Proceedings(2)

Sponsors and Participating organizations GLOBAL OBSERVATION OF FOREST AND LAND COVER DYNAMICS

MONGOLIAN GEOSCIENCE AND REMOTE SENSING SOCIETY

NATIONAL UNIVERSITY OF MONGOLIA

MONGOLIAN UNIVERSITY OF SCIENCE AND TECHNOLOGY SCHOOL OF MATERIALS SCIENCE

INTERNATIONAL INSTITUTE FOR GEO-INFORMATION SCIENCE AND EARTH OBSERVATION (ITC), NETHERLAND

CEReS, CHIBA UNIVERSITY, JAPAN

RESEARCH INSTITUTE OF EMERGENCY SERVICES

SUN ERDENE TOUR, MONGOLIA

Proceedings of The 2nd International Conference On Land Cover /Land Use Study Using Remote Sensing And Geographic Information System And The GOFC-GOLD Regional Capacity Building Meeting In Mongolia

June 08-09, 2006 Organized by Mongolian Remote Sensing Society and GOFC-GOLD

(Editors) N. Tugjsuren and R. Tsolmon

Welcome Message to the participants of The 2nd International Conference on Land cover / Land use study using Remote Sensing and Geographic Information System and the GOFC-GOLD regional capacity building meeting in Mongolia

Distinguished International Scientists, Conference participants, ladies and gentlemen, On behalf of the Government of Mongolia, the Members of the Mongolian Parliament, I would like to welcome all of you to this event, and express our gratitude to you for coming to Mongolia to support and contribute to this event.

Two years ago, we held the very successful First National Conference on Land Cover Study Using Remote sensing and GIS, sponsored by the Mongolian Geosciences and Remote Sensing Society and the ITC from the Netherlands and Center Environmental Remote Sensing Center Chiba University in Japan. Since then, there have been many remote sensing initiatives in Mongolia. One of the major events has been the establishment of the NUM-ITC-UNESCO Laboratory for Remote Sensing and GIS, at the National University of Mongolia, funded by UNESCO and the ITC. As a result, this laboratory became a centre for geoscientists, researchers, police-makers, students, and volunteers, all of whom have found a need for the information provided by remote sensing and geographical information systems to advance the technical requirements of their given field. The types of scientific studies concerning the remote sensing and applied branches of scientific, social and economical sectors of Mongolia have increased within this joint laboratory, with excellent hardware and modern Remote Sensing/GIS Software. I know that the GOFC-GOLD, supported by FAO, UNESCO, UNEP, ICSU and WMO is a most powerful, multifaceted international strategy to bring the Earth’s land cover under continuous observations. It has a vision to share information, data and knowledge. It consists of a coordinated program of activities to ensure that earth observation and other data are used effectively for global monitoring of terrestrial resources and the study of global change. It operates through a network of participants implementing coordinated demonstration and operational projects. I would like to underline that the objectives of conference and discussion topics are indeed, of immense importance and actual problems for Mongolia and a main goal of GOFC-GOLD is contemporary forest dynamics. For instance, I would like to share with you my views about Mongolia’s forest capacity. The forest area of Mongolia is only 10 million hectors, in other words, 6.5% of total territory of country. This little land cover is faced with a large amount of damage. In 1999-2000 deforestation rate decreased by 0.72% each year, but between 2000-2005, this rate increased intensively by 0.77% each year. For example, in 2005, the consequence of 180 forest fires destroyed more than 450 thousands hectors of forest. Another reason for deforestation is the insect impact. On the one hand, results of your detailed studies and successive cooperation are very important for detection and determination of how many territories are impacted by insect. On the other hand, the remote sensing method can make an important contribution to study the impact of insects on forests, monitor the spread of infestations, etc. In the last few years, the frequencies and amplitudes of drought and dzud calamities has intensified. Between 1999-2000, 10.4% (3.4 million of heads) of total livestock sector, and in

2000-2001 another 15.2% (4.8 million) of livestock died from the dzud, which has decimated for the Mongolian livestock farming system. The desertification and dust storm issues have also intensified in total area of Mongolia, particularly in Gobi Desert. Thousands of lakes, rivers, springs and wells have dried. I can list so many difficulties concerning ‘Mongolian’ calamities which have occurred in recent times in our country. As a result of this destructive environmental change, the next chain of events have effected Mongolia’s demography: the herders have become poor, they have migrated to urban areas, so, urban life is faced with many new difficulties and poverty persists. Poor people influence also further contribute to deforestation in consequence with the large wood uses for fuel. In order to overcome all these difficulties, Mongolia must set forth upon multilateral and regional cooperation with other countries to aid with Mongolia’s many environment and development issues. I would like to reassure you that the results of this conference, and your future cooperation in Mongolia will be supported by Mongolian Parliament, Government of Mongolia and myself. I wish you all very fruitful discussions and a successful conference, plus, I hope you will all enjoy some of our Mongolian culture during your stay in our country. Thank You.

Mongolian Parliament Member, B. Munkhtuya

Contents Challenges in the mapping and monitoring of forests using remote sensing John R. Townshend ------------------------------------------------------------------------------------------------- 1 Ground survey of the Mongolian rangelands on plant onset trend T. Chuluun, J. Sanjid, I. Tuvshintogtoh, B. Oyun, B. Tserenchunat and Dennis Ojima ------------------- 5 Trends in Northern Eurasia’s land cover derived on SPOT-VGT time series analysis 1998-2005 M. Herold, C. Hɶttich, C. Schmullius, and S. A. Bartalev ----------------------------------------------------- 6 Application of Multitemporal Optical and SAR Data for Different Forest Studies Damdinsuren Amarsaikhan ---------------------------------------------------------------------------------------- 7 Brief description on land cover and current activities in Korea Nam-Sun, Oh, Hong-Yeon Cho, and Geun-Sang Lee --------------------------------------------------------- 12 Glacier change estimation using Landsat TM data M. Erdenetuya, P.Khishigsuren, M. Otgontugs -----------------------------------------------------------------18 Forage Monitoring Technology to Improve Risk Management Decision Making by Herders in the Gobi Region of Mongolia J. P. Angerer, L.Bolor-Erdene, D. Tsogoo, M.Urgamal and S. Granville-Ross ---------------------------- 22 Monitoring environmental degradation in Mongolia with NPP and rainfall data. M. Tyburski, R. Tsolmon, D. Sodnomragchaa, R. Harris1 and A. Warren ---------------------------------- 23 Projects and Initiatives addressing Environmental Impact Studies in Northern Mongolia and the Lake Baikal Region K. Frotscher, C.C. Schmullius ------------------------------------------------------------------------------------- 31 Digital Asia -Information Network for Sustainable Future Hiromichi Fukui ----------------------------------------------------------------------------------------------------- 32 Annual variation of aerosol optical thickness derived from PAR observation in Mongolia T. Takamura, T. Karasuyama, N. Tugjsuren, G. Batsukh, and H. Takenaka ------------------------------- 33 Determination Of The Photosynthetically Active Radiation For Vegetation Growth Period Of The Mongolian Grain Farm Region Tugjsuren Nasurt ---------------------------------------------------------------------------------------------------- 37 Coherent time in cloud analysis using 95GHz FM-CW cloud profiling radar Y. Nakanishi, t. Takano, k. Akita, h. Kubo, y. Kawamura, h. Kumagai, T. Takamura and t. Nakajima ---------------------------------------------------------------------------- --------- 41 Land cover mapping of mongolia Sh. Munkhtuya ------------------------------------------------------------------------------------------------------- 45 Development of a Better Atmosphere and Soil Resistant Vegetation Index for Forestry Monitoring in Taiwan G. Dashnyam, G. R. Liu, C. K. Liang, T. H. Kuo C. W. Lan, T. H. Lin, Y. C. Chen --------------------- 49

Sharing ground truth data for land cover mapping – GLCNMO

Ryutaro Tateishi ----------------------------------------------------------------------------------------------------- 52 Ecosystem changes mapping for Eastern Shore of Lake Hovsgol from satellite imagery and GIS a case study B.Gantsetseg --------------------------------------------------------------------------------------------------------- 53 GIS application on micro relief development Altangerel, B., Schwanghart, W. & Walther, M. --------------------------------------------------------------- 56 Vegetation mapping of the Great Gobi A strictly protected area A.Tsolmon & H. von Wehrden ----------------------------------------------------------------------------------- 57 Climate change impact on rangeland productivity in the Eurasian steppe Dennis S. Ojima and Togtohyn Chuluun ------------------------------------------------------------------------ 61 Classification of Multitemporal InSAR Data for Land Cover Mapping in Selenga River Basin, Mongolia Damdinsuren Amarsaikhan ---------------------------------------------------------------------------------------- 65 Urban Land Cover Change Studies Using Multitemporal RS Images D.Amarsaikhan, M.Ganzorig and B.Nergui --------------------------------------------------------------------- 69 Surface Water Pollution Of The Ulaanbaatar City (determined by BOD, dissolved O2, NH4+, NO2-, NO3-, PO4-3, Cr+6, COD) (between 1996-2004) Ch.Gonchigsumlaa, O.Altansukh -------------------------------------------------------------------------------- 76 Analysis on the Land Use in General and Neighborhood Commercial Areas of Ulaanbaatar city using RS and GIS B.Chinbat, D.Amarsaikhan and Tae-Heon Moon --------------------------------------------------------------- 78 The Heat Island Experiment over the Western Taiwan Plain with MODIS satellite and concurrent helicopter-borne IR imager data Chia Wei Lan*, Gin-Rong Liu, Tsung-Hua Kuo, Kun-Wei Lin, Tang-Huang Lin, Ming-Chang Hsu, Yen-Ju Chen, Chia-Chi Liu ---------------------------------------------------------------------------------------- 85 Global vegetation continuous field tree cover products and Siberian forest types M. Herold , D. Knorr, K. Kornhaə, A. Shvidenko , O. Cartus and C. Schmullius ------------------------ 89 Possible use of fuzzy-knowledge for improving the geographic boundary representation Sang-Jun Kim, Ju-Whan Kang, and Soung-Yong Yun -------------------------------------------------------- 90 Diagnoses for the Drought and Dzud Frequencies in Mongolia T. Ulaanbaatar ------------------------------------------------------------------------------------------------------- 99 Evaluation of atmospheric optical characteristics In the mongolian territory G. Batsukh, B. Daariimaa, T. Narangarav----------------------------------------------------------------------- 109 Landscape unit map of uvs nuur and adjacent areas Michael walther------------------------------------------------------------------------------------------------------ 110 Some initial results of using The 6p forest inventory method in Mongolia Sh. Tsogtbayar------------------------------------------------------------------------------------------------------- 111 Pasture land classification using remote sensing data D.Narangerel, N.Monkhoo, B.Suvdantsetseg, B.Batzorig, A. Saruulzaya ---------------------------------- 112

Use of Caesium-137 for the agriculture soil degradation in Central part of Mongolia O.Batkhishig, N.Enkhbat, B.Burmaa-----------------------------------------------------------------------------113 The Mongolian forest characteristic and ecological changes. G.Tsedendash -------------------------------------------------------------------------------------------------------- 114 A method to estimate soil moisture using l-band synthetic aperture radar data Javzandulam Tsend-Ayush, Josaphat Tetuko S.S, Ryutaro Tateishi, Tsolmon Renchin ---------------- 116 Moving into international geographic information standards Bolorchuluun.Ch; Battsengel.V ----------------------------------------------------------------------------------- 120 LUCC and Terrestrial Study based on RS, GIS and Ecological Observation & Proposal for International Collaboration Jiyuan Liu Lin Zhen Yunfeng Hu Qian Zhang --------------------------------------------------------------- 127 Remote Sensing Parameterization of Land Surface Heat Fluxes over Arid and Semi-arid region in Mongolia Jadamba Batbayar , Nas-Urt Tugjsuren ------------------------------------------------------------------------- 129 A Possibility of Cooperation in Detection of Water and Heat Losses of District Heating System in Ulaanbaatar Ulaanbaatar T., Legden M. and Danbayar ----------------------------------------------------------------------- 135 Monitoring of Saxaul forest in Gobi of Mongolia B. Suvdantsetseg, Y. Aruinzul and D.Narantuya --------------------------------------------------------------- 139 Dust and Sandstorm Monitoring of Mongolia Using NOAA AVHRR data L.Ochirkhuyag, R.Tsolmon; S.Khudulmur; J.Sumyasuren; L.Natsagdorj; D.Jugder --------------------- 144

Challenges in the mapping and monitoring of forests using remote sensing. John R. Townshend Department of Geography, University of Maryland College Park, Maryland 20742 USA Abstract The need for improved observations of the Earth’s changing forests is essential for many different types of stake-holders. Remote sensing data from satellites can now contribute greatly to the monitoring of forests. But there are many challenges in ensuring that long term operational monitoring is achieved. These are largely non-technical and relate to long term funding commitments and ensuring appropriate distribution of products. Of crucial importance in increasing the uptake of remote sensing data is eliminating charging for data from governmentfunded satellites wherever possible. This would help most users and also would help stimulate service providers relying on remote sensing.

I. INTRODUCTION The world’s forests continue to change rapidly primarily as a result of anthropogenic changes both direct and indirect. The need for reliable information on forests has never been stronger. They play a major role in climate change through their impact on the carbon cycle both through sequestration and through release of CO2 especially during forest fires. Forests also often are locations of high biodiversity. They help reduce floods, supply drinking water and prevent erosion. Forests are themselves key natural resources in developing and developed countries. Consequently there are many stake-holders for reliable observations, including the global change science community, those concerned with international environmental agreements as well as natural resource managers. II. CONTRIBUTION OF REMOTE SENSING SATELLITES National capabilities to monitor forest cover vary greatly. Remote sensing data are often unavailable because of high costs or inadequate satellite acquisition strategies. Comparisons between countries are often thwarted because of different definitions and protocols. The Forest Resource Assessment (FRA) of FAO uses a tree canopy cover limit of only 10% but in practice many forest agencies use a threshold of 35-40% and may include harvestable lands whether these are actual or potential forests. There are many types of satellites with the potential to contribute to the monitoring of forests. Moderate spatial resolution sensors (250m-1km) such as NASA’s Moderate Resolution Imaging Spectrometer (MODIS) typically provide a daily overview of the whole Earth. There are many fine resolution systems in orbit such as Landsat’s Enhanced Thematic Mapper (ETM+), SPOT’s HRVIR (High Resolution Visible Infrared), the AVNIR (Advanced Visible and Near Infrared Radiometer) instrument of the Indian Remote Sensing Satellite Systems, the Japanese ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) or the CBERS (Chinese Brazilian Earth Resource Satellite) of China and Brazil. These sensors provide much more detail globally but less frequently than moderate resolution sensors - usually only once every approximately16 days. Ultrafine resolution images (<5m resolution) are usually from commercial satellites, though the Japanese Advance Land Observing Satellite (ALOS) now has this capability; these provide a very detailed look though only for sample areas with relatively limited coverage. Satellite data have been increasingly used for monitoring forests though there is a paucity of internationally agreed protocols and standards for extracting the information in the creation of information products. Results from satellite based analyses often show substantial disagreements compared with statistics from international agencies. For example work by Defries et al (2002) combining coarse and fine resolution remotely sensed data for the tropics indicated that the satellite-derived estimates of forest change agree with FAO estimates in Latin America and Tropical Asia for the 1990s, but are substantially lower for the 1980s. The net rate of tropical forest clearing increased approximately 10 percent from the 1980s to 90s, most notably in Southeast Asia, in contrast to an 11 percent reduction reported by FAO. Hence for this period the rate of tropical deforestation is increasing and not decreasing. In terms of carbon this suggests that the net mean annual carbon fluxes from tropical deforestation and regrowth are 0.6 (.4-1.0) and 1.0 (.5-1.4) Gt/yr for the 1980s and 1990s respectively.

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III. CHALLENGES IN MOVING TOWARDS OPERATIONAL SYSTEMS FOR THE LAND COVER A key question for many who wish to utilize products from remotely sensed data is whether they can be assured that they will continue to be available in the future. If not then it can be risky for operational agencies to adopt remote sensing. Any survey of remote sensing instruments shows that there are currently many missions in orbit. But many of these have no long term continuity planned; capacity to generate products in the ground segment is often limited and poor data policies may militate against wide use. Overall for land remote sensing cooperation in the use of satellite assets is weak compared with weather satellites. For moderate resolution remote sensing there are now quite hopeful indications that long-term observations are assured through the present MODIS instrument and the successor VIIRS (Visible/Infrared Imager/Radiometer Suite) instrument which will be on the next generation of meteorological polar orbiters called NPOESS (National Polar-orbiting Operational Environmental Satellite System). The NPOESS Preparatory Project (NPP) has a VIIRS instrument and this should fill in any gap between MODIS and the first VIIRS on NPOESS. For fine Landsat-class instruments the situation is much less certain at least in the near to medium term. The ETM + of Landsat 7 has major deficiencies due to the failure of its Scan Line Corrector mirror and currently there is heavy reliance on the ancient TM of Landsat 5, whose data are only available in sight of receiving stations. Efforts in many countries are on-going to use other fine resolution assets or increase receiving station capabilities for Landsat 5 data. Although many other systems similar to Landsat exist (outlined in section 2), there is no coherent plan to try and duplicate the acquisition strategy of Landsat 7 and consequently the current availability of this class of data is much poorer now than in the early years of this decade. Unfortunately this situation may persist for another 5 years. It is to be hoped in the longer term that nations such as the US, EU, China and India will work together to create an integrated constellation of fine resolution satellites better able to meet the needs of the many users of this class of data. The latter should include more frequent imaging that that achieved by Landsat. Landsat 5 and Landsat 7 working together provided an 8 day frequency of imaging. Coordination between agencies in choice of orbits of future Landsat-class missions could result in frequencies of once every four or even two days and ultimately in daily imaging. Alternatively increasing the swath width of future sensors could also significantly increase the frequency of imaging. Recently considerable advances have been made in the quality of data available from radar for land cover monitoring, by use of these sensors in an interferometric mode notably in L-band. Regular global coverage of products suitable for land cover monitoring is not feasible as yet, if only because of computational challenges, but such data could have a significant complementary role in the cloudier parts of the world, where optical remote sensing is rarely capable of allowing regular acquisition of high quality data. Identification of such areas, followed by their systematic imaging by radars could make an important contribution to operational global monitoring of forests. In Table 1 there is an attempt to identify the main challenges hindering the regular supply of the main categories of remote sensing data needed for forest monitoring. Three types of challenge are identified: Technical challenges: These are challenges associated with the design and construction of sensors to meet the needs of users. Continuity challenges: These challenges are those in obtaining committed resources for systematic long-term observations into the foreseeable future. Only sensors on meteorological platforms currently fall into this category. To ensure full operational status there should be back-up missions available. Distribution challenges: Even though missions may be in orbit this does not mean that users will have ready access to data suitable for there needs. For example there may be restrictions due to under-scoping the ground segment in relation to user demands for data; costs and other aspects of data policy may greatly hinder use; formats of products may make it difficult for users readily to integrate their data with other data sets. What Table 1 implies is that the challenges are now largely not technical. Sensors can be built satisfying many of our needs (Townshend et al 2004). The only major exception is for canopy lidars, which are needed to provide information on the vertical structure of canopies but these challenges likely -2-

will be overcome in the next few years. Although the VIIRS has some deficiencies relative to MODIS and the data processing system has still to be completed, it appears that all of the continuity and distribution challenges are being overcome for MODIS-class instruments. For all the other types of sensors there remain significant continuity and distribution challenges not withstanding the number of such missions in orbit or being planned. Many systems may appear to be sufficient to meet user needs but there are often significant difficulties in users obtaining the data or ensuring that the data are acquired when and where users need them. Furthermore countries rarely will commit to long term funding to ensure that there are no gaps in the record. It is to be hoped that burgeoning organizations such as the Group on Earth Observations (GEO) can improve cooperation and coordination between countries to overcome some of these challenges.

Landsatclass Thermal IR (<120m) MODISclass Radar Canopy Lidar

Technical Continuity Distribution Challenge Challenge Challenges s s Y Y No No

Y

Y

No

No

No

No Y

Y Y

Y Y

Table 1 Characterization of remote sensing capabilities in terms of technical challenges, continuity challenges and distribution challenges.

IV. IMPORTANCE OF CHARGING POLICIES Charging for data undoubtedly inhibits applications of remote sensing. The US is unique in not charging for the vast majority of its remote sensing from government funded satellites However Landsat data, essential for forest and land cover change, are usually charged for, though once purchased they can be copied without restriction. In contrast for comparable data from other sources it may be impossible to purchase them: one simply buys the rights to use the data and in such circumstances normally one is not allowed to copy the data and distribute it to others. In the case of government-financed remote sensing systems there is little justification for charging for data. Tax-payers have already paid for the satellites and sensors and the cost of the data is a marginal fraction of the total cost of the system. Furthermore there is little evidence after 30 plus years that terrestrial remote sensing can be commercially viable except possibly for ultra-fine resolution systems and even for these systems it is only massive government data purchases that makes them commercially viable. There is however likely to be a growing commercial future for services based on remote sensing data, and this commercial sector would be stimulated by not charging for data. Hence it is fair to conclude that charging for data reduces the economic benefits of remote sensing. It is true that scientists are sometimes charged less for data or even may be charged zero, but there is no logical reason why this class of user should be charged less than those in developing countries concerned with alleviating poverty or even those in the commercial sector aiming to bring economic development to a region. V. CONCLUSIONS Forests are changing rapidly and their ability to provide services is declining in many areas. There is therefore a crucial need for reliable monitoring of forest cover change. Remote sensing capabilities to carry out this task have improved substantially in recent years, but there are many deficiencies in operational capabilities especially those concerned with maintaining observational continuity and ensuring products are distributed to users. The likely gap in Landsat coverage is a major blow to our ability to monitor the Earth and improved coordination is needed in the short and longer terms to remedy current deficiencies. There is a vital need to maintain open data policies and to eliminate any charging for data from government-funded systems.

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REFERENCES 1. DeFries, R., Houghton, R.A., Hansen, M., Field, C., Skole, D., Townshend, J.R.G. 2002 Carbon Emissions from Tropical Land Use Change Based on Satellite Observations for the 1980s and 90s, Proceeding National Academy of Sciences, 99, 14256-14261. 2. Townshend, J.R.G., Justice, C.O., Skole, D.L., Belward, A., A. Janetos, Gunawan, I., Goldammer, J., Lee, B. (2004) Meeting The Goals Of GOFC: an evaluation of progress and steps for the future. In Gutman, G. et al (Eds.) Land Change Science, Kluwer Academic Publishers, Dordrecht, Netherlands, pp 31-52.

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Ground survey of the Mongolian rangelands on plant onset trend T. Chuluun1, J. Sanjid2, I. Tuvshintogtoh2, B. Oyun3, B. Tserenchunat1 and Dennis Ojima4 1

National University of Mongolia, phone 976 9911-1301, Email: [email protected] 2 Institute of Botany, Mongolian Academy of Sciences 3 Department of Ecology & Environmental Sciences, Inner Mongolia University 235 West University Road, Hohhot,Inner Mongolia, P.R. China 010021 4 Natural Resource Ecology Laboratory, Colorado State University, Fort Colllins, Colorado 80523, USA Plant onset trends of grassland ecosystems of the Mongolian Steppe have been analyzed using a long-term RS data identifying the zones with delayed or advanced plant onset trends in the Mongolian rangelands (Ellis et al., 2002). The region of advanced green-up covers much of the eastern steppe, extending south from the forest zone to near the northern edge of the Gobi desert. This is the region with the highest rainfall in the Mongolian steppe, with precipitation ranging from 200 mm per annum in the south, to more than 400 mm near the forest zone. The delayed green-up zone forms band along the boundary area of the dry steppe and the Gobi desert steppe and covers the desert steppes located in the southern slopes of high mountains such as Altai, Hangai and KhanKhohii. Mean annual rainfall in most of the delayed green-up zone is 100-200mm. The delayed green-up in dry ecosystems seen here could be linked to lower photosynthetic rates, lower CO2 uptake and reduced primary production rates. These changes would certainly viewed as negative for the herbivores and human that depend on high latitude grassland environment for their sustenance and support. A ground survey was conducted to better understand factors impacting on plant onset trends in the Mongolian rangeland ecosystems. Generally, land use was a major factor rather than climate change to impact on vulnerability of rangelands in Inner Mongolia, which was opposite in Mongolia, being climate change a major factor influencing on vulnerability of rangelands. However, there were areas where both climate change and land use inter-acted causing serious land degradation in Mongolia with signs of desertification. More extensive field survey was done for rangelands in Mongolia during 2001-2003 to better understand how different types of ecosystems and landscapes respond differently to climate change. The soil moisture availability in the spring time was a main factor for the green-up trends of the rangelands. The eastern steppe, grasslands on the northern slopes of the Altai and Khan Khohii Mountains and some oasis or sandy lands, where the soil moisture was available in early spring, had an advanced green-up trends. Extensive band along the boundary area of the Gobi and steppe, and desert ecosystems located in the southern slopes of the Altai and Khan Khohii Mountains had delayed green-up trends making these rangelands vulnerable in spring time. The grasslands located in the dry end of the steppe region seemed more vulnerable to spring drying trend than the desert steppe located in the northern edge of the Gobi due to adaptive capacity of the Gobi plants to drought. Thus, the Mongolian rangeland ecosystems vulnerable to climate change have been identified. Further field study is necessary to better understand climate change effects on functional groups of ecosystems and defining adaptive rangeland management strategies in these vulnerable regions.

Key words: Climate change effects on grassland ecosystems, ground validation and rangeland vulnerability

Literature 1. Ellis, J., K. Price, R. Boone, Yu. Fangfang, T. Chuluun and Yu. Mei. 2002. Integrated assessment of climate change effects on vegetation in Mongollia and Inner Mongolia. In: Togtohyn Chuluun and Dennis Ojima (eds.) Symposium Proceedings “Change and Sustainability of Pastoral Land Use Systems in Temperate and Central Asia”, pp. 26-34.

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Trends in Northern Eurasia’s land cover derived on SPOT-VGT time series analysis 1998-2005 M. Herold1, C. Hɶttich1, C. Schmullius1, and S. A. Bartalev2 (1)

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Friedrich-Schiller-University Jena, Department of Earth Observation Russian Academy of Sciences, Space Research Institute, Boreal Ecosystems Monitoring Laboratory

The Boreal and Tundra ecosystems of the mid-high latitudes provide sensitive indicators of environmental impacts both of climate change and human activities. A number of studies have emphasized changes and trends in Eurasia due to drivers of natural and human induced land cover dynamics, in particular for the period 1982-1999. The investigations of this study focus on the very recent years of 1998-2005 and cover the whole boreal ecosystems of Northern Eurasia with its geographical dimensions of 42°N to 75°N and 5°E to 180°E. This northern hemispheric belt includes to its most extent the national territory of Russia but also partly its abutter nations, the Scandinavian Nations and the central and south-European nations, as well as, Mongolia. The study focus on linear trends from 1998-2005 using inter-annual and inter-seasonal trends in the SPOTVGT mosaics. Significant trends could be detected in the NDVI and NDWI time series from 1998 -2005. The trends differ by season (spring, summer, autumn), land cover type and latitude. The spring trends show significant positive NDVI regression slopes and strong negative NDWI slopes over the evergreen needleleaf-, needleleaf/broadleaf-, and mixed forests of the Russian and Scandinavian boreal zone, which indicates an onset of the vegetation greenup dates (NDVI trends) over eight years linked with earlier snowmelt (NDWI trends). Most affected are the forests in high altitudes like the Ural region, the central Siberian forests and the forests of middle Sweden. Similar vegetation dynamics can be exposed in the fall. Positive NDVI slopes over nearly all vegetation classes indicate a longer durance of the vegetation period. Contrary trends were detected in the tundra. The tundra ecosystems of the northern Eurasia latitudes seemed to be affected by trends of negative NDVI and positive NDWI slopes. The significant trends could be acquired in the prostrate shrub tundra followed by sedge and shrub tundra. This may be explained by earlier snowmelt from higher temperature anomalies since the last eight years. The estimates over these mainly climate controlled processes can be consolidated by analyzing the surface temperature anomalies from 1998 to 2005. In comparison with the base from 1951 to 1998, positive surface temperature anomalies are observed particular in spring and fall season. The analysis of the summer NDVI trends with GLC2000 cropland classes on Oblast level in Russia point at hot spots of agricultural land changes in the south-western part of Russia. Comparisons with multi-temporal Landsat data showed natural succession on former agricultural land is in particular located at remote areas, along river lines, forests and along the state and Oblast borders.

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Application of Multitemporal Optical and SAR Data for Different Forest Studies Damdinsuren AMARSAIKHAN Institute of Informatics and RS, Mongolian Academy of Sciences Faculty of Geography and Geology, National University of Mongolia

Abstract The aim of this study is to demonstrate different applications of the passive and active sensor data for different forest studies in Mongolia. For this purpose, Landsat TM data of 1988, SPOT XS image of 1997, ERS-1/2 tandem pass SAR images of 1997, ERS-2 SAR and JERS-1 SAR intensity images of 1997, and other thematic information are used and different digital image processing techniques are applied. The results indicated that the integrated use of optical and microwave data can be successfully used for different forest studies as well as for differentiation between the fuzzy boundaries of different forest and vegetation classes. Key words: Optical, SAR, Multitemporal, Forest change, Reflectance, Backscatter 1. Introduction Optical remote sensing (RS) data sets taken from different Earth observation satellites such as Landsat and SPOT have been successfully used for forest monitoring and management since the operation of the first Landsat launched in 1972. SAR images taken from space platforms have been widely used for different forest applications since the launch of the ERS-1/2, JERS-1 and RADARSAT satellites. The combined application of data sets from both sources can provide unique information for different forest studies, because passive sensor images will represent spectral variations of the top layer of the forest classes, whereas microwave data with its penetrating capabilities can provide some additional information about forest canopy [3].

The aim of this research is to demonstrate different applications of the passive and active sensor d ata for different forest studies in Mongolia that is a) to conduct a forest change study using multite mporal optical RS images, b) to create a forest biomass map using SAR images and c) to analyze t he boundary between fuzzy classes: grass-herb and young forest using both optical and SAR imag es. As a test site, Bogdkhan Mountain situated in central part of Mongolia, near the city of Ulaanb aatar has been selected. To reach the final goals, different RS and GIS techniques have been appli ed. 2. Study area and data sources As a test site Bogdkhan Mountain situated in central part of Mongolia, near the city of Ulaanbaatar has been selected. The mountain is a protected area and has a territory of 41651ha, of which 55% is covered by forest. The mountain has 588 species of high plants, which are related to 256 genuses of 70 families. 135 species such as carex, artemisa, oxytropis that relate to 11 main genuses comprise 22.9% of all species distributed on the mountain. Cedar and larch dominate in the forest cover but pine, birch, spruce and poplar are also occur [1]. The data used consisted of Landsat TM data from summer of 1988, SPOT XS image of 19 June 1997, ERS-1/2 tandem pass SAR single look complex (SLC) images acquired on 10 and 11 October 1997, ERS-2 SAR intensity image of 25 September 1997 and JERS-1 SAR intensity image of April 1997. In addition, a topographic map of 1984, scale 1:50,000 and a forest taxonomy map, scale 1:100,000 were available. -7-

Figure 1. a) Forest taxonomy map of Bogdkhan Mountain, b) The selected part of the study area, c) Landsat TM image of 1988, d) SPOT XS image of 1997, e) Classified image of Landsat TM, f) Classified image of SPOT XS.

3. Forest change study using multitemporal optical RS images In the test area, most of the mixed forests that represent fuzzy boundaries among different forest classes were situated in the central and western parts of the mountain. Therefore, for the analysis, the areas situated in these parts have been considered (figure 1a,b). Initially, the optical images (i.e. Landsat TM data of 1988 and SPOT XS image of 1997) were thoroughly analyzed in terms of brightness and geometric distortion and the images were of a good quality. Then, the SPOT XS and Landsat TM images were successively georeferenced to a UTM map projection using a topographic map of the study area, scale 1:50,000. The ground control points (GCP) were selected on clearly delineated sites and in total 9 regularly -8-

distributed points were chosen. For the actual transformation, a second order transformation and nearest neighbour resampling approach [6] have been applied and the related root mean square (RMS) errors were 0.68 pixel, and 0.76 pixel, respectively. In order to demonstrate the forest changes, the selected multitemporal optical images were classified using the traditional statistical maximum likelihood classification (MLC) [7,9]. For the actual classification green, red and near infrared bands of the images were used and the images were classified into just two classes: forest and non-forest. The original SPOT XS and Landsat TM images and the results of the MLC are shown in figure 1c-f. As seen from the figure 1, different local changes had occurred in the mountain within a 9 year period. 4. Creation of a forest biomass map using SAR images In the present study, for the creation of a forest biomass map of Bogdkhan Mountain, ERS-1/2 tandem pass SAR images and JERS-1 SAR intensity image have been used. Initially, we had to extract coherence and amplitude images from the ERS-1/2 data sets and for this purpose, the techniques used in Amarsaikhan and Sato (2004) have been applied.

Figure 2. Forest biomass map of Bogdkhan Mountain

In general, the coherence is a measure of the variance of the phase difference of the imaged surface in the time between the two SAR data acquisitions. The coherence values range between 0 and 1. If some land surface changes had occurred in a target area between the two image acquisition periods, then coherence is low and if no changes had occurred, then the coherence is high [10]. In general, the coherence over a dense forest and shrub will be the lowest, while for the bare soil, the coherence will be the highest. Based on this characteristics of coherence, it is possible to define different forest volumes which are directly related with forest biomass. To create a forest biomass map, initially the SAR images were successively georeferenced to a UTM map projection using a topographic map of the study area, scale 1:50,000. Then, the combined SAR bands were classified using the MLC defining such biomass classes as very high, high, moderate and low (figure 2). It was not possible to define the amount of biomass in the areas affected by radar layover.

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5. Analysis of the boundary between fuzzy classes using ERS-2 SAR image In some areas of the forest classes represented on the optical images, the boundary between fuzzy classes: grass-herb and young forest could not be distinguished due to their similar spectral characteristics. However, these two classes might be distinguished on the SAR image because they have different structure that can cause different backscatter return. These two fuzzy classes have the following backscattering properties [2,5,8]. From forest canopy, at different radar wavelengths, volume scattering derived from multiple-path reflections from leaves, twigs, branches and trunks can be expected. However, in case of the ERS-2 SAR data with its VV polarization only volume scattering from the top layer can be expected, because the wavelength is too short to penetrate the forest canopy. The backscatter will also be influenced by the local incidence angle as well as the underlying topography. In total, the forest area will behave as a diffuse reflector due to volume scattering although some other scattering might also be expected depending upon the height and geometry of the trees. As a result, the area will have brighter appearance on the radar image. Grass-herb will behave as a mixture of grass and soil and the backscatter will depend upon the volume and characteristics of either of them. In C-band frequency, such a class will have components of both diffuse and specular reflection depending on the plant characteristics and incident angle. The backscattering of soil will also depend on different surface and system parameters. Specifically, the backscatter from a soil layer is very much dependent on the moisture content and the higher the water content the more reflection is expected. However, in this mountain area, the moisture content cannot be high enough to cause high reflection, and the reflection from the soil will most probably be dominated by specular reflection. As a result, the backscatter from this class will not be as high as in the case of volume scattering, thus resulting in lower to middle brightness.

a

b

c

d

Figure 3. (a) The original speckle suppressed image. (b) SFCC image (7x7 variance=R, 5x5 edge enhancement=G, and 5x5 mean euclidian distance=B). (c) A subset from (a) indicating some of the selected sites. (d) A subset from (b).

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Initial visual inspection of the speckle suppressed SAR image gave some distinctions between different features, but further interpretation highly required local knowledge about the sites and backscatter properties. To improve the image interpretation and increase the tonal discrimination between different forest and non-forest types, a synthetic false colour composite (SFCC) image has been created. To create such an image, at first the gammamap filtered SAR image was filtered by the use of different texture analysis and high pass filters of different sizes. Then, the results of three different filter operations were assigned to the red, green and blue (RGB) colours, respectively. The best colour image to represent the tonal variations was obtained by the combination of the results of 7x7 variance, 5x5 edge enhancement, and 5x5 mean euclidian distance filters. Figure 3 shows the comparison between the original speckle suppressed image and the created SFCC image. As seen from figure 3, despite the radar layover and foreshortening effect, the SFCC demonstrates more tonal variations between the two fuzzy classes: young forest and natural vegetation, which were not distinguishable on the optical images. The radar image in these areas shows brighter colour if there is a forest due to volume scattering. The tonal variations change to darker colour when there are classes that cause less volume scattering. 6. Conclusions The aim of this study was to demonstrate different applications of the optical and microwave data sets for different forest studies in Mongolia. Within the framework of the study a) a forest change study using multitemporal optical RS images, b) creation of a forest biomass map using SAR images and c) analysis of the boundary between fuzzy classes: grass-herb and young forest using both optical and SAR images, were carried out. Overall, the study demonstrated that the integrated use of optical and microwave data can be successfully used for different forest studies as well as for differentiation between the fuzzy boundaries of different forest and vegetation classes. References 1.

Adyasuren, Ts., Shiirevdamba, Ts., and Darin, B., 1998, Ecosystems Atlas of Bogdkhan Mountain, Ulaanbaatar, Mongolia, pp40. 2. Amarsaikhan, D., and Ganzorig, M., 1999, Interpretation and comparison of AirSAR quad-polarised radar images. Proceedings of the 20th Asian Conference on RS, Hong Kong, China, 22-26 November 1999, 695-700. 3. Amarsaikhan, D., Ganzorig, M., Batbayar, G., Narangerel, D., and Tumentsetseg, Sh., 2004, An integrated approach of optical and SAR images for forest change study, Asian Journal of Geoinformatics, No.3, 2004, pp.27-33. 4. Amarsaikhan, D. and Sato, M., 2004, Integration of RS and GIS for sustainable forest management, International Boreal Forest Research Association Conference, Alaska, USA, 3-6 May 2004. 5. Amarsaikhan, D., Ganzorig, M. and V.Battsengel, 2005, Knowledge Acquisition in C-band and Lband Radar Frequencies, CD-ROM Proceedings of the Asian Conference on RS, Hanoi, Vietnam, pp.DTP4-1_1-4. 6. ERDAS, 1999, Field guide, Fifth Edition, ERDAS, Inc. Atlanta, Georgia. 7. Mather, P.M., 1999, Computer Processing of Remotely-Sensed Images: An Introduction, 2nd edition (Wiley, John & Sons). 8. Richards, J.A., Milne, A.K., and Forster, B.C., 1987, Remote sensing with synthetic aperture radar. Centre for Remote Sensing, UNSW, Sydney, Australia. 9. Richards, J.A., 1993, Remote Sensing Digital Image Analysis-An Introduction, 2nd edition (Berlin: Springer-Verlag). 10. Weydahl, D.J., 2001. Analysis of ERS SAR coherence images acquired over vegetated areas and urban features. International Journal of Remote Sensing, 22, 2811-2830.

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Brief description on land cover and current activities in Korea Nam-Sun, Oh1, Hong-Yeon Cho2, and Geun-Sang Lee3 1

Department of Ocean Civil Engineering, Mokpo National Maritime University, Mokpo, S. Korea 530-729, Tel) +82 061-240-7078, Fax) +82 061-240-7284, Email: [email protected] 2 Korea Ocean Research and Development Institute (KORDI), Ansan, S. Korea 425-600, Tel) +82 031-400-6318, Fax) +82 031-408-5823, Email: [email protected] 3

Korea Water Resource Corporation (KOWACO), Daejeon, S. Korea 305-391 Tel) +82-42-860-0354, Fax) +82-42-860-0319, Email: [email protected] Abstract This paper describes brief description on land cover and current activities in Korea. In 1998, the Korean Ministry of Environment (MOE) constructed the land cover map on the basis of the satellite data in an attempt to support main environmental policy formulation such as non-point source evaluation, environmental impact assessment and riparian buffer zone establishment. The MOE have constructed three different levels of the land cover map (coarse, medium, and fine) for various purposes. Use of the land cover map has been increasing in broader areas, such as hydrology, agriculture, engineering, and atmosphere as well as environment.

1. Introduction The Korean community recently stands on the brink of a new era of environmental and water resources management with the advent of the remotely sensed data handled by broad geographic information system (GIS). Traditionally environmental problems have been concentrated on the control of point source but non-point source, which is hard to manage systematically, is becoming a main interest to scientist. The pollution resulting from non-point source is involved with both natural characteristics and artificial activities. Thus, the impact of non-point source on social and economic activities is considerable and complicated. In order to deal with the complicated problems of pollution process induced by non-point source, broad information on surface characteristics needs to be collected and quantified. Comprehensive, precise, and rapid analysis is simultaneously necessary. In 1998, the Korean Ministry of Environment (MOE) constructed the land cover map on the basis of the satellite data in an attempt to support main environmental policy formulation such as non-point source evaluation, environmental impact assessment and riparian buffer zone establishment. Old fashioned documental information is converted into the digitally formatted information such as satellite photograph, land cover map, and natural ecology system. The remotely sensed data has no restriction in temporal and spatial scale and then data availability is very flexible. In typical fashion, the land cover map constructed by MOE is classified into specific classes and the land cover map, along with the soil texture map and DEM, is easily accessible to people without special knowledge. More people in broader areas, such as hydrology, agriculture, engineering, and atmosphere as well as environment, are using the land surface map and the MOE is trying to upgrade the land surface data.

Table 1. Change in environmental management in Korea. In the 80’s, environmental management in Korea was focused on local and point source but the trend was changed into national and nonpoint source after 2000. Accordingly the RS and GIS skill was becoming more important.

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Period

1980's - regulation of pollutant source environmental - pollution level management measurement - passive evaluation data - point source data coverage Tools

- local - simple source - COBOL - FORTRAN

1990's

After 2000

- daily environmental - nature-friendly environment management management - regulation of pollutant - regulation in total maximum daily load (TMDL) source - active evaluation - passive evaluation - plane source data - regional - statistical analysis, comprehensive report

- spatial source data

- RDBMS - EXCEL

- RS - GIS

- national - mapping, GIS tool

2. Mapping the land cover (MOE activities) The MOE started mapping the land cover in 1998 and activities are continued, finishing the fifth stage in 2005. At the first stage, the MOE constructed the coarse land cover map (hereafter named coarse level: 7 classes) in an attempt to grasp the land use/cover characteristics nationwide, which is calibrated to the 215 points of the ground samples. The activities are becoming more specified and functionalized in need of practical application. At the second stage in 2000, the MOE classified the land cover into 23 classes (hereafter named medium level) and 48 classes (hereafter named fine level) as shown in table 3 to more effectively retrieve necessary information for environmental management. Especially, for Seoul cosmopolitan area, medium level of the land cover map was constructed using 5m resolution of IRS image. 6 areas were also selected for intensive analysis and fine level of the land cover map was constructed. The guideline to making the fine level of the land cover map is given to local governments so that they can make the local land cover map for their own purpose. All the geographic information constructed by the MOE is freely available to people on the internet (http://egis.me.go.kr/egis/intro.asp). At the third stage in 2002, the project was focused on the urban area. The IKONOS image was used to characterize the urban cover such as building and road. During this stage, the standardized classification was suggested on the basis of the precedent results to make the classification processes systematic. The output of the project was tested with water quality modeling in the small basin called Gyeongan-Cheon and examined the accuracy and usefulness of the land cover classification. At the fourth stage in 2003, the SPOT image (2.5m resolution), which is monitored since 2002, was used. Some information was not open to public because of its security but the secured information was available with permission by the MOE (called infranet service). At the fifth stage in 2005, medium level (23 classes) of the land cover map was constructed on a nationwide scale. Subsequently applicability of the data was investigated in various areas: water quality, flood forecasting, drought monitoring, and soil loss estimation etc. Relative comparison of the existing methods was performed to improve the quality of the land cover information. The followings show the chronicle of the land cover map in Korea.

Table 2. Land cover map construction by a year budget($) 1st stage (1998.11~1999.11)

77,000

Activities - mapping coarse level of the land cover ࡮South Korea area(90's), 1:50000 scale, 238 sheets - digital elevation map construction ࡮South Korea area(90's), 1:50000 scale, 238 sheets - sampling points (215 points)

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2nd stage (2000.12~2001.6)

3rd stage (2002.2~2002.12)

4th stage (2003.2~2003.12)

5th stage (2004.6~2005.4)

- mapping coarse level of the land cover ࡮South Korea area(80's), 1:50000 scale, 238 sheets ࡮North Korea area(80's), 1:50000 scale, 249 sheets ࡮North Korea area(90's), 1:50000 scale, 249 sheets - mapping medium level of the land cover ࡮Seoul metropolitan area, 1:25000 scale, 121sheets 2,300,000 - mapping fine level of the land cover ࡮6 test areas, 1:5000, 7 sheets - aerial photographs scanning and manufacture ࡮aerial photographs scanning, 20,569 sheets ࡮aerial photographs manufacture, South Korea - sampling points (200 points) - constructing internet service system

896,000

- mapping fine level of the land cover map ࡮Han river and Geum river basin, 1:25000 ࡮Standardized and guideline suggested - case study ࡮water quality modeling to a Gyeongan-Cheon basin. - enforcement of internet service system

- mapping medium level of the land cover map ࡮Nakdong rive basin, 1:25000 950,000 - broadening internet service system - construction of intranet service system - case study. - mapping medium level of the land cover map ࡮Yeongsan river basin and Jeju island, 1:25000 1,190,000 - construction of aerial photographs ࡮South Korea, 1:25000, 101 sheets

3. Mapping processes As mentioned earlier, the land cover map can be used for various areas but it is true that the MOE performed the mapping processes with emphasis on environmental management such as non-point source, watershed management, and basis for modeling approach. The followings briefly describe the mapping processes; ƒ

ƒ ƒ

ƒ

The overseas classification systems (USGS and/or CORINE) were thoroughly reviewed and each class is selected on the basis of these systems. Then, each name was adjusted to Korean environment in which surface conditions are more diverse and individual patches are relatively small. Some peculiar items like the bushes in high mountain region were excluded in the list. The classification system also considered seasonal variation such as vegetation fraction, rice farming area, and tidal flat. The classification system was made in such a way that coarse level of the land cover classification includes several medium levels of the land cover classifications (see table 3). In that case you can track coarse level of the land cover classification from medium level of the land cover classification. Coarse level of the land cover was structured for prompt response, while medium level of the land cover system was structured for assisting monitoring or providing a basis for modeling setting in national-wide management plans.

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Table 3. Coarse and medium level of the land cover classification A large-scale classification Class Code

Urban and dry area

A middle-scale classification Class Code

100

Agriculture region

200

Forest region

300

Grassland

400

Wetlands

500

Barren area

600

Water area

700

Residential district.

110

Industrial area. Commercial area.

120 130

Recreation facilities

140

Transport facilities

150

Public facilities

160

A rice field A dry field Glass culture An orchard Other culture non-conifer forest

210 220 230 240 250 310

A coniferous forest

320

Mixed forest

330

A nature grassland

410

A golf course Other grassland Inland wetlands Coastal wetlands A mining region Other barren area A inland water A marine water

420 430 510 520 610 620 710 720

Fig.1 presents the land cover map constructed by the MOE. The left panel shows the SPOT 5 derived satellite image and the right panel shows the corresponding land cover map. The symbols for each land cover and location are marked under the map.

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Fig 1. A sample of land cover map Fig.2 and 3 shows coarse and medium level of the land cover map for Andong lake basin which is located in South Korea. The table 4 and 5 shows vegetation fraction which corresponds to Fig.2 and Fig.3

Fig 2. Land cover map(coarse)

Fig 3. Land cover map(medium)

Table 4. Analysis of land cover map of Fig.2 (coarse) - 16 -

Land cover

percentage(%)

Area(ᓚ)

Urban area

22.532

1.417

Rice field

43.750

2.750

Dry field

145.194

9.128

1,307.775

82.215

Grassland

6.544

0.411

Wet land

9.813

0.617

Unused area

16.467

1.035

Water area

38.587

2.427

1,590.662

100.000

Forest

Total

Remarks

Fraction of farm land (11.878%)

Table 5 Analysis of land cover map of Fig.2 (medium) Class

Code

Area(ᓚ)

Residential district.

11

12.251

0.770

Industrial area

12

0.870

0.055

Commercial area

13

1.574

0.099

Recreation facilities

14

0.377

0.024

Transport facilities

15

6.503

0.409

Public facilities

16

0.957

A rice field

21

43.750

A dry field

22

123.763

Glass culture

23

0.277

An orchard

24

18.687

1.175

Other culture

25

2.467

0.155

non-conifer forest

31

371.797

A coniferous forest

32

681.280

42.848

Mixed forest

33

254.698

16.019

A nature grassland

41

2.904

A golf course

42

0.040

Other grassland

43

3.600

Inland wetlands

51

9.813

A mining region

61

2.835

0.178

Other barren

62

13.632

0.857

Water area

71

38.587

2.427

1590.662

100.000

Total

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Occupation ratio(%)

0.060 2.752 7.784 0.017

23.383

0.183 0.002 0.226 0.617

Glacier change estimation using Landsat TM data M. Erdenetuya* (PhD), P.Khishigsuren**, M. Otgontugs* * National Remote Sensing Center/Information and Computer Center ** Agency of Land Affairs, Geodesy and Cartography [email protected], [email protected], [email protected]

1. Introduction By the geographical position and “ecotone” formation whole territory of Mongolia was selected as main part of study areas in Northeastern Asia. In Mongolia located Altai, Khangai and Khuvsgul high mountains and formulated permanent snow and ice. As mentioned the glacier is most important land cover type to keep the freshwater resources and as indicator of the climatic temporal variability. Since the middle of the last century, the global climate is changing drastically and as a result the current climate experiences more frequent extremes in Mongolia causing big losses amongst animal and land degradation. Also the National Program on Climate Change mentioned that for every increase of 3 degrees in air temperature, there will be a 10 percent reduction in carbon (C) and nitrogen (N) contents of the plant-soil ecosystem, a 3-10 percent reduction in pasture vegetation and a biomass reduction of 21.5 percent1. Nowadays, the air temperature has already increased by 1.9 degrees2. The global warming factor could strongly influence to melt the permanent snow and ice on top of higher mountain system in Western Mongolia at the same time to decrease fresh water resources. In order to estimate glacier area we have used the Landsat TM data from different period and applied NDSI (normalized difference snow index) calculation method and maximum likelihood classification approach. Also the SRTM/DEM data have been used for 3 dimensional processing and for calculation of the glaciers area. 2. Data and Methods There have been used Uvs lake hydrology data of 1970-2002 and meteorological data observed at Ulaangom and other stations, located in the basin. For estimation of glacier area dynamics have been used Landsat of 3 different time period data for glacier massifs as Kharkhiraa, Turgen Tsambagarav, Munkhkhairkhan and Sair Mts. The Landsat TM and ETM+ scenes were selected from 141-144 paths and 26-27 rows and obtained on following dates. x 16 September 1990 (used for only Munkhkhairkhan Mt. Noted with* in Table 1) x 25 June 1992 x 10 Sep., 2000 /Kadota and Davaa, 2003/ x 08 August 2002 For mapping the glacier area from the satellite image we have applied several approaches such as, x To identify spectral characteristics of glacier in each Landsat band x To apply bands combination method for glacier extraction x To apply both supervised and unsupervised classification methods x To calculated normalized difference snow index x To analyze three dimensional view of images x To compare calculated areas In order to distinguish snow from similarly bright soil, rock and cloud we have calculated NDSI (normalized difference snow index) by following formulae: NDSI

(TM 2  TM 5) (TM 2  TM 5)

(1)

In Fig. 1 shows Landsat TM data fragments of 2 mountains in different periods.

1 2

National Program on Climate Change, 2000 L. Natsagdorj – Assessment of Climate factors to Mongolian pasture degradation, 2006

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A. B. Fig. 1. Landsat TM, ETM+ images of glaciers in 1990 and 2002 (A – Tsambagarav, B – Sair mountain) 3. Results and discussions On the background of the paleoclimate data, more precise climatic chronology has been given by the research on reconstruction of climate data with tree ring indices. One (Khalzan Khamar) of the tree ring chronology sites were selected in the Altai Mountains. It has permanent snow fields, ice and permafrost and located near the timberline where temperature appears to be limiting factor for growth. Similar fluctuations have been derived from the tree-ring width indices record, taken from the sample of the Turgen Mountain. Reconstructed with 5 year moving average of tree ring width indices (r=0.60) of the Turgen and instrumental, annual temperature at Ulaangom station were well correlated and show that highest temperatures are in last decades. Melting of the ice masses in the mountains, the end of pediment formation on the lower mountain slopes and the slow regeneration of plant cover owing to rising temperatures and increased precipitation all lead to a relative quick rise in lake level, since the basins filled up with both rainwater and melt water from the rapidly melting glaciers. Therefore, pretty good relationship exists between 5 year moving average of tree ring width indices and annual average of water level of the Uvs lake for the period of 1970-2002. Reconstructed with 5 year moving average of tree ring width indices (r=0.76) of the Turgen and observed water level of the Uvs lake were well correlated and show that highest water levels are observed in the last decade. It is obvious that dynamics of the water balance elements of the Uvs Lake were following water level fluctuations. However, it is possible to estimate water balance elements in last 40 years. Spectral characteristic of glacier The maximum, minimum and mean spectral values of each of land cover class were calculated from high resolution Landsat images, based on that spectral reflectance of each land cover types is different in each wave length of electro-magnetic radiation [6]. On Landsat ETM+ data the glacier spectral values accounted as 255, 145-255, 191-255, 116-217, 20-31 and 3-18 in each spectral bands 1-5 and 7 respectively. The calculated spectral values of land cover classes used as a reference value for glacier classification. Also for identification of glacier have been used band combination method and the glacier was extracted in each 3,2,1 and 4,3,2 and 5,4,3 combinations of bands as showed in Fig. 2.

Fig. 2 Landsat ETM+ data combination (Tsambagarav mountain) - 19 -

For extraction of glacier area we have used the NDSI (normalized difference snow index) calculation to distinguish snow from similarly bright soil, rock and cloud from entire images.

Fig. 3 NDSI images of Kharkhiraa and Sutai mountains

Also the SRTM/DEM data have used for 3 dimensional processing and identification of the glaciers area. In next step of processing we will integrate DEM data to calculate real or volume area of glaciers.

A. Fig. 4 Three Dimensional Landsat images (A – Kharkhiraa, B – Tsambagarav

B.

Reason of reduction of evaporation from water surface area of the lake can be the decrease in water temperature due to the increase in melt water, draining to the Uvs Lake primarily in the form of underground flow. Retreat of Kharkhiraa and Turgen glaciers is drastically increasing since 1940s. Kharkhiraa, Turgen, Kharkhiraa, Tsambagarav and Tavanbogd glacier areas were 50.13, 43.02, 105.09 and 88.88 sq. km, estimated from topographic map, scaled as 1:100 000 and compiled in 1940s [3]. Areas of the Kharkhiraa, Turgen, Munkhkhairkhan, Tsambagarav and Sair glaciers were decreasing by 45.5, 33.7, 25.8, 21.4 and 42.5 percent since 1992 till 2002, respectively (Table 1).

Table 1. Changes in glacier areas Glacier massif Kharkhiraa Turgen Munkhkhairkhan Tsambagarav Sair

1940s topo map 25 June 1992 10 Sep 2000 43.02 57.37 36.08 50.13 51.03 34.74 36.96* 105.09 90.98 74.8 11.51 -

- 20 -

8 Aug. 2002 31.29 33.83 27.42 71.52 6.62

Retreat of glaciers is intensified in last decades and many of glacier peaks got ice free especially in very dry year 2002. We have analyzed Landsat TM, ETM+ data from 1992, 2002 and compared their massif changes (Fig. 5).

Fig. 5 Comparison of Turgen and Tsambagarav mountains glacier massif in 1992 (red) and 2002 (blue) 4. Concluding remarks Comprehensive investigation focusing on glacier mass balance, ground and surface water interaction, dating and the environment changes are desired in the near future. Compilation of glacier inventory is important issue, using remote sensing and ground observation data, especially vertical air photographs, which are basic information for development of hydro-climate-glacier and integrated water resource management studies. For satellite data application on glacier mapping we still need fresher (2005 and 2006) and higher resolution satellite (ASTER, IKONOS) imageries. References [1]. Davaa G. Dashdeleg N. Tseveendorj N., The dynamics of water balance elements of the Uvs lake, Proceedings of International symposium on “Global change- Uvs lake”, Ulaanbaater, Mongolia, 1991, pp.18-19 [2]. Erdenetuya M. and Khudulmur S. Glacier assessment using Landsat satellite data. Proceeding of First National Conference on Remote Sensing and GIS Applications. Ulaanbaatar, Mongolia, May 2005, pp103-106 (in Mongolian). [3]. Grunert J. F. Lehnkuhl, Walter M., Paleoclimatic evolution of the Uvs Nuur basin and adjacent areas (Western Mongolia), Quaternary International 65/66 (2000), pp. 171-192. [4]. Kadota T. and Davaa G. A preliminary study on Glaciers in Mongolia, proceedings of International workshop “Terrestrial Change in Mongolia”, Japan, 2003, published in Mongolia, Ulaanbaatar, 2004, pp. [5]. Lovilius N.V., Davaajamts T. and Gunin P.D., 1992. Dendroindications of forest growth conditions in Mongolia and possibilities of forecasting (in Russian), Russian Academy of Sciences, Puchino, Moscow, pp. 32-49. [6]. Munkhtuya Sh., (2004): Remote sensing methodology and technology for land cover classification. Dissertation, UB, Mongolia.

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Forage Monitoring Technology to Improve Risk Management Decision Making by Herders in the Gobi Region of Mongolia J. P. Angerer1, L.Bolor-Erdene2, D. Tsogoo2 , M.Urgamal2 and S. Granville-Ross2 1

-Dept of Rangeland Ecology & Management, Texas A&M University, College Station, Texas, USA; [email protected] 2 -Mercy Corps, P.O. Box 761, Ulaanbaatar - 49, Mongolia ; [email protected]; 976-11-461145

Due to increased frequency and coverage area of drought and "zud" disaster in Mongolia in recent years, the pastureland vegetation, its structure and composition has considerably been changed and the natural and ecological balance distorted that directly affects the livelihood of the herders. Therefore, the predefinition of present and future pasture conditions, distribution of relevant information to the herders to raise their awareness, undertaking appropriate preparatory and organizational actions and consulting are urgently needed. Therefore we are workong on developing risk management technologies to provide drought and winter disaster early warning to improve rural business in the livestock sector of the Gobi region. Our objectives are developing a regional forage monitoring system that provides near-real time spatial and temporal assessment of current and forecasted forage conditions and a communication infrastructure to provides herders with forage condition information to assist in making timely and specific management decisions. We established 246 monitoring sites were established in the Gobi region to parameterize the forage production model and to enable mapping of available forage. Vegetation parameter collection has been completed at each monitoring site. Grazingland communities being monitored range from mountain steppe to desert grasslands. Across the monitoring sites, 390 unique species of plants were encountered. Of these, 258 were forbs, 69 were grass or grass-like, and 65 were shrubs Communication protocols are being developed to provide herders, administrative, and government personnel in the region with current forage conditions and 90-day forecasts on a 16-day cycle.

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Monitoring environmental degradation in Mongolia with NPP and rainfall data. M. Tyburski1,2, R. Tsolmon2, D. Sodnomragchaa3, R. Harris1 and A. Warren1. 1

Department of GeographyUniversity College London,London, UK

2

NUM-ITC-UNESCO Remote Sensing and GIS Laboratory, National University of Mongolia Ulaanbaatar, Mongolia 3

Mongolian Research Institute of Emergency, Ulaanbaatar, Mongolia

Abstract Environmental degradation was assessed with net primary production and precipitation estimates for Mongolia during the period 1982-2000. Umnugobi aimag was the most degraded aimag during the study period and future studies should use high resolution satellite remote sensing imagery to determine why degradation occurred in this aimag. We suggest future research on environmental degradation use a greater suite climate variables than just rainfall data.

Introduction Mongolia became a signatory to the United Nations Convention to Combat Desertification in 1994. 90% of Mongolia is considered vulnerable to desertification pressures (MNEM, 1997). Desertification, i.e. environmental degradation in arid environments (UNCCD, 1994), can be defined as a reduction in the biological productivity of the land (Reynolds & Stafford-Smith, 2002). 76.5% of Mongolia is used for pasture land and pasture yields have decreased by 19-24% during the period 1977-2002 (UNCCD, 2002). It is therefore necessary to determine whether the environmental degradation of Mongolian pasture land is attributable to human activity or climate change. In this study, we assess environmental degradation in Mongolia with satellite remote sensing imagery and modeled precipitation data during the period 19822000 in an effort to deduce where Mongolian pasture land has been most likely degraded by anthropogenic forces. Remote sensing of environmental degradation Prince (2002) reviews various methods used by satellite remote sensing to monitor environmental degradation. Of these approaches, the Rain Use Efficiency (RUE) model (Prince et al., 1998; Nicholson et al. 1998; Diouf & Lambin, 2001; Yu et al. 2004; Hein & De Ridder, 2006) is the most useful method for monitoring large-scale environmental degradation in environments with less than 1000 mm of annual rainfall. However, the RUE model has been criticized for the incorrect assumption that in the absence of degradation, the RUE amount should be constant over time (Hein & De Ridder, 2006). Prince (2002) also proposed a DNPP model to quantify environmental degradation in environments receiving greater than 1000 mm of annual rainfall. At finer spatial scales, other methods have been developed to accommodate for factors such as soil type, elevation, distance from water source, etc. (Stafford-Smith & Pickup, 1993; Pickup,1996; Pickup et al., 1998;Wessels et al., 2004), which contribute to the spatial variability of biological productivity in the context of a specific locality. Therefore, monitoring environmental degradation by satellite remote sensing imagery is scale dependent (Prince, 2002) and requires more detailed parameterization at increasing spatial resolution. Materials and Methods Data Net Primary Production (NPP) is the biomass amount for a given time (Geider et al., 2001), and we use annual amounts of NPP to measure biological productivity. NPP estimates were derived from the Global Production Efficiency Model (GLO-PEM). GLO-PEM is a terrestrial ecosystem model that is driven by reflectance data from satellite remote sensing imagery (Prince & Goward, 1995). The annual NPP data derived from GLO-PEM were downloaded online from the University of Maryland’s Global Land Cover - 23 -

Facility (downloadable at: http://glcf.umiacs.umd.edu/index.shtml). Cao et al. (2004) performed the GLO-PEM modeling procedures and described in detail the inter-annual NPP change for the GLO-PEM data set we used in this study. The GLO-PEM data was modeled with NDVI estimates from the NOAA AVHRR Pathfinder data set, which provides global coverage at 8km2 pixel resolution in 10-day increments during the period 1982-2000. The University of East Anglia’s Climate Research Unit’s (CRU) global 0.5 degree precipitation data (Mitchell & Jones, 2005) were used to calculate annual rainfall estimates for Mongolia. Vegetation zone (MNRSCICC ,1999) and soil data (CWG, 2004) were used to aggregate vegetative productivity in Mongolia. Annual and inter-annual biological variability for different vegetation zones were reported in past field-based case studies (Fernandez-Gimenez & Allen-Diaz, 1999; Fernandez-Gimenez & Allen-Diaz, 2001). Mongolian nature reserves were used to control for natural amounts of biological productivity. IUCN Protected Areas boundaries (WDPA Consortium, 2004) were downloaded from the University of Maryland’s Global Land Cover Facility (downloadable at: http://glcf.umiacs.umd.edu/index.shtml). Nature reserves were only sampled after the reserve was established (Table 1). Quantitative Methods First, change analysis by a linear regression method (y = a+bx) was used to quantify NPP change during the period 1982-2000. Second, we calculated the annual Rain Use Efficiency (RUE = NPP / Rainfall) between 1982-2000 and performed a linear regression on the annual RUE estimates to quantify RUE change. Third, an aggregated method was applied to quantify environmental degradation. Mongolia was aggregated by vegetation zone (Figure 1) and soil variability (Figure 2). Nature reserves were used as controls (Table 1). We assumed nature reserves would have the least amount of environmental degradation related to anthropogenic agency, and biological variability would thus function under natural conditions. Mean annual NPP and rainfall estimates were sampled for individual soil polygons within each vegetation zone inside nature reserves and we then quantified the relationship by regression analyses. Soil polygons with less than five pixels sampled were discarded due to statistical and pixel uncertainties (Young & Harris, 2005), which may cause discrepancies between mean annual NPP amounts. The function of the NPP and rainfall relationship was used to calculate a Potential NPP (PNPP) amount from the CRU annual precipitation estimates falling in each vegetation zone. The annual Difference in NPP (DNPP) between the Actual NPP estimates (ANPP), derived from the GLO-PEM NPP data, and modeled PNPP amounts were calculated for each vegetation zone. Linear regression was used to determine DNPP change during the period 1982-2000. Since we used different functions for individual vegetation zones to quantify PNPP, the DNPP amounts for different vegetation zones were assumed to be quantifiably incomparable. Relative DNPP amounts were quantified so that DNPP amounts in different vegetation zones could be then comparable throughout Mongolia. To calculate relative DNPP amounts over time, we use the linear function y = a + bx, where relative DNPP change = (bx/a) * (a/abs(a)). (a/abs(a)) was used for the possibility of a negative y-intercept. Also, we reclassify a for DNPP amounts between -1 and 0 to equal -1 and DNPP amounts between 0 and 1 to equal 1. We reclassify so that DNPP amounts for a between -1 and 1 are not confounded by small relative DNPP amounts. For example, if pixel "1" has bx at 20 and a at .5 and pixel "2" has bx at 20 and a at 1, pixel "1" will have a relative DNPP amount of 40 and pixel "2" will be 20. This is a misleading result for environmental degradation. Results/Discussion Figure 3 shows the results for regression analyses between mean annual NPP and rainfall for individual soil polygons of different vegetation zones in nature reserves. In the desert vegetation zone (Figure 3a), there was a weak relationship in terms of the coefficient of determination (r2 = 0.28), but there was an obvious linear pattern in the annual NPP and precipitation relationship. This pattern suggests a relationship between annual NPP and precipitation, but other environmental factors, such as potential evapotranspiration or plant biology, may also limit NPP in deserts. There was a stronger relationship between annual NPP and precipitation in the desert steppe (Figure 3b) than the desert vegetation zone in terms of coefficient of determination (r2 = 0.56), and there was a linear pattern between the NPP and rainfall relationship. This pattern suggests that annual rainfall was a dominant factor for determining annual NPP in the desert steppe vegetation zone. In the steppe vegetation zone (Figure 3c), there was a significant relationship in terms of the coefficient of determination (r2 = 0.71), a higher a-value compared to desert and desert steppe vegetation zones, suggesting the annual NPP amount is greater in the steppe, - 24 -

and there was a linear pattern between the annual NPP and rainfall relationship. In the mountain (forest) steppe (Figure 3d), there was a weaker relationship in terms of the coefficient of determination (r2 = 0.39) when compared to desert steppe and steppe vegetation zones, but there was a linear pattern between the annual NPP and rainfall relationship and a much higher a-value compared with desert, desert steppe and steppe vegetation zones. There was no relationship or pattern between annual NPP and rainfall in either the taiga or alpine vegetation zones (Figures 3e & d). Figure 4 illustrates the results for NPP change, RUE change and relative DNPP change in Mongola. Table 2 depicts the results for the mean amount of annual NPP, RUE and relative DNPP change per aimag. Arkhangai aimag had the greatest mean amount of negative NPP change. Umnugobi aimag had the greatest mean amount of both negative RUE change and relative DNPP change. Conclusion x NPP change is not a good indicator for environmental degradation by human agency in Mongolia, because it does not account for climate variability. There is no relationship between rainfall and NPP in Alpine and Taiga vegetation zones, and therefore, the RUE model is not useful for monitoring environmental degradation in Alpine and Taiga vegetation zones in Mongolia. More detailed, high resolution land use and land cover change assessments are required to understand the local context (Warren, 2002) of environmental degradation in the Omnogovi aimag during the period 1982-2000. x In theory, the implementation of the RUE model to monitor environmental degradation in environments receiving less than 1000 mm of annual rainfall is confounded by biological and soil constraints within different vegetation zones, and perhaps other climatic variables, such as radiation and temperature. Nemani et al. (2003) use a mix of precipitation, radiation and temperature data to determine dominant global constraints on NPP change. We suggest future environmental degradation work, both in Mongolia and in other countries, to focus upon the relationships between NPP and precipitation, radiation and temperature in nature reserves. Potential NPP should then be modeled with the function derived from multivariate analyses between NPP and precipitation, radiation and temperature. References 1. Cao M, Prince SD, Small J,Goetz SJ (2004) Remotely Sensed Interannual Variations and Trends in Terrestrial Net Primary Productivity 1981–2000. Ecosystems, 7, 233–242 2. (CWG) Cryosol Working Group. 2004. Northern and Mid-Latitude Soil Database, Version 1. Data set. Available on-line [http://www.daac.ornl.gov] from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A. 3. Diouf A, Lambin E (2001) Monitoring land-cover changes in semiarid regions: Remote sensing data and field observations in the Ferlo, Senegal. Journal of Arid Environments, 48, 129-148. 4. Fernandez-Gimenez ME & Allen-Diaz, B (2001) Vegetation change along gradients from water sources in three grazed Mongolian ecosystems. Plant Ecology, 157, 101-118. 5. Fernandez-Gimenez ME & Allen-Diaz, B (1999) Testing a non-equilibrium model of rangeland vegetation dynamics in Mongolia. Journal of Applied Ecology, 36, 871-885. 6. Geider RJ, Delucia EH, Falkowski PG, et al. (2001) Primary productivity of planet earth: biological determinants and physical constraints in terrestrial and aquatic habitats. Global Change Biology, 7, 849-882. 7. Hein L, De Ridder N (2006) Desertification in the Sahel: a reinterpretation. Global Change Biology, 12, 1-8. 8. Mitchell T, Jones D (2005) An improved method of constructing a database of monthly climate observations and associated high-resolution grids. International Journal of Climatology, 25, 693712. - 25 -

9. (MNEM) Ministry for Nature and the Environment of Mongolia (1997) National Plan of Action to Combat 10. Desertification in Mongolia. Ulaanbaatar, Mongolia. 11. (MNRSCICC) Mongolian National Remote Sensing Center/Information and Computer Center (1999). Ulaanbaatar, Mongolia. 12. Nemani RR, Keeling CD, Hashimoto H, Jolly WH, Piper SC, Tucker CJ, Myneni RB, Running SW (2003) Climate-Driven Increases in Global Terrestrial Net Primary Production from 1982 to 1999. Science, 300, 1560-1563. 13. Nicholson SE, Tucker CJ, Ba MB (1998) Desertification, drought, and surface vegetation: An example from the West African Sahel. Bulletin of the American Meteorological Society, 79, 1-15. 14. Pickup G (1996) Estimating the effects of land degradation and rainfall variation on productivity in rangelands: An approach using remote sensing and models of grazing and herbage dynamics. Journal of Applied Ecology, 33, 819-832. 15. Pickup G, Bastin GN, Chewings VH (1998) Identifying trends in land degradation in nonequilibrium rangelands. Journal of Applied Ecology, 35, 365-377. 16. Prince SD (2002). Spatial and temporal scales of measurement of desertification. In: Global desertification: Do humans create deserts? (eds. Stafford-Smith M, Reynolds JF), pp. 23-40. Dahlem University Press, Berlin. 17. Prince SD, Brown de Colstoun E, Kravitz L (1998) Evidence from rain use efficiencies does not support extensive Sahelian desertification. Global Change Biology, 4, 359-374. 18. Prince SD, Goward SN (1995) Global primary production: A remote sensing approach. Journal of Biogeography, 22, 815-835. 19. Reynolds JF, Stafford Smith M (2002) Do humans create deserts? In: Global desertification: Do humans create deserts? (eds. Stafford-Smith M, Reynolds JF), pp. 1-22. Dahlem University Press, Berlin. 20. Stafford Smith DM, Pick-up G (1993). Out of Africa, looking in: Understanding vegetation change. In: Range Ecology at Disequilibrium: New Models of Natural Variabilty and Pastoral Adaptation in African Savannas (eds. Behnke Jr RH, Scoones I, Kerven C), pp. 196-244. Overseas Development Institute and international Institute for Environment and Development, London. 21. Tucker CJ, Dregne HE, Newcomb WW (1991) Expansion and contraction of the Sahara desert from 1980-1990. Science, 253, 299-301. 22. UNCCD (2002) Mongolian National Report to United Nations Convention to Combat Desertification. 23. UNCCD (1994) United Nations Convention To Combat Desertification In Those Countries Experiencing Serious Drought And/Or Desertification. United Nations General Assembly, New York. 24. Warren A (2002) Land degradation is contextual. Land Degradation and Development, 13, 449459.

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25. WDPA Consortium. World Database on Protected Areas" 2004 . Copyright World Conservation Union (IUCN) and UNEP-World Conservation Monitoring Centre (UNEP-WCMC), 2004. 26. Wessels KJ, Prince SD, Frost PE, van Zyl D. (2004) Assessing the effects of human-induced land degradation in the former homelands of northern South Africa with a 1 km AVHRR NDVI timeseries. Remote Sensing of Environment, 91, 47–67. 27. Young S, Harris R (2005) Changing patterns of global-scale vegetation photosynthesis, 1982-1999, International Journal of Remote Sensing, 26, 4537-4563.

28. Yu F, Price KP, Ellis J; Feddema JJ, Shi P (2004) Interannual variations of the grassland boundaries bordering the eastern edges of the Gobi Desert in central Asia. International Journal of Remote Sensing, 25, 327-346.

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1/1/1992 1/1/1992 1/1/1992 1/1/1992 1/1/1993 1/1/1996 1/1/1975 1/1/1975

1/1/1993 1/1/1993 1/1/1995 1/1/1996 1/1/1996 1/1/1996 1/1/1975 1/1/1993 1/1/1993 1/1/1993 1/1/1993 1/1/1993 1/1/1996 1/1/1996 1/1/1996 1/1/1996 1/1/1965 1/1/1977

Khovsgol Lake Khan Khentee Khan Khentee Otgontenger Uvs Nuur Basin Khangai nuruu Great Gobi Great Gobi

Sharga-Mankhan Gobi Gurvansaikhan Eej Khairkhan Erglyn Zoo Small Gobi Small Gobi Great Gobi Sharga-Mankhan Uvs Nuur Basin Uvs Nuur Basin Sharga-Mankhan Gobi Gurvansaikhan Erglyn Zoo Ikh nart Small Gobi Zagiin us Khorgo Terkh Zagaan Nuur Khukh Serkhyn Nuruu

1993-2000 1993-2000 1995-2000 1996-2000 1996-2000 1996-2000 1982-2000 1993-2000 1993-2000 1993-2000 1993-2000 1993-2000 1996-2000 1996-2000 1996-2000 1996-2000 1982-2000 1982-2000

1992-2000 1992-2000 1992-2000 1992-2000 1993-2000 1996-2000 1982-2000 1982-2000 Desert Desert Desert Desert Desert Desert D. Steppe D. Steppe D. Steppe D. Steppe D. Steppe D. Steppe D. Steppe D. Steppe D. Steppe D. Steppe M.F. Steppe M.F. Steppe

Alpine Alpine Alpine Alpine Alpine Alpine Desert Desert

28

Burkhan Buudai Khangai nuruu Batkhaan Bogdkhan Mountain Eastern Mongolian Steppe Mongol Daguur Sharga-Mankhan Ganga Lake Sharga-Mankhan Hustain Nuruu Khorgo Terkh Zagaan Nuur Khovsgol Lake Khan Khentee Gorkhi-Terelj

Otgontenger Ugtam Mountain Uvs Nuur Basin Uvs Nuur Basin Sharga-Mankhan Uvs Nuur Basin Gorkhi-Terelj Alag Khairkhan 1/1/1996 1/1/1996 1/1/1957 1/1/1978 1/1/1992 1/1/1992 1/1/1993 1/1/1993 1/1/1993 1/1/1993 1/1/1965 1/1/1992 1/1/1992 1/1/1995

1/1/1992 1/1/1993 1/1/1993 1/1/1993 1/1/1993 1/1/1993 1/1/1995 1/1/1996

Table 1 List of nature reserves sampled. D. Steppe is Desert Steppe and M.F Steppe is Mountain (forest) Steppe. Date Est. Sampled Veg. Sample Nature Reserve Date Est. Nature Reserve Khorgo Terkh Zagaan Nuur 1/1/1965 1982-2000 Alpine Bogdkhan Mountain 1/1/1978 Khasagt Khairkhan 1/1/1965 1982-2000 Alpine Mongol Daguur 1/1/1992 Khukh Serkhyn Nuruu 1/1/1977 1982-2000 Alpine Eastern Mongolian Steppe 1/1/1992 Khovsgol Lake 1/1/1992 1992-2000 Alpine Nomrog 1/1/1992

1996-2000 1996-2000 1982-2000 1982-2000 1992-2000 1992-2000 1993-2000 1993-2000 1993-2000 1993-2000 1982-2000 1992-2000 1992-2000 1995-2000

1992-2000 1993-2000 1993-2000 1993-2000 1993-2000 1993-2000 1995-2000 1996-2000

Sampled 1982-2000 1992-2000 1992-2000 1992-2000

M.F. Steppe M.F. Steppe Steppe Steppe Steppe Steppe Steppe Steppe Steppe Steppe Taiga Taiga Taiga Taiga

M.F. Steppe M.F. Steppe M.F. Steppe M.F. Steppe M.F. Steppe M.F. Steppe M.F. Steppe M.F. Steppe

Veg. Sample M.F. Steppe M.F. Steppe M.F. Steppe M.F. Steppe

Table 2 Mean change per aimag.

Mean NPP change Aimag Arkhangai -30.0271 Bayan-Ulgii 29.2072 Bayankhongor 9.0231 Bulgan -3.2581 Dornod -2.3570 Dornogobi -10.7844 Dundgobi -6.2522 Zavkhan -8.4938 Gobi-Altai 4.7270 Khentii -11.7224 Khovd 15.1625 Khuvsgul 2.2329 Umnugobi 0.6552 Uvurkhangai -8.7405 Selenge 11.7548 Sukhbaatar -6.7816 Tuv -18.6570 Uvs -1.9268

Mean RUE change -0.0015 -0.1908 -0.2195 0.2197 0.2250 0.1108 -0.0201 0.0128 -0.2419 0.3226 -0.0885 0.1209 -0.2861 0.0063 0.1172 0.3384 0.1047 0.0583

Mean relative DNPP change -1.0186 -0.4444 -2.2930 0.6145 2.4493 0.8510 -0.5537 -0.6901 -1.1372 3.1339 0.0539 -1.3943 -3.1386 -0.5003 1.0649 2.0418 0.3996 -0.4796

Vegetation Zone Desert Desert Steppe Steppe Forest Steppe Taiga Alpine Lake

Figure 1 Vegetation zones of Mongolia.

Figure 2 Soil aggregation for Mongolia.

b) 400 350 300 250 200 150 100 50 0

NPP

NPP

a)

y = 0.6265x + 64.419 R2 = 0.2786 0

50

100

150

200

900 800 700 600 500 400 300 200 100 0

250

y = 1.4964x + 0.8198 R2 = 0.5567 0

100

200

Rainfall (mm)

400

500

d) 1200

900 800 700 600 500 400 300 200 100 0

1000 800 NPP

NPP

c)

600 400

y = 1.7902x + 88.021 R2 = 0.7131 0

100

200

300

400

y = 1.3778x + 229.95 R2 = 0.3871

200 0 0

500

100

200

300 Rainfall (mm)

Rainfall (mm)

e)

300

Rainfall (mm)

f)

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400

500

600

800

600

NPP

NPP

700

500 y = -0.13x + 595.89 R2 = 0.0245

400 300 0

100

200

300

400

500

600

700

580 560 540 520 500 480 460 440 420 400 200

y = 0.0363x + 486.23 R2 = 0.0066 250

300

Rainfall (mm)

350

400

450

500

550

600

Rainfall (mm)

Figure 3 The relationship between mean annual NPP (gCm-2) and rainfall for individual soil aggregates in the a) desert (n = 1065), b) desert steppe (n = 293), c) steppe (n = 88), d) mountain (forest) steppe (n = 243), e) alpine (n = 135), f) taiga (n = 150) vegetation zones.

a)

b)

576

7.5

-738

-5.5

Aimag boundary

Aimag boundary

c)

201

-96 Aimag boundary

Figure 4 a) NPP change (gCm-2yr-1) between 1982-2000. b) RUE Change (gCmm-2yr-1) between 1982-2000. c) Relative DNPP change (gCm-2yr-1) between 1982-2000.

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Projects and Initiatives addressing Environmental Impact Studies in Northern Mongolia and the Lake Baikal Region K. Frotscher & C.C. Schmullius, Friedrich-Schiller-University Jena, Germany

Fast-growing economies and worldwide growing consumer demands have a considerable impact on natural resources and thus on the way Earth Science data community addresses the acquisition, storage and analyses of spatial data. Forest resources of Mongolia and the Lake Baikal region came in the foreground since China’s exports of wood products have been fast growing over the last decade and hence their imports of timber. Furthermore the region is rich in many types of mineral resources which are of interest to international investors since bullish market indices and proximity to major metal markets in China and Japan. Under these circumstances an issue that must be addressed is the monitoring of the region and the subsequent analyses pertaining to environmental impacts. The University of Jena carries out several investigations using multidimensional satellite data. Reliable and up-to-date information on land surface characteristics and changes are therefore required by decision makers in order to fulfil several international and national treaties and for its own policy. One of the essential components is the use of open standards as recommended by the World Wide Web Consortium (W3C®). Open standards enable easy integration between systems and support the easy retrieval of geospatial information in a distributed environment. The FAO-funded Technical Cooperation Programme Mongolia for example used the full capacity of area-wide and mostly free available Remote Sensing data to support the development of participatory forestry. Moreover technology consultancy and the provision of advanced training on the use of Image Processing/GIS encouraged capacity building. The SELENGA initiative provides a generic methodology using Space-based Earth Observation data to overcome information gaps for evaluating processes as controlled by the Selenga River and other large and inaccessible watersheds. These are relevant for assessing the hydrological balance in the catchment area and understanding related sediment mass and pollutant transfer to the Lake Baikal. The Irkutsk Regional Information System for Environmental Protection (IRIS) will assess the current status and dynamics of the Region’s forestry environment, influenced by man-made changes and anthropogenic impact arising from pollution sources and other negative anthropogenic drivers located in the region and in adjacent areas. The ‘GMES (Global Monitoring for Environment and Security) Service Element Forest Monitoring’ provides another powerful tool for effective forest monitoring and inventory at regional scale. Based on open standard technologies the Siberian Earth System Science Cluster (SIB-ESS-C) will be developed as a spatial data infrastructure for remote sensing product generation, data dissemination and scientific data analysis.

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Digital Asia --- Information Network for Sustainable Future Hiromichi Fukui Professor, Faculty of Policy Management, Research Director, Global Security Research Institute, Keio University Secretary General of GIS Association, Japan 5322 Endoh, Fujisawa, Kanagawa, 252-8520 Japan Tel. +81-466-49-3497,Fax. +81-466-49-1334, E-mail : [email protected] Abstract Our current status of environmental issues on a global scale clearly indicates that we can no longer consider the capacity of the natural system that provides foundation to human activities, such as water, atmosphere, soil to be infinite, nor can we ignore the scope of human activities or their speed of expansion as negligible. We should always consciously maintain an overall perspective, always zooming in on various aspects while zooming right back to the wide overview of the whole picture. We have tried to look at the concepts and technologies of the Digital Earth that would enable this approach.Digital Earth is a virtual representation of the planet, encompassing all its systems, and life forms, including human societies. It is designed as a multi-dimensional, multi-scale, multi-temporal, and multi-layer information facility. The Digital Earth vision incorporates a computerized Earth, as its interface, whereby a corresponding virtual body of knowledge, or global encyclopedia of the real Earth and its digital representation for understanding the oneness of the Earth and its relevant phenomena. An overview of the scope and breadth of these innovative activities in Japan, such as the project “Digital Asia (Digital Asia Research Centre for Strategic Design, Keio University as Academic Frontier Project, Matching Fund Subsidy from MEXT(2004-2009))” is showcased. Digital Asia is an initiative to provide people and communities with easy access to geo-spatial information and events over the Internet through open sharing of GIS & Remote Sensing Data and News among all the countries of Asia. Digital Asia will form the Digital Asia Node Network (DANN) to bring together all participating people and agencies, and to provide a place where they can obtain useful information for developing their applications and demonstration systems. Digital Earth technology play key roles in economic and social sustainable development, environmental protection, disaster mitigation, conservation of natural resources and to improve humankind’s standard of living. Through seamless visualization of information ranging from the global to the local level and an easily understandable overall representation of global issues, we can expect a formation of “knowledge of the global community” based upon shared sympathy of the majority of the global population. The potential of the Digital Earth as a media to nurture a tangible sense of being part of one connected planet and one humankind is enormous. We have strong expectations that building such world will further enrich us and realize concepts such as “global citizenship” and “global society.”

Key words: GIS, Digital Earth, Media Browser, Spatial Data Infrastructures, Interoperability biographical note Dr. Hiromichi FUKUI has graduated from Nagoya University in 1980. He holds a Doctor of Science in Earth Sciences from Nagoya University in 1987. He is a Professor of Faculty of Policy Management, the Graduate School of Media and Governance and Research Director of Global Security Research Institute of Keio University. Before joined Keio University in 1996, Prof Fukui worked as a chief scientist in the Research Institute of Japanese Bank. He has extensive experience in technical assistance overseas as an expert of GIS for JICA and World Bank. His current research interests include regional planning, ecological development and global environment issues with emphasis on spatial information sciences. He has served on the secretary general of GIS Association and ISDE-Japan. He also served on a member of the board of directors in Center for Environment Information Sciences, senior scientist of JAXA and on the guest professor of Chinese Academy of Science. His specializations are in Geo-spatial Informatics and Environmentlogy.

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Annual variation of aerosol optical thickness derived from PAR observation in Mongolia T. Takamura1, T. Karasuyama2, N. Tugjsuren3, G. Batsukh4, and H. Takenaka2 (1) Center for Environmental Remote Sensing, Chiba University; [email protected] (2) Graduate school of Science and Technology, Chiba University; [email protected] (3) S\chool of Materials Science, Mongolian University of Science and Technology; [email protected] (4) School of Physics and Electronics, National University of Mongolia; [email protected] Abstract The PAR(Photo-synthetic Active Radiation) has been observed in Mongolian grassland for a long time. It is useful for not only vegetation but also atmospheric research, especially for aerosols, because of its major part of solar radiation. In this study, a method to estimate optical thickness of aerosol(AOT) has been developed with an error analysis and then the AOT has been derived from the PAR data in Mongolia during a period of 1985 to 2000. There are two remarkable features shown in the analytical results; the AOT in 1992 is temporarily increasing possibly due to the effect of Pinatubo eruption(June 1991), and after 1997 the AOT is gradually increasing and has a clear seasonal trend compared before 1995.

Introduction PAR(photosynthetic Active Radiation) regime is an efficient part of the solar radiation for all the vegetation through the energy of growth. So it is one of the most important parameters for vegetation research as well as water and has been observed in Mongolian grassland for a long time(Tugjsuren and Takamura, 2001). PAR ranges over about 400 nm to 700 nm where is sensitive to photosynthetic activity and also most strongest region in the solar spectrum. So the intensity in the PAR region is strongly dependent on an atmospheric condition, such as cloud, aerosol and so on. On the other hand, the PAR region includes no strong absorption bands for the standard atmospheric constituents, which means the “visible atmospheric window”. Therefore, the PAR data can reflect aerosol information under clear sky conditions. The PAR in the Mongolian territory has been observed at the top of the building of the National University of Mongolia in Ulaanbataar for a long time. A pyranometer and pyrheliometer with two kinds of sharp cut-off glass filters, 380 nm and 710 nm, have been working. Then the PAR data have been derived from the difference between data of both filters. In this study, the aerosol optical thickness(AOT) in the Mongolian grassland is estimated from the observed PAR data.

Methods The PAR region has two absorption bands of ozone and water vapor, where ozone absorption called Chappuis band extends widely from about 500 nm to 700 nm piled up with very weak water vapor absorption bands. Water vapor is the most remarkable and effective species in the solar radiation, but it is negligible in the PAR region so that an aerosol optical thickness can be made possible to estimate without knowledge of water vapor content in the atmosphere. That is one of advantages on estimating AOT from PAR data. Also, an ozone amount has weak change in time and space that the influence of ozone might be estimated using monthly mean data from TOMS. When aerosol is lightly loaded in the atmosphere, the extinction of the direct solar radiation can be assumed to be single-scattered. Therefore, the direct PAR is as follows,

FDirectPAR

cos T R2

³

710

380

I 0 O u exp mW O dO ,

(1)

whereWO is the optical thickness of the atmosphere at wavelengthO, m the relative air-mass which is roughly proportional to the inverse of cosine of the solar zenith angle TR the relative distance in

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astronomical unit between the Earth and the Sun. The variable ,RO shows the input solar flux at the top of the atmosphere and FdirectPAR the direct PAR irradiance received at the surface. The optical thickness of the atmosphere consists of three components, air molecule scattering(WRayleigKOҏ), aerosol extinction(WaerosolO) and gas absorption, which has several species, but in the PAR region ozone is the most effective(WozoneO) and water vapor is negligible, as described,

WO

W Rayleigh.O  W ozone.O  W aerosol .O ,

(2)

where WRayleigKOandҏWozone̤Oҏcan be estimated from an atmospheric pressure and TOMS data, but the wavelength dependence of aerosols is due to their size distribution and refractive index. So in the analysis the Angstrom’s relationship is assumed,

W aerosol .O

§ O · E¨ ¸ © 0 .5 ¹

D

,

(3)

where is the optical thickness of aerosols at 500nm. The direct radiation of PAR can be easily simulated using Eq.1 with an assumption of variables Dand E in Eq. 3. In the present analysis, the variables are assumed to be 0, 0.5, 1.0, and 1.5 as Dand to be 0, 0.05, 0.1, 0.2, 0.3, 0.5, 0.8, 1.0, 1.5, 2.0 as E As these simulated results are corresponding to the observed direct PAR FdirectPAR.obs, the most suitable value of mWcan be estimated using a polynomial equation derived from the calculated results. Figure 1 shows an example of May 8, Fig.1 A relationship between FdirectPAR and mW simulated 2000 in Ulaanbataar, Mongolia. The using aerosol parameters, D and E, in Angstrom’s relation variation in each calculated point is due to shown in Eq.3. an effect of wavelength dependence (D) of aerosol optical thickness(AOT), so that the approximated curve gives some errors on estimating an AOT. The pattern of the curve is a function of solar zenith angle T. In the figure, the effect of different aerosol types(size distribution, refractive index) becomes surely smaller with a smaller AOT. In the calculation, the ozone comes from monthly mean data of TOMS provided by NASA. In the database, it lacks during a period from May 1993 to July 1996, so the monthly mean with other years is used.

RESULTS AND DISCUSSION The direct PAR data used in this study have been collected at Ulaanbattar(47.9N in Latitude, 106.9E in longitude, 1160m MSL) during a period of January 1985 to December 2000. The original data are shown in Fig. 2, where the year 1995 has no data due to an instrumental trouble and also a period of a latter half of 1992 to the end of 1994 has sparse data. Figure 3 shows the annual trend of AOT(500nm) derived from the direct PAR corresponding to Fig.2. Fig.2 Direct PAR irradiance observed at Ulaanbattar The red dots in Fig.3 represent monthly mean data of during a period of 1995 to 2000. (unit:W/m2) AOT. It should be noted that it is difficult to - 34 -

discriminate a very thin cloud such as thin cirrus, so these aerosol data might include an effect of thin cloud. The bias to AOT by this effect, however, should be small because observations have been performed basically under sky-clear conditions with eye-watching. The annual trend of AOT shows clear features as follows; 1. The AOT is temporarily increasing and decreasing with a peak period of 1992. The increase in this feature is due to the volcano dust of Mt. Pinatubo, the Philippines, which erupted on June 15, 1991. After eruption, the effect of the volcano dust were gradually extended to the northern hemisphere and observed in the same period at many places(e.g., Guasta et al. 1994; Russell et al. 1993). The aerosols coming from the Mt. Pinatubo has been staying mainly in the upper troposphere and the lower stratosphere with an AOT Fig.3 Aerosol optical thickness(500nm) derived from of about 0.2(Takamura et al. 1994). The excess of the direct PAR observed at Ulaanbataar. AOT is also the similar, as shown in Fig.3. 2. After 1997, the AOT is gradually increasing. Figure 3 also shows a trend of increase in AOT after 1997 and a clear seasonal variation. It is curious why the trend is changed. One of possible reasons might reflect the Mongolian economical situation, but there is no clear evidence in the viewpoint.

Fig.4 Seasonal variation of AOT at Ulaanbataar during a period of 1985 to 2000. After 1997, it is clear that an increase in AOT is remarkable in summer season.

Figure 4 shows a seasonal variation of AOT. Except for the effect of Mt. Pinatubo, an increase in AOT after 1997 is distinguished in summer season, compared with the trend before 1991. Takamura et al.(1984) showed an seasonal trend of AOT observed at Sendai, Japan. According to their results, the AOT is bigger in summer than in winter. They have simulated an increase in AOT utilizing a growth theory of aerosol particles dependent on relative humidity. Simultaneously this simulation can give a change of optical properties such as complex refractive index of aerosols. They suggested that the summer aerosols grow up with adsorption of water vapor due to higher RH because they have lighter absorptive index of refraction. Ulaanbataar is in the dry and highland area inside the Asian continent, so it is hard to explain the seasonal trend using an effect of relative humidity, but there is some possibility that the aerosols during these years have changed in chemical quality of aerosols. When the optical characteristics such as single scattering albedo or complex refractive index will be made clear, these might be also clear.

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ERROR ESTIMATION This method has several error sources in measurement and analysis. In this analysis, two main sources are anticipated, one of which is due to an assumption of wavelength dependence of aerosol, and the other due to ozone absorption. An assumption of the wavelength dependence of Table 1 Error caused by different wavelength dependence aerosols such as Angstrom’s relation may of AOT introduce some uncertainty into AOT in the FdirectPAR W500.aerosol 'W500.aerosol Percentage error(%) 2 analysis. However, there is no information on (W/m ) this dependence only from the PAR observation. 318.7 0.02 0.00 0 Therefore, possible ranges of parameters and 205.7 0.34 0.02 5.9 have been assumed. The error can be estimated 100.4 0.87 0.06 6.9 with the extreme cases of parameters far from the mean value in the assumed ranges. We try three cases of FdirectPAP , 100.4, 205.7 and 318.7 W/m2 corresponding to Fig. 1. The simple results are summarized in Table 1. The error due to this assumption is dependent on AOT itself and shows less than 10 % even for the heavy aerosol loadings. The effect of ozone has been also examined. The maximum and minimum value of monthly mean of ozone amount during a period from Jan. 1985 to Dec. 2000 is 449DU and 289DU, respectively, and the averaged monthly mean is 350.0DU with a standard deviation of 36.8DU. So we put an average ozone amount with plus 50% and minus 50% for error estimation. The resultant approximation equations for two extreme cases are almost the same with negligible errors. Therefore, the effect of ozone amount to AOT is negligible. SUMMARY A PAR radiation has been observed for long time in Ulaanbataar, Mongolia. It is very useful not only for vegetation research but also for atmospheric research of aerosols, because the spectrum region of PAR has negligible water vapor absorption and other minor gases except for ozone. The key of this method is not necessary to consider water vapor amount in the atmosphere. The PAR data during a period of Jan. 1985 to Dec. 2000 are analyzed to estimate an AOT with a correction of ozone using TOMS data. As a result, an annual trend of AOT shows interesting features, one of which is an effect of Mt. Pinatubo's eruption and the other is an increase trend after 1997. Also in the latter case, there is a clear seasonal variation, which shows that AOT is increasing in summer and smaller in winter. This trend is not shown before 1994 and may reflect a change of aerosol situation in Mongolia. It suggests that the air quality should be examined by many kinds of way such as physical and chemical methods. References 1. Yamauchi, Toyotaro, 1995: Statistical Analysis of Atmospheric Turbidity over Japan: The Influence of Three Volcanic Eruptions. J. Meteor. Soc. Jpn., 73, 91-103. 2. Guasta, M. Del, M. Morandi, L.Stefanutti, B.Stein, and J.P.Wolf, 1994: Derivation of Mount Pinatubo Stratospheric Aerosol Mean Size Distribution by Means of a Multiwavelength Lidar. Appl. Opt., 33, 5690-5697. 3. Russell, P.B., J.M. Livingston, E.G. Dutton, R.F. Pueschel, J.A. Reagan, T.E. Defoor, M.A. Box, D. Allen, P. Pilewskie, B.M. Herman, S.A. Kinne, and D.J. Hofmann, 1993: Pinatubo and PrePinatubo Optical-Depth spectra: Mauna Loa Measurements, Comparisons, Inferred Particle Size Distributions, Radiative Effects, and Relationship to Lidar data. J. Geophys. Res., 98, 2296922985. 4. Takamura, T., Y. Sasano, and T. Hayasaka, 1994: Tropospheric aerosol optical properties derived from lidar, sun photometer and optical particle counter measurements. Appl. Opt., 33(30), 71327140. 5. .Takamura, T., M.Tanaka, T.Nakajima,1984: Effects of Atmospheric Humidity on the Refractive Index and the Size Distribution of Aerosols as Estimated from Light Scattering Measurements. J. Meteor. Soc. Japan, 62(4), 573-582. 6. Tugjsuren, Nas-Urt, and Tamio TAKAMURA, 2001: Investigation for Photosynthetically Active Radiation Regime in the Mongolian Grain Farm Region, J. Agric. Meteorol., 57(4), 201-207. 7. TOMS ozone data: http://toms.gsfc.nasa.gov/ozone/ozoneother.html - 36 -

Determination of the Photosynthetically Active Radiation for Vegetation Growth Period of the Mongolian Grain Farm Region By Tugjsuren Nasurt Mongolian University of Science and Technology,Ulaanbaatar, Mongolia E-mail:[email protected] AbstractReality, worldwide routine network for the measurement of photosyenthetically active radiation (PAR) is not yet and PAR is often calculated as a nearly constant ratio of the broadband solar radiation. This study focuses on the PAR to crop vegetation period of Mongolian grain farm region and presents and discusses some specific aspects related to the wheat phenological phases. Photosynthetically active radiation (PAR) contributes significantly in comprehensive studies of radiation climate, remote sensing of vegetation, radiation regimes of plant canopy and photosynthesis. The PAR radiation covering both photon and energy terms lies between 400 and 700 nm or 380–700 nm in the solar spectrum . However, today's more commonly accepted spectral interval 400–700 nm does not cause misunderstanding.

1. Introduction

Photosynthetically active radiation (PAR) contributes significantly in comprehensive studies of radiation climate, remote sensing of vegetation, radiation regimes of plant canopy and photosynthesis. The PAR radiation covering both photon and energy terms lies between 400 and 700 nm or 380–700 nm in the solar spectrum . However, today's more commonly accepted spectral interval 400–700 nm does not cause misunderstanding. Most published experimental results use measured values of global PAR (QP) and global solar radiation (Q) to determining the PAR fraction of the broadband solar radiation. In the literature, there exist three distinct measuring techniques for determining PAR: (i) spectrally, by integrating spectral irradiance distribution measurements over the waveband 400–700 nm; (ii) indirectly, through combined filtered data; and (iii) directly, via spectral PAR measurements (400–700 nm) by means of quantum sensor (QP). Published values for the PAR fraction of global irradiance are around 0.45 or 2 E J-1 for photon efficiency. Nevertheless, the range of the PAR fraction suggests the desirability for recalibration accounting for local climatic differences. Thus, the present analysis aims to quantify temporal variations of the PAR efficiency for various atmospheric conditions at Ugtaal, Mongolia (48°3'N, 105°25'E, 1160 m above the sea level), and to identify reasons for such variations. Fraction of photosynthetically active radiation is defined as the fraction of photosynthetically active radiation absorbed by a plant canopy in vegetation period. It excludes the fraction of incident PAR reflected from the canopy and the fraction absorbed by the soil surface or the combination of forest floor and understory, but includes the portion of PAR which is reflected by the soil and absorbed by the canopy on the way back to space. 2. Measurement and data processing In this study, we used investigative data at the Ugtaal (48°3'N, 105°25'E, 1160 m above the sea level), which is located in the Mongolian grain farm region. Data were collected at the Ugtaal for every hour in the daytime during 1986-1996. In order to measure the PAR received at the Earth’s surface, a thermo-electric actinometer (AT-50) was mounted. This instrument has two filters (BS-8, KS-19), which can selectively pass only PAR. The direct solar radiation in the full wavelength and the direct PAR can be obtained by the filter operation, manually. We simultaneously measured global and diffuse radiation using a thermo-electric pyronometer (M-80M) with a black and white type sensor. With this instrument, the global and diffuse radiation can be observed with and without a shadow disk for direct solar flux. Measurements of photosynthetically active radiation at Ugtaal, combined with simultaneous meteorological parameters such as soil and atmospheric temperature, soil and atmospheric humidity, wind - 37 -

speed and its direction, cloud cover or sunshine duration.We were determined QP at Ugtaal for every phenological phase of wheat ( wheat sort -Buriad-34 ) development during 11 years (1986-1996). 3. Results and discussion Global solar radiation is one of the most essential factors for vegetation. Results of the investigation show that a mean value of integrated solar radiation over all wavelengths at Ugtaal for 11 years, is 4.4-4.7 GJ/m 2 for the whole year, and 2.3-2.7GJ/(m 2 yr) for the vegetation period. The study of PAR in Mongolia shows that the average ratio of yearly global solar radiation to global PAR in Ulaanbaatar and Ugtaal are 0.469 and 0.472, respectively (N.Tugjsuren 1996, 2004). The ratio (QP/Q) exhibits seasonal dependence, high values in spring-summer (0.48-0.47) and lower and more variable values (0.44) in winter. The higher proportion of PAR irradiance occurred in May (0.49), while the respective lower proportions occurred in December and January (0.436). The analysis of hourly values also reveals significant diurnal variation of the ratio during daylight hours. The sky clearness and brightness indices and path length caused substantial changes in the PAR fraction. The ratio of PAR dependence on vegetation period is included in table 1. A number of researchers have proposed various strategies that need to be adopted in order to develop sustainable food production systems in the country and the same time reduce the vulnerability of small farmers to crop failures. The search for drought-adopted species of short cycle, well adapted to the arid and semi-arid environments is also at the top of the research agenda of Mongolian agricultural researchers. The plants’ phenological development and water and PAR requirements are important attributes that need to be considered when evaluating their suitability for a given climatic environment. Besides phenology and crop water requirements, we need to be tested on eco-physiological properties such as responses to high temperature and water stress. The response of annual crops to water stress depends upon the developmental stage of the plants at the time water becomes limiting a factor. The analyses of hourly or daily measurements of PAR in period May 1986 to October 2006 for grain farm region of Mongolia, are briefly summarized as follows (Fig.1): The mean values of QPAR and phase duration of derivation and tillering stages of Buryad-34 wheat has been found 93.6 MJ/m 2 and 123.9 MJ/m 2 and 9 and 13 days, respectively. Then, QPAR and phase duration for stem production, heading and flowering stages are 138.7 MJ/m 2 , 119.8 MJ/m 2 and 83.7 MJ/m 2 and 15, 15, 11 days, respectively. The mean values of QPAR and phase duration of this sort of wheat for milk, wax and full maturity has been found 101.0, 87.2 and 96.0 MJ/m 2 and 14, 12 and 14 days, respectively. It should be pointed out here that the differences in days to maturity between were 34-53 days.

1986 1987 1988 1989 1990 1991 1992 1993 1994 1995

Y 21.1 13.5 7.3 6.9 6 4 14.4 14.6 10 5 10.3

T 7 11 8 8 12 6 9 7 11 12 9

Qp 100.9 106.9 78.3 69.4 113.2 61 106.9 100.7 99 100 93.6

T 10 10 14 14 16 17 11 13 14 14 13

Qp 95.2 97.9 138.8 138.7 135.6 161 135 109.4 112 115 123.9

T 14 13 20 18 17 16 14 14 14 14 15

Qp 133.4 117.3 194.7 111.8 143.8 132.7 182.2 114.8 138 118 138.7

T 20 13 13 14 16 14 16 14 13 15 15

Qp 104.6 117.3 124.2 118.9 118.6 119.6 126.9 129.4 118.9 120 119.8

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T 12 4 9 6 14 8 14 14 13 14 11

Qp 114 31.4 82.6 48.5 94.9 62.2 104.2 118.6 118.9 91 83.7

T 14 16 10 14 17 13 14 15 14 14 14

Qp 88.1 125.6 87.1 110.2 113.2 97.8 69.6 113.2 101 95 101

T 9 12 8 16 16 19 9 10 13 12 12

Qp 78.6 146.7 69.7 88.1 79 112.2 99.4 32.2 84 82 87.2

Full maturity

Wax maturity

Milk maturity

Floweing

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Stem productio n

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Yiuld

Derivatio n

Table.1.

T 18 6 10 20 20 13 14 14 12 12 14

Qp 146.4 40.7 67.7 125.5 79 87.6 90.4 169.5 98.8 95.5 96

QPAR and dermination

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Figure1. Photosynthetically active radiation for vegetation period of Ugtaal, Mongolia

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2000

CONCLUTION 1. The mean values of QPAR and phase duration of derivation and tillering stages of Buryad-34 wheat has been found 93.6 MJ/m 2 and 123.9 MJ/m 2 and 9 and 13 days, respectively. 2. QPAR and phase duration for stem production, heading and flowering stages are 138.7 MJ/m 2 , 119.8 MJ/m 2 and 83.7 MJ/m 2 and 15, 15, 11 days, respectively. 3. The mean values of QPAR and phase duration of this sort of wheat for milk, wax and full maturity has been found 101.0, 87.2 and 96.0 MJ/m 2 and 14, 12 and 14 days, respectively. It should be pointed out here that the differences in days to maturity between were 34-53 days. 4. The agricultural advisory service will in future play an extremely important role in bringing about the necessary modification of the cropping systems. 5. We need to investigate the possibilities to use of satellite remote sensing techniques for crop monitoring and yield forecasting. The satellite data are integrated with agrometeorological models to improve crop monitoring and yield forecasting.

References 1. G.Papaioannou, G.Nikolidakis, D.Asimakopoulos, D.Retalis, 1996: Photosynthetically active radiation in Athens, Agric.For.Meteorol. 81(1996), pp287-298 2. D.Rijks, J.M.Terres, P.Vossen, 1998: Agrometeorogical applications for regional crop monitoring and production assessment, Joint Research Centre, European Commission. 3. Tugjsuren N., Takamura T, 2001: Investigation for photosynthetically active radiation regime in the Mongolian grain farm region, J. Agric. Meteorology, 57(4), 201-207. 4. Tugjsuren N, 1996: Investigation of solar radiation regime of dry and cool zone crop-growing region of Mongolia, Science and Technology Report, Science and Technology Information Center, Ulaanbaatar, Mongolia 75 pp, (In Mongolian). 5. Tugjsuren N, 2003: Latitudinal distribution of solar radiation under clear and cloudy conditions on the territory of Mongolia, Proceedings of the CEReS International Symposium on Remote Sensing , ‘Monitoring of Environmental Change in Asia’, Desember 16-17, CEReS, Chiba University, Japan, pp 129-133 6. Tugjsuren N, 2003: Aerosol pollution of the atmosphere and its sources features in Mongolia, Proceedings of the CEReS International Symposium on Remote Sensing , ‘Monitoring of Environmental Change in Asia’, Desember 16-17, CEReS, Chiba University, Japan, pp 153159. 7. Tugjsuren N, 2004: Investigation of photosynthetically active radiation in Mongolia, Proceedings of First International Workshop on Land Cover Study of Mongolia using Remote Sensing /GIS, 8-10 June 2004, p 45-55

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Coherent time in cloud analysis using 95GHz FM-CW cloud profiling radar Y. Nakanishi(1), T. Takano(1), K. Akita(1), H. Kubo(1), Y. Kawamura(3), H. Kumagai(4), T. Takamura(1,5) And T. Nakajima(6) (1) Graduate School of Science and Technology, Chiba University (2) Center for Frontier Electronics and Photonics, Chiba University (3) Faculty of Engineering, Chiba University (4) National Institute of Information and Communications Technology (5) Center for Environmental Remote Sensing, Chiba University (6) Center for Climate System Research, The University of Tokyo



Abstract A low-power and high-sensitivity cloud profiling radar with a frequency-modulated continuous wave (FM-CW) of 95 GHz has been developed in order to survey an internal cloud structure by ground-based observations. Millimeter wavelength at 95 GHz is more suitable for cloud profiling than a traditional one with 35 GHz because of its higher sensitivity for cloud particles. An FM-CW radar has several advantages to a pulse radar, such as a lower output of power, easy operation, low cost and so on. The radar developed with a 500mW output power has almost the same performance as the pulse radar with 1.6 kW output power of the National Institute of Information and Communications Technology. Doppler function is also applicable for a vertical motion of cloud particles.  In the previous study, Doppler sensitivity has not reached to the expected level even if the signal sensitivity has been raised theoretically by a signal averaging technique. The reason might be turbulence inside the cloud, which means that coherency reflects the droplet motion of cloud. Therefore, coherent time should be considered when averaging reflected signal for an improvement of Doppler sensitivity. A dependency of length of the coherent time on internal motion of cloud has been discussed in the present study. 

 1. Introduction Cloud in the atmosphere plays an important role for the Earth climate through radiation budget. As described in the ISCCP report, however, cloud behaviors in each GCM gives some different aspects due to unknown cloud processes in formation. These are key issues for climate studies. We have many tools for cloud research, such as satellite- and ground-based instruments. At the ground, cloud observation is so limited in space but can be performed by many methods. A radar system is a powerful tool in order to make internal structure of cloud clear. Traditional radar has a frequency of 35GHz, which is very sensitive to rain drops and bigger ice crystals, but insensitive to cloud particles, especially water clouds. In these cases, new radar system with higher frequency of 95GHz has been developed. FM-CW radar is one of the most powerful tools for cloud research, which has continuously transmitted and frequency-modulated wave of 94GHz. Table 1 Specific features of RADAR The power emitted from an antenna can be Type pulse Frequency-modulated reflected by cloud and then received by Peak power High (1600W) Low (0.5W) another antenna with a Doppler effect. The Instrumental Large Small(solid state cloud features such as cloud density and size made) particle speed can be derived from the frequency shifts caused by its traveling time Except antenna and Doppler shift. FM-CW radar has a big Portability Hard(Heavy) Easy(relatively light) advantage rather than traditional pulse radar Cost Relatively high Relatively low because of its lower power, smaller size with semiconductor-components and lower cost performance. These features can make it possible to have observation at many sites. - 41 -

2. Basic concept of FM-CW radar and its signal analysis Basic concept of FM-CW in signal processing radar is different from pulse radar. It requires conversion of time-frequency domain to distance from the emitted source, while pulse radar is simple conversion of reflected time to distance of target. Therefore, its signal analysis is a little more complicated for FM-CW radar signal than that for pulse radar. The principle and a photograph of an FM-CW radar are shown in Figs.1 and 2. The signal frequency is modulated in the range of f0 ҙҡҟҏF. Transmitted signal from one of the antennas is reflected by cloud particles, returns, and is received by the other antenna with a delay time of t relative to the original transmitted signal. Mixing the transmitted and received frequencies, beat frequencies fb are observed in the spectra, which are caused by ensemble of clouds particles: fb = 4 F r / (c Tm)

(1),

where r is the height of the clouds, Tm is the modulation interval, and c is light velocity. When theobjects move line of sight, the frequencies of reflected signals change by fd : fd = -2 ( f0 / c ) ( dr / dt ) (2). Spectra of the obtained beat frequency, corresponds to the cloud ranging profiles.

the in the

fb ,

Fig.1. Principle of a

FMCW Radar

3. Observation of clouds Using the developed millimeter-wave FM-CW radar at 95 GHz, we observed clouds on Mirai, a Japanese scientific research vessel shown in Figs.3 and 4, in the Arctic Ocean and southwest Pacific Ocean in 2004 and in the northern half of the Pacific Ocean in 2005 (Fig.5). Fig.6 shows an example of observed cloud profiles in the Arctic Ocean on September 3 to 6, 2004 with our FM-CW radar at 95 GHz. Thin clouds at 5-10km height were detected as well as low-height clouds and precipitation as shown in Fig.6.

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Fig.2 Developped FM-CW radar at 95GHz

Fig.3 Japanese research vessel Mirai

Fig.4 Container of the FM-CW radar on Mirai Fig.5 Cruise track of Mirai in 2004 and 2005 4. Coherency of signal from clouds and simulation of coherent integration Received signal of the FM-CW radar would be an ensemble of scattered waves from cloud particles. The modulation interval Tm is 1 msec and single frequency spectrum corresponding to a cloud profile is obtained each 1 msec. There are two ways to integrate signals in random noise for improving S/N ratio: coherent integration and incoherent integration. If the signal from clouds is coherent in a longer period than 1 msec, coherent integration is more effective to improve S/N ratio by factor 2 than incoherent integration. If the coherent time of scattered wave from clouds is, however, shorter or comparable to 1 msec, incoherent integration should be done to avoid reduction of S/N ratio. Fig.7 shows a result of computer simulation of improvement in S/N ratio of observed signal. The input signal is 100 times smaller in amplitude than a random white noise Figs.7(a),(b). Assuming that the coherency of the signal is maintained in the period, coherent integration shown in Fig.7(c) present much better S/N ratio than the case of incoherent integration shown in Fig.7(d).

5. Conclusion The result of simulation shows that coherent integration is fairly effective if the signal is coherent in the period of integration. In order to realize coherent integration, however, it is very important to know how long coherency is maintained in clouds and precipitations. Investigation of coherent time in various clouds would be interesting on the standpoint of microphysics of clouds. We, therefore, will measure dependence of S/N ratio on coherent integration period in the near future.

References 1. Toshiaki TAKANO, Ken-ichi AKITA, Hiroshi KUBO,  Youhei KAWAMURA, Hiroshi KUMAGAI , Tamio TAKAMURA, Yuji NAKANISHI, and Teruyuki NAKAJIMA, “Observations of Clouds with the Newly Developed Cloud Profiling FM-CW Radar at 95GHz”, The International Society of Optical Engineering, Symposium on Remote Sensing, Bruges (Belgium), Sept.19, Vol.5979, No.07, 2005.

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(b) spectrum of random white noise whose amplitude is 100 times larger than the signal.

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(c) 50 times coherent integration of the (d) 50 times incoherent integration of the signal and noise. The signal appears signal and noise. The signal cannot be in the spectrum above noise. seen in the spectrum. 䎔䎑䎓䎓

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Fig.7 Simulation of coherent integration of received data.

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LAND COVER MAPPING OF MONGOLIA Sh. Munkhtuya GIS Specialist/ IT Asia Gold Mongolia LLC Email: [email protected]

Abstract: The goal of this research is to develop a land cover classification system for Mongolia, a method for integrated information processing techniques which would produce a land cover map for multi-class area, that meets international standards using Landsat thematic mapper information. To achieve this goal the following specific objectives were set: 1. To study Mongolian and International land cover classification system and to evaluate it, 2. To study the possibility of a land cover classification system using spectral parameters measured by satellite sensors, 3. To create a database incorporating satellitte imagery, thematic and topographic maps, field measurement and linking them by spatial and temporal scales and by contents, 4. To develop a methodology and information processing technique for digital classification and land cover mapping, 5. To develop a methodology for assessment of land cover change, 6. To produce a multi-class land cover map at a scale of 1:50,000 for a selected area. This research goal and objectives have been achieved succesfully. The developed system and methodology have been tested for 11 different scenes of Landsat satellite. 1:50,000 scale land cover maps have been produced for 4.8 mln hectare of main crop area of Mongolia. This research study was done at the Information and Computer Center of the National Agency for Meteorology, Hydrology and Environmental Monitoring (NAMHEM) of the Ministry of Nature and Environment (MNE) of Mongolia, ERDAS IMAGINE image processing system and ArcINFO geographic information system based on an UNIX operating platform. Practical value of this research: The developed system is a theoretical and methdological base for real time land cover mapping of Mongolia using Landsat TM data. It would be used for 1:50,000 scale land cover mapping for the remaining area of Mongolia. Land cover information is useful in practical implementation of new system for land monitoirng and management. Also it can be used for thematic mapping, such as vegetation, soil, forest, water and land use, for collecting of reference information for modeling of ecosystem. Study Area The study areas for land cover mapping were selected based on different landscape types of Mongolia. It includes different ranges of land cover, such as Khangai mountains, Taiga upland, Gobi and Steppe area of Mongolia, which covered 12 Landsat scenes (Figure 1.1).

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Data Used RS Data: x 12 scenes of Landsat ETM from 1989 to 2000 GIS Data: 1. Main geographic features like administrative boundaries, lakes, roads, rivers, and relief (scale 1:500,000) 2. Land use map of Selenge aimag (scale 1:500,000) 3. Ecosystem map of Selenge region (scale 1:500,000) 4. Fire map, which produced from NOAA AVHRR data of 2000 5. Parcel map (scale 1:100,000) (Figure 1.5) 6. Desertification and degradation map (scale 1:1,000,000) Statistical Data: - Local agronomist data (Figure 1.6) - Parcel data from LMA, MAF and National Census

2. CLASSIFICATION APPROACH PROPOSED FOR LAND COVER MAPPING Concerning the topic land cover mapping initial analysis came to the result that a part of the mapping carried out by supervised classification. Specific classes and issues had to be mapped/refined by a following interpretation of the data. Based on the available data (in a first step data of 1989 were used) a general work approach has been worked out which is replicable for 2000 data sets, re-using training areas, adapted techniques and applications. The main working steps are as follows: x Geocoding of satellite data x Preparation of material for ground survey and execution (carried out for Selenge) x Develop land cover classification and interpretation keys x Supervised classification using existing information and ground survey results x Post-processing and filtering of systematic errors x Interpretation and refinement of classification results x Generalize land cover classes into big classes x Generate land cover change map 3. RESULTS 3.1 Land cover classification system Depending on the data used in this research work, the second level land cover classes were classified. 7 land cover classes and 38 subclasses were developed over Mongolia by comparing international and Mongolian previous land cover classification systems. Mongolian land cover classification system 1. Grassland 1.1 Mountain tundra 1.2 Mountain meadow steppe 1.3 Mountain steppe 1.4 Valley steppe 1.5 Plain steppe 1.6 Burnt steppe 1.7 River valley meadow 1.8 Sparce vegetated land 2. Cultivated land 2.1 Agricultural land 7. Settlement 2.2 Fallow

5. Wetland 5.1 Swamp 5.2 Clay and solonchacks 5.3 Bulrush 6. Water body 6.1 Salt lake 6.2 Fresh lake 6.3 Pond 6.4 River 6.5 Water basin 7.1 Urban area - 46 -

2.3 Abandonned land 3. Forest 3.1 Coniferous forest 3.2 Decidous forest 3.3 Sparce forest 3.4 Shrub land 3.5 Mixed forest 3.6 Burnt forest 3.7 Diseased forest 4. Bare land 4.1 Rocks 4.2 Glacier 4.3 Dried land 4.4 Desert 4.5 Sand 4.6 Developing sand 4.7 Stones 4.8 Dry valley 4.9 Mining

7.2 Road, communication lines 7.3 Constructions out side from city

3.2 Land cover maps

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4. CONCLUSION 1. Development of land cover classification system and its implementation technology is theoretical base of land cover mapping. 2. Developed system, methodology and technology can be used for land cover classificaton of other areas of Mongolia in a 1:50,000 scale using Landsat imagery. 3. Land cover information is useful for generating basic information of thematic mapping, such as grassland, soil, forest, water and land use, bio-physics, and ecosystem modeling in real situation, by a large area and in a short time. 4. It can be used for land cover suitability assessment, land cover database generation. 5. Spectral values of land cover types, such as forest, bare land, meadow, water and wetland give high contrast from each other in the Landsat image bands of 4, 5, 7 but spectral values of grassland cover are more complex. 6. Land cover monitoring study has been started due to the land cover classification having been done in a certain time interval and to determine its change detection. Land cover classification for the whole territory of Mongolia is very useful information for other thematic study and to implement this goal we need to process a total of 110 Landsat scenes to cover the entire area of Mongolia.

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Development of a Better Atmosphere and Soil Resistant Vegetation Index for Forestry Monitoring in Taiwan G. Dashnyam1 *, G. R. Liu2, C. K. Liang1, T. H. Kuo2 C. W. Lan1, T. H. Lin2, Y. C. Chen1 1

2

Institute of Atmospheric Physics, National CentralUniversity, Chung-Li, Taiwan 320 Center for Space and Remote Sensing Research,National Central University, Chung-Li, Taiwan 320 *corresponding author: [email protected]

Forests are considered as an important sink in capturing atmospheric CO2. The Tokyo Protocol places special emphasis on how much forest areas a country owns. With the rising concern over global warming, the monitoring and preservation of forest areas have become an increasingly crucial task for the entire human race. The main purpose of this study is to develop a more accurate forest area assessment via satellite data. It is well known that the effects from atmospheric radiation can degrade satellite image quality, rendering inaccurate interpretation of the data. For example, although the NDVI index can provide surface vegetation information, the atmospheric effect can cause significant errors. With the launch of the more advanced MODIS sensor, which is equipped with more spectral bands, more accurate vegetation indices have been devised. In this paper, the EVI, AFRI, and SARVI vegetation indices have been chosen to compare their atmosphere and soil resistant capabilities in finding the most suitable index for forestry monitoring in Taiwan.

Keywords: Forest, Satellite, MODIS, Vegetation index

1. Introduction Vegetation is considered one of the most important components of the ecosystems. It influences the planet’s energy balance, climate, hydrological and bio-geochemical cycles, and so forth. Knowledge about the vegetation species, distribution patterns, and changes in the phonological cycle can give relevant information about the climatic and geological characteristics of an area. Therefore, understanding in the vegetation distribution and its changes is very crucial. This is where satellite remote sensing plays a vital role in its ability to generate spectral indices of the vegetation indicator from their multiple spectral bands. The main objective of the study is to estimate various vegetation indices over the Taiwan region via satellite data, where an optimized vegetation index is chosen. 2. Vegetation Index (VI) Several vegetation indexes----- Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil and Atmosphere Resistant Vegetation Index (SARVI), Aerosol Free Vegetation Index (AFRI), and the Atmospherically Resistant Vegetation Index (ARVI)-----were investigated in this study. Among these indexes, the NDVI is the most well-known indicator for vegetation mapping. However, it has already been in use for decades. Currently, there are several other remotely sensed channels that can be utilized in reducing the atmospheric influence and soil effects. For example, the EVI adopts the blue ray spectrum, which can better correct the atmospheric haze, while the SARVI has been clamed for its capability to correct both the soil and atmospheric noise. In addition, the AFRI vegetation index owns the capability to resist the existence of man made smoke (Rouse et al., 1973; French et al., 1997; Kaufman and Tanre, 1992; Kaufman et al., 1997; Santer, 1999). 3. Data Description Data from the MODerate resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua satellites are mainly employed in the study. The satellites scan the Earth twice a day. The first scan occurs in the morning, and the second scan is conducted in the afternoon. The same location in scanned every sixteen days. The MODIS sensor has 36 spectral bands, situated between 0.4 to 14.2 microns. It has - 49 -

a maximum spatial resolution of 250m for bands 1-2, 500m for bands 3-5 and 1000m for bands 6-29, respectively. For our present study, the MODIS-Terra satellite bands 1 (0.62-0.67 um), 2 (0.84-0.87 um), 3 (0.450.47 um), and 7 (2.10-2.15 um) with 1 km resolutions were used. The MODIS-Terra level 1B data were collected between November - December 2004 and January - May 2005. This particular time period was chosen, where the maximum amount of cloud free images with a near nadir view could be obtained. Also, the data from SPOT VI were used in this study. 4. Analysis and Result In order to obtain an accurate vegetation computation, the geometrical correction is very important, especially for regions such as Taiwan, because of its complex landuse/landcover distribution, mountainous terrain and fine agriculture farming. Fig. 1 is an example demonstrating the vegetation computation errors caused by misregistration. Due to this aspect, a precise MODIS data geo-correction procedure was developed. This was done by conducting an image matching process to a reference SPOT geo-corrected image set, along with a subsequent fine resampling procedure. Results show a great improvement in the sup-pixel registration accuracy. As this study seeks to find a vegetation index that is more atmospherically resistant, the various computed vegetation indexes from SPOT-4 VEGETATION data were compared with the ground measured aerosol optical depth (AOD) of South Korea’s Anmyon station, one of the Aerosol Robotic Network (AERONET) observation sites. From the slope of Fig. 2, we see that the NDVI index value goes down steeply with an increasing AOD. This is consistent with the fact that a higher AOD causes the suspended particles in the atmosphere to scatter the red band more. This increased scattering may produce a larger reflectance in the red band than the NIR for the sensor to detect, and thus render the value of the NDVI index to drop. As for the scatter plot of the AFRI index with the AOD, the regression line slopes a little upward with an increasing AOD (not shown). This indicates that the SWIR can still be slightly affected from an increasing AOD, but not as much as the red band. Finally, for the ARVI index (Fig. 3), when gamma equals 1.3, the regression lines tilts up a little; when gamma equals 1, it becomes nearly horizontal; and when gamma equals 0.7, it slightly edges down. The more horizontal regression line becomes, the more likely it is not so easily affected by the AOD. At this point, the lower correlation coefficients indicate that that model is less sensitive to the AOD variation. In other words, the model, which has the lowest correlation relationship, is the one we want. Therefore, it seems logical to assume that the AFRI index and the ARVI index with a gamma value of 1.0 would be the most capable of overcoming the influence of aerosols. Following a similar procedure used by Kaufman et al (1997) in defining AFRI2:1, an alternative EVI (EVI SWIR) was developed by replacing the blue ray with the short wave infrared band. The composite EVI and EVI SWIR images are shown in Fig. 4. The EVI values are very high in the western plain areas, which appears to be a forestry area. Yet, this is not true. The EVI SWIR does not show such high values in these regions. This demonstrates a more reasonable landuse/landcover distribution. Subsequent detailed comparisons also supported that the EVI SWIR was better than the EVI. 5. Summary Through comparisons of the three vegetation indexes, it appears that the AFRI and ARVI index when Ȗ=1, seems to be the most capable in reducing the atmospheric effects. In contrast, an over-correction appears to occur when Ȗ=1.3, and an under-correction seems to occur when Ȗ=0.7. In addition, the value of 0.7 demonstrated the lowest fluctuation throughout the changes in the AOD. This contradicts to the fact that the gamma value of 1 is supposedly the most capable of decreasing the atmospheric influence. Consequently, more research should be done regarding the value of 0.7. Furthermore, it is also suggested that additional gamma values ranging from 0.7 and 1.3 should be tested in the future. Meanwhile, the addition of the blue ray or short wave channels could provide additional information in evaluating the atmospheric condition (AOD, turbidity, or smoke). It could help reduce the noise, when computing the vegetation index. This can be seen from the consideration of the short wave data utilized in - 50 -

this study. Basically, any single vegetation index is unable to provide a perfect delineation of the vegetation canopy distribution. Needless to say, a better atmospheric and soil resistant vegetation index could be established by compositing the different vegetation index’s advantages where our analysis. References 1. French, A.N., Schmugge, T. J. and Kustas, W.P., 2000: Discrimination of Senescent Vegetation Using Thermal Emissivity Contrast. Remote Sensing Environ., 74, 249~254. 2. Kaufman, Y. J. and Tanre, D., 1992: Atmospherically Resistant Vegetation Index (ARVI) for EOS-MODIS. IEEEE Trans. Geosci. Remote Sensing., 30 (2), 261~270. 3. Kaufman, Y. J., Wald, A. E., Remer, L. A., Gao, B. C., Li, R. R. and Flynn, L., 1997: The MODIS 2.1ȝm Channel---Correlation with Visible Reflectance for Use in Remote Sensing of Aerosol. IEEEE Trans. Geosci. Remote Sensing., 35 (2), 1286~1298. 4. Rouse, J.W., Haas, R.H., Schell, J.A. and Deering, D.W., 1973: Monitoring vegetation systems in the great plains with ERTS. Third ERTS Symposium, Goddard Space Flight Center, Washington, DC. NASA SP-351, 390~317. 5. Santer, R., V. Carrere, Ph. Dubuisson and J. C. Roger, 1999: Atmospheric corrections over land for MERIS. International 0,25 0,2 0,15 0,1 0,05 0 Red_ref

NIR_ref

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Fig.1 Change in the NDVI values due Fig. 2 Comparison of the SWIR reflectance and AOD-500nm. misregistration.

Fig. 2 Comparison of the NDVI reflectance Fig.4 Monthly composite EVI (left panel) and AOD-500nm. and EVI SWIR (right panel) for May 2005.

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Sharing ground truth data for land cover mapping – GLCNMO Ryutaro Tateishi CEReS, Chiba Univeristy, 1-33 Yayoi-cho Inage-ku Chiba 263-8522 Japan E-mail: <mailto:[email protected]>[email protected] Ground truth data in land cover mapping are important as training data and validation data. The quality of mapping result is highly dependent on training data, and validation is an essential step in the mapping. However collection of ground truth data needs much work. Land cover ground truth data for global area are being prepared by national mapping organizations with the initiative of Chiba University in the Global Mapping project (http://www.iscgm.org/). These data will be used for the Global Land Cover mapping by National Mapping Organizations (GLCNMO), and also they will be available to anyone in order to promote sharing ground truth by any organizations and projects.

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Ecosystem changes mapping for Eastern Shore of Lake Hovsgol from satellite imagery and GIS a case study B.Gantsetseg Institute of Geoecology, Baruun Selbe-15,Ulaanbaatar-211218, Mongolia Fax :976-11-321862, E-mail : [email protected]

ABSTRACT. Analysis of the watersheds on the eastern-shore of Hovsgol Lake in relation to soil, pasture and vegetation was done using remote sensing techniques through the use of Arcview, ENVI (the environment for visualizing images) and ERDAS IMAGINE and ArcGIS geographic information systems software. Then, depending on the priority of the factors to be considered for alignment, proper weight ages were selected for different features. The Turag valley has the largest catchment area (22635.1 ha) and also has the longest river in the study area. The area of intensively burned forest area was estimated to cover 2903.6 ha using a supervised classification map. The Permafrost and forest pest insect distribution maps are shown for the Eastern part of Lake Hovsgol, prepared by building a model operation in ArcGIS (based on topographic parameters of solar radiation, elevation, wetness index, slope, NDVI and landcover). KEY WORDS : Landsat-TM, ASTER, Supervised classification, INTRODUCTION Watershed studies are essentially the collection of information on a wide range of parameters of static and dynamic processes related to the landscape, hydrology, vegetation, land use, soils, geomorphology, drainage condition, climate etc. Remote sensing provides valuable data over vast areas in a short time about natural resources, meteorology and environment leading to better resource management and accelerating national development. The data collected through aerial and satellite sensing is either visually interpreted or analyzed on the computer for specific resource applications. Normally both visual and computer techniques are employed to tackle the complex problems involving more disciplines. The main advantages of automatic analysis are classification of many resource themes covering a large area in a short time. While, the remote sensing data is operationally being utilized for mapping various resources, the need is to step ahead towards integrating these resource maps with other resource related information and socioeconomic data for generating action plans (Rao et al.,1994). A Geographic Information System is a specific information system applied to geographic data and is mainly referred to as a system of hardware, software and procedures designed to support the capture, management, manipulation, analysis, modeling and display of spatially referenced data for solving complex planning and management problems (Burroughs, 1986). The GIS will have to be the workhorse of integrated database system, as both spatial and non-spatial data for habitat suitability need to be handled. More intensely developed approach to information integration: attribute information is associated with point, line and polygons as spatial entities that describe features occurring in the real world. MATERIALS AND METHODS Materials The project study area includes six watershed valleys along the eastern shore of Hovsgol Lake, an area that is mountainous and mostly covered by taiga forest. Landsat-7 imaging bands 4(R), 7(G) and 2 (B), as well as digital data dated 28 June 2000 and ASTER imaging (with 14 channels of digital data dated 27 August 2001) were used to prepare forest cover type and landcover maps on a scale of 1:100,000. Topographis maps, scale 1:100,000, sheets M47-22, M47-34, M47-46, M47-23, M47-35 and M47-47 produced by Mongolian Government Survey Agency, were used as base map for the sampling design and generating a Digital Elevation Model.

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Methodology In the first step Landsat TM and ASTER images were used for visual interpretation and pre-classification in order to create thematic maps of forest covering and density. The next was image processing which included image classification, interpretation and distribution analysis. The comparison between spectral signatures of different important cover types such as burnt forest, unburnt forest and of image from ASTER was performed. Finally, GIS was conducted to investigate the relationship between different research fields and the environmental factors. The research flowchart is shown in figure 1. Fieldwork carried out during JulySeptember 2004-2005. The ASTER image was classified by using Supervised Classification techniques and maximum Likelihood Classifier. RELEVANT PARAMETERS USED TO RELATIONSHIP FIELD STUDY

Digitize

Contour

Ancillary data

SATELLITE IMAGE

TOPOSHEETS

ASTER

LANDSAT

Top point GEOMETRIC CORRECTION

DEM ASTER

LANDSAT Filter (DX, DY)]

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Influenced factor for forest pest insect distribution

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Remote sensing data

Analog data

NDVI

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i n s e c t

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d a t a

e c o l o g y

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Forest age, fire

Air tem, solar rad, precip. etc

Vegetation map

Family location and income

Relationship between research fields

FLOW CHART FOR METHODOLOGY ON GIS MAPPING Relationship between Thematic maps research fields

Distribution map

Family location

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Vegetation, geomorphology, landscape

Family Distribution income for

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Distribution for diatom

Forest pest insects’ distribution

terrestrial insects

(Toposheet) Hydrology Ancillary data (Field data)

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Climate data

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Figure 1. Methodology flow chart

RESULTS AND DISCUSSIONS The ASTER image 2001 was classified to the specific three classes: intensive burnt, low burnt and unburnt forest. The intensive burnt areas cover 0.4% or 440.50 ha and low burnt area covers 20% or 23010.9 ha of the study area. The unburnt forests cover 12.3% or 13522 ha of the total area. The toposheet and field data for pasture type, grazing area and vegetation dominant species were used to prepare pasture map on a scale of 1:100,000 for study area. The permafrost distribution map prepared based on topographic parameters of solar radiation, elevation, wetness index, ndvi and landcover. The permafrost distribution values, as active permafrost layer low in steppe and grassland areas and the active layer ground temperatures fell and permafrost was present, particularly on the north facing slopes of the mountains. These altered parameter values showed that on the north facing slopes of mountains and in riparian zones were much more widespread. The forest pest insect distribution maps prepared by building model operation in arcgis (based on topographic parameters of solar radiation, elevation, wetness index, ndvi and landcover, air temperature and slope). Gypsy moth lays their eggs in rock outcrops located in warm and sheltered south facing slopes, so their larvae distribution is obviously near the rock is shown in the distribution map. The sample data of defoliated area by gypsy moth is overlapping to high distribution area identified by influence factors. It is allowing to conclude us that slope is the relatively higher influence factor to the distribution of gypsy moth. REFERENCES 1. Annual report 2004., Institute of Geoecology of MAS Project “The Dynamics of Biodiversity Loss and Permafrost Melt in Lake Hovsgol National Park, Mongolia” 2. Annual report 2002., Institute of Geoecology of MAS Project “The Dynamics of Biodiversity Loss and Permafrost Melt in Lake Hovsgol National Park, Mongolia” 3. Richard D.Hunter 2004 “Climatologically aided mapping of daily precipitation and temperature”, Journal of applied meteorology, 44 4. Michael N.Demes 2002., “GIS modeling in Aster” 5. David O’Sulvivain, David J.Unwin 2003., “Geographic Information Abalysis” 6. Paul A.Longley, Michael F.Goodchild, David J.Maguire, David W.Rhind 2004., “Geographic Information Systems and Science” 7. Eva Solbjord Flo Heggem 2005, “Mountain permafrost distribution and ground surface temperature variability in Southern Norway and Northern Mongolia-spatial modeling and validation” 8. Burrough, P.A. 1988. Principles of Geographical Information Systems for Land Resources Assesment. Clarendon Press, Oxford. 9. Manual for ArcGIS software

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GIS application on micro relief development Altangerel, B1., Schwanghart, W2. & Walther, M.1 1. 2.

MOLARE Research Centre, NUM, [email protected] Free University of Berlin, [email protected]

Research area; The research area is about 1 km south of Ugii Nuur. Ugii Nuur is located 350km west to Ulaanbaatar. There could be observed 3 closed depressions located at one line next to each other. These kinds of form are rarely to be found in Mongolia’s nature. Local nomads called the area as “Gurvan togoo”. The geographical coordinates E 102o 46i 46ii N 47o 43i 53ii Nowadays especially erosion processes are heet flushes. That means in summer time severe rain showers happen in short periods of sometimes only one hour. Field measurement; 1. mapping the area focused on the surface development. 2. measurement by differential GPS. Highest elevation error possibility is 20 cm in total more than 2300 points were measured during the field work. 3. save all data as vector data on notebook. 4. make TIN calculation 5. convert into the Digital Elevation Model (DEM) Result: Visualisation of the depression in a 3D model from DEM file. Hypothesis based on the result: What is the genesis of the forms? The depressions could be interpreted as Pingo relicts in a former lake depression of Ugii Nuur. This is evidence for a large extension. Conclusion (based on additional geomorphological field work): 1. depressions are Pingo relicts 2. Paleoclimate condition. Caused a higher lake level to be proved by a higher shoreline. 3. Paleotemperature must be less than -7’C mean annual temperature. Bay of the former lake was 100 m higher than present lake level.

Reference: 1. Strahler, A. & A. Strahler (2002): Physical Geography.- John Wiley and sons, New York, USA. 2. Lillisand, M. & W. Kiefer (2004): Remote sensing and image interpretation.- John Wiley and Sons, New York, USA. 3. Tsegmid, Sh. (1969): Physical Geography of Mongolia.- National Publishing House, Ulaanbaatar, Mongolia. [in Mongolian]

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Vegetation mapping of the Great Gobi A strictly protected area A. Tsolmon1 & H. von Wehrden2,3 1)

2)

Institute of Botany, Mongolian Academy of Sciences, Mongolia Research Institute of Wildlife Ecology, University of Veterinary Medicine, Savoyen Strasse 1, A-1160 Vienna, Austria 3) Institute of Geobotany, Martin-Luther University of Halle-Wittenberg, Germany

Spatially explicit information on the distribution of habitats and vegetation types is a key requirement for resource management. The Outer Mongolian Republic has designated some of the world's largest nature reserves in the last years, yet some protected areas are situated in remote semi-desert regions with limited baseline data available. Here, we present a recent study on remote-sensing based vegetation mapping in the probably driest reserve in Mongolia (HIJMANS et al. 2005), the Great Gobi A special protected area situated in the deserts und semi-deserts of the Transaltay Gobi. Field-work followed a Braun-Blanquet approach (MUELLER-DOMBOIS & ELLENBERG 1974) with deliberate selection of study sites; sampling was guided by visual interpretation of unsupervised classifications from Landsat satellite data (CAMPBELL 1996). A similar approach was already successfully applied in other protected areas in southern Mongolia (VON WEHRDEN & TUNGALAG 2004; VON WEHRDEN et al. in print). Relevɣs were classified by phytosociological table work following the general framework provided by HILBIG (1995, 2000). This classification was compared to the Russian classification scheme based on dominant species (RAýHKOVSKAYA & VOLKOVA 1977), to cluster analysis (PODANI et al. 2000), and to a COCKTAIL classification (BRUELHEIDE 2000). Indirect and direct gradient analysis techniques (standard DCA and CCA, see HILL & GAUCH 1980; JONGMAN et al. 1995; TITEUX et al. 2004) were used to infer ecological relationships among relevɣs and species; secondary data for the ordination included climatic data from a public domain climate model, digital elevation data and remote sensing information in addition to survey data gathered during fieldwork (VON WEHRDEN & WESCHE 2005). Phytosociologically classified plots served as ground checks for a supervised classification of Landsat 7 data in order to create a vegetation map (see VON WEHRDEN et al. in print on further details of the method). Prior to classification, plots had to be manually enlarged as relevɣs were not large enough for the employed classification algorithm. Isoclass unsupervised classifications of the raster data were the first approach to identify homogenous areas within the Landsat scenes. As vegetation cover in the study area is mostly below 20 % several NDVI transformations were tested to assess primary productivity and vegetation cover. However, tasselled cap transformations proved to be a better tool for individual enlargement of the plot data. Enlarged plots were finally implemented into a maximum likelihood classification, results of which were smoothed using a nearest neighbour 7x7 pixel mean filter. Classification accuracy was assessed by cross-validation with an independent data set. The final map was implemented in a GIS and compared to maps produced with the same methods for the neighbouring Great Gobi B strictly protected area (VON WEHRDEN & TUNGALAG 2004) and the Gobi Gurvan Saikhan national park (VON WEHRDEN et al. in print). Sound spatially information on the distribution of habitats and vegetation types is a necessity for resource management. The Outer Mongolian Republic has designated some of the world's largest nature reserves in the last years and decades, yet some protected areas are situated in remote semi-desert regions with limited baseline data available (see map 1). Here, we present a recent study on remote-sensing based vegetation mapping of the driest reserve in Mongolia (based on data from HIJMANS et al. 2005), the Great Gobi A strictly protected area situated in the deserts und semi-deserts of the Transaltay Gobi. Sampling was undertaken during summer 2004 and contained two surveys within the working area (see map 2). Field-work followed a Braun-Blanquet approach (MUELLER-DOMBOIS & ELLENBERG 1974) with deliberate selection of study sites; sampling was guided by visual interpretation of unsupervised classifications and RGB-transformations from Landsat satellite data (CAMPBELL 1996). A similar approach was already successfully applied in other protected areas in southern Mongolia (VON WEHRDEN & TUNGALAG 2004; VON WEHRDEN et al. in print). Relevɣs were initially classified by phytosociological table work following the general framework provided by HILBIG (1995, 2000). This classification was compared with the Russian classification scheme based on dominant species (RACHKOVSKAYA & VOLKOVA 1977), with cluster analyses (PODANI et al. 2000), and with a COCKTAIL classification - 57 -

(BRUELHEIDE 2000). Indirect and direct gradient analysis techniques (standard DCA and CCA, see HILL 1974; GAUCH 1994) were used to infer ecological relationships among relevɣs and species; secondary data for the ordination included climatic data from a public domain climate model, digital elevation data and remote sensing information in addition to survey data gathered during fieldwork (VON WEHRDEN & WESCHE 2005). The environmental parameters of each community were analyzed using Boxplots; the results were partly used as stratification key for the raster data classification. The phytosociological system is currently in preparation for publication (VON WEHRDEN et al. in prep.). By now eight vegetation units were derived; however some 13 contained sub-association or sub-communities. Altogether 18 units were classified, out of which 13 were included in the preliminary classification. Compared to vegetation maps from other Gobi regions this is seemingly lower (VON WEHRDEN & TUNGALAG 2004; VON WEHRDEN et al. in print). This might be due to the aridity of the here presented region (see table 1). Phytosociologically classified plots served as ground checks for a supervised classification of Landsat 7 data in order to create a vegetation map (see VON WEHRDEN et al. in print on further details of the method). Prior to classification, plots had to be enlarged as most relevɣs were too small for the employed classification algorithm. Isoclass unsupervised classifications of the raster data were the first and initial approach to identify homogenous areas within the Landsat scenes. As vegetation cover in the study area is mostly below 20 % several NDVI transformations were tested to assess primary productivity and vegetation cover. However, tasselled cap transformations proved to be a better tool for individual enlargement of the plot data. To asses the mapping possibilities based on the available ground truth data, spectral separability was checked; units which were closely related regarding their spectral values were therefore merged or excluded. However this was only the case regarding some few extrazonal communities, which contained comparable vegetation cover and structure. The training data was finally implemented into a maximum likelihood classification; the results were smoothed using a nearest neighbour 7x7 pixel mean filter. Classification accuracy was assessed by cross-validation with an independent data set. Thus kappa statistics were compiled and accuracy checks performed, which yielded around 91 %. Deserts and semi-desert ecosystems are more homogenous, and therefore showed a higher accuracy. The dry steppe units occurred mainly at higher elevations on more heterogenous hills and mountains, and therefore showed an overall lower accuracy. The final map was implemented into a GIS and compared to maps produced with the same methods for the neighbouring Great Gobi B strictly protected area (VON WEHRDEN & TUNGALAG 2004) and the Gobi Gurvan Saikhan National Park (VON WEHRDEN et al. in print). For future investigation we want to compare the results with available public-domain climate datasets (HIJMANS et al. 2005) and thus examine the climatic variance of these communities. Using MODIS timelines we plan to identify NDVI variability for each mapping unit. This approach was already widely applied in central Asian ecosystems, yet on a broader resolution regarding vegetation units and working area (e.g. YU et al. 2003; YU et al. 2004). The NDVI timelines will be correlated with rainfall estimates. These will be linked with Khulan observations obtained from collared animals using ARCOR. A combination with other animal observation (such as Ibex, wild Camel, Argali, Gobi Bear, Gazelle etc.) would be desirable to understand the habitat conditions of these endangered animals. The final goal of this project is to give baseline data for better protection of the fragile Gobi ecosystem; some applications are currently tested in some protected areas of the region (visit www.takhi.org; KACZENSKY et al. in prep.). Acknowledgements: Fieldwork would not have been possible without the support of the staff of the Great Gobi A strictly protected area office. The project was supported by the DAAD (German Academic Exchange Service) and the Austrian Science Foundation (FWF project P18624). Tab. 1: Variance of the precipitation of the protected areas within southern Mongolia. All values represent millimetres of precipitation per year, assessed using data from HIJMANS et al. 2005.

AREANAME

MIN

MAX

RANGE

MEAN

STD

Great Gobi "B"

69

177

108

96

17

Great Gobi "A"

33

124

91

54

11

Gobi Gurvan Saykhan

39

222

183

103

35

Small Gobi "A"

63

115

52

84

8

Small Gobi "B"

113

173

60

139

14

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Map 1: Protected areas within southern Mongolia.

Map 2: Route of our surveys; the thin black outline surrounds the protected area; the thick black outline shows the border to China.

References 1. BRUELHEIDE, H. 2000: A new measure of fidelity and its application to defining species groups. Journal of Vegetation Science 11: 167-178. 2. CAMPBELL, J. B. 1996: Introduction to Remote Sensing. - New York. 3. GAUCH, H. G. 1994: Multivariate analysis in community ecology. - Cambridge. 4. HIJMANS, R. J.; CAMERON, S. E.; PARRA, J. L.; JONES, P. G. & JARVIS, A. 2005: Very high resolution interpolated climate surfaces for global land areas. - Int. J. Climatol. 25: 1965-1978. 5. HILBIG, W. 1995: The vegetation of Mongolia. - Amsterdam. - 59 -

6. HILBIG, W. 2000: Kommentierte ɖbersicht ɶber die Pflanzengesellschaften und ihre hɰheren Syntaxa in der Mongolei. - Feddes Repert. 111: 75-120. 7. HILL, M. O. 1974: Correspondence analysis: a neglected multivariate method. - Journal of Applied Statistics 23: 340-354. 8. KACZENSKY, P.; WALZER, C. & GANBAATAR, O. in prep.: Niche separation of the two native Asian equids: the Przewalski's horse and the Asiatic wild ass in the Gobi areas of SW Mongolia. 9. MUELLER-DOMBOIS, D. & ELLENBERG, H. 1974: Aims and methods of vegetation ecology. - New York, London, Sydney, Toronto. 10. PODANI, J.; CSONTOS, P. & TAMȻS, J. 2000: Additive trees in the analysis of community data. Community ecology 1. 11. RACHKOVSKAYA, E. I. & VOLKOVA, E. A. 1977: Rastitelnost Zaaltayskoy Gobi. - Biol. Res. prir. Uslov MNR 7: 46-74. 12. VON WEHRDEN, H. & TUNGALAG, R. 2004: Mapping of vegetation units of the Great Gobi B National Park. - Final report for the Przewalski Horse Project. - Univ. Marburg, Fac. of Geography. 13. VON WEHRDEN, H. & WESCHE, K. 2005: Mapping Khulan habitats - a GIS-based approach. Erforsch. biol. Ress. Mongolei 10. 14. VON WEHRDEN, H.; WESCHE, K. & HILBIG, W. in prep.: Plant communities of the Mongolian Transaltay Gobi. - Feddes Repertorium. 15. VON WEHRDEN, H.; WESCHE, K.; REUDENBACH, C. & MIEHE, G. in print: Mapping of large-scale vegetation pattern in southern Mongolian semi-deserts - an application of LANDSAT 7 data. Erdkunde. 16. YU, F.; PRICE, K. P.; ELLIS, J.; FEDDEMA, J. J. & SHI, P. 2004: Interannual variations of the grassland boundaries bordering the eastern edges of the Gobi Desert in central Asia. - International Journal of Remote Sensing 25: 327-346. 17. YU, F. F.; PRICE, K. P.; ELLIS, J. & SHI, P. J. 2003: Response of seasonal vegetation development to climatic variations in eastern central Asia. - Remote Sensing of Environment 87: 42-54.

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Climate change impact on rangeland productivity in the Eurasian steppe Dennis S. OJIMA1 and Togtohyn CHULUUN2 1-Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, Colorado 80523-1499, U.S.A. 2- Mongolian Academy of Sciences, Ulaanbaatar - 46, Mongolia Abstract Dramatic changes have occurred in pastoral systems of Mongolia, China, Central Asia and Russia over the past several decades. Integrated assessment of these changes on the environment and quality of life is essential for sustainability of the region. Integrated assessment entails determining the interactions and impacts of various management strategies on the environment and human systems. Recently, an evaluation of the pastoral systems has been conducted in the region. Pastoral systems, where humans depend on livestock, exist largely in arid or semi-arid ecosystems where climate is highly variable. Thus, in many ways, pastoral systems are adapted to climatic variability. It is plausible to assume direct connection between climate variability, ecosystem dynamics and nomadic land use system in Mongolia. Interactions between ecosystems and nomadic land use systems co-shaped them in mutual adaptive ways for hundreds of years, thus making both the Mongolian rangeland ecosystem and nomadic pastoral system resilient and sustainable. We also recognize the pervasive role of demographic, political and economic driving forces on pastoral exploitation. The general trend involves greater intensification of resource exploitation at the expense of traditional patterns of range utilization. This set of drivers is orthogonal to the described climate drivers. Thus we expect climate-land use interactions to be modified by socio-economic forces. Nevertheless, the complex relationship between climate variability and pastoral exploitation patterns will still form the environmental framework for overall patterns of land use change. Integration of knowledge and delivery of this knowledge to scientists, policy makers and land users is critical for regionally sustainable development.

Introduction In the semiarid regions of the Mongolian steppe, nomadic pastoralism has been the dominant agronomic activity for many centuries. Recent changes in cultural, political and economic factors have caused changes in how the pastoral systems operate within the region. These systems encompass a range of grazing patterns (i.e., frequency, intensity of grazing and the types of animals), and have incorporated new breeding stocks that are potentially not suitable for certain climate regimes (e.g., drought conditions of the Gobi desert, cold hardiness against severe winter storms in the Mongolian steppe region). These changes in pastoral management have altered the nomadic patterns of the region. Pastoral systems, where humans depend on livestock, exist largely in arid or semi-arid ecosystems where climate is highly variable. Thus, in many ways, the historical pastoral livestock systems are intimately adapted to climatic variability. There is a direct relationship between climate variability and the spatial scale of pastoral exploitation. Extensive nomadic systems are found in the most variable regions; less extensive, more intensive modes of livestock management occur in less variable grazing lands. Climate change in drylands can thus be expected to have important implications for the dynamics and viability of pastoral people, and their exploitation patterns on land cover and land cover change. We also recognize the pervasive role of demographic, political and economic driving forces on pastoral exploitation. The general trend involves greater intensification of resource exploitation at the expense of traditional patterns of extensive range utilization. This set of social factors tend to over-ride the climate drivers during periods of economic change. Thus we expect climate-land use-land cover relationships to be modified by the socio-economic forces. Recent political and economic changes (i.e., in the past 50 years) in land use management have resulted in a more sedentary livestock management system. These changes have led to more intensive stocking rates in localized areas and change in the breeds of animals used. More recent changes in the social-economic setting have forced new changes in pastoral management due to relaxation of central government controls and the implementation of a more "free-enterprise" system. What will result from these recent changes is unclear, and the effect on the human and natural resources of these arid and semi-arid regions needs to be determined.

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Climate Trends In the last 60 years, the mean annual air temperature increased by 1.560C, due to winter warming (Mongolia National Action Program on Climate Change 2000). Changes in warming are more pronounced in the high mountains and mountain valley, and less in the Gobi desert and the steppe. There is a slight increase in the annual precipitation in the last 60 years (Natsagdorj 2000). During 1940-1998, the annual precipitation increased by 6%, while summer precipitation increased by 11% and spring precipitation decreased by 17%, with the main spring dryness happening in May. The frequency of extreme events such as drought, flood, dust storm, thunderstorm, heavy snow, and flash flooding, has increased over the past 30 years (Natsagdorj 2000). In addition, the forest and steppe fire frequency is increasing because of the extremely dry springs. The economic losses of these extreme events is estimated to be 1 to 3 billion Togrog (1 to 3 million US$) per year. Economic losses due to drought range annually from 5 to 7 million Togrog (5 to 7 thousand US$). Livestock losses from 1999-2000 zud (the Mongolian term used for severe for livestock winter condition) were 2.4 million, and for the winter of 2001 was over 1.3 million livestock lost by February 25, 2001. It is likely that the boundary zone between the Gobi desert and steppe is already affected by global warming and land use impact. Analysis of onset of green-up, an indicator of spring thaw and the initiation of the growing season, during the 1982 to 1991 time period indicates that large portions of eastern Mongolia and Inner Mongolia are experiencing earlier green-up (Yu et al. 1999), the authors suggest that this is associated with warming of winter and spring temperatures during this decade. This region of advanced green-up is dominated by Meadow Steppe and relatively mesic areas of typical steppe. There are also large portions of the desert steppe and dry areas of the typical steppe, where there is a strong trend towards delayed green-up. The old herders from central regions of Mongolia also complained that the plant productivity decreased from onethird to one-half during their lifetime (Ellis and Chuluun 1993). According to the GCM scenarios, the annual mean temperature in Mongolia is projected to increase by about 1.8-2.80C in the first quarter of the 21st century (Mongolia National Action Program on Climate Change 2000). This increase is projected to double during 2025-2050. An increase in total precipitation by 20-40% can be projected in the first half of new century, but precipitation is expected to decline from 2040 to 2070. Increased plant productivity is expected until the 2040’s, when less favorable conditions will follow. Land Use Effects Covering nearly 120 million hectares, Inner Mongolia accounts for 12.3% of China’s total area (Grasslands and Grassland Sciences in Northern China 1992). In 1989, Inner Mongolia had 86.7 million hectares of grassland, of which 18.7 million hectares were deemed “unusable” and another 29.9 million hectares were considered “deteriorated” or “seriously deteriorated”, leaving only 38.1 million hectares both usable and in good condition. Compared to 1965, the area of the region’s grasslands is said to have decreased by 6.2 million hectares, deteriorated grasslands have increased by 28.7 million hectares, and total grass production has dropped by 30%. Currently, Inner Mongolia has about 8 million hectares of cropland (Enkhee 2000). Over 80% of land in Mongolia (123 million hectares) is rangelands (Tserendash 2000). Arable lands occupy only less than 1% of total agricultural lands (1.35 million hectares) in Mongolia. A comparative study of culture and environment in Inner Asia conducted by Humphrey and Sneath (1999) found that pasture degradation was associated with the loss of mobility in pastoral systems. Pasture degradation was most severe at the research sites from Buyatia and Chita Oblast’ (Russia), where the sedentarisation level was the highest, compared to other research sites from Mongolia, Tuva (Russia), Inner Mongolia and Xinjiang (China). Thus, they concluded that mobile pastoralism still remains a viable and useful technique in the modern age. Our simulation studies using CENTURY (Chuluun & Ojima 1996; Ojima et al. 1998, Chuluun & Ojima 1999) confirmed that the Mongolian grasslands could lose significant amounts of carbon in continuous year-long or summer heavy grazing systems. The effect of different seasonal and year-long grazing treatments on soil carbon levels was simulated for 50 years. Summer or year-long heavy grazing for 50 years resulted in the largest loss of total soil carbon relative to other seasonal grazing scenarios. Heavy summer grazing resulted in a 15% soil carbon loss. The influence of spring grazing on soil organic carbon levels was slightly greater than winter and fall grazing. A total soil organic carbon level decrease of about 25% was observed in the heavy grazed Xilingole research site treatments compared to control treatments. However, a decrease in soil organic carbon was not observed in the heavy or overgrazed grassland sites in Mongolia relative to control sites. Thus, we have to be cautious when we quantify carbon loss because of - 62 -

overgrazing. Not all land use changes were negative for grassland health and carbon storage. The crop sector has been collapsing in Mongolia during the transition to a market economy since 1990. In 1995, sown areas have decreased by half, and crop production decreased by about one third of the 1990 levels. The growing of plant fodder and cereals other than wheat has practically been eliminated by 1995. This agricultural failure decreased food security of the country. Livestock reduction in Central Asia and cropping industry failure in Mongolia could potentially contribute to a restoration of degraded pastures and eroded arable lands. Restoration would have the added benefit of increasing carbon storage and consequently these lands can serve as potential sinks for atmospheric carbon. Regional implications

Ellis and lee (1999) analyzed rain use efficiency of different vegetation communities of the lake balkash basin in southeastern kazakhstan, between 430 and 470 north latitude, using remote-sensed avhrr-ndvi data. Site ndvi data were compared to annual rainfall figures from the closest weather station. Improved rainfall use efficiency by typical steppe and shrub steppe vegetation communities (indication of grassland recovery) for last decade has been observed, and this change has coincided with a shift from intensive year-long grazing, to very light or no grazing pressure for those grasslands. analysis of rangeland recovery in central asia indicates that as growing season increases, there is more carbon uptake into soils, suggesting that there is potential for carbon sequestration. These improved grazing management practices may prove useful for the mongolian steppe situation, which is undergoing large increases in livestock numbers. Implementation of improved grazing practices can lead to sustainable carbon storage in these ecosystems, and offset some of the negative impacts of climate warming by conserving soil moisture in the rangelands. Summary Dramatic changes in land use have occurred in this region of East Asia during the last several decades. The extent of grassland conversion into croplands and grassland degradation is large due to increased human population and political reforms of pastoral systems. Rangeland ecosystems of this region are vulnerable to environmental and political shocks, and response of pastoral systems to these shocks have varied across the region. Indeed, this part of the world has experienced many perturbations in the recent decades, such as, the collapse of the livestock sector in some states of central Asia, expansion of livestock in China and Mongolia, intensification of cropland conversion, multiple zud of 1999-2001 in Mongolia, and recent intensive dust storm events. However, this region has great potential for rangeland improvements and carbon sequestration, if appropriate land management practices are adopted. Traditional pasture management with greater mobility should be incorporated in rural development policies. For instance, traditional pastoral networks should be encouraged with new land reform policy in Mongolia. The traditional pastoral networks emerged as dissipative structures during the past in areas of limited natural resources (water and soil organic matter) and highly variable environment conditions. These strategies improved the resilience and sustainability of grazing of grazing lands in Mongolia and surrounding pastoral regions in Eurasia.

Literature Cited 1. Chuluun, T. and Ojima, DS. 1996. Rangeland management. U.S. Country Studies Program Mongolia’s Study Team, Mongolia’s Country Studies Report on Climate Change: Mitigation Analysis, Ulaanbaatar, Mongolia, vol. 4: 57-78. 2. Chuluun, T. and Ojima, DS. 1999. Climate and grazing sensitivity of the Mongolian rangeland ecosystem. Proceedings of the VI International Rangeland Congress on “People and Rangelands: Building the Future”, Townsville, Queensland, Australia, 1999, vol. 2: 877-878. - 63 -

3. Ellis, J. and Chuluun, T. 1993. Cross-country survey of climate, ecology and land use among Mongolian pastoralists. Report to Project on Policy Alternatives for Livestock Development (PALD) in Mongolia. Institute of Development Studies at the University of Sussex, UK. 4. Ellis, J. and Lee, RY. 1999. Collapse of the Kazakhstan livestock sector: Catastrophic convergence of ecological degradation, economic transition and climate change. Report on “Impacts of privatization on livestock and rangeland management in semiarid Central Asia”, Overseas Development Institute, London, 1999. 5. Enkhee, J. 2000. The Mongolian tradition of legal culture and the grassland management in Inner Mongolia today, Proceedings of the International Symposium on “Nomads and use of pastures today”, 2000, 194-199. 6. Humphrey, C. and Sneath, D. 1999. The End of Nomadism?: Society, State and the Environment in Inner Asia, Durham: Duke University Press, 1999. 7. Batjargal, Z., Dagvadorj, D. and Batima, R. (eds.). Mongolia National Action Programme on Climate Change. 2000. JEMR Publishing, Ulaanbaatar. 8. Natsagdorj, L. (2000). Climate Change. In Climate change and its impacts in Mongolia, ed. R. Batima and D. Dagvadorj, JEMR Publishing, Ulaanbaatar. 9. Ojima, D.S., Xiangming, X., Chuluun, T., Zhang, XS. 1998. Asian grassland biogeochemistry: factors affecting past and future dynamics of Asian grasslands. In Asian Change in the context of global climate change, ed. James N. Galloway and Jerry M. Melillo, IGBP publication series, Cambridge: Cambridge University Press 3: 128-144. 10. Tserendash, S. 2000. Pasture resource – utilization and management in Mongolia. Proceedings of the International Symposium on “Nomads and use of pastures today”. Pp: 141-143. 11. Yu, F., Price, KP., Lee, RY., and Ellis, J. 1999. Use of time series of AVHRR NDVI composite images to monitor grassland dynamics in Inner Mongolia, China. Proceeding of ASPRS’99.

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Classification of Multitemporal InSAR Data for Land Cover Mapping in Selenga River Basin, Mongolia Damdinsuren AMARSAIKHAN

Institute of Informatics and RS, Mongolian Academy of Sciences & Faculty of Geography and Geology, National University of Mongolia

Abstract The aim of this study is to evaluate different features extracted from the multitemporal spaceborne interferometric synthetic aperture radar (InSAR) data sets for a rural land cover mapping. For the actual land cover classification, the traditional statistical maximum likelihood classification and neural network method are performed and the results are compared. Overall, the research indicated that the multitemporal InSAR data sets have a valuable contribution to efficient land cover mapping.

Keywords: InSAR, Multitemporal, Classification, Accuracy

1. Introduction At present, InSAR data sets are being widely used for land cover/use and other resources mapping. Unlike the traditional single frequency and single polarisation SAR, the InSAR uses both the amplitude and phase information from a pair of single look complex (SLC) SAR images. From this pair of SLC images, different SAR products such as amplitude and coherence images as well as a digital elevation model can be generated. These derived products or their enhanced features combined with other data sets can be successfully used for different classifications to increase the performance of the applied decision rules [2,4]. In the present study, we wanted to discriminate rural land cover types in Mongolia using the features derived from multitemporal InSAR data sets. As a test site, the Selenga River Basin, Northern Mongolia has been selected. The area represents a forest-steppe ecosystem and is characterized by fertile for agriculture chestnut soil. In the area, such classes as forest, agricultural fields, swampy area, natural vegetation, soil and water were available. For the discrimination of the selected classes, the traditional statistical maximum likelihood (MLH) and neural network (NN) classifications have been applied and compared. The actual classifications have been performed using a) the original InSAR products, b) the SAR derivative features and c) the results of a principal component analysis (PCA), and the final results were compared. Overall, the research indicated that the multitemporal InSAR data sets have a valuable contribution to the efficient land cover mapping. 2. Data sources As data sources, multitemporal interferometric ERS-1/2 SLC SAR data with a spatial resolution of 25m acquired with 1 day repeat pass interval on 16 and 17 October 1997, and 8 and 9 August 1998 were used. In addition, for ground truth checking topographic maps of 1984, scales 1:100,000 and 1:200,000 as well as soil and vegetation maps, scale 1:200,000 were available. 3. Derivation of the InSAR coherence and amplitude images The coherence and amplitude images have been derived as follows [1,4]: ƒ

Initially, 200 ground control points (GCP) regularly distributed over the images were automaticall - 65 -

ƒ ƒ ƒ ƒ

y defined using the satellite orbit parameters and the two SLC images were co-registered with 0.1p ixel accuracy. Then, a course registration followed by a fine registration was performed. Coherence has been calculated using 10x3 size window and the coherence image was generated. From the complex images, amplitude images were generated. The preliminary SLC images were converted from the slant range onto a flat ellipsoid surface. The true size (5800x5800) SAR images were generated using image undersampling applying 3x3 s ize low pass filter.

4. Derivation of the texture features To derive texture features occurrence and co-occurrence measures were applied to the coherence and average amplitude images of the multitemporal ERS-1/2 data sets. The occurrence measures use the number of occurrences of each grey level within the processing window for the texture calculations, while the co-occurrence measures use a grey-tone spatial dependence matrix to calculate texture values [5]. By applying these measures, initially 36 features have been obtained, but after thorough checking of each individual feature only 12 features, including the results of the data range, mean and variance filters applied to the original SAR products were selected. 5. Principal component (PC) images To reduce the dimensionality of the dataset, the PCA [7,8] has been performed to the extracted SAR features. For the PCA 16 features, including the multitemporal ERS-1/2 coherence and average amplitude images, and 12 texture features were used. As it was seen from the PCA, PC1 which contained 49.68% of the overall variance was dominated by the variance (loadings of 0.72) of the ERS amplitude image of 1997 looked very similar to the original variance dominating image and did not contain useful information, while PC2 which contained 21.82% of the overall variance was also dominated by the variance (loadings of 0.63) of the ERS amplitude image of 1997 and did not contain much information. Moreover, PC3 and PC4 which were dominated by the variances of the ERS coherence images of 1997 and 1998 also did not contain useful information. However, PC5, PC6 and PC7 which were dominated by the variances of the mean filtered coherence image of 1998, data range filtered coherence image of 1998 and data range filtered amplitude image of 1997, respectively, contained just 1.6% of the overall data variance, but contained very useful information. These PC images delineated clear spectral views of the selected classes of objects. Therefore, these PC images have been selected for further analysis and the other PCs were rejected. 6. Classification of the InSAR features As it is known, before classification of SAR images, the speckle noise of the SAR images should be reduced, because, the reduction of the speckle increases the spatial homogeneity of the classes which in turn improves the classification accuracy. In this study, to reduce the speckle of the selected features a 5x5 size frost filter has been applied [5]. After the speckle suppression, from the features, 2-3 areas of interest (AOI) representing the six selected classes have been selected using a polygon-based approach. Then, training samples were selected on the basis of these AOIs. The separability of the training signatures was firstly checked on the feature space images and then evaluated using JM distance [8]. Then the samples which demonstrated the greatest separability were chosen to form the final signatures. For the classification, the following feature combinations have been used: 1. The coherence and amplitude images of InSAR products (4 bands), 2. 12 features selected from the occurrence and co-occurrence measures, 3. The PC5, PC6 and PC7 derived from the PCA. For each of these feature combinations, the statistical MLH classification and NN method have been applied and the results were compared [7,8].

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Figure 1. The original SAR image of the test area (a) and the results of the MLH classification ((b) original InSAR products, (c) 12 features, (d) PCs). (dark green-forest, brown-agricultural fields, pink-swampy area, green-natural vegetation, cyan-soil and blue-water). For the accuracy assessment of the final classification results, the overall performance [4] has been used. As ground truth information, for each class several regions containing the purest pixels have been selected. In all cases, the performance of the MLH classification was better than the performance of the NN method. The overall classification accuracies of the selected classes in the selected features are shown in table 1. As seen from table 1, the performances of the classifications using the 12 features were higher than any other combinations. The original SAR image of the test area and the results of the MLH classification are shown in Figure 1.

Table 1. The overall classification accuracy of the classified features. - 67 -

Feature combinations The original InSAR products 12 features The PCs

Overall accuracy of MLH method (%)

Overall accuracy of NN method (%)

81.02

76.16

82.29 78.54

77.86 71.47

7. Conclusions The aim of this research was to evaluate different features extracted from the multitemporal spaceborne InSAR data sets for a rural land cover mapping. For the classification of the individual and extracted features, the statistical MLH and NN classifications were used and the results were compared by measuring the overall accuracy. In all cases, the performance of the MLH classification was better than the performance of the NN method. Overall, the study indicated that the multitemporal InSAR data sets could be efficiently used for a land cover mapping. References

1. 2.

3. 4.

5. 6.

Amarsaikhan, D. and M.Ganzorig, 2001, Application of spectral and scattering knowledge for interpretation of RS images, Journal of Informatics, Ulaanbaatar, Mongolia, pp. 87-95. Amarsaikhan, D. and Sato, M., 2003, Feature extraction and multisource image classification, Proceedings of the Asian Conference on Remote Sensing and International Symposium on Remote Sensing, pp.597-600, Busan, Korea, November 2003. Amarsaikhan, D., and Douglas, T., 2004, Data fusion and multisource data classification, International Journal of Remote Sensing, No.17, Vol.25, pp.3529-3539. D.Amarsaikhan, M.Ganzorig, M.Sato, 2005, Application of Multitemporal Interferometric SAR Data for Land Cover Mapping in Mongolia, CD-ROM Proceedings of the Asian Conference on RS, Hanoi, Vietnam. ENVI, 1999, User's Guide, Research Systems, USA. ERDAS, 1999. Field guide, Fifth Edition, ERDAS, Inc. Atlanta, Georgia. nd

7.

Mather, P.M., 1999. Computer Processing of Remotely-Sensed Images: An Introduction, 2 edition (Wiley, John & Sons).

8.

Richards, J.A., 1993. Remote Sensing Digital Image Analysis-An Introduction, 2 edition (Berlin: Springer-Verlag).

nd

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Urban Land Cover Change Studies Using Multitemporal RS Images D.Amarsaikhan, M.Ganzorig and B.Nergui

Institute of Informatics and RS, Mongolian Academy of Sciences Abstrac The aim of this study is to compare the changes in the main urban land cover classes of Ulaanbaatar city, Mongolia occurred during a centralized economy with the changes occurred during a market economy and describe the socio-economic reasons for the changes. For this purpose, multitemporal remote sensing (RS) and cartographic data sets as well as census data are used. To extract the reliable urban land cover information from the selected RS data sets, a refined parametric classification algorithm that uses spatial thresholds defined from the local and contextual knowledge is constructed. Overall, the study indicated that during the centralized economy significant changes occurred in the ger area of the city, while during the market economy the changes occurred in both ger and building areas.

Keywords: RS; Urban change; Building area; Ger area; Classes 1. Introduction At present, Mongolia is facing a problem with the urban expansion and the growth of population in the main cities. In general, much of Mongolia's urban growth has taken place since the middle of 1970s, because, at that time, the government encouraged migration to urban areas, specifically to Ulaanbaatar in the belief that this would increase the industrialization and productivity in the country. To accommodate the growing population in the capital city, the government mainly constructed high-rise apartment blocks [5]. However, they were far to satisfy the demands of the growing population. Therefore, rural people when migrated to Ulaanbaatar, usually used gers (Mongolian national dwelling) for their accommodation and built them up usually in urban fringes. In 1990, Mongolia entered the market economy and it totally changed the lives in the society. For the isolated rural people, it had become very difficult to reach the central market. Meanwhile, almost everything started to centralize in the capital city and Ulaanbaatar had become the dream for many rural people. Therefore, many rural families officially and unofficially moved to Ulaanbaatar. As a result, the population of Ulaanbaatar had been significantly increased and the city area had significantly expanded. To analyze such changes, the urban planners need detailed regularly updated maps, however, there are no such maps. In this case, an updated map generated through an information extraction procedure from RS data with an acceptable resolution can give them an impression about the changes in the city area where some planning actions are considered [1,2]. In general, it should be interesting to study the urban growth in Ulaanbaatar city comparing the growths occurred during the main industrialization period, that is the period in between 1969 and 1990 when Mongolia had socialist economy with the changes occurred during a certain period of the market economy. In this research, we wanted to study the urban growth in the capital city considering six classes such as building area, ger area, forest, grassland, soil and water. For the final urban change analysis, the changes occurred in Ulaanbaatar area in between 1969 and 1990 were compared with the changes occurred in between 1990 and 2001 using a topographic map as well as multitemporal RS images, and the socioeconomic reasons for the changes were described. To extract the reliable urban land cover information from the selected RS data sets, a refined parametric classification algorithm that uses spatial thresholds defined from the local and contextual knowledge was used. 2. Test site and data sources As a test site Ulaanbaatar, the capital city of Mongolia has been selected. Ulaanbaatar is situated in the central part of Mongolia, on the Tuul River, at an average height of 1350m above sea level and currently has nearly 1 million inhabitants [10]. The selected part of the capital city is about 28kmx20km and in the selected area, such land cover classes as building area, ger area, forest, grass land, soil and water can be identified.

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As the data sources, Landsat TM data of 10 September 1990 with a spatial resolution of 30m, SPOT PAN data of May 1990 with a spatial resolution of 10m, and Landsat ETM (+) data of 31 August 2001 were used. The ETM data consisted of a panchromatic band resampled to a pixel resolution of 14m and 7 multispectral bands resampled to a pixel resolution of 28m. In addition, topographic maps of 1969 and 1984, scale 1:50,000, and a general urban planning map were available. Figure 1 shows recent look of the test area in the ETM image and some examples of its land cover.

Figure 1. ETM image of Ulaanbaatar area (Red=band5, Green= band4, Blue= panchromatic band). The size of the displayed area is about 28kmx20km. 3. Radiometric correction and georeferencing of the multi-sensor images At the beginning, all the available images were thoroughly analyzed in terms of radiometric quality and geometric distortion. For the SPOT PAN data of 1990 some line dropouts occurred, while the panchromatic band of the ETM data had some radiometric noise. In order to correct the error of the SPOT PAN data, averaging of the upper and lower lines of the lines that had to be corrected was applied, whereas for the panchromatic band of the ETM data, a 3x3 size average filtering [8,9] was applied. In case of the multispectral images, band 1 (i.e., blue band) of both TM and ETM had noise from the atmosphere and it was difficult to correlate their radiometric values with other bands. Therefore, they were excluded from the analysis. Moreover, as band 6 of TM and bands 61 and 62 of ETM are thermal bands, they were excluded from the further analysis, too. In order to merge data sets with different spatial resolutions, a high geometric accuracy and good geometric correlation between the data sets are needed. Initially, the panchromatic image of the ETM data was georeferenced to a UTM map projection using a topographic map of 1984, scale 1:50,000. The ground control points (GCP) have been selected on well defined cross sections of roads, streets and other clearly delineated sites. In total, 16 regularly distributed points were selected. For the transformation, a second order transformation and nearest neighbour resampling approach [7] have been applied and the related root mean square (RMS) error was 0.92 pixel. In order to georeference other images, 16 more regularly distributed GCPs were selected on each image comparing the locations of the selected points with other information such as the already georeferenced panchromatic image of the ETM and the selected topographic maps. Then, the images were successively georeferenced to the UTM map projection using the - 70 -

topographic map of 1984. For the actual transformations, a second order transformation and nearest neighbour resampling approach were applied. The RMS errors of the image transformations were 0.98 pixel for the SPOT PAN, 0.65 pixel for the TM and 0.79 pixel for the ETM, respectively. In each case of the georeferencing, an image was resampled to a pixel resolution of 14m. 4. The refined classification method Over the years, multispectral RS data sets have been widely used for urban land cover mapping at a regional scale and for the generation of land cover information, diverse classification methods have been applied. The early methods mainly involved supervised and unsupervised methods and hence, many techniques have been developed [3,4]. In this study, to extract the reliable urban land cover information from the selected RS data sets, a refined parametric classification algorithm that uses spatial thresholds defined from the local and contextual knowledge has been used. Generally, in the classification process, it is desirable to include only the features in which the signatures of the selected classes are highly separable from each other in a multidimensional feature space [6,12]. In the present study, as the features, infrared bands of TM and ETM as well as panchromatic bands were used. To define the sites for the training signature selection, initially, from the images, several areas of interest (AOI) have been selected for each available class using the local and contextual knowledge. The local knowledge has been defined from the historical GIS data sets (i.e. topographic and urban planning maps), while the contextual knowledge was defined on the basis of the spectral variations of the land surface features as well as the texture information delineated on the colour images. The separability of the selected training signatures was firstly checked in feature space and then evaluated using JM distance [12] and it revealed that high statistical overlaps exist between the classes: building area and ger area. Then, the samples which demonstrated the best possible separability were chosen to form the final signatures. The final signatures included about 126-498 pixels. For the actual classification, the Mahalanobis distance classifier (MDC) has been used. The MDC is a parametric method, in which the criterion to determine the class membership of a pixel is the minimum Mahalanobis distance between the pixel and the class centre [7]. Initially, in order to check the performance of the standard method, the selected PC features were classified using the MDC. However, on the classified images there were different mixed classes between the classes: building area and ger area. This should be evident, because the previous signature analysis indicated that the signature distributions of these classes had significant overlaps in the multidimensional feature space. To separate these statistically overlapping classes, different spatial thresholds determined on the basis of the local and contextual knowledge have been used. The local knowledge was based on the knowledge about the site as well as the historical GIS data sets, whereas the contextual knowledge was based on the spectral and textural variations of the selected classes in different parts of the colour images. To determine the initial spatial thresholds, firstly the appropriate polygon boundaries related to the selected classes were defined from the historical GIS data sets. As these polygon boundaries represented old information, it was necessary to update them. For this purpose, new polygon boundaries were defined from the PC images on the basis of the contextual knowledge (i.e., defining class boundaries in relation to its neighbourhood) and added to the initial spatial thresholds. The results of the classifications using the defined spatial thresholds are shown in figure 2a,b. For the accuracy assessment of the classification results, the overall performance has been used [12]. As ground truth information, different AOIs containing the purest pixels have been selected. The confusion matrices produced for the refined parametric classification method showed overall accuracy of 91.98% for the 1990 data sets, while for the 2001 data sets it was 92.89%, meanwhile indicating an accuracy of more than 90% for each of the selected classes. This means that these classification results can be reliably used for further analysis. 5. Urban change analysis In this study, we wanted to compare the general changes occurred in the main land cover classes (i.e., building area, ger area, forest, grass land, soil and water) of Ulaanbaatar area during the centralized economy (i.e., in between 1969 and 1990) with the changes occurred during a market economy (i.e., in

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between 1990 and 2001) using multitemporal RS images, and explain the socio-economic reasons for the changes. Initially, to create the primary historical GIS data, the classes: building area, ger area, forest and water were digitized in a UTM map projection from a topographic map of 1969, scale 1:50,000. On this topographic map, the areas related to the grass land were not delineated. Therefore, it was not possible to distinguish between grassland areas and soil classes. The apartments, residential houses, industrial buildings and all other building areas were included in the building area class, because on the RS images it was not possible to distinguish among these classes, due to their very similar spectral characteristics. To define the total area of each class, the vector map has been rasterized with a pixel resolution of 14m and then a number of pixels falling into each of the classes was calculated. Likewise, from the classified PC images, the total number of pixels falling into each of the classes has been calculated. The total areas related to each class defined from the digitized map as well as classified multitemporal RS images are shown in table 1. Although, we have census data of Ulaanbaatar city, it is not possible to directly relate it to the current analysis, because our study area does not cover all the areas from where the final census data is collected. However, as the test area covers the majority of the area belonging to the capital city, it is possible to use the census data for a comparison of the general population increase with the actual urban expansion process. Table 1. The total areas for each class in different years, evaluated from multitemporal GIS and RS data sets.

Land cover classes

The total areas for each class in different years (ha)

Building area Ger area Forest Grass land Soil Water Total

1969 2498.92 978.86 11896.12 N/A N/A 1078.84 56408.19

1990 3996.71 2719.65 11075.94 11626.26 26273.23 716.40 56408.19

2001 4812.64 4304.26 10324.26 9153.98 27359.04 454.01 56408.19

As seen from table 1, in 1969 in Ulaanbaatar city, the building area and ger area covered 2498.92ha and 978.86ha, respectively, whereas in 1990 these two urban classes covered 3996.71ha and 2719.65ha, respectively. Moreover, it is seen that the forest and water resources had been reduced. The available census data indicated that in 1969 the population of the capital city was 267,400, while in 1990 it had become 574,900. As seen, within the 21 year period of the centralized economy, the building areas were increased by only 59.94%, whereas the ger areas were increased by more than 2 times. Meanwhile, the population had been increased by more than 2 times. These changes are related with the following: 1) Due to the industrialization process, many people came from different parts of the country seeking for better lives. 2) During the centralized economy, the government constructed mostly high-rise apartment blocks with many stories to accommodate the growing population. These residential blocks, though could contain families of many ger districts, occupied much smaller land parcels than the ger parcels. For example, one apartment block with five stories which could accommodate 60 families occupied just about 1000sq.m, while one family living in a ger district occupied usually 600-800sq.m, because the gers in urban areas are surrounded by fences.

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Figure 2. Comparison of the classification results for the selected classes. (a) Classified image using 1990 data sets, (b) Classified image using 2001 data sets.

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3) During the centralized economy, many people came from rural sites with their gers to the city. Although, the government constructed high-rise apartment blocks, they could not satisfy the growing demands of the increasing population. Therefore, those people who could not get apartments usually used their gers as dwelling houses. Therefore, the ger districts had such a significant expansion. Furthermore, as seen from table 1, within the 11 year period since the country entered the market economy, the building area and ger area had been increased by 20.41% and 58.26%, respectively. Meanwhile, forest, grass land and water resources had been reduced, expanding the areas of bare soil. The previous study, conducted by Amarsaikhan et al. (2001) had found that within a 7 year period of a market economy, in the central part of Ulaanbaatar city the building area and ger area had been increased by 48% and 50%, respectively. However, the current study covering much larger area than the previous study has revealed that over recent years, the ger areas in the capital city have been significantly expanded, specifically in the urban fringes. The reasons for these changes are: 1) Due to the market economy many people moved to the areas with good infrastructures, thus increasing the population. For example, census data indicated that in 2001, the population of the capital city had become 812,500. 2) Since the country entered the market economy, the government has not constructed new residential apartments. The apartment prices of the existing construction companies are very high, so, most ordinary people cannot afford them. Therefore, most people when moved to Ulaanbaatar built up their gers surrounded by fences, thus significantly expanding ger districts. 3) During the market economy, most of the constructed buildings are western cottage style houses or buildings with few stories, not like the former apartment blocks. Therefore, they occupied larger areas than the high-rise apartments and also contributed to the expansion of the city areas. 4) At present in Mongolia there is boom to own land. Because, the new land law issued in 2002 [11] gives a distinct right to land owners and they can use their land for receiving bank loans. Therefore, the interests of people to own land parcels have greatly increased. 6. Conclusions The overall idea of the research was to compare the changes in the main urban land cover classes of Ulaanbaatar city, Mongolia occurred during a centralized economy with the changes occurred during a market economy and describe the socio-economic reasons for the changes using multitemporal RS data sets. To extract the reliable urban land cover information from the available RS data sets, a refined parametric classification algorithm that uses spatial thresholds defined from the local and contextual knowledge was constructed. As seen from the classification results, the spatial thresholds defined from the local and contextual knowledge could significantly improve the performance of the classification and for the accurate classification, proper spatial thresholds should be applied. As seen from the urban land cover change analysis, during the centralized economy significant changes occurred in the ger area of the city, while during the market economy the changes occurred in both areas. Moreover, as seen from the analysis, during all this time, the natural resources such as forest and water had been reduced. Because of the spatial resolution of the used RS data sets, the results of this study could be used for a decision-making process at a regional scale. For detailed analysis, large scale maps and very high resolution RS images covering all areas should be used.

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References 1. AMARSAIKHAN, D. GANZORIG, M. and SAANDAR, M., 2001, Urban Change Study Using RS and GIS, Asian Journal of Geoinformatics, December 2001, No2, Vol2. pp. 73-79. 2. AMARSAIKHAN, D. and SATO, M., 2003, The role of high resolution satellite images for urban area mapping in Mongolia. In ‘Reviewed Papers’ part of Proceedings of the Computers for Urban Planning and Urban Management (CUPUM)’03 International Conference, Sendai, Japan, pp.1-12, May 2003. 3. AMARSAIKHAN, D. and DOUGLAS, T., 2004, Data fusion and multisource data classification, International Journal of Remote Sensing, 17, pp. 3529-3539. 4. AMARSAIKHAN, D. and SATO, M., 2004, Validation of the Pi-SAR data for land cover mapping, Journal of the Remote Sensing Society of Japan, No.2, Vol.24, pp. 133-139. 5. AMARSAIKHAN, D., GANZORIG, M. and Moon, T.H., 2005, Application of multitemporal RS and GIS data for urban change studies, Proceedings of the Korean GIS Conference, Busan, Korea, pp.190-215. 6. BORAK, J. S. and STRAHLER, A. H., 1999, Feature selection and land cover classification of a MODISlike data set for a semiarid environment. International Journal of Remote Sensing, 20, pp. 919-938. 7. ERDAS, 1999, Field guide, Fifth Edition, ERDAS, Inc. Atlanta, Georgia. 8. GONZALEZ, R. C. and WOODS, R. E., 2002, Digital Image Processing, Second Edition, Upper Saddle River, (New Jersey: Prentice-Hall). 9. MATHER, P.M., 1999, Computer Processing of Remotely-Sensed Images: An Introduction, Second Edition, (Wiley, John & Sons). 10. MONGOLIAN STATISTICAL YEAR BOOK, 2006, National Statistical Office of Mongolia, Ulaanbaatar, Mongolia. 11. NEW LAND LAW OF MONGOLIA, 2002, New Land Law of Mongolia, Ulaanbaatar, Mongolia. 12. RICHARDS, J.A., and JIA, X., 1999, Remote Sensing Digital Image Analysis-An Introduction, Third Edition, (Berlin: Springer-Verlag).

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SURFACE WATER POLLUTION OF THE ULAANBAATAR CITY (determined by BOD, dissolved O2, NH4+, NO2-, NO3-, PO4-3, Cr+6, COD) (between 1996-2004) (1) Professor, Ch.Gonchigsumlaa, (2) MSc, O.Altansukh National University of Mongolia, Faculty of Earth Sciences, Department of Geoecology-Land Management, [email protected]

Key words: Chemical analysis, surface water, water pollution, water quality, permissible limit, standard of water quality Introduction: Chemical component of river water, is running near by Ulaanbaatar city (Tuul, Terelj, Uliastai, Selbe and Dund rivers), divided two sections of water quality when compared with Mongolian standard of water quality. 1. Section with fresh water - in permissible limit 2. Section with polluted water - out permissible limit Method:  Used the chemical analysis results of river water that did in the Central Laboratory of Environmental Monitoring.  Compared with Mongolian standard of water quality  Drew by ArsView 3.3 Result: 1) Section with fresh water - in permissible limit  Tuul-Uubulan  Tuul-Nalaih  Tuul-Bayanzurh  Tuul-Zaisan  Tuul-Sonsgolon  Tuul-Upper Songino  Terelj-Terelj  Uliastai-Ulaanbaatar  Selbe-Ulaanbaatar 2)

Section with polluted water - out permissible limit  Tuul-Lower Songino  Tuul-Shuvuun fabric  Tuul-Khadanhyasaa  Tuul-Altanbulag  Dund-Ulaanbaatar

Chemical component of river water increased downstream until city center. Near by Ulaanbaatar city, river water quality is decreasing when it is running through the city. There is having several reasons: 1) Industrial waste water is pouring into river 2) Increasing number and density of population in city center 3) Decreasing natural water cleaning capacity near city In addition, there is having a biggest factor of water pollution. This is a central sewage treatment station of city. This station is source of section with polluted water. Suggestion: - 76 -

 

Immediately, to change technique and technology of central sewage treatment station. To improve industries treatment station.

Surface water pollution of the Ulaanbaatar city BOD, P, dissolved O2, NO-3, NO-2, NH+4, COD, Cr+6 (between 1996-2004)

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Research point Clean water - in permissible limit Polluted water - out permissible limit Unstudying river Studying river Tehnogen area Territory of the ulaanbaatar city

Analysis on the Land Use in General and Neighborhood Commercial Areas of Ulaanbaatar city using RS and GIS3 B.Chinbat1, D.Amarsaikhan1,2 and Tae-Heon Moon3 1

Faculty of Geography, National University of Mongolia, Ulaanbaatar, Mongolia E-mail: [email protected] 2 Institute of Informatics and RS, Mongolian Academy of Sciences av.Enkhtaivan-54B, Ulaanbaatar-51, Mongolia E-mail: [email protected] 3 ERI, Urban Engineering Major, Construction Division, Gyeongsang National University 900 Gazwa Jinju City, Gyeongnam 660- 701 Korea E-mail: [email protected] Abstract The aim of this study is to analyze the land use of general and neighborhood commercial areas of Ulaanbaatar city using RS and GIS techniques. For this purpose, two urban land use sites representing these areas have been selected in different locations. For the analysis, historical GIS data as well as SPOT 5 and Quickbird images of 2002 have been used. The analysis was carried out using the ArcGIS and Erdas Imagine 8.6 installed in a PC environment and to reach the final goal, different RS and GIS techniques have been applied.

Keywords: Land use, commercial area, RS, GIS, analysis 1. Introduction In recent years, Ulaanbaatar, the capital city of Mongolia has faced different urban development problems, similar to many cities in developing countries. In the city, various problems had been accumulated during the communist era and they have been accelerated by the reforms of the entire political and economic systems, unregulated market development and the rapid population growth caused mainly by migration from rural areas. Since the transition to a market economy, the Ulaanbaatar city has experienced much more developments, which resulted in changes of the spatial and functional structures of the city and the most significant changes have been the increase of commercial functions in the city centre and inner city area; the expansion of the urbanized areas along with the growth of formal and informal ger-settlements; the formation of satellite nodes with clusters of commercial functions, and the residential suburbanization in the outer city by single family houses [5]. At present, in the country there are missing urban-oriented research activities, based on the modern urban geographical theory and methodologies, because the research on detailed urban studies, including functional and spatial differentiation of urban areas is a relatively new research direction in urban geography of Mongolia. One of the fundamental problems in urban study could be the research on how different urban features with various profiles and duties can be spatially located better coping with each other in order to be ecologically, economically and socially efficient and satisfy the requirements of the sustainable development [4,5]. In this study, we wanted to analyze the land use of general and neighborhood commercial areas of Ulaanbaatar city using RS and GIS techniques. For this purpose, 2 urban land use sites representing these areas have been selected and for the analysis, historical GIS data as well as SPOT 5 and Quickbird images of 2002 have been used. The analysis was carried out using the ArcGIS and Erdas Imagine 8.6 installed in a PC environment. 2. The selected sites and data sources In this study, as a general commercial area III-IV microdistrict located in the north western part, whereas as a neighborhood commercial area Sansar microdistrict located in the central part of Ulaanbaatar city have been selected. The locations of these sites represented in a SPOT 5 image of 2002 are shown in Figure 1. 3

Paper to be published in Proceedings of 2nd International Conference on Land cover /Land use study using RS and GIS

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As the RS data sources, multispectral SPOT 5 image of 2002 resampled to a pixel resolution of 4m and Quickbird image of 2002 with a spatial resolution of 70cm have been used. In addition, a topographic map of 1984, scale 1:50,000 and a topographic map of 2000, scale 1:10,000 as well as a general urban planning map were available. The digital forms of these maps were considered as historical GIS data sets.

Figure 1. SPOT 5 image of Ulaanbaatar city (Red=band 1, Green=band 3, Blue=ban 2). 1-General commercial area (III-IV microdistrict), 2-Neighborhood commercial area (Sansar microdistrict).

3. Georeferencing of the SPOT 5 and Quickbird images In order to georeference the Quickbird image to a Gauss-Kruger map projection, a topographic map of 2000, scale 1:10,000 has been used. The ground control points (GCP) have been selected on well defined cross sections of roads, streets and building corners and in total, 12 and 9 regularly distributed points were selected for the III-IV microdistrict and Sansar microdistrict, respectively. For the transformation, a linear transformation and nearest neighbour resampling approach [6,8] have been applied and the related root mean square (RMS) errors were 1.56 pixel and 1.79 pixel, accordingly. Likewise, two subsets selected from the multispectral SPOT 5 image have been georeferenced to a Gauss-Kruger map projection [3] using the same topographic map of the test area. For the transformation the same number of GCPs has been used and the related RMS errors were 1.21 pixel and 0.98 pixel, accordingly. In each case of the georeferencing, an image was resampled to a pixel resolution of 70cm. 4. Image fusion In the present study, in order to enhance the spectral and spatial variations of different land use classes as well as to merge the images with different spatial resolutions, two image fusion techniques such as Brovey transform and intensity–hue–saturation (IHS) transformation have been used and compared. After applying corrections, data with different spatial resolutions can easily be integrated. The image fusion is the integration of different digital images in order to create a new image and obtain more information than can be separately derived from any of them [2,9]. In the case of this study, the panchromatic image provides more spatial information due to its higher spatial resolution, while the multispectral images provide the information about the spectral variations of the urban classes. Image fusion can be performed at pixel, feature and decision levels [1,9]. In this study, the fusion has been performed at a pixel level. Before applying the fusion techniques, a 5x5 size high pass filtering [7,10] has been applied to the panchromatic images in order to enhance the edges. Brovey transform: In this method, multispectral images with a lower spatial resolution are integrated with an image with a higher spatial resolution, thus creating spectrally and spatially enhanced color images [11]. To create spectrally and spatially enhanced color (RGB) images, the sum normalized multispectral bands are multiplied by the image with a higher spatial resolution as shown below:

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R=(B1/B1+B2+B3)*B4 G=(B2/B1+B2+B3)*B4 B=(B3/B1+B2+B3)*B4 where B1,B2 and B3 - multispectral bands, and B4 - band with a higher spatial resolution. In the present study, for the Brovey transform, the bands of SPOT 5 were considered as multispectral bands, while Quickbird image was considered as higher spatial resolution band. IHS transformation: The IHS method is the most widely used data fusion technique. This method assumes that the H and S components contain the spectral information, while the I component represents the spatial information [1,8]. A detailed review of this approach is given in Mather (1999). For the IHS transformation, the RGB image created by green and near infrared bands of the SPOT 5 data as well as panchromatic band of Quickbird data have been used and the panchromatic band was considered as the I. When the IHS image was transformed back to the RGB colour space, contrast stretching has been performed to the I and S channels. In order to obtain a reliable color image that can illustrate the spectral and spatial variations of the selected land use classes, different band combinations have been used and compared. Although, the images created by the Brovey transform contained some shadows that were present on the panchromatic images, they still illustrated good results in terms of separation of the available land use classes. The images created by the IHS method contained less shadow effects, however, it was very difficult to analyze the final images, because they contained too much color variations. Therefore, for the interpretation of the selected land use classes, for both test sites, the images created by the Brovey transform have been used. The fused images of SPOT 5 and Quickbird are shown in Figure 2.

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Figure 2. The fused images of SPOT 5 and Quickbird data. a) and c) Brovey transformed images, b) and d) IHS transformed images.

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5. Land use analysis of general and neighborhood commercial areas

At the beginning, from the Brovey transformed images of both test sites, the available land use types have been digitized using the ArcGIS system (Figure 3). The general commercial region consists of the trade and service street stretched about 2 km along the Ayush avenue, which separates the III and IV microdistricts. As seen from the results of the interpretation, in this region the high rise and middle rise residential houses occupy 15,2 hectares (ha) or 49,9%; general educational schools, crɢches and kindergartens occupy 6,39ha or 21%, trade and services areas occupy 3,75ha or 12,4%; research institutions, schools occupy 1,3ha or 4,3%; companies and banks occupy 0,85ha or 2.8% of the total area, thus forming the prevailing portion of the land use. These microdistricts which were constructed in 1970s and 1980s consist of several residential apartment blocks and neighborhoods together with schools, kindergartens, trade and services centers. The average radius of the apartment or neighborhood was planned to be 400-500 meters and their trade and services centers to be located neighboring each other in the Ayush avenue. During the process of the transition period which started in 1990, on the bases of the old apartment blocks, trade and services buildings there were centered many new types of land use such as supermarkets, garment shops, electronics, cosmetics diversified groceries, department stores, banks, companies as well as universities and institutes, hotels and restaurants. As results of this, this street started to play the role of a general commercial or commercial center with own local character. Moreover, inside of the residential blocks of the microdistricts there were established and opened a great number of offices, hotels, restaurants, night clubs, private universities and institutes, supermarkets, which diminished the environmental value and quality of the given residential area. In addition, one of the negative phenomena occurring in the current land use of the residential areas is that there are being constructed too many low-rise buildings designated for auto garages and other construction facilities of cheaper value and quality. This deteriorates the friendly environment and living conditions of the people inside of these areas. Neighborhood commercial zone refers to commercial area within the residential area. As seen from the results of the interpretation, in this microdistrict, the middle rise apartments occupy 1,9ha or 42,2%; highrise residential houses occupy 0,61ha or 13,5%; trade and services facilities occupy 0,234ha or 5,2%; general educational schools, crɢche and kindergartens occupy 0,69ha or 15,3% of the total area. The service radius of the given service center is 500 m. The services center was planned from the beginning and constructed with a supermarket, grocery shop for consumer goods, bookshop and communal services center. During the transition period, all of these including the sewing, hairdressing, photographic, postal, cinema and dry cleaning services as well as TV repair shop were privatized. After the privatization, the profile and designation of significant part of these services had been altered and changed to night clubs, entertainment places, hotels and so on. Such a change deteriorates the surrounding environment and makes too much noise, thus worsening the living conditions of people living in this region. 6. Conclusions The aim of this study was to analyze the land use of general and neighborhood commercial areas of Ulaanbaatar city using RS and GIS techniques. For this purpose, two urban land use sites representing these areas have been selected in different locations. For the analysis, two urban land use sites were selected and historical GIS data as well as SPOT 5 and Quickbird images of 2002 were used. As seen from the analysis, since the irreversible transfer of the country into the market economy, commercialization became the most important process in these regions and it has influences on the changes of spatial and functional structures. Also, it is seen that there are being formed new types of land use which might deteriorate the residential zones of the urban population. Therefore, thorough urban planning and management based on modern theory and methodologies are urgently needed.

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Figure 3. The land use types interpreted from the Brovey transformed images. a) General commercial area, b) Neighborhood commercial area.

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References 1.

Amarsaikhan, D., and Douglas, T., 2004, Data fusion and multisource data classification, International Journal of Remote Sensing, No.17, Vol.25, pp.3529-3539. 2. Amarsaikhan, D., Ganzorig, M. and Moon, T.H., 2005, Application of multitemporal RS and GIS data for urban change studies, Proceedings of the Korean GIS Conference, Busan, Korea, pp.190215. 3. ArcGIS, User’s guide, ESRI, Inc. Atlanta, Georgia, USA. 4. Chinbat, B., 2005, On a new land use classification and zoning scheme of Ulaanbaatar, Mongolia, Proceedings of Mongolia-Korea Conference in Urban Planning, Ulaanbaatar, Mongolia. 5. Chinbat, B., Bayantur and Amarsaikhan, D., 2006, Investigation of the internal structure changes of Ulaanbaatar city using RS and GIS, Paper presented at the ISPRS Mid-term Symposium, ITC, Enschede, The Netherlands. 6. ERDAS, 1999, Field guide, Fifth Edition, ERDAS, Inc. Atlanta, Georgia. 7. GONZALEZ, R. C. and WOODS, R. E., 2002, Digital Image Processing, Second Edition, Upper Saddle River, (New Jersey: Prentice-Hall). 8. MATHER, P.M., 1999, Computer Processing of Remotely-Sensed Images: An Introduction, Second Edition, (Wiley, John & Sons). 9. POHL, C., and VAN GENDEREN, J.L., 1998, Multisensor image fusion in remote sensing: concepts, methods and applications. International Journal of Remote Sensing, 19, 823-854. 10. RICHARDS, J.A., and JIA, X., 1999, Remote Sensing Digital Image Analysis-An Introduction, Third Edition, (Berlin: Springer-Verlag). 11. VRABEL, J., 1996, Multispectral imagery band sharpening study. Photogrammetric Engineering and Remote Sensing, 62, 1075-1083.

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The Heat Island Experiment over the Western Taiwan Plain with MODIS satellite and concurrent helicopter-borne IR imager data Chia Wei Lan*, Gin-Rong Liu, Tsung-Hua Kuo, Kun-Wei Lin, Tang-Huang Lin, Ming-Chang Hsu, Yen-Ju Chen, Chia-Chi Liu Center for Space and Remote Sensing Research, National Central University, Jhong-Li, Taiwan 320, Corresponding author: Chia Wei Lan, [email protected]

Abstract Previous studies have shown that the urban air temperature can be 4 к hotter than the surrounding rural areas, which is usually referred to as the “heat island effect”. The effect is mainly induced by a combination of human activities and the different types of landcover/landuse. Different types of landcover/landuse render different specific heat values of the land surface. The higher temperature produced from the effect could have a serious impact on the local, regional, and even global environment. It could also affect the public’s health, and waste a country’s energy resources. Furthermore, the heat island effect is a very important factor in global change studies, because it can substantially influence weather and climate systems. With the rising concern over the impact from the heat island effect, a series of observations were conducted over western Taiwan. Concurrent datasets from the satellite-borne MODIS sensor and helicopter-borne thermal IR imager were both employed. In addition, the ground truths from surface stations and automatic weather recorders were also utilized during the same time period. The sampling data in this experiment covered an area of more than 100 km2. Based upon these comprehensive observations, a heat island pattern of Taiwan’s western plain was delineated. The relationship between the temperature increase rate and landcover types was also investigated. The result shows that the surface canopy and the air humidity together play an important role in the surface temperature increase. In this mid-term report, the procedure in the ground truth observation, along with the study’s preliminary results is essentially explained.

Keywords: Heat island, MODIS, Landcover/Landuse

1. Introduction A growing amount of evidences obtained from ground truth and satellites show that the influence from human activities can seriously affect the earth’s climate and biosphere (Gallo et al., 1999; Hung et al., 2006). For example, Taiwan nearly has the highest population density over the world. More than 20,000,000 people live in an island, which covers an area of about 36000 km*2. Unfortunately, more than 2/3 area of this island is mountainous terrain, rendering most of the population to be situated within the western plains of the island. The urbanization evidently changes the pattern of landuse/landcover types and causes the so-called heat island effect (Hoard, 1833, Kato et al., 2005; Rosenzweig et al., 2005, Streutker, 2003). Therefore, a heat island observation experiment covering a large area was conducted for the first time in Taiwan to understand the urbanization influence in the heat island effect on Taiwan’s western plains. Observations were conducted by the MODIS satellite, coupled with ground truths collected from weather stations and helicopter-borne thermal IR Imager observations. 2. Data The data used in this study are shown as follows: Helicopter-borne thermal IR imager (ThermoVision A40M), MODIS, and weather station data. The first two platforms supplied the thermal IR images for the surface temperature retrieval, while the weather stations provided the surface temperature, humidity, and wind speeds.

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The concurrent observations were conducted on 2006/02/22. Most of the IR images were taken between the altitude of 600 to 800 meters.

3. Result Since several of the different landcover types could generally appear during a FOV scanning procedure, the statistical medium values gathered from this analysis could be considered as the nominal temperature of the single scanning observation. The IR images acquired by the helicopter could be converted into surface temperature maps. The maps clearly showed that the surface temperature was strongly dependent with the corresponding landcover types. In order to obtain a comprehensive picture of the heat distribution in this large area, the MODIS data was utilized. From the thermal bands in the MODIS platform, a large land surface temperature (LST) map could be obtained (Figure 1). The temperature map clearly revealed that the left part of the test area obviously had a higher temperature distribution than the right portion, and the temperature values decreased as the vegetation canopy increased. The temperature values varied from 18 to 36 °C. The highest values occurred in the eastern parts of Pu-Tzu, while the lowest values were seen in the eastern part (Ta-Hu and Chang-Nao-Liao) of the observation area. This probably was caused by the low cloud layers and higher landscape. Once again, we used the MODIS red and near IR bands to calculate the NDVI map, which was a simple but efficient indicator for finding the dense or isolated vegetation areas (Figure 2). The contrast between Figure 1 and 2 demonstrates that the main surface heating was caused from deforestation, reduction of vegetation areas, and increment of artificial surfaces. After obtaining the surface temperature and vegetation data, the relationships between the MODIS LST, IR Imager LST and station measured temperatures for the sampling locations can be seen in Figure 3. The line plots showed a fine consistency between the three land surface temperatures. It is reasonable that the results showed a high correlation between the temperature and humidity (not shown). However, the temperature showed a null relationship with the wind speed. More detailed analysis regarding these parameters and the roles they play in the heating process and flux mechanism should be investigated in the future. Needless to say, the (decreasing) change of the vegetation canopy (with a correlation coefficient, R=0.93) plays a very important role in the growth of heat islands (Figure 4).

4. Summary This study has so far successfully conducted one helicopter observation during a very clear sky condition. Basically, the thermal temperature derived in the MODIS and IR Imager data acquired in February of 2006 were seen as a dataset representing a winter heat island pattern. More detailed analysis is currently undergoing. To understand the seasonal influence, another helicopter observation is being planned around this June or July to obtain a second dataset in delineating the summer heat island pattern. In addition, the temperature difference from this summer data could aid in the investigation of the variations between the atmospheric parameters. Based upon our current analysis, some conclusions can be drawn out. Both from the helicopter and satellite observations, the heat island pattern is obvious in the western Taiwan Plains. The roles of the surface canopy will be detailed further for investigation. The influence of the atmospheric effect in the MODIS and IR Imager observations must be computed or modeled from the radiation transfer models. Meanwhile, the influence of the FOV resolution to the mean surface temperature should be seriously treated. If possible, an accurate model taking into account the influence of aerosols, artificial surfaces, and surface waterforms is recommended to compute the heat flux, where a better understanding of the sources and sinks of the heat island heating process can be obtained.

Acknowledgements - 86 -

We would like to gratitude to the Academia Sinica in supporting the grans of theme project “Heat island effect over Taiwan’s western plain and its impact on climate changes”, project number is AS-93-TP-A04. In addition, we appreciate the help of staffs in Academia Sinica and Remote Sensing Center in NCU.

References 1. Gallo, K., T. W. Owen, 1999, Satellite-based adjustments for the urban heat island temperature bias. Journal of Applied Meteorology, 38, 806-813. 2. Howard, L., 1833, The climate of London, Vols. I-III, Harvey and Dorton. 3. Hung, T. D. Uchihama, S. Ochi, Y. Yasuoka, 2006, Assessment with satellite data of the urban heat island effects in Asian mega cities. International Journal of Applied Earth Observation and Geoinformation. 8, 34-48. 4. Kato, S., Y. Yamaguchi, 2005, Analysis of urban heat-island effect using ASTER and ETM+ data: separation of anthropogenic heat discharge and natural heat radiation from sensible heat flux. 2005, 99, 44-54. 5. Rosenzweig, C., W. D. Solecki, L. Parshall, M. Chopping, G. Pope, R. Goldberg, 2005, Characterizing the urban heat island in current and future climates in New Jersey. Environmental Hazards, 6, 51-63. 6. Streutker, D. R., 2003, Satellite-measured growth of the urban heat island of Houston, Texas. Remote Sensing Environment, 85, 282-289.

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Figure 1 The land surface temperature map derived by concurrent observations from MODIS thermal IR bands.

Figure 2 Same as Figure 1, but an NDVI map.

NDVI to MODIS-LST

40 35

0.5

30 25 20 15

0.3

10 5 0

0

0.1 -0.1 -0.2

A

LSTΰdegreeα

0.2

NDVI

0.4

o Ch H -Ko an is g- -K N ou ao -L ia N o eiP Ta u -H La u i-T o CY u N G iao S K Hsi uo n -K Y ou uLi Pu ao -T zu

Temperature (degree)

Temperature & NDVI

MODIS-Lst IR-avg

2

R = 0.8804 0.1

0.3 NDVI

NDVI

Figure 3 Relationship between the different surface temperatures with the MODIS-derived NDVI index.

y = -24.067x + 32.696

-0.1

AT _1m

Station

40 35 30 25 20 15 10 5 0

Figure 4 The relationship between the MODIS measured surface temperature with the NDVI values.

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0.5

Global vegetation continuous field tree cover products and Siberian forest types M. Herold (1), D. Knorr (1), K. Kornhaə (1), A. Shvidenko (2), O. Cartus (1) and C. Schmullius (1) (1) Department of Geoinformatics and Remote Sensing, Friedrich Schiller University, Jena, Germany ([email protected]) (2) International Institute for Applied Systems Analysis, Laxenburg, Austria

Global vegetation continuous fields (VCF) products offer a different perspective on land surface characteristics than traditional discrete classifications. By presenting each pixel as a percent coverage, spatial heterogeneity may be better represented with significant advantages for vegetation modelling. A common VCF product representing the percentage of tree canopy cover per pixel was generated from monthly MODIS composites. Currently, this product is available for 2001 with a spatial resolution of 500m. The following four years with a resolution of 250 m are expected to be available in the near future (http://glcfapp.umiacs.umd.edu:8080/esdi/index.jsp?productID=20). Despite the obvious potential of such global datasets, there is only limited validation and insufficient comparability with traditional forest inventory data. Thus, this study aims at building understanding and confidence in VCF tree cover products by a systematic comparison and analysis with large scale boreal forest inventory information. In the framework of the EU funded project SIBERIA-II, the International Institute for Applied Systems Analysis (IIASA) provided a GIS based vector database of 74 test sites at scale 1:50.000 with detailed forest inventory data as well as an aggregated vegetation data base at scale 1:1 Mio. The latter covers the entire 3 Mio km² SIBERIA-II study region in Central Siberia. This provides unique database to verify the VCF tree cover product with detailed inventory information and for a large region covering the full range of boreal forest types. Empirical attempts to find statistical relations between the VCF tree cover, representing canopy closure, and forest parameters are promising and show both potentials and obvious limitations of the datasets. The best linear regressions could be found between the VCF tree cover and tree height (R² = 0.44) and growing stock (R² = 0.35) including all data available. However, the regressions strongly vary for different tree species and eco-regions of this large study area. Prominent correlations between VCF tree cover and growing stock could be found for larch (R² = 0.53). Multivariate geographically weighted regressions showed even higher correlations and reveal interesting spatial patterns. 63% of large residuals found in the multivariate geographically weighted regression coincide with forest disturbances detected by satellite remote sensing. Further investigations provided initial understanding of the link between ERS-SAR coherence, VCF tree cover, and forest inventory information.

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Possible use of fuzzy-knowledge for improving the geographic boundary representation Sang-Jun Kim1, Ju-Whan Kang2, and Soung-Yong Yun3 1

Department of Civil Engineering, Kyungwon University, Seongnam, S. Korea 461-701 Tel) +82 031-756-0248, Fax) +82 031-756-0248, Email) [email protected]

2

Department of Civil Engineering, Mokpo National University, Mokpo, S. Korea 534-729 Tel) +82 061-450-2473, Fax) +82 061-452-6468, Email) [email protected]

3

Department of Civil Engineering, Ansan College of Technology, Ansan, S. Korea 425-080 Tel) +82 031-490-6077, Fax) +82 031-490-6075, Email) [email protected]

Abstract The polygon boundaries on the digital map of land surface characteristic are conventionally represented as a sharp change and this leads to discrepancy between real world phenomena and the information presented by boundaries on digital map. As an alternative to the sharp-change representation commonly seen in crisp thematic map, this study presents a fuzzy-based method to represent the possible gradual transition along the geographic boundary in the numerical modeling approach. To test acceptability of the newly suggested method, the Revised Universal Soil Loss Equation (RUSLE) was performed with emphasis on the soil erodibilty factor (K). The new approach was facilitated at a small basin in Korea and the results of the RUSLE using the conventional sharp edged boundaries and the fuzzy boundaries are tested against field measurement. The results show the fuzzy representation of geographic boundary provided better performance.

Keywords: Fuzzy, GIS, Soil Loss, RUSLE 1. Introduction In typical fashion, the digital map is represented as a categorical format and polygon boundaries delineate and thereby distinguish areas with different surface characteristics. Conventionally the polygon boundaries on the digital map are represented as a sharp change, which results in discrepancy between real world conditions and the information presented by boundaries on map (Burrough, 1986; 1992). Each zone with an abrupt line is a cadastral map where abrupt boundary definition is required to differentiate land parcels that have unique property (Hunter and Williamson, 1990). It is especially true for soil properties because boundaries of soil properties are continuously changing and rarely sharp or crisp. In reality, localized partial change, gradual transition, and other non-sharp changes coexist in soil properties (Burrough, 1986; Burrough and Frank, 1996). The fuzzy representation of geographic boundary is an advanced geographic boundary representation which enhances the expressive properties of polygons. In this study, a new approach is attempted to improve the representation of geographic boundary in the RUSLE model, examining probable impact of the representation of geographic boundary on soil erosion. The model results show the fuzzy boundary provides better performance and the impact of the fuzzy representation on the RUSLE model is considerable. 2. Numerical Experiment The RUSLE is facilitated at the Yongdam basin, southern part of Geum river. The center of the basin is 127° 33ƍ E, 35° 45ƍ N, which is about 200km south of the capital of Korea and it covers about 575.21 ໊ . Its annual average temperature and humidity are approximately 14ଇ and 74%, respectively and its annual average precipitation (=1,238mm) is slightly lower than the Korean national average (=1,283mm). - 90 -

The RUSLE model computes soil erosion using six major factors which describe land surface characteristics (e.g. soil erodibility, topography, and land use and management) and meteorological conditions (e.g. rainfall erosovity). The cell-based representations of map features used in the RUSLE offer analytical capabilities for continuous data and allow fast processing of map layer (Fernandez et al., 2003). The mean annual gross soil erosion is calculated on the cell basis using the combination of the product of six factors as follows;

A

Ru K u Lu S uC u P

(1)

where A denotes the average soil loss due to water erosion (in ton ˜ ha 1 ˜ yr 1 ). R denotes the rainfall and runoff factor (in MJ ˜ mm ˜ ha 1 ˜ yr 1 ). The soil erodibility factor, K reflects the ease with which the soil is detached by splash and surface flow. The RUSLE describes topographic effect by means of the L- and S- factor, which accounts for the effect of slope length and slope gradient on erosion, respectively. The C-factor reflects the effects of cropping and management practices on erosion rates. The P-factor is a reflection of soil loss due to the flow pattern change, gradient, direction of surface runoff, and reduction of runoff rate resulting from variable cultivation and particular support pratices (Renard and Foster, 1983). The L, S, C, and P are all dimensionless. The amount of soil loss generation is calculated on a yearly basis for the spatial resolution of 22m in this study. It is hard to directly measure soil erosion on a basin scale in an effective way and the measurement of the basin sediment yield was used for the study. The basin sediment yield can be defined as the quantity of sediments which is routed to the basin outlet for a certain time of period. Considering that only some of the eroded soils are routed to the basin outlet, knowing the ratio between the basin sediment yield at the basin outlet and soil erosion over the basin, which is called sediment delivery ratio (SDR), is important for the decision makers but the SDR is involved in numerous uncertainties including temporal discontinuity and spatial variability (Wolman, 1977; Walling, 1983). The RUSLE calculates soil loss forced by rainfall but doesn’t take the sediment yield into account. To generate the sediment yield at the outlet, an equation (6) for SDR, which is an empirical equation derived form the filed experimental data carried out in Korea (KICT, 1992), was introduced as follows; YJ

( R u K u L u S u C u P) u SDR

(2)

where YJ denotes the unit sediment yield and SDR 152.581u Aba sin

0.577

(3)

where, SDR is the sediment delivery ratio and Aba sin is the basin area. The SDR physically means the ratio of the sediment routed to the outlet over the basin (both overland and channel). Figure 1 shows spatial distribution of the RUSLE factors and table 1 shows basic statistics of the six factors. The rainfall erosivity factor, R is in the range of 413-563. The mean values of the L- and S- factor are 2.695 and 3.864, respectively, while the standard deviation (SD) of the L- and S- factor are 1.904 and 3.307, respectively. The SD of L- and S- factor is relatively large and it might be a direct reflection of high topographic variation. The average C- factor was evaluated as 0.028, while the P-factor factor was estimated as 0.759. 3. Theory

Three different types of models are, in general, used to effectively represent geographic boundaries in GIS; abrupt change (Type I), large change (Type II), and gradual change (Type III) (Vincent, 1991; Wang and Hall, 1996).

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The membership function of a set defines how the 'grade of membership' of an individual with an attribute value of x is determined. The membership function converts attribute values x to membership function values ( MFx ). For conventional crisp sets of the polygon boundaries on the digital map, the membership function can be represented as follow; MFx 0

for x  b1

MFx 1

for b2 t x t b1

(4)

MFx 0 for x ! b2 where b1 and b2 define the exact upper and lower limits of the set. For fuzzy sets, the limits b1 , b2 define the central concept of the set. The fuzzy membership function (FMF) defines how the possibility of membership varies continuously from 0 (for individuals that are completely outside the set) to 1 (for objects that within the central concept). The attribute value at the point where 'the grade of membership = 0.5' is called the 'crossover point’ (Burrough, 1992). Rather than the binary membership conditions of classic set theory (1 or 0), a fuzzy membership condition allows more realistic modeling of geographic properties with high spatial within-class variability, whereby membership grades accommodate the extreme classical case, as well as all other possibilities in between (Wang and Hall, 1996). The following fuzzy classification models, which are suitable for soil property data, are an extension version of Kandel (1998). It is a simple model and a general symmetric bell-form FMF. FMFx

FMFx 1

1 [1  {( x  b) / d }2 ]

for x ! P

for 0 d x d P

(5)

The parameter b defines the value of the attributes x at the central concept of the standard index of the set. The form of the membership function and the position of the crossover points can be easily changed by changing the value of the dispersion index, d. The parameter d gives the width of the bell curve at the crossover point, which defines the transition zone around the central core of the set in the same units as the central concept. Accordingly the shape of FMF strongly depends on the parameters b and d and these parameters affect the fuzzified distribution of the K-factor and soil erosion eventually. 4. Application of fuzzy boundary to Soil Loss

We made two simulation scenarios; one is for the conventional representation of sharp change and the other is for the fuzzy representation of within-class variability described in equation (7). Hence, the only source that would differentiate the amount of soil loss generation among the two simulation scenarios is the different description of geographic boundaries. In typical fashion, a value of soil erodibility factor, K is assigned for each grid cell (22m) according to the soil typesoil erodibility conversion. Each soil type has its own sand %, clay %, and silt % from the sampling test (KICT, 1992) and then, K-factors were retrieved from the Erickson’s triangle diagram (Erickson, 1997). Consequently the basin is divided into several patches that are homogeneous in terms of soil properties and it is called the conventional representation of sharp change (Type I in figure 2). Each soil patch is assigned one soil type (one K-factor) and is distinguished by another boundary of soil type. However, the K factor has different values depending in part on how to specify soil type in the RUSLE boundary cell and it can be explicitly controlled as mentioned earlier. To make - 92 -

the soil boundary of soil map more realistic, the simple FMF is then used and the image of figure 3 shows a detailed fuzzy-knowledge based boundary description with 500m Euclidian distance. In Korea, soil samples were taken at 1km spacing to calibrate the 2-D digital soil texture map. The boundary area near which soil characteristics varies was arbitrarily bisected to discern different soil texture for each category and these uncertainties have an influence on soil erosion estimation in the RUSLE. It is, hence, assumed that the Euclidian distance of 500m is covered by the fuzzy representation from the boundary on the basis of ground sampling spacing (1km) (Ahamed et al., 2000) and the fuzzy distance is independent of the soil type, the tillage, and topography. In other words, the FMF to 500m spacing on both directions of the soil boundary is considered to calculate soil erodibility factor, K as shown in figure 3. The application of the fuzzy approach to the RUSLE is as same as that of the conventional approach but the fuzzified K-factors (sharply changed K-factors for the conventional approach as in type I of figure 2) only in the boundary area. Figure 4(a) shows the 2-D imagery map for the soil erodibility, K of the conventional sharp change, while figure 4(b) shows the 2-D imagery map for the soil erodibility, K as a result of the fuzzy representation. The fuzzy-knowledge based boundary in figure 4(b) (blurry area) is called the fuzzy representation of geographic boundary (Type II in figure 2) and then it is regarded as reproducing more real condition in relative terms. Table 1 presents basic statistics of the value of K-factor for both methods. With the fuzzy representation, the mean value of the K-factors is slightly high and the standard deviation is low, while the maximum/minimum values keep constant. Table 2 presents basic statistics of annual soil erosion calculated by the RUSLE for each method. The mean (standard deviation) of soil loss for the fuzzy representation (figure 4(d)) is higher (lower) by 3.1(4.4)% and the maximum loss for the fuzzy representation is higher by 1.8% than that of the conventional boundaries (figure 4(C)). The total soil erosion simulated by the RUSLE for the fuzzy boundary is 1474781 ton / yr , while 1429339 ton / yr for the conventional boundary. The sediment yield is given by SDR (3.9% by equation 7) as 57516 and 55744 ton / yr for the fuzzy and conventional boundary, respectively. Consequently the RUSLE sediment yield for the fuzzy representation of geographic boundary results in lower error (2.9%) comparing to field measurement (unit sediment yield=103 ton / km 2 / yr (KICT, 1992)) as shown in table 3. It implies the fuzzy boundary shows better performance, ignoring the selection of model, the quality of geospatial data, measurement accuracy, and the basin characteristics. 5. Summary and conclusions

A fuzzy representation of geographic boundary is presumably better description of soil properties in that it includes within-class variability concept, which can not be properly described by membership in a single set of sharp change. The results of the RUSLE using the conventional sharp edged boundaries and the fuzzy boundaries are tested against field measurement. The primary conclusions of the study are as follows; ƒ ƒ ƒ

With the fuzzy representation, the mean value of the K-factors is slightly high and the standard deviation is low, while the maximum/minimum values keep constant. Accordingly, the mean (standard deviation) of soil loss for the fuzzy representation is higher (lower) by 3.1(4.4)% and the maximum loss for the fuzzy representation is higher by 1.8%. The sediment yield is 57516 and 55744 ton for the fuzzy and conventional boundary, respectively. The fuzzy representation of geographic boundary in the RUSLE results in lower error (2.9%) and shows better performance.

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To some degrees, error may be different for every new basin of interest, but the method used herein should work anywhere. More realistic description of geographic boundary such as the fuzzy representation is desirable in dealing with soil properties of the soil loss model. References

1.Ahamed, T.R., Gopal Rao, K., Murthy, J.S.R. 2000. GIS-based fuzzy membership model for crop-land suitability analysis. Agricultural Systems 63, pp. 75-95. 2.Ahamed, T.R., Gopal Rao, K., Murthy, J.S.R. 2000. Fuzzy class membership approach to soil loss modeling. Agricultural Systems 63, pp. 97-110. 3. Burrough, P.A., 1986. Principles of geographical information systems for land resource assessment. Clarenden Press, Oxford, UK 4.Burrough, P.A., 1992. Fuzzy classification methods for determining land suitability from soil profile observations and topography. Journal of Soil Science 43, 193-210 5.Burrough, P.A., and Frank, A.U., 1996. Geographic Objects with Indeterminate Boundaries. Taylor & Francis, pp.3-28. 6. Erickson, A.J., 1997. Aids for estimating soil erodibility – K value class and soil loss tolerance. U.S. Department of Agriculture, Soil Conservation Services, Salt Lake City of Utah. 7. Fernandez, C., Wu, J.Q., McCool, D.K., and Stockle, C.O., 2003. Estimating water erosion and sediment yield with GIS, RUSLE, and SEDD. Journal of Soil Water Conservation, 58, 128–136. 8.Hunter, G.J., and Williamson, I.P. 1990. The development of a historical digital cadastral database. International Journal of Geographic Information Systems, 4,169-180. 9. Korea Institute of Construction Technology ( KICT), 1992. The development of selection standard for calculation method of unit sediment yield in river. KICT 89-WR-113 Research Paper (In Korean) 10. Renard, K.G., and Foster, G..R., 1983. Soil Conservation-Principles of erosion by water, In: Dregne, H.E., Willies, W.O. (Eds.), Dryland Agriculture, American Society of Agronomy. Soil Science Society of America, Madison, WO, USA, pp 155-176. 11. Vincent, P. 1991. Modeling binary maps using ARC/INFO and GLIM. In Proceedings of the European Conference on Geographic Information Systems (EGIS 90), (Utrecht: EGIS Foundation), pp. 1108-1116. 12. Walling, D.E., 1983. The sediment delivery problem. Journal of Hydrology, 65, 209-237. 13. Walsh, SJ, 1989. User considerations in landscape characterization. In the accuracy of spatial databases, Taylor and Francis, London, UK, pp. 35-44 14. Wang F and Hall GB, 1996. Fuzzy representation of geographical boundaries in GIS. International Journal of Geographic Information System, 10(5), 573-590 15. Wolman, M.G., 1977. Changing needs and opportunities in the sediment field. Water Resources Research, 13, 50-59.

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R K L S C P

Table 1. RUSLE factors and soil erodibility factor, K between the conventional sharp change (Type I) and fuzzy representation (Type II) used for the study. Min Max Mean S.D. 413.420 562.571 459.286 24.099 Conventional 0.100 0.390 0.251 0.055 Fuzzy 0.100 0.390 0.255 0.054 0.072 9.372 2.695 1.904 0.049 14.927 3.864 3.307 0.000 0.500 0.028 0.079 0.100 1.000 0.759 0.266

Table 2. Basis statistics of soil erosion for each method. Min Max Mean SD ( ton / ha / yr ) ( ton / ha / yr ) ( ton / ha / yr ) ( ton / ha / yr ) Conventional

0.000

4931.244

24.849

59.374

Fuzzy

0.100

5021.634

25.639

56.742

Table 3. Comparison of annual soil erosion calculated by both methods. The measured unit sediment yield is 103 ton / km 2 / yr (KICT, 1992). Total erosion SDR Sed. Yield Measurement Error (%) (%) ( ton / yr ) ( ton / yr ) ( ton / yr ) Conventional 1429339 55744 5.91 3.9 59246 Fuzzy 1474781 57516 2.92

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Illustration captions

Figure 1: Spatial distribution of the RUSLE factors: (a) R-factor. (b) K-factor, (c) L-factor, (d) Sfactor, (e) C-factor, and (f) P-factor

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Figure 2: Three different types of models used to effectively represent geographic boundaries in GIS; abrupt change (Type I), large change (Type II), and gradual change (Type III)

Figure 3: Detailed fuzzy-knowledge based boundary description with 500m Euclidian distance

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Figure 4: 2-D imagery map of soil erodibility factor (K) and soil erosion calculated by the RUSLE. The (a) and (c) is for the conventional representation of sharp change, while the (b) and (d) is for the fuzzy representation of geographic boundary.

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Diagnoses for the Drought and Dzud Frequencies in Mongolia T. Ulaanbaatar Geophysical Department, National University of Mongolia, [email protected] Abstract. The largest natural disasters are the drought and dzud in Mongolia. The studies concerning drought and dzud in Mongolia indicate that the amplitudes and frequencies drought and dzud increased during last 60 years due to the Global Warming. The paper shows some reasons and consequences of these disasters such as desertification, and dust storm based on space and time spectra of their frequencies. A possibility to mitigate the drought, desertification and dust storm within the southwest region of Gobi Desert by regional cooperation is also presented.

Background In Mongolia, the agricultural sector employed 48.5% of the labor force and contributed 26.0% to GDP as of 2001. Approximately 90% of agricultural production came from the animal products sub-sector. In 2001, animal products (mainly cashmere, wool, leather and fur) accounted for about 52.3% of all exports. However, the calamity of ‘Dzud’ (disaster due to harsh winter or weather) occurred continuously in the period 1999 and 2000, causing loss of about 10% of the livestock that died from hunger and frost. The total number of livestock that died from this calamity reached 5.75 million heads, and more than 12000 herders lost their livestock caused by the dzud and as a consequence, agricultural output dropped by about 30%. [The Study…, 2006] At the end of 1999 the number of livestock is highest since 1918 or 33568.9 million heads, but during the 1999-2000 years dzud is destroyed 3391.1 million (10.4%). At the winter of 2001 (after the large summer drought of 2000) 4758.8 million (15.2%) heads of livestock died by dzud. [Batima P. and others, 2005] Moreover, dzud is becoming the main factor of increasing the poverty in the countryside as well as intensifying the mass migration of rural people to the urban areas [Sarantuya, G., 2005] In order to mitigate the damage from Dzud, the Government of Mongolia decided to implement “The National Programme to Protect the Livestock from Drought and Dzud”. Our study concerning with the Drought and Dzud, and their consequences consists of all the territory of Mongolia and period 1943 to 2005. In this paper we attempt to explore answers to next questions. What is the precursor to begin the drought and dzud? What natural disasters are dominant? Are there any connections between the natural disasters? Are there any peculiarities or oscillations to occur the drought and dzud in time and space (within limits of Mongolian territory)? Are there any way to mitigate the drought and dzud? Dzud The term “Dzud” is unknown in world scientific community. Its description is also not accepted commonly. Some have written that this disaster is due to harsh winter or weather (The Study…, 2006), some said dzud have some variations, in example, there are “white dzud”, “black dzud”, “stormy dzud”, “cold dzud”, “joint dzud” and “foot dzud” (Lesson of 1999-2000…, 2000) and others determine only the natural disaster by death-rate of men. Sarantuya G., [2005] wrote that dzud is determined by losses of livestock in Mongolia. The longer anomalous climatic condition, which continues by season or extremely hot or cold months and seasons play of immense negative role in the agricultural life of Mongolia. In the world the understanding of harsh and severe winter and extremely cold snowy periods is familiar, however they do not know this kind of calamity, which dominates in deserts and steppe with continental climate and influences directly on the nomadic livestock farming as Mongolia. We would like to underline that the “Dzud and drought are like a type of “mass extinction” occurred during the Earth’s history. - 99 -

The frequencies of dzud are shown by dominant aimags (Figure 1 and Figure 2). We summarize that as follow: Ÿ During last 60 years Zavkhan was 29 years (48.3%) under the Dzud, except for 24 of 60 years with good climate conditions (40%), Ÿ In 1944-1972, 13 times in Zavkhan (Northeast territory of Western Mongolia ), 12 times in Tuv (Central Mongolia) and, Dornod and Sukhbaatar (Eastern Mongolia). So, the Dzud focuses abovementioned territories, but Gobi Desert enters into this calamity, Ÿ In 1972-1980, Zavkhan and Tuv aimag are still the hot points of Dzud, but Sukhbaatar participates more intensively, Ÿ Dzud increases intensively since 1992 by intervals 1992-1994, 1995-1998 and 1999-2002 35

Number of Dzud

30

Number of Dzud during last 60 years by Aimags

25 20 15 10

Aimags

5

So u

th G Se obi le G lng ob e i-A Ea lta st i Ba G ya obi nU lg Bu i i Ar lga n k C han en ga Ba tra j ya l G nk ob ho i ng D or o U vu rno rk ha d ng a Kh j en t K ii Su ho kh vd ba a H tar uv sg ul U vs Tu Za v vk ha n

0

Figure 1. The Dzud frequency by Aimags The Figure 2 shows that the dzud increases in 1943-1945, which is so-called Large Monkey-Year Dzud, 1951-1957, 1965-1968, 1974-1977, 1980-1987, 1998-2002 and decreases in 1948-1952, 1967-1975, 1976-1986, 1987-1992 and 1999-2001. 20 18 16 14 12 10

6 4

Ⱥɣɦɝɢɣɧ ɬɨɨ

8

2 0 1943- 1947- 1950- 1952- 1954- 1956- 1958- 1960- 1962- 1964- 1966- 1968- 1970- 1972- 1974- 1976- 1978- 1980- 1982- 1984- 1986- 1988- 1990- 1992- 1994- 1996- 1998- 20001944 1948 1951 1953 1955 1957 1959 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001

Figure 2. The frequencies of Dzud during last 60 years The precursor and existence of dzud does not only depend upon the winter meteorological conditions, but mainly on the behavior of the previous summer. (Sarantuya G., 2005)

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Drought Drought causes losses of livestock, and also very seriously affects environment: land degradation, desertification, fire and water deterioration. 68% of Mongolia is drought prone, most affected by drought is central and southern regions, in which every year drought over 40-50 days occur once 2-3 years. (Bayasgalan, M., 2005) The PDSI (Palmer Drought Severity Index) trend for the last 62 years from 1940 to 2002 is shown in Figure 2. PDSI is averaged value over the whole territory of Mongolia for growing season, from June to August. Periods of PDSI increasing approximately are 1944-1959, 1980-1994, of decreasing 1959-1980, 1994-2002. According to the Palmer’s drought severity classification severe drought occurred 7 times in1944, 1978, 1980 and consecutively 1999-2002, moderate 10 times in 1942,1946, 1947, 1951, 1972, 1981, 1982, 1989, 1998 and slight 8 times in 1941, 1948, 1949, 1952, 1957, 1968, 1979 and 1997. Since 1994 drought intensity has been increased significantly. (Bayasgalan, M., 2005) (Figure 3)

Figure 3. PDSI changes for 1940-2003

Figure 4. Map of drought frequency in Mongolia (Erdenetuya, M., ICC, Ministry of Natural Environment)

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Figure 5. Desertified area in Mongolia by average NDVI

Connection of Drought and Dzud Comparing Figure 2, Figure 3, Figure 5 and Figure 6, we can see easily that high possibility of occurrence of very dangerous dzud, when very dry summer follows harsh winter. Table 1. Frequency of Drought and Dzud

rough t

0

8

zud

5

3

8

3

0

6

7

4

4

7

9 0 0 0 4 0 4 57 92 14 http://www.fao.org/ag/agl/swlwpnr/reports/y_ea/z_mn/mntb262.htm

221 201 181

Frequency

161 141 121

Dzud

101

Drought

81 61 41 21 1 19 8 8

19 8 9

19 9 0

19 9 1

19 9 2

19 9 3

19 9 4

19 9 5

19 9 6

19 9 7

19 9 8

19 9 9

2000

2001 2002

Figure 6. The drought and dzud frequencies in Mongolia during last 16 years

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2003

Based on Table 1 and Figure 6, we can conclude that drought follows dzud by 6 months to one year later and there is whatever oscillation between the drought and dzud. The drought does not occur after dzud without 1999-2001 years. Consequences of Drought and Dzud Contemporary Earth’s climate behavior is multiplicity, which includes all the characters of Warming and Glaciation: floods, land degradation, forest and steppe fire, desertification, deforestation, drought, rainfall, sandstorm, snowstorm, duststorm, eliminations in water resource, heavy cold and hot condition, environment pollutions and so on. The impacts of these calamities differs by space and time, the score of damage in each country and nations. We must select and group the natural disasters by the feedback mechanisms and chains. For example, the Central Asian Arid Climate Zone as a group consisting of main types of natural calamity: drought, Dzud. This group has next types of calamity: 1. Decreasing the water resources 2. “Mass Extinction” in biodiversity and losses of livestock 3. Wild Fire 4. Land degradation 5. Desertification 6. Deforestation 7. Poverty 8. Mass migration to urban area 9. Air and water pollutions 10. Dust Storm and so on. From this list we conclude that at first, consequences of Global Warming are the decreasing of water resources, drought, wild fire, land degradation, deforestation and dzud. The consequences of drought and dzud are “mass extinction”, poverty, mass migration to urban area and so on. In this paper we could limit our study by some important types of this group and explore their reasons. Exploration of Reasons Abovementioned group calamity must have whatever order and connection for each other and reason for their existences. To mitigate negative impact of this group we need to determine the first reason, second reason, third and so on. There is two large reasons: The Global Warming and deficit of water resource. The Global Warming The largest reason is, of course, the man-made Global Warming or Global Change. It goes by natural and anthropogenic way itself. According to our study [Ulaanbaatar T., 1994; 1998a; 1998b; 1999] the natural global warming has some gradual increases which may tell that they are indivisible steps each other and follow in next order: 1. Stabilizing effect of water vapor on thermal storage near the earth surface (Insolation-Water vapor positive feedback), 2. Decreasing the albedo of snow cover and ice sheets off, (Insolation-Albedo feedback and Ice/snow- Albedo negative feedback) 3. Decrease of daily and yearly temperature range and, temperature contrast between the equator - 103 -

and poles, (Feedback on Isotherms of Heliogeothermosphere -Global average temperature of planet), 4. Wave length of terrestrial radiation becomes shorter than previous climate epoch by blocking influences of water vapor and clouds, finally, wet and warm climate covers throughout the earth surface, (Negative Feedback on Increasing of air temperature-Shorting of wave length of terrestrial radiation) 5. Continuous cloudiness exists in equatorial regions, (Positive feedback on InsolationCloudiness) 6. Disturbance in underdeveloped atmospheric global circulations due to the continuous cloudiness, (Negative feedback on Insolation-Cloudiness) 7. Decreasing the permafrost off, (Negative Feedback on flow of substance in LithospherePermafrost) 8. Increasing the atmospheric CO2 content in consequence of volcanic and tectonic activities, (Positive Feedback of Volcanic activity-Warming) 9. Rising the isotherms of heliogeothermosphere due to rising of sea level i.e. transgression, (Positive Feedback on Isotherms of heliogeothermosphere -Global average temperature of planet). If the Earth’s climate system naturally remains without the human-influences as like above, this World Climate SuperCirculation (so-called by Ulaanbaatar T.) would continue some hundred millions of years. Unfortunately, new man-made feedbacks in all of these steps can effect powerfully and most quickly than natural variability into the climate system. Today, the Earth’s climate goes already at first 3-4 steps. On a word, human hazardous activities can influence some thousand-fold beyond the volcanic activities producting carbondioxide. If we cannot immediately impact in mitigation of our dangerous activities and climate change, present climate system becomes incontrollable critically. As results, in latest few years a multitude worst climate event are occurring throughout the Earth. Situated deep in the interior of Asia and unpenetrated by the air currents from the oceans, Mongolia has conspicuous continental climate, with highly changeable temperature, sharp difference in temperature between day and night, abundant sunshine, intense evaporation and little precipitation. The Gobi Desert of Mongolia is very sensitive to Global Warming. The Water Resources x

Surface Water

More than half the country is covered by permafrost, which makes construction, road building, and mining difficult. All rivers and freshwater lakes freeze over in the winter, and smaller streams commonly freeze to the bottom. In consequence of global warming in whole territory of Mongolia more than 400 hundreds of rivers and lakes are depleted during the last 30 years. 37% of the territory of Gobi and Great Gobi Protected Areas have no any sources of water supply, which have caused the elimination of hundreds of oases, springs, rivers and wells. x

The Ground Water

Demands of water in Mongolia are mainly met from the ground water sources. But in course of last 16 years the ground water table lowers down due to the drying of surface water and decreasing of precipitation. The level of lowering down of ground water varies by 2-3 meters in the Shallow Well and Shaft Well, and 1-2 m in Production Well, but there dominates no water in Traditional Well in the Mountain Steppe. The level of lowering down in Mountain regions is more than in the Deserts and Steppes. The lowering increases also in the Gobi Desert. We have experienced the lowering down of Gobi ground water table, when T.Ulaanbaatar participated in JICA water exploration expedition in Dundgobi, Umnugobi and Dornogobi, and when met herdsmen.

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x

The Precipitation

Main sources of precipitation in Mongolia dominates as follow: o The Arctic Oscillation The arctic cycle depends mainly on the precipitations of whole territory, specially, Northwest of Western Mongolia, Eastern and Central Mongolia. o The west trade-wind. All year, the west and northwest wind stream dominates in Mongolia. The northwest trade-wind generates from Arctic Circulation, and slight west stream into the Gobi Desert is from the Caspian Sea and the Mediterranean Sea. (Figure 7) o The Pacific Circulation, influence of which is slightly on a very little zone of Khalhin Gol River in Eastern Mongolia. (Figure 8) There is a short rainy season in July and August during which most of the yearly rain falls. Around 67-78 percent of all precipitation falls during these three summer months.

http://www.wunderground.com/global/Region/AS/WindSpeed.html

Figure 7. Trade-wind map of Asia

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Figure 8. 1961-1990 Mean annual precipitation of Mongolia From Figure 4, Figure 5 and Figure 8 we can see the west region of Gobi Desert has a very little annual precipitation and very large frequency of drought. Now we would like to show a possibility to improve the ecosystem of the southwest Gobi Desert. The regional intensive cooperation can mitigate only some local problems concerning desertification, drought and dust storm of Gobi Desert. Can We Mitigate Dust Storm Generating from Gobi Desert? According to our study the main reason of the drought in Mongolia is the lowering down of ground water table, and next reason is the decreasing the humidity (precipitation) on and near the earth surface. Very sensitive vegetation system of Gobi Desert irrigates on the one hand, by ground water, on the other hand, by the humidity and rare precipitation. The vegetation system of southwest Gobi Desert irrigates only on the one hand by ground water, and on the other hand, seasonal humidity (precipitation) delivering from territory of XinJing Uigur Autonomous Region, China by west trade-wind (Figure 7). This type of humidity and precipitation is very little, however, very useful for the life of sensitive and thirsty vegetation of southwest Gobi Desert. The level of ground water table in this region also lowers down. Gong S.L. and others [2004] wrote that “depending on the degree of desertification, newly formed deserts covered 15% to 19% of the original desert areas and would generate more dust storm, ranging from 10%-40%, under the same meteorological conditions for spring 2001. …There are two major non-Chinese dust sources: Mongolian Gobi over north of China and Kazakhstan/Uzbekstan deserts in the northwest. The non-Chinese deserts (mainly from Mongolia) contribute about 30-60% surface dust concentrations in Northeast China and 40-50% in Korea and Japan”. http://gsa.confex.com/gsa/inqu/finalprogram/abstract_54086.htm The desertification is not only Chinese problem, it is also problem of Mongolia, furthermore Korea and Japan. For this reason, if we contribute in the framework of the artificial precipitation project of NW China, we can mitigate the desertification and dust storm generating from Mongolia Qiang, Mingrui, and Chen, Fahu (2003) reconstructed the dust history in Northwestern China for the last 2000 years at a 5-10 year resolution and showed quite clearly an increased trend in dust storm events over the last 20 years. http://gsa.confex.com/gsa/inqu/finalprogram/abstract_54086.htm “Northwest China's Xinjiang Uygur Autonomous Region plans to explore air water resources through artificial efforts to ease the shortage of surface water and improve the local environment. Meteorologist Zhang Jianxin said that the region has been technically prepared to launch the artificial precipitation project. Currently, two-thirds of Xinjiang's land area and more than 12 million of its population are threatened by desertification, which spreads by 350 square kilometers annually. The air in Xinjiang is rich in water vapor, which totals 1.38 trillion cubic meters annually, Zhang said, adding that only 17.6 percent of the total amount transforms into rain and snow on yearly basis. Zhang said, if one percent more of the vapor is transformed into rain and snow through artificial technologies, an additional 1.4 billion cubic meters of water could be utilized by the region annually. That amount is equal to one-sixth of the region's current runoff volume. Xinjiang first launched artificial precipitation in the 1960s and has achieved encouraging results.” www.hartford-hwp.com/archives/55/469.html “Zhang and his Lanzhou Arid Climate Research Institute are now engaging themselves into an ambitious "heaven water seeking program," a part of the Ministry of Science and Technologies' West Development Scientific and Technological Program Package. Started at the end of 2004, the program is to research the cloud resources in the air above the Qilianshan Mountain in the province and provide solutions on exploiting the air water resources. - 106 -

"If the program goes smoothly, it will bring 370 million cubic meters more water to the Qilianshan Mountain and the continental rivers of the nearby Hexi Corridor in Gansu," Zhang said. "This will greatly improve the environment of the areas and create a profit of 600 million yuan (about 72.6 million US dollars)." "China defines the water conservancy holding more than 100 million cubic meters of water to be large-scale reservoirs and the program will bring nearly four large-scale reservoirs to the province," said Senior Engineer Qi Xinhui from the provincial flood control and drought relief headquarters, which is believed to be one of the biggest beneficiaries of the program. As a matter of fact, the "heaven water seeking program" is not only carried out in Gansu, but also in the neighboring Shanxi Province and Ningxia Hui Autonomous Region. In these regions, the per capita water resources is below 1,000tons per person, less than half of the water-needy China's average.China's per captia water resource possession is 2,200 tons, only one quarter of the world's average. Liu Chunzhen from the Ministry of Water Resources' informationcenter said the continuing drought and unplanned water use worsensthe water shortage in China's northwest. To ease the situation, local governments have already started to apply the artificial precipitation. The statistics from Gansu artificial precipitation office said the province has established more than 300 operation sites for artificial rainfall and snowfall and will perform more than 1000 operations every year with the help of rockets, cannons and airplanes. Gansu has benefited about one billion cubic meters of precipitation from 2004 to now thanks to the artificial operations. Zhang said the development of the cloud water resources will bring the provinces "some invisible and long-term benefits." http://news.xinhuanet.com/english/2005-05/25/content_3002822.htm Gong S.L. and others [2004] wrote that some regions that are mainly in northern China close to the extensive desertification areas could reach reduction by 40% to 50%. There are many advantages in NW China Region to Launch Artificial Precipitation Project, but some disadvantages too. We think that more than 300 operation sites for artificial rainfall and snowfall and more than 1000 operations every year with the help of rockets, cannons and airplanes can influence powerfully on expansions of desertification and increasing of the drought frequency in Gobi Desert. Conclusion 1. Frequencies of dzud and drought increase due to the Global Warming. Approximately every 60-year an extremely harsh dzud may be occur throughout the country. 30-years oscillation is indicated. 2. Drought follows dzud by 0.4-0.7 years late of phase. 3. Drought is reason of the hazard of Dzud, difficulties of livestock farming system, poverty and other negative phenomena. 4. The drought does not occur generally after dzud without 1999-2001 years. 5. There is a right way to mitigate the frequencies and amplitudes of drought, desertification and dust storm in Gobi Desert: To expand the NW China Region to Launch Artificial Precipitation Project to southwest Gobi Desert and to intensity the regional cooperation. Reference 1. Batima P., Oyun R., Erdenetuya M., Erdenetsetseg B. and T. Ganbaatar, Affects of Livestock Farming 2. System by Climate Change, UNEP, START and AIACC, Ulaanbaatar, 2005 3. Bayasgalan, M., Drought monitoring, Proceedings of First International Conference on Studies on 4. Mongolian Environmental Issues Using Remote Sensing and Geographical Information System, 5. 99-103 pp, Chiba University, Japan, 2005 6. Bayasgalan M. “Drought monitoring in Mongolia”, Dissertation, Ulaanbaatar, 2006. - 107 -

7. Erdenetuya M., Remote sensing methodology and technology for pasture vegetation assessment, 8. Dissertation, Ulaanbaatar, 2004. 9. Gong, S. L., Zhang, X. Y., Zhao, T.L., and L. A. Barrie, Sensitivity of Asian dust storm to natural and 10. anthropogenic factors, Geophysical Research Letters, Vol., 31, L07210, doi:10.1029/2004GL019502, 2004 11. Sarantuya, G., Studies of dzud occurring in Mongolia and possibilities of assessment, National University 12. of Mongolia, Ulaanbaatar, 2005 13. The Study for Improvement Plan of Livestock Farming System in Rural Area, Draft Final Report, 14. (Mongolian counterpart person is Ulaanbaatar T.), Japan International Cooperation Agency 15. (JICA), Ministry of Food and Agriculture Mongolia (MFA), Pacific Consultants International and 16. Mitsui Mineral Development Engineering Co. Ltd, January, 2006. 17. Ulaanbaatar T., Radiative properties of water vapor, Symposium: Radiative effects of water vapour on 18. climate, IAMAS, XXII General Assambly, IUGG, Birmingham, UK, Vol. A, 260, 18 July-2 19. August, 1999, 20. Ulaanbaatar T., Mathematical modeling for the thermal regime of the Earth’s surface and cryosphere, Ph.D 21. Thesis, Mong. Univ.of Technology, Ministry of Science and Education, Ulaanbaatar, 1994. 22. Ulaanbaatar, T., Climate Supercirculation of the Earth, Scientific Translation, 3(132), 246-264, Nat. Univ. 23. Mong., Ulaanbaatar, 1998a, 24. Ulaanbaatar, T., Feedbacks in the World Climate Supercirculation and their interacts, Scientific 25. Translation, N6(147), 124-139, Nat. Univ. Mong, Ulaanbaatar, 1998b.

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EVALUATION OF ATMOSPHERIC OPTICAL CHARACTERISTICS IN THE MONGOLIAN TERRITORY G. Batsukh1, B. Daariimaa2, T. Narangarav3

Department of Geophysics, NUM [email protected]

x x x

Abstract: In this paper, we have determined the yearly and long-term changes of atmospheric aerosol optical thickness in photosynthetically active spectral region (380-710nm) and atmospheric optical thickness in photosynthetically active (380-710nm), infra red (>710nm) and biologically active (<510nm) spectral regions in Ulaanbaatar. We have used hourly measurements of direct solar irradiance, obtained by AT-50 actinometer with glass filters in Ulaanbaatar (1980-2004), Ugtaal (1987-1996), Sainshand (1993-1997) and Darkhan (1992-1998), where the population and climate are different. The aerosol optical thickness and atmospheric optical thickness in region of photosynthetically active radiation were increased sharply in long-term change, especially, during the 1982-1984 and 1991-1993. It is related to the change of aerosol optical thickness for upper atmosphere. And, we have shown how the integral coefficient of transparency expresses the change of aerosol optical thickness in long-term change. When the Sun’s altitude increases, the integral coefficient of atmospheric transparency decreases, and the aerosol optical thickness and atmospheric optical thickness increase. This relationship is same for all seasons. We have shown that long-term change of atmospheric optical characteristics change the same at any altitude of Sun and in any season. The atmospheric optical characteristics are greater in summer than in winter, because the Sun’s altitude is greater in summer than in winter.

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LANDSCAPE UNIT MAP OF UVS NUUR AND ADJACENT AREAS Michael Walther National University of Mongolia MOLARE Research Centre, Director [email protected]

Uvs Nuur Basin is located around 1.200 km west of Ulaanbaatar (Mongolia) in the western province of Uvs Aimak (Capital: Ulangom). The lake covers a surface of 3.350 sqkm, has a diameter of around 75 km and a maximum depth of 22 m. The endorehic lake has an inlet in the eastern part of Tes Gol. Based of geomorphologic field studies in the late nineties different landscape units are subdivided into: 1. recent lake surface 2. paleo lake surface 3. paleo shorelines 4. pediments 5. alluvial fans (different old stages) 6. fluvial meadow bottoms 7. fluvial high flood beds 8. rivers 9. tectonic lines 10. dunes (different old stages) 11. basement (mountain ranges) More information is published in: 1. Grunert, J., F. Lehmkuhl & M. Walther 2000: Paleoclimatic evolution of the Uvs Nuur basin and adjacent areas (Western Mongolia). - Quaternary International, 65/66: 171 - 192. 2. Grunert, J, & D. Dasch 2000: Binnendɶnen im nɰrdlichen Zentralasien. - in Walther, M. et al. (Eds) 2000: Proceedings of the Congress Mongolia 2000, Freie University of Berlin, Berliner Geogr. Abh., 205: 2 - 8; Berlin. 3. Klein, M. 2001: Binnendɶnen im nɰrdlichen Zentralasien. - Mainer Geogr. Studien, 47: 182 S.; Mainz 4. Klinge, M. 2001: Glazialgeomorphologische Untersuchungen im Mongolischen Altai als Beitrag zur jungquartɞren Landschafts- und Klimageschichte der Westmongolei. - Aachener Geogr. Arbeiten, 35:125 S.; Aachen. 5. Naumann, S. & M. Walther 2000: Mid-Holocene lake level fluctuations of Bayan Nuur (NorthWestern Mongolia). - in: Miehe, G. & Y. Zhang (Eds): Environmental Changes in High Asia; Marburger Geogr. Schriften, 135: 15 - 27; Marburg. 6. Walther, M. 1999: Befunde zur jungquartɞren Klimaentwicklung rekonstruiert am Beispiel der Seespiegelstɞnde des Uvs Nuur Beckens (NW-Mongolei). - Die Erde, 130: 131 - 150; Berlin. 7. Walther, M. et al. (Eds) 2000: Proceedings of the Congress Mongolia 2000, Freie University of Berlin, Berliner Geogr. Abh., 205: 2 - 8; Berlin.

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SOME INITIAL RESULTS OF USING THE 6P FOREST INVENTORY METHOD IN MONGOLIA

Sh. Tsogtbayar [email protected] Department of Forestry, Faculty of Biology, NUM The 6P forest inventory method was used in Mongolia to determine stand characteristics for a 900 ha site in Mongonmorit soum, Tov province of Mongolia. The data was compared to data collected by traditional inventory methods used in Mongolia, to make an evaluation of the 6P method. The research was conducted in cooperation with German professionals working under the natural resource sustainable development project framework of German Technical Cooperation (GTZ). One hundred and fifty (150) plots were laid out on a satellite image of 1:50000 scale. The ground distance between plots was 100 m. In the field, each plot was located, and the latitude, longitude and altitude for each plot were measured at its center. From the center, a 12.62 m radius circle was constructed and the six nearest trees to the center were selected. For each selected tree the following characteristics were recorded: species type, DBH (diameter at breast height), and distance from center. The height of the tallest of the six trees was also measured. In addition to the selected tree measurements, natural regeneration was described, presence or absence of previous forest fire recorded, and site quality was measured. After measurements and records for a given plot was completed, the next plot center 100 m away was identified by GPS and the procedure was repeated. In total 150 plots in the 900 ha site were selected and described in this manner. The data from the 6P method were compared to data acquired through traditional inventory methods used in Mongolia. The results were summarized on a per hectare basis. From an analysis of the data, the following results can be concluded. First, Basal area increment and resource per hectare will increase as distance from the sixth selected tree to the center decreases. Second, measurement of the tallest trees in the sample plot is automatically increasing resource per hectare. Due to this study, we focused to produce most comfortable methods for Mongolian forest inventory study.

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PASTURE LAND CLASSIFICATION USING REMOTE SENSING DATA

D.Narangerel Informatics & RS Institute, Mongolian Academy of Sciences, [email protected] N.Monkhoo Agency for Land Administration Geodesy and Cartography, Ministry of Construction & Urban development, B.Suvdantsetseg “NUM-ITC-UNESCO” laboratory for Remote Sensing/GIS , The National University of Mongolia, E-mail: [email protected]

A.Saruulzaya Institute of Geography Mongolian Academy of Science B.Batzorig Ministry of Food and Agriculture of Mongolia

Abstract: Pasture land classification is one of the emerging issues in Mongolia. Since 2002 when land privatization and privatization of pasture land and surroundings of Winter, summer & autumn camps become reality; the Mongolian Government needs to have and to implement advanced techniques for assessment of natural and land surface resources & their potential.

Current paper describes methods of pasture lands classification system used at present and application of new advanced technologies such as high precision Remote sensing ASTER data and global land cover classification system, which can be applicable for Mongolia.

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Use of Caesium-137 for the agriculture soil degradation in Central part of Mongolia O.Batkhishig1 , N.Enkhbat2 and B.Burmaa3 1 Institute of Geography, MAS 2 Nuclear Research Centre, NUM 3 Ministry of Food and Agriculture Land degradation and soil erosion becoming very serious problem in Mongolia. According to statistics over 90% of total cropping land is eroded and the yield loss on eroded area comprises of 24-50%. Due to heavy migration of rural habitants to the central market area, over 70% of total pastureland is overgrazed. According to the National Statistics the number of lifestock in 2005 reached upto 30 million head. The another factor affected to the soil erosion and pasture degradation is continuous drought and poor farming practices. Soil degradation appears thus as a major limitation for increased food production. It is then essential to document the magnitude of the problem and develop conservation approaches adapted to the Mongolian agri-environmental conditions. Use of Caesium-137 radionuclide of for solving soil erosion, land degradation problems very important for assessment, monitoring and land degradation process and designing effective soil conservation measures. Our study region situated in Erdenesant somon Tuv aimag. Soil erosion is a prime cause of loss of productivity of land. Compared 30 years cultivated field and non cultivated field down to 0-5, 5-10, 10-15, 15-20, 20-25 cm depths. This soil is by Mongolian soil classification Dark Kastanozem soil. Thickness of humus horizon 30 cm, soil reaction neutral pH ranges 6.65-7.13, humus content varied 7.206 – 2.217 %. Result of long cultivation soil becoming sandy, soil fertility declining. Soil humus content in the upper 5 cm of non-cultivated soil is 7.206 %, but in agriculture fields soil humus content falling down to the 3.186 % or more than 2 times. Caesium-137 radionuclide measurement of soil erosion fields show following results. In the non-cultivated fields Caesium-137 has a 3.1, 2.1, 1.4 kBq/m-2, but in the 30 years cultivated fields this value ranges 1.5, 1.5, 1.6, 0.7 kBq/m-2. Result of cultivation soil erosion rate nearly 2 times increased. Mostly by wind deflation. Upper 10 cm most changed by erosion. The results of this study showed that soil erosion in the Erdenesant Soum of Central aimag of Mongolia is accelerating, should be an immediate concern to conservationists and development planners at all levels. Further research focused involve to satellite images for the soil erosion and degradation combining radionuclide techniques.

Key words: Caesium-137, Soil erosion, degradation, Mongolia, use of radionuclide. E-mail address: [email protected]; [email protected]

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The Mongolian forest characteristic and ecological changes. G.Tsedendash1 Institute of Botany, Mongolian Academy of Sciences, Ulaanbaatar, Mongolia

1

Mongolian forest located in taiga of Eastern Sibiria-Southern Baikal, between the Central-Asian steppe and desert as mountain forest and the most southern part of southern spruce ( Larix) forest. However, totally square of Mongolian forest almost 10 percent consists of Mongolian total territory, its ecological efficient is the most important for ecosystems. Totally square forest of Mongolia is 17515.2 ha and the total resource is 1334.6 million cub. Selenge river’s water collecting square has 73% of forest land and has differences by its vegetation, regeneration and dynamics, which separated it into four zones: tundra, taiga, pseudo-taiga, sub-taiga. The lower belt of forest situated at 650m ( Tujiin nars), but higher belt situated at to 2600m elevation ( Khangain nuruu). On the basis of above-mentioned differences of forest and vegetation, total forest territory separated into the three provinces, ten regions ( Karta lesov MNR, 1983, Tsedendash,1996). Over 2500 species of vascular plants grow in Mongolia, ¼ of them grow in forest zone and about 1/3 of medicinal plants existed in Mongolia. Anthropogenic impact to forest ecosystem during the last 100 years shows that 40% affected by human activities, and regenerated 12% by birch and aspen forest, 1.6% changed by ecosystem, other 9.2% is lost its forest characteristics (Methodi otsenki, 1990). Comparison of 1974 years forest covered territory with 2004 years, shows decrease number by 1187.6 thousand ha. In spite of this open woodland and felled area has increased by 10-15 times during this period. Forest water is high efficiency. Khentii Nuruu’s larch forest crown keeps rain water 19.2% in itself and 80.8% to soil. Larch forest with forbs litter is 2-3cm2 , resource is 9.5-16.8 thousand ha (Lesa MNR,1983). The melted snow stream of felled area 90-200 times more than virgin forest and 20-50 times of summer hibernal stream. The maximum temperature of 2m height forest is 29.40C, logged forest 29.70C, minimum temperature 3.40C, -5.90C (Lesa MNR). The soil surface temperature of felled area reached in Khangai 60.00C, in Khuvsgul 53.50C. The air humidity in July ( 13 PM of daytime) in forest 76%, in felled area 69%, which indicated mild impact to the climate. Soil litter of taiga forest ( 3-5cm), resource9-23 thousand ha, humidity capacity 6-11mm. Water permeability of soil is 2.0-9.9mm/min, soil surface stream of all precipitation is not more than 3% and erosion coefficient 1*10-5-12*10-5. Annually 600-1000t/km2 soil has erosion in felled area. The soil surface stream of burnt larch forest affected 12 years before has 2.2-3.4mm, erosion coefficient 74*10-4-100*10-4, annually soil erosion 1.52-9.62 t/km2. By vegetation coefficient related to natural forest-felled area after a year 24.1%, after 12 years 22.9%, after 30 years-18.9%. If the grass layer of forest resource is 2.1cent/ha, but 30 after logging is 11.9cent/ha, moss from 23.3cent/ha into 3.5cent/ha. The temperature regime of felled area forest has changed and affected to the eternal frost, which follows mire and growing of willow stand, birch, elm forest moss. The mire will be changed into the bunchgrass steppe or condition for growing birch forest. The process from coniferous forest to larch forest change is common. The resource of forestry is already used and started to cutting in green area, stricter lines and even affected the trees not-reached Y class of growing. In 1996-1997, lost forest by fire are over 5 million ha forest, approximately annually 500 thousand ha in vary intensive forest fire. Forest fire separated into the three intensifications and has mixed specifics like crown fire, surface fire, which follows different impact. The grass layer, seedlings, young growth are burnt in intensive fire of surface. Some scientists have noticed that surface fire has positive impact to the regeneration of forest with rich vegetation.

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Comparison of years between 1988 in 1981, the shows forest land reducing by 11.2 thousand ha or 25%, pine forest land by 10.4 thousand ha ( 14.3%), aspen forest 0.3 thousand ha ( 30%) increasing in Shariin gol region. After 13 years, felled area of burnt forest have no any signs of regeneration. From Tujiin nars research, the forest has Cerambycidae, Scolytidae in the second summer of burnt region. These larva’s are wintering in trees, in autumn of the second year. Therefore, impossible to have standard cuttings after the second year of fire affected forest. The burnt tree even its not alive still efficient in decreasing of wind speed, shading someway the soil surface and influencing in losing the humidity. The forest main issue is to study the forest regeneration conformity, the reforestation suited in its specifics. It is considered that more over 2-3 thousand of 6-10 years young growth is satisfied, not satisfied if lower than a thousand. From research in Khangai nuruu, the first year we have calculated 451 thousand seedlings in 1 ha, 135 thousand in the second year, decreased into 38 thousand in the 3-rd year and recovering work is started from 1972 in Mongolia. During this period the reforestation is done in 105 thousand ha land. The reforestation is provided only in 30% or over 30 thousand ha in felled area. A long-term, cyclical drying of climate is causing a slow northerly retreat of its forest. For instance, forest distribution certified by trees of Khentii , Khangain nuruu, Gobi Altai nuruu’s different forests. Conclusion. x Forest ecosystem changes of Mongolia, deforestation of eternal frost taiga x Reforestation of felled areas, non-decrease of vitality coniferous forest x Needs of appropriate method, research suited in climate and landscape of Mongolia different from European technology x Selection and putting into operation of data exchange system of training-monitoringunion-emergency agency for disaster preparedness.

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A METHOD TO ESTIMATE SOIL MOISTURE USING L-BAND SYNTHETIC APERTURE RADAR DATA JAVZANDULAM TSEND-AYUSH*, JOSAPHAT TETUKO S.S**, RYUTARO TATEISHI**, TSOLMON RENCHIN*** *Department of Geoinformatic Engineering, Inha University, 253, Yonghyun-Dong, Nam-Gu, Incheon, 402-751, ROK **Center for Environmental Remote Sensing (CEReS), Chiba University, 1-33 Yayoi-cho, Inage-ku, Chiba, 263-8522, Japan *** Geophysical department, Mongolian National University, Ulaanbaatar, Mongolia Tel: 82-32-874-7660 Fax: 81-43-290-3857 E-mail: [email protected]

Abstract. This study focused on development of a method to estimate the soil moisture from l-band synthetic aperture radar (sar) data in arid area. To develop a method relationship between backscattering coefficient of remotely sensed data and soil moisture, and relationship between dielectric constant properties and soil moisture were investigated. Based on the dielectric constant properties of soil samples, the relationship between the backscattering coefficient and soil moisture was obtained by calculation using the developed a multi-layer modeling analysis. Then by using this model, the soil moisture had been estimated by means of derived backscattering coefficient from japanese earth resources satellite-1 (jers-1) sar data.

Key words: Soil moisture, Dielectric constant, and JERS-1 SAR Introduction Soil moisture is an important component of the hydrological cycle. The use of remote sensing to measure soil moisture has been researched on for the last 20 years, with the use of both passive and active microwave instruments (Ulaby et al., 1986). Quantitative measurements of soil moisture in the surface layer of soil have been most successful using passive remote sensing in the microwave region. SAR data have great potential for terrestrial observations, as has been demonstrated by L-band SAR on Seasat (Ulaby et al., 1983). The objective of this research was to develop a method to retrieve soil moisture in arid and semi arid area of Mongolia using SAR data. For this purpose, the relationship between radar response and dielectric constant, and the relationship between dielectric constant and soil moisture are investigated. Based on the dielectric properties of the collected samples, the relationship between backscattering coefficient and soil moisture was estimated by developing a multi-layer modeling analysis. 1. Study area and ground measurement data Our experiment was conducted on arid area of Mongolia, which is located in the southern part (103o00’E to 104o00’E, 43o20’N to 44o20N) of country . A generally arid climate prevails in the study area, dominated by low precipitation, with average rainfall less 50mm per year. Field survey was conducted in July 2003 to collect soil samples to measure soil moisture. Soil samples were collected from 12 points in the large homogeneous sparse vegetated arid area for dielectric constant measurements. The soil moisture content may be expressed by weight as the ratio of the weight of water present to the dry weight of the soil sample. To determine the ratio for a particular soil sample, the sample is weighed before and after drying it at temperatures of 100o - 110oC. Water content of the sample is calculated as:

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Ki where

Ki

mwi  mdi mdi  mb

(1)

is soil water content of ith sample , mwi , mdi and mb are weights of ith wet soil sample and

box, weight of ith dry soil sample and box, and empty box, respectively. 2. Analysis method Based on enhanced analysis method, developed by Tetuko et al. (2003b), a model of scattered waves Z total air

Eo

Ti

air ZC1

[1

ZL1

[i

i th soil

ZCi

[1 [i

st (Z1 ) 1 soil

(Z i )

i th soil

ZLi

trapped waves

limestone rock

f

ZL

(Z f ) limestone rock

(b)

Fig 1 . (a) Two-dimensional analysis model that is composed of multi-layer of media; infinite length of air, thickness [i of ith layer of topsoil, and infinite depth of limestone rocks. (b) The equivalent circuit of the model.

from different amount of soil moisture is considered. Fig 1a shows a two-dimensional model of analysis that is composed of multi-layer of media; infinite length of air, thickness [i of ith layer of soil, and infinite depth of rocks. In this research, to simply the analysis, the impact of surface roughness on the scattered waves was considered because the average of roughness is too smallest than the JERS-1’s wavelength. The incident wave was assumed to be a plane wave with an incident angle T i . The equivalent circuit of the model used in this analysis is shown in fig 1.(b), where the effective series impedance of the ith layer of soil, the parallel impedance of ith layer of soil, and total input impedance are ZCi , Z Li and Ztotal respectively. To simplify the analysis, the parallel impedance of soils ( Z Li ) is neglected and assumed as zero, and the bedrock layer is assumed to be an infinitely deep perfect conductor, consequently, Zf is zero. Based on transmission line theory method, the total input impedance Ztotal is derived from fig 1.(b) and determined by f

Z total

¦Z

(2)

i

i 1

Zi

Z Ci

Z Li  Z Ci tan J Ci[ i Z Ci  Z Li tan J Ci[ i

(3)

where J Ci and [i are propagation constant and thickness of ith layer of soil, respectively.

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By considering the propagation of the wave transmitted from the air to the soils. Referring to this figure, the propagation constant J Ci is derived from Maxwell’s equation as: J Ci

j

2S

Oi

H ri Pri cosTti

(4)

where H i , Pri and Oi are complex dielectric constant, complex specific permeability, transmission angle, and wavelength of ith layer of soil respectively. j is equal to  1 . The wave impedance ZCi of transmitted wave in soil is obtained from component of electromagnetic field perpendicular to the propagation axis. ZCi

where

§ Z0 ¨ ¨ ©

P0 H0

· ¸ ¸ ¹

E yti H zti

Z0

P ri cosTti H ri

(5)

is the wave impedance in air or free space (=120 S ohms). Based on Snell’s law,

the relationship between incidence angle T i and transmission angle T ti at the boundary yield sin T i H ri P ri (6) sin T ti By substituting (4) to (6) into (3), wave impedance Ztotal of incident wave in ith layer of soil becomes

Zi

§ 2S[ i H ri P ri  sin 2 T i H ri P ri  sin 2 T i tan ¨¨ j H ri © Oi Z0

· ¸ ¸ ¹

(7)

Furthermore, the total input impedance is obtained by substituting (7) into (2), therefore the reflection coefficient becomes: *

Z total  Z 0 cos T i Z total  Z 0 cos T i

(8)

o Then the backscattering coefficient V is defined as:

V0

20 log( * )

(9)

The other hand let relationship between dielectric constant H ri and soil moisture Ki of ith soil is defined to

H ri where

f (K i )

(10)

f (Ki ) is the correlation function of dielectric constant and soil moisture. By substituting

the (10) into (7), hence the relationship between backscattering coefficient V o and soil moisture of soil can be obtained from equations (7) to (9). Dielectric constants H ri of ith soil were measured experimentally using dielectric probe kit HP58070B in frequency range from 0.3 to 3.0 GHz.

3. Results and discussion In order to develop a method to retrieve soil moisture, firstly, the relationship between dielectric constant and soil moisture was investigated. Several studies have been conducted over the past few decades to study the relationship between dielectric constant and soil moisture (Ulaby and Batlivala,

118

1976; Ulaby et al., 1978). In this study, the relationship between dielectric constant and soil moisture was obtained by using measured dielectric constant and soil moisture of the soil samples. Figure 3 shows statistical relationship between the dielectric constant and soil moisture. The correlation coefficient of

H

real part of dielectric constant and soil moisture is 0.76. Dielectric constants r of soil were measured experimentally using dielectric probe kit HP58070B in frequency range from 0.3 to 3.0 GHz. The equation (10) describes empirical relationship between dielectric constant and soil moisture

H 'r

2.68e 0.1K

H rcc 0.004K 2  0.01K  0.1

(10)

where H r , H r ,K is real part of dielectric constant, imaginary part of dielectric constant and soil moisture respectively. We considered the average thickness of soil layer on the bedrock in study area as 45cm. And by o [ substituting (11) and =45cm into (8), the relationship between backscattering coefficient ( V ) and soil moisture was obtained. The equation (13) describes the relationship backscattering coefficient and soil moisture.

c

cc

K 1.81e 0.23V where K , V

0

(12)

o

are soil moisture and backscatterin coefficient respectively. The backscattering coefficient

( V ) were calculated using the equation V =20logI-68.2dB (Shimada 2002), where I is the pixel intensity of JERS-1 SAR data. Employing the developed method, soil moisture was estimated using JERS-1 SAR L-band data in study area. It was found that the percent soil moisture ranged between 4.5% and 9%. o

o

Estimated soil moisture with 12 points of ground measured soil moisture of July 2003. The correlation coefficient was 0.94 and standard error of moisture percentage was 1.66.

References 1. Tetuko S.S., J., Tateishi, R., and Tateuchi, N., 2003a, Estimation of burnt coal seam thickness in central Borneo using a JERS-1 SAR image. International Journal of Remote Sensing, 24, 879-884. 2. Tetuko S.S., J., Tateishi, R., and Tateuchi, N., 2003b, A physical method to analyze scattered waves from burnt seam and its application to estimate thickness of fire scars in central Borneo using LBand SAR data. International Journal of Remote Sensing, 24, 3119-3136. 3. Ulaby, F. T., and Batlivala, P. P. (1976), Optimum radar parameters for mapping soil moisture. IEEE transactions on Geoscience and Remote Sensing 14, 81-93

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Moving into international geographic information standards Bolorchuluun.Ch; Battsengel

School of Earth Sciences, National University of Mongolia, Email: [email protected]; [email protected] Key words: standards, GIS, geospatial information, ISO standards

Absract The purpose of this paper is to provide an introduction to standards and an overview of the standardization process in terms of the development, implementation, and deployment of international standards for spatial data infrastructures (SDIs) such as GSDI. The introduction identifies the multiple functions of standards. Within the context of the International Organization for Standardization (ISO) Technical Committee 211, Geographic Information/Geomatics, this introduction describes the functionality of ISO standards beyond being just technical solutions. These functions include: standards serving as compromises, as forms of technology transfer, as democratic and as research mechanisms. It also discusses the consensus process and its implications for standardization. The necessity for worldwide standardization of Geographic Information is well known. Standardization is important for the production and use of geographic data, the GIS industry and the application of their products. The development of national and international Geographic Information Systems has been proceeding rapidly for many years. GIS standards, the relationship between the GIS standards infrastructure and spatial data infrastructures are also discussed. This paper concludes with a brief review of the status ISO/TC 211. In the Geo ICT environment, access to different datasets and grid computing becomes more applicable using the Internet for cross-border datasets. However, still, in many applications, one suffers from the possibility the transfer meaningful data to each other's applications.

Introduction The information technology environment has become far more integrated. Web Services, EGovernment, Federated Architectures and Grid Computing, have benefited government and businesses, helping them to gain access to a substantial amount of data, including geospatial data. Mongolia is in transition from a centrally planned economy to a market oriented one. It is a historically unique process without any given rules and scenarios. Transformation of standardization is one example of deep changes, which generally have had to be made, reflecting quite different understanding of social and economic systems. The depth of these changes is evident when we realise existing differences between the concept of standardization and the function of standards in planned and market economies.  The characteristic features of the earlier approach to standardization were: - mandatory standards, - strict state control of standardization - financing of standardization from the state budget One of the consequences of adopting free market principles is a need to transform the way that standards are developed and adopted within the society. This requires fundamental decisions by government which lead towards: - a movement from mandatory to voluntary standards - adaptation of the national system to conditions relevant to a market economy - harmonization of national standards with the international standards We are going to solve these problems step by step. Standards are often deceptive because they serve different functions. Generally, standards are perceived as consensus accepted technical solutions. Within the International Organization for Standardization (ISO), consensus does not necessarily imply unanimity or approval by majority. The notion of consensus, within this context, refers to the absence of sustained objection. Closer scrutiny reveals that standards are more likely to be political compromises that may have significant roles and implications in the management, policy, and financial considerations of governments, industry, and user - 120 -

communities. In this regard, the approved standard is less than likely to be a superior technical solution. Standards frequently serve as forms of technology transfer between advanced and emerging countries. The traditional technology lag existing between developed and emerging countries is disappearing because emerging countries are now joining technical committees within standardization organizations as participating members or as observers. Who wants geographic information standards? The answer is that all businesses that produce, distribute, or utilize spatial information, either alone or in conjunction with non-spatial information, benefit from spatial standards. These range from geographic information, decision support, data mining, data warehousing, to modelling and simulation. Application areas include – but are not limited to – automated mapping, geo-engineering, computer-aided drafting and design, entertainment, modelling, and simulation. These broad categories span the planning, design, construction, operation, and maintenance of facilities and their supporting infrastructure such as communications, transportation, and utilities. A common way to describe the market is to divide it up into segments: the traditional geographic information systems (GIS) market, business support systems (BSS), and personal productivity (PP). Let's look at each: _ GIS (geographic information systems): – Spatial information (contributes the most value) – Traditional market for spatial technology _ BSS (business support systems): – Spatial information (does not contribute the most value) – Spatial technology embedded in business applications _ PP (personal productivity): – Users want to communicate by use of maps/geographic information – Follows the office suite market A new emerging market is location based mobile services (LBMS), shown left. Many industry sectors within the marketplace will benefit significantly from interoperable access to spatial information and services, including such areas as the travel and tourist industries, the mapping and routing industries, communications, utilities, transportation, national defense, agriculture, disaster management and public safety, location/mobile services, inventory management, real and synthetic environmental modelling and gaming, and the emerging needs of electronic commerce for spatial information. Achieving more interoperability requires a proactive coordination of spatial standards at both the abstract and implementation levels. Proactive cooperation among spatial standards activities should also help to use available resources more efficiently by minimizing technical overlap, wherever this occurs. Such coordination and cooperation should lead to more market-relevant spatial standards, and could serve as a useful roadmap for all interested parties. Mongolian standards evolution The standardization agency of Mongolia was initially established in 1953 under the name "Price and Standardization Bureau". Since that time its structure and responsibilities have been changed several times. However, it has been consistently engaged in nation-wide management and coordination of standardization work. Today these functions have been assumed by Mongolian National Center for Standardization and Metrology (MNCSM). MNCSM approves and publishes all Mongolian standards and represents Mongolia in International Organization of Standardization. MNCSM is a member of ISO since 1979. And today it is a P-participating member of 47 ISO technical committees and subcommittees as well as O-observer member of 69 ISO/TC and SCs. Nowadays, MNCSM has 25 Technical committees. One of them is a standardization technical committee on Information technology, which is open to everybody who is interested to take part. However, members of technical committees unable to participate in international activities due to pure financial resources and well-trained experts. We have got a significant result in harmonization of our National standards with international ones. We have adopted ISO Directives into Mongolian national standardization system. These are: - 121 -

x x x

Part 1. Procedure for technical work. MNS1.1: 99 Part 2. Methodology of standards work. MNS1.2: 99 Part 3. Drafting and presentation of standards. MNS1.3: 99 For the promotion of alignment of the MNS (national standards) with international ones, MNCSM successfully implemented the concept of standards harmonized to ISO/EIC Directives. There are over 3800 Mongolian standards. The percentage of harmonized national standards with international ones is reached 18.4% in all Mongolian standards for last three years. It meant that the percentage increased 3.4 times more than in 1996 and at the end of 2002 it expected to be reached 35 percents. MNCSM now has 20 Technical Committees, through which all interested parties can participate in standardization on concerned fields. Membership in committees is voluntary and it is open to all who are interested to take part in their work at their own expenses. Each technical committee may establish subcommittee (SC) and working group (WG) to cover different aspects of its work. Each technical committee has a Secretary who employed by MNCSM. Mongolian National Center for Standardization and Metrology (MNCSM) is a governmental regulatory agency and national standards body responsible for the coordination and management of the MSTQ activities in Mongolia. The preparation, application and promotion of National standards are set out in the Mongolian law on “Standardization and Quality Certification” adopted in 1994. The Mongolian script character set is a coding proposal of Mongolian scripts which includes Mongolian, Todo, Sibe and Manchu letters, punctuation marks, digits and control characters. The written languages, Todo, Sibe and Manchu, all share Mongolian letters. Many Mongolian characters have different forms according to their position in the word (initial, middle or final). According to the relevant principles of ISO/IEC 10646, only one of those presentation forms has to be encoded. This form is named as basic character. Due to the official use of Cyrillic character as a writing media of Mongolian language there were approved the standards for Cyrillic characters based on Microsoft Code page 866 and 1251 respectively for MS DOS and WINDOWS. Historically, Mongolia had been and keeping traditional tabby for protection environmental since including water, pasture-lands, plans and fauns. Mongolia has adopted and ensured the implementation of several Environmental Laws. These laws, however, did not consider natural processes as ecosystem as a whole. Instead, they focused primarily on securing right to possess natural resources to the relevant Ministers. The entire has been mapped geologically at a scale of 1:1,500,00, much of the country to 1:1,000,000 and 1:50,000, and selected areas to larger scales. Gravity, aeromagnetic and geochemical data are also available. For the promotion of alignment of the national standards with international ones, MNCSM successfully implemented the concept of national standardization system standards harmonized to ISO Directives. MNCSM sets a priority in IT standardization in order to: - Facilitate international, regional and national trade, - Achieve mutual understanding in intellectual, scientific, technical and economic fields, - Implement and transfer advanced technology and technique, - Computerize information exchange - Support mutual understanding and cooperation between governmental authorities, non-governmental organizations, manufacturing companies and businessman of industry and commerce. Standardization of geographic information The International Standards Organization (ISO), the Technical Electro-technical Commission (IEC), and the Telephone Consultative Committee (CCITT), all located in Geneva, are responsible for the standardization at the international level. In ISO, the national standards bodies of some 120 countries cooperate in activities that aim to facilitate the international exchange of goods and services by creating uniform standards with global validity, and to stimulate cooperation in the scientific, technical and economic fields across national frontiers. Since 1994, DGIWG and IHO have played a special role in the development of geospatial standards. More recently the OGC, which has now entered into a cooperative agreement with ISO/TC211, has a significant impact on the standards formulating process. The results of this effective cooperation are ISO Standards. FIG takes special note of the ISO activities in order to transfer knowledge about ISO/TC211 standards to its members for practical use. A survey conducted by FIG shows that there is a general lack of - 122 -

knowledge and practice of the official standardization. Many other useful links to ISO have been started by a special task force on standards, established at the FIG congress in 1998 in Brighton. Since ISO/TC211 was established, in 1994 (Secretariat NTS, Norway) this committee has been steadily increasing. There are now 33 P-(participating) members and 19 O-(observer) members. The ISO 19100 is a series of standards for defining, describing, and managing geographic information. This standard defines the architectural framework of the ISO 19100 series of standards and sets forth the principles by which this standardization takes place. Standardization of geographic information can best be served by a set of standards that integrates a detailed description of the concepts of geographic information with the concepts of information technology. A goal of this standardization effort is to facilitate interoperability of geographic information systems, including interoperability in distributed computing environments. Figure 1 depicts this approach. Geographic Information Framework and Reference Model

•Spatial reference •Temporal reference •Spatial properties •Spatial operations •Topology •Quality •………...

Reference Model, Overview Conceptual schema language, Terminology, Conformance and testing

Geographic Information Services • Positioning services Portrayal Services Encoding

Data Administration

Information Technology •Open Systems Environment (OSE) •Information Technology Services •Open Distributed Processing (ODP) •Conceptual Schema Languages (CSL) •………………………….

Cataloguing Spatial Reference Descriptive Reference Quality Quality Evaluation Procedures Metadata

Data Models & Operators Spatial schema Temporal Schema Spatial Operators Rules for Application Schema

Profiles & Functional Standards

Figure 1 — Integration of geographic information and information technology The ISO 19100 series of geographic information standards establishes a structured set of standards for information concerning objects or phenomena that are directly or indirectly associated with a location relative to the Earth. This standard specifies methods, tools and services for management of geographic information, including the definition, acquisition, analysis, access, presentation, and transfer of such data in digital/electronic form between different users, systems and locations. In figure 1, the ISO 19100 series of geographic information standards can be grouped into five major areas, each of which incorporate information technology concepts to standardize geographic information. These major areas describe the: - The framework for the ISO 19100 series of geographic information standards including ISO 19101, Geographic information  Reference model. The framework and reference model cover the more general aspects of the ISO 19100 series of standards. The reference model identifies all components involved and defines how they fit together. It relates the different aspects of the ISO 19100 series of standards together and provides a common basis for communication. - Geographic information services define the encoding of information in transfer formats and the methodology for presentation of geographic information that is based on cartography and the old traditions of standardized visualisations. - Data administration is concerned with the description of quality principles and quality evaluation procedures for geographic information datasets. Data administration also includes the description of the data itself, or metadata, together with feature catalogues. This area also covers the spatial referencing of geographical objects - either directly through coordinates, or more indirectly by use of, for instance, area codes like postal or zip codes, addresses, etc. - Data models and operators are concerned with the underlying geometry of the globe and how geographic features and their spatial characteristics may be modelled. This area defines important spatial characteristics and how these are related to each other.

- Profiles and functional standards consider the technique of profiling. Profiling consists of putting together “packages/subsets” of the total set of standards to fit individual application - 123 -

areas or users. This supports rapid implementation and penetration in the user environments due to the comprehensiveness of the total set of standards. Equally important is the task of “absorbing” existing de facto standards from the commercial sector and harmonizing them with profiles of the emerging ISO standards. ISO/TC211 Working Groups and Projects are 31 projects handled by 5 working groups and 2 project teams work directly under ISO/TC211 (ISO/TC211-N1042, 2001). Working group 1-Framework and reference model 19101 Geographic information – Reference model 19102 Geographic information – Overview 19103 Geographic information – Conceptual schema language 19104 Geographic information – Terminology 19105 Geographic information – Conformance and testing IS 19121 Geographic information – Imagery and gridded data TR 19124 Geographic information – Imagery and gridded data components Stage 0 Working group 2 – Geospatial data models and operators 19107 Geographic information – Spatial schema 19108 Geographic information – Temporal schema 19109 Geographic information – Rules for application schema 19123 Geographic information – Schema for coverage geometry and functions 19110 Geographic information – Feature cataloguing methodology 19111 Geographic information – Spatial referencing by coordinates 19112 Geographic information – Spatial referencing by geographic identifiers 19113 Geographic information – Quality principles 19114 Geographic information – Quality evaluation procedures 19115 Geographic information – Metadata 19126 Geographic information – Profile – FACC Data Dictionary NP 19127 Geographic information – Geodetic codes and parameters NP Working group 4 – Geospatial services 19116 Geographic information – Positioning services 19117 Geographic information – Portrayal 19118 Geographic information – Encoding 19119 Geographic information – Services 19125 Geographic information – Simple feature access–Part 1: Common architecture 19125-2 Geographic information – Simple feature access–Part 2: SQL options 19125-3 Geographic information – Simple feature access – Part 3: COM/OLE options Working group 5 – Profiles and functional standards 19106 Geographic information – Profiles Final 2.CD 19120 Geographic information – Functional standards 2.CD 19120 Amd. 1Geographic information – Functional standards – Technical amendment NP Project directly under the ISO/TC211 19122 Geographic information/Geomatics –Qualification and Certification of personnel NP 19128 Geographic information - Web Map server interface The major clauses of the Reference model are Conceptual modelling, the Domain reference model (clause 8), the Architectural reference model, and Profiles. These clauses detailed in ISO 19101 are related to the major areas of the ISO 19100 series of geographic information standards. These relationships are summarized in figure 2, and explained in the paragraphs that follow.

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Architectural reference model (Clause 9)

Conceptual modelling (Clause 7)

Geographic information services •Positioning services •Portrayal •Services •Encoding

Data administration

Domain reference model

•Cataloguing •Reference by coord. •Reference by geo. id. •Quality •Quality evaluation procedures •Metadata

Data models & operators

Profiles & functional standards

•Spatial schema •Temporal schema •Spatial operators •Rules for application schema

Profiles (Clause 10)

(Clause 8)

Figure 2 — Relationship of the Reference model to other standards in the ISO 19100 series of geographic information standards The strategic direction of an international deployment of geographic information standards The mandate for ISO/TC 211 – that at least which is implied – is to develop an integrated set of standards for geographic information. Equally important, if not more so, is the unstated strategic direction of the international deployment of such standards. Accordingly, the strategic directions for ISO/TC 211 can be viewed in terms of development, deployment, and the underlying coordination/consensus process that integrates both these phases in successful standardization. Over and above standardization of traditional geographic functionalities, innovative, new, and unknown technology and application domains present challenges that transcend the established process of Geographic standardization. In terms of development, the major issues include: the technical development of standards, the organizations developing geographic or related standards, the priorities within standards, standards and interoperability testing, and the speed of developing technical specifications. As to deployment, the key issues are: the implementation of standards, standards education/training, and the user communities supporting ISO/TC 211 standards. Inherently present and all-pervasive throughout the standards-development process, the deployment of standards and their coordination/ consensus process are considerations for the implementers and users of geographic standards: such items as data transfer standards that are implemented by vendors, or data cataloguing standards implemented by data producers, or metadata standards implemented by vendors, data producers, and general users of geographic information. Implementers and user requirements need to be considered in conjunction with the standards development, deployment, the process of integrating such requirements. Traditionally, geographic information was produced and used by the geographic community. Conclusion In this age of globalization, the human communication, it can only be achieved through shared efforts and information technology that effort to the needs of tomorrow’s ever more interdependent and ever more technologically advanced world. As we entered a new century, we need to build a shared information and ensure a shared growth benefits through integrating developing countries or countries of economies in transition. We need to establish the Mongolian Geospatail Standardization System. However the size of the task of considering, adopting and publishing Mongolian geographic information standards cannot be underestimated. MNCSM recognises it has much to do and that it needs to do all it can to speed up the process. - 125 -

The geospatial information community is a mature and well developed community, which over an extended period has proven its worth in developing tools to make geospatial data accessible from multiple technologies and software vendors. At the same time organisations have started to develop their policies and data management practices to ensure they are ready for the new demands and potentials of interoperability. Increasingly, geographic information is being created and used by everyone else, especially people in the business community.

1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17.

References The SDI Cookbook, version 1.0, GSDI, www.gsdi.org International Organization for Standardization, www.iso.ch ISO/TC 211 Geographic information/Geomatics, www.statkart.no/isotc211/ Joint Steering Group on [AFNOR, 1992], Association Franɡaise de Normalisation, see: http://www.afnor.fr [CEN, 1992], Comitɣ Europɣen de Normalisation CEN/TC 287 - Geographic Information; see: http://www.cenorm.be [EU,2003], European Union, framework programmes; see:http://europa.eu.int/pol/rd/index_en.htm Eurogeographics, 2003], a network of National European Mapping Agencies (NMAs), see: http://www.eurogeographics.org [EUROGI, 1993], European Umbrella Organisation for Geographic Information see:http://www.eurogi.org [DIGEST, 2003], DIgital Geographic Information Exchange Standard; See:http://www.digest.org of the Digital Geographic Information Working Group - DGIWG [INSPIRE, 2002], Data Policy and Legal Issues Position Paper, 2002, see: http://inspire.jrc.it [INSPIRE, 2006], Infrastructure for Spatial Information in Europe Initiative; see: http://inspire.jrc.it [ISO, 2006], International Organisation for Standardisation; see: http://www.isotc211.org [SNV, 2003], (Schweizerische Normen-Vereinigung, see: http://www.snv.ch

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LUCC and Terrestrial Study based on RS, GIS and Ecological Observation & Proposal for International Collaboration Jiyuan Liu Lin Zhen Yunfeng Hu Qian Zhang (Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, PRC)

Land Use/Cover Changes (LUCC) is a core element leading to terrestrial ecosystem dynamics and socio-economic consequences, and the relevant research has been the focus of many institutions and scientists worldwide. The Institute of Geographic Sciences and Natural Resources Research (IGSNRR) is a leading institute in China in the field of LUCC and terrestrial research through application of sophisticated technology and method such as RS, GIS and ground based ecological observations. Over the decades, the Institute has contributed significantly to theoretical development and practical application of LUCC and terrestrial study, and ultimately to sustainable management of land systems and ecosystems of the country in specific and the surrounding Central/North-east Asian countries in general. This paper summary the major achievements made in those fields, and propose points for future collaboration with international partners. Major research achievements: ƒ

ƒ

ƒ

ƒ

ƒ ƒ

LUCC study based on RS and GIS: both spatial (eg. TM/ETM㧘MODIS) and attribute data covering land use, environmental and social and economic conditions were gathered for exploration of LUCC dynamics and major drivers of China over the past 20 years. Land surface key parameters (eg. NDVI, LAI, EVI, fPAR) inversion algorithm and software, land surface temperature, Leaf Area Index and annual changes, and tree distribution of China have been developed through a long term based research and monitoring. The database of LUCC and environmental changes of the entire country has been established, and the LUCC zones were identified for improved management. Terrestrial ecosystem observation and research: ChinaFLUX (Chinese Terrestrial Ecosystem Flux Research Network) and CERN (Chinese Ecosystem Research Network) covering typical terrestrial ecosystems with powerful long-term quantitative observation functions have been established. There are now 36 and 8 countrywide stations respectively. The networks are providing strong basis for quantifying carbon budget, estimating terrestrial ecosystem NPP (eg. Cropland, forest through developing and using CEVSA model), simulating carbon cycle, assessing the effects of increasing atmospheric CO2 and climate variability, and land use changes through integration of both topdown and bottom-up approaches. Integrated use of process-based ecosystem model (CEVSA Model) and data from weather stations, FLUX and natural resource surveys, and RS model (eg. GLO-PEM model) driven by Remote Sensing data are the main features of the network function. Integrated Ecosystem Assessment of Western China (MAWEC) is a successful example of terrestrial ecosystem assessment via application of all those means. It was found, for example, that the NPP in western China was approximately 70% of the total amount in China in twenty years, and policy options for improvement of ecosystem services of the region have been made and adopted by the government. Ground-based observation for research of impact of climate changes on ecosystem services, land degradation and permafrost changes. It was visualized, for instance, the permafrost changes in the form of digital map by means of GIS analysis in Tibet. The research is to be expanded to Mongolia through using PulseEKKOPRO ground-penetrating radar. Urbanization monitoring: mapped spatio-temporal changes of urban land expansion in China using high-resolution Landsat Thematic Mapper and Enhanced Thematic Mapper data since the 1980s. It was found that China’s urban expansion had a high spatial and temporal variability. Case studies of the thirteen large cities showed that urban expansion has been driven by demographic changes, economic growth, and changes in land use policies and regulations. Forest fire monitoring: large scale forest fire maps were developed using MODIS data, and have been used for the fire monitoring and forecasting. Powerful platform and support: Data-Sharing Network of China Earth System Science and GIS software-SuperMap, self-developed GIS software with powerful functions are strong basis for - 127 -

conducting relevant researches. International collaboration and proposal for further research: The IGSNRR has been keeping close contact with international partners in the field of LUCC and terrestrial ecosystem research under the framework of GOFC-GOLD. Major contributions and plans are listed, but not limited as follows: ƒ

ƒ

ƒ

Strengthening capacity building: the IGSNRR has already kicked-off GIS training program for the scientists from the Institute of Geography, Mongolian Academy of Sciences, and scheduled to offer training courses for researchers from Russia, Kazakstan, and North Korea. Apart from international activities, the Institute is focusing on domestic needs and strengthening capacity building for economically backward western regions. The future training will cover the fields of land system, terrestrial ecosystem assessment, carbon cycling, water cycling, socio-economic systems, urban and settlement development, etc. Enhancing collaboration with economic powers such as Japan and South Korea to establish working groups (WG) in the East Asian Region. The WG will take a leading role in the sustainable development of the Central/North-east Asian nations. A network and data sharing system among the countries will be established. Promoting joint research activities: the IGSNRR has been implementing joint research projects with Mongolia and Russia in the field of ecosystem and environmental changes and sustainable development. Those activities will be further expanded to other surrounding Central/North-east Asian countries following the framework of GOFC-GOLD.

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REMOTE SENSING PARAMETERIZATION OF LAND SURFACE HEAT FLUXES OVER ARID AND SEMI-ARID REGION IN MONGOLIA Jadamba Batbayar , Nas-Urt Tugjsuren Mongolian University of Science and Technology E-mail: [email protected] Abstract Remote sensing in the thermal infra-red spectrum is essential for the estimation surface temperature and the components of the surface energy balance. The intent of this study is to evaluate the applicability of the remotely sensed surface energy balance method to a regional land surface heat fluxes distribution for arid and semi arid surface with homogeneous area in Mongolia. The Dundgovi region is selected to apply surface energy balance method for heat fluxes distribution in arid and semi-arid region of Mongolia. The surface heat fluxes derived from Landsat+ETM in the morning overpass satellite, and computed on a pixel-by-pixel basis on 10.August.1999.

Key words: Net radiation, Soil heat flux, Sensible heat flux, Latent heat flux Introduction The study on the energy exchanges between the land surface and atmosphere is important to understand for arid and semi-arid regions environmental changes. Remote Sensing from different satellite sensors offers the possibility to derive regional distribution of land surface heat fluxes. Many new remote sensing algorithms to estimate surface energy balance components have been proposed during the last 10 years. An overview of the most common one is presented in Kustas W. P, et al. (1994), Moran M. S, et al. (1994, 1995). Remote sensing of surface temperature (i.e. radiative surface temperature) together with some ground-based data has been widely used in conjunction with simple one-dimensional models to estimate components of the energy balance equation from field to regional scales (Jackson, R, D. 1985). Briefly, the method uses the energy balance equation with an estimate of the surface temperature from an IR sensor; wind speed, air temperature and incoming solar radiation come from ground measurements. The purpose of this study is to estimate regional land surface heat fluxes distribution for large arid and semiarid surface with homogeneous area condition. The method is based on Surface Energy Balance and Normalized Difference Vegetation Index (NDVI) as well as surface parameters. The parameters will be derived from LANSAT+ETM satellites data, taken on 10.August, 1999 supplemented by ground observations. THE DATA SET AND STUDY AREA The Landsat-ETM satellite images on 10 August 1999 were evaluated for Land Surface Heat Fluxes distribution in Dundgovi of Mongolia. These images overpass time was 10:00 AM of Landsat-ETM path/row 131/28 on local time. These images had favorable weather conditions without clouds in study area. Data from ground measurement area were available to assist the calculation of the Land Surface Heat Fluxes in the locations of study area. (Lat: 45.49N, long: 106.12E). Table 1.summarizes the meteorological ground conditions at the study area of satellites overpass time on 10.August, 1999. Table1. Ground measurement data with selected satellites overpass in Mandalgovi (lat: 45.49N, long: 106.12E) of Mongolia on 10. August, 1999. Time

10

Cloud (0-10) 0

Wind Speed (m/s) 9.4

Air pressure (hp) 848.8

Air temperature (oC) 18.65

Relative humidity 23.9

Ground temperature (oC) 27.1

Incoming solar radiation 701.6

Incoming longwave IR (Wm-2) 281.9

The study area is Dundgovi aimag (province) of Mongolia. The coordinates are 44000cN and 46000cN, 103000cE and 109000cE, and the area is 78 thousand km2 an arid and semi-arid region. Annual mean precipitation is about 150-250 mm and 85-90 percent of the annual precipitation falls

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as rain during the summer of which 50-60% in July and August. Cloudy skies are infrequent, and 4,467 hours of sunshine per year. METHOD The surface energy balance method is an image-processing algorithm which calculates energy exchanges at the earth’s surface using digital image data collected by Landsat or other remote sensing satellites by measuring visible, near-infrared and thermal infrared radiation. Instantaneous net radiation values were computed according to incoming and reflected solar and thermal radiation. Rn is computed for each pixel using albedo and transmittances computed from short wave bands and long wave emission computed from the thermal band and some ground data. (See Eq 1.) Soil heat flux (G) is predicted using vegetation indices computed from combinations of bands and net radiation. Sensible heat fluxes (H) is calculated from several factors: surface temperature and wind speed measurements from ground data, and estimated surface roughness and surface –to-air temperature differences predicted from vegetation indices. LE is latent heat flux, which is the energy used to evaporate water. All computations were made pixel by pixel. Net radiation Usually, net radiation can be estimated by:

Rn

4

(1  D ) Rs  HG (H a Ta  Ts4 )

(1)

Where Rs is the incoming short-wave solar radiation, D the surface short-wave albedo, H the surface emissivity, G the Stefan–Boltzman constant, and ea the effective atmospheric emissivity, with (Brutsaert, 1975)

HD

§e 1.24¨¨ a © Ta

· ¸¸ ¹

1/ 7

(2)

Where eD is the atmospheric vapour pressure. The surface emissivity is calculated as a weighted average between bare soil and vegetation. Where Hv is the emissivity of vegetation assumed to be 0.95, Hs, the emissivity of bare soil assumed to be 0.85 and fv the fractional vegetation cover.

H H v f v  H s 1  f v H v | 0.95 H s | 0.85

(3)

. The fv vegetation coverage in a pixel, is yielded by a relationship between f and NDVI (Gutman 1998):

f

NDVI  NDVI min NDVI max  NDVI min

(4)

Where NDVImax and NDVImin are the maximum and minimum NDVI in a whole growth of vegetation. Soil heat flux The soil heat flux can be empirically estimated using the net radiation and LAI or a satellite derived vegetation index, such as the normalized difference vegetation index, NDVI. In this case the Moran et al. (1989) model was adopted as:

G

Rn >0.58 exp 2.13NDVI @

(5)

Where NDVI=(NIR_RED)/(NIR+RED), and NIR and RED are the near-infrared and red reflectance’s, respectively.

Sensible heat flux - 130 -

The sensible heat flux can be written as:

H

UC p (Ts  Ta ) / ra

(6)

Where UCp is the thermal capacity of the atmosphere, (ra) aerodynamic resistance and (Ta) is the air temperature at the reference height where meteorological measurements are available. The land surface temperature (Ts) can be derived from a remotely sensed radiometric surface temperature. Latent Heat flux The regional latent heat flux is derived as the residual of the energy balance theorem for the land surface, i.e., (7) LE Rn  H  G Where Rn is the net radiation, H is the sensible heat flux, G is the soil heat flux, and LE is the latent heat flux (W/m2).

Results The main output of Surface energy balance method is the partitioning of energy balance. The figure 1 shows the results of the different land surface heat fluxes (Rn, G, H, LE) distribution for the study area and surface variables (NDVI, LST) have been calculated using LandsatETM data with ground observations over the homogeneous areas of Mandalgovi on 10.August, 1999. The distribution range of land surface heat fluxes over the study area is shown in Table2.

Net radiation (Rn)

751

874

Soil heat flux (G)

Wm-2

240

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258

Wm-2

Sensible heat flux (H)

Latent heat flux (LE)

Wm-2 212

Wm-2

467

136

368

25

40

NDVI -0.15

0.3

Vegetation indices (NDVI)

Co

Land surface temperature (LST)

Figure 1. Maps of land surface fluxes and surface variables for the arid and semi arid region of Dundgovi aimag. Land surface fluxes calculated from Landsat-ETM+ satellite data on 10.August, 1999. Lines are illustrated administrative boundary of soum level. Table 2. The distribution of land surface heat fluxes over study area Surface heat Range of Dundgovi area Mandalgovi (station) fuxes (Wm-2) (Wm-2) -2 Rn (Wm ) 751 - 874 836.92 Go (Wm-2) 240 - 258 250.71 H (Wm-2) 212 - 467 339.81 LE (Wm-2) 136 - 368 246.4 The map for vegetation indices (NDVI) shows high values 0.1-0.3 in the northern part of the Dundgovi, which in vegetated area with low values of land surface temperature 25-28Co. And sparsely vegetated and bare soil regions have low values of vegetation indices –0.150.01 related with high land surface temperature 29-40 Co. Table 3. Shows comparison of ground measurement data with overpass satellites data in Mandalgovi of Dundgivi (lat: 45.49N, long: 106.12E) on 10.august, 1999 Ground Incoming solar Incoming Time temperature ( ) radiation (Wm-2) longwave IR, (Wm-2)

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10 AM

27.13

701.568

282.934

Ground measurement 10 AM 30 647.00 304.98 Landsat-ETM The some results are presented in Table 3, which illustrated comparison of satellite data with ground measurement data. But satellite estimated surface heat fluxes (net radiation flux Rn, soil heat flux G, sensible heat flux H, latent heat flux LE) are did not made comparison, due to insufficient of ground measured data.

Conclusions The surface energy balance method has been applied for regional distributions of land surface variables (land surface temperature and vegetation index) and land surface heat fluxes (net radiation Rn, soil heat flux G, sensible heat flux H, latent heat flux LE). The land surface temperature, incoming solar radiation and incoming long wave radiation derived from Landsat-ETM satellite was approximately 80% near with ground observations. It’s concluded satellite data is better convenient to the estimation of the distribution of land surface fluxes and land surface variables for large scale of real time observations. The study has demonstrated that surface energy balance method can be determined successfully from satellites. In future studies, more attention should be paid to the validation of satellite estimation and ground measurements of land surface variables and land surface heat fluxes for arid and semi-arid region of Mongolia. References 1. J.Batbayar, N.Tugjsuren, (2005). Net radiation estimation using MODIS-TERRA data for clear sky days over homogeneous region of Mongolia, Proceeding of the CEReS International Symposium, Chiba , Japan, 2005,p 206-213. 2. Jackson, R. D., Pinter, Jr., P.J., and Reginato, R. J., (1985). Net radiation calculated from multispectral and ground station meteorological data, Agric. For. Meteorol., 35, 153-164. 3. Kustas, W. P., Moran, M. S., Humes, K. S., Stannard, D. I., Pinter, P. J. Jr., Hipps, L. E., Swiatek, E., & Goodrich, D. C. (1994). Surface energy balance estimates at local and regional scales using optical remote sensing from an aircraft platform and atmospheric data collected over semiarid rangelands. Water Resources Research, 30 (5), 1241– 1259. 4. Moran, M. S., Kustas, W. P., Vidal, A., Stannard, D. I., Blanford, J. H., & Nichols, W. D. (1994). Use of ground-based remotely sensed data for surface energy balance evaluation of a semiarid rangeland. Water Resources Research, 30 (5), 1339–1349. 5. Moran, M. S., Jackson, R. D., Clarke, T. R., Qi, J., Cabot, F., Thome, K. J., & Markham, B. L. (1995). Reflectance factor retrieval from Landsat TM and SPOT HRV data for bright and dark targets. Remote Sensing of Environment, 52, 218– 230. 6. Stewart, J. B., Kustas, W. B., Humes, K. S., Nichols, W. D., Moran, M. S., & de Bruin, H. A. R. (1994). Sensible heat flux — radiometric surface temperature relationship for 8 semi-arid sites. Journal of Applied Meteorology, 33, 1110 – 1117. 7. Tugjsuren N, Batbayar J (2005): Satellite detection of the atmospheric aerosol for some region of Mongolia, First National Conference on RS and GIS applications, May 02-03 Ulaanbaatar, 2005, p 77-82 8. Wukelic, G. E., Gibbons, E. E., Martucci, L. M., & Foote, H. P. (1989). Radiometric

calibration of Landsat Thematic Mapper thermal Band. Remote Sensing of Environment, 28, 327–339.

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A Possibility of Cooperation in Detection of Water and Heat Losses of District Heating System in Ulaanbaatar Ulaanbaatar1 T., Legden2 M. and Danbayar1 1

Geophysical Department, National University of Mongolia, [email protected]; 2 NewCom Group

Abstract. The heat and water losses in the underground pipelines and structures of District Heating System in Ulaanbaatar are very large. The detection method of location and intensity of losses and underground repair and maintenance of leakage are also carried out by multitude difficulties. In this paper a new initiative to cooperate for detection of heat and water losses in District Heating Network by remote sensing indicators as high resolution satellite IR images, airborne thermal methods and in-situ is shown.

Introduction. The pipelines and structures of District Heating System are established in 1970, 1983 and 1987. For this reason, the pipelines of the over ground and under ground are exceedingly aged. The water and heat losses become very large. 10% of total heat energy production losses whatever way, but more than 50% of this loss is in the underground pipelines.

Figure 1. Additional water (million tons) in 1991-1996

62% of water losses was in the distribution, transmossion and branch piplelines. In results of project implementation of Asian Development Bank and DANIDA the water losses decreased in 1999-2003. (Figure 2) 4600 4400 4200 4000 3800 1998

1999

2000

2001

2002

2003

2004

Figure 2. Additional water (million tons) in 1999-2003

Now we would like to show by layouts

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Determination of difficulties and problems

Main goals Company is

of

UB

District

Heating

in

detection

of

Difficulties defects R

x

i

tl

ti

Increasing the expenditure of water E ceeding the additional ater (losses of

Problems: x x

To improve the marketing of heat transport economic compensation and compensate the heat distribution To evaluate correctly the losses of heat energy producing from power plants within the distribution d i i i li Today 15-20%

Losses by norm are

50-100 times

Large part of heat and electric energy producing Electric Power Plants expends to pump and to heat the additional water

To new technology To best technology

Remote Sensing - 135 -

Conclusion For decreasing the water and heat losses, and detection of location and intensity of their leakages the time serious high resolution images of ASTER and IKONOS need. Reference 1. Ulaanbaatar, T., Tsolmon, R., and M. Erdenetuya, Detection of heat and water leakages in Ulaanbaatar District Heating Network by Remote Sensing, First National Conference on Remote Sensing and Geographical Information System Applications, Mongolian Geoscience and Remote Sensing Society, National University of Mongolia, Ulaanbaatar, 2 May, 2005. 2. www.worldbank.org/html/fpd/esmap/pdfs/247-01_1.pdf 3. www.adb.org/printer-friendly.asp?fn=/Documents/News/1996/nr1996145.asp 4. www-wds.worldbank.org/servlet/.../Rendered/INDEX/multi0page.txt

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By the Russian norms of 1999

Influencing Factors: x Length of pipelines x Insulation x Environment

In result of change of pipelines by pre-isolated pipelines the losses will decrease ¾ 2-5 times.

1.1. Branch pipeline 10-

Real heat losses: x In central pipelines 5-30%

From insulation of the pipelines by

Heat

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x Changes by siliphon compensator x Partial compensation of density

By the Russian norms of 1999

Influencing factors: x Condition of exploitation in pipeline system x Densitization of pipelines x Deadline of exploitation

Resident Building

Additional water since 1994 in District Heating System has increased 2 times and 300 repairs each year

water losses

losses

District Heating

Heat Losses

Monitoring of Saxaul forest in Gobi of Mongolia 1

B. Suvdantsetseg1, Y. Aruinzul 2 D.Narantuya 3 “NUM-ITC-UNESCO” laboratory for Remote Sensing/GIS , The National University of Mongolia, E-mail: [email protected] 2 “State Specialized Inspection Agency, E-mail: [email protected] 3 . Ministry of Mongolian Nature and Environment E-mail: [email protected]

Abstract The paper aimed at determining the relative proportions of saxaul forest cover in the Gobi region of Mongolia. A Linear Mixing Model was applied in the study area to map the saxual forest. Three endmembers (saxaul forest, vegetation, soil) were identified based on the image and a constrained least-square solution was used to unmix the image. The reflective channels of SPOT-4/ VEGETATION satellite over years 1998-2003 were used for the analysis and monitoring for Saxaul forest. The output results area a contribution to research on vegetation cover change in southern region of Mongolia

Key words: Saxaul forest, Linear Mixing model, desertification

Introduction The main objective of the study is to monitor and map the saxaul forest in Mongolia. This has never before been completed with satellite remote sensing imagry. The saxaul forest is dominated by an endemic brush type plant and is found in the deserts of Mongolia ( figure 1). It is presumed that many hectars of saxaul forest are disappearing every year due to logging and its gathering for firewood. The saxaul forest grows in an area within the Gobi of about 1650 km wide, and about 360 km in diameter from north to south. There are about 39 subprovinces and 7 provinces where the Saxaul forest is presumed to grow in Mongolia. Few scientists have ever studied the saxaul forest. The first scientific studies of the Saxual forest were completed in the 1940’s and only random notes were taken in previous years. Two censuses of the saxual forest were completed during the years 1960 and 1997. Between 1961 to 1968, J.Gal (ref) completed several studies on the saxual forest, where he described the natural history of the saxual forest, such as its area, growth patterns etc., as well as it’s use by local inhabitants. J Gal (ref) also completed other research on the fund of zag and its enemies and diseases. The importance of the saxaul forest to Mongolia is that it stabilizes active sand dunes and reduces the effect of sand storms. The saxual forest does this because of its root size and depth, which holds most of the soil moisture in arid and hyper-arid environments. The sparsely structered spatial pattern of individuals within saxual forest stands also contributes to the saxual forest being a natural defense against sand storms. If the saxual forest is decreasing in area due to logging and gathering for firewood, it would be one reason why sand storms are becoming worse in Mongolia. It is therefore necessray to monitor the saxual forest over large areas in Mongolia. In this study, we attempt to map and monitor the area of the saxual forest with satellite remote sensing techniques. Remote sensing is defined as the science and art of obtaining information about an object, or phenomenon througth the analysis of data acquired by a device that is not in contact with the object, area or phenomenon under investigation (Lillesand 1994). Land cover classifications are among the most important applications of remote sensing; however, classifying vegetation cover is problematic because there is no standardised approach for classifying and mapping different land cover types. This is related to many factors, such as the spatial heterogeneity of different vegetation structures, vegetation classification and plant species idenification, plant geometry and biomass. The linear mixing model (LMM) approach is one of the most often used methods for handling the mixed pixel problem.

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Figure 1. Saxaul forest in the Gobi

Study area Figure 2 shows the seven Gobi region provinces where the saxaul forest is presumed to be located, and these provinces are generally in the east south and west south parts of the Mongolia (41° 30’ -49° N and 90°43/- 113°E). These provinces border China to the south, west and east, and have a total area of 639.000 km2. The basic economy is livestock ranching. The topography of these provinces consists mainly of a plateaus and mountain ranges.

Figure2 The location of the study area

The climate of these province is characterized by short dry, summers and a long cold winter season. The plant diversity varies between populus diversifolia, saxaul, tamarisk, hippobopac, peplars, willows are along the humid areas and larch, curgana pygmaya, sympegma and bramble. Wild fauna consist of wild sheep, ibex, deer, mountains-funas, snow-leopard, lynx, corsa, wild cat, wolf, fox, ermin and marmot. There are also wild-camel, wild-horse, saiga, tatarica Mongolia, black tailed antilope, and gobi-bear in the semi-desert. Methodology Several techniques (Smith et al. 1985, Shimabukuro 1987, Adams et al. 1989) have been developed to solve the mixture problems in a number of fine spatial resolution data sets from Multispectral Scanner System(MSS); Thematic Mapper (TM) data (Adams and Adams 1984, Shimabukuro 1987); and AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) data (Gillespie et al.1990). All of the above techniques produced similar results (Shimabukuro 1987) and their uses are usually dictated by the investigators personal preference. As there are the same number of equations as there are unknowns, proportion was solved directly as opposed to least-squares (Shimabukuro and Smith 1991) methods to minimize the error. Quarmby et al. - 139 -

(1992) applied linear mixture modeling to AVHRR data to estimate crop coverage. The mixture model (e.g., Settle and Drake 1993) was used to separate out the green vegetation component from other components. The resulting land cover map was then used as a boundary condition in biosphere models to estimate the exchange of radiation between the boosher-atmosphere, as well as sensible and latent heat parameters and surface roughness (Dickinson 1995, Sellers et al.1997) . Studies by Cross (Cross et.al 1991) showed the model produced image outputs in which pixel intensities indicated the proportion of forest cover per square kilometer. Unmixing had already been applied to coarse resolution data in a number of studies, especially for vegetation monitoring. While some were based on the first two channels of NOAA-AVHRR (Quarmby et al. 1992, Hlavka and Spanner 1995) others used the reflective part of the third channel as well (Holben and Shimabukuro 1993, Shimabukuro et al. 1994). The first four AVHRR channels were used by Cross et al. (1991) for unmixing and was able to differentiate tropical forest from non-forest, with satisfactory results compared with TM images. More recent studies (Bastin 1997, DeFries et al. 1997) reflect the ongoing interest in sub-pixel analysis using coarse resolution satellite imagery. The linear mixture model based on Optimization Method (R.Tsolmon 2003) was applied in this study to the saxaul forest monitoring using SPOT/VEGETATION -4 10 day composite, 1-10 August , 1 km2 resolution data from 1998 to 2003. The saxaul percentage images was derived (Figure 3) using spectral bands of the VEGETATION sensor. For this study, three bands were used, band1-reflectance at 0,500,59µm, band2-reflectance at 0,61-0,68µm, band3-reflectance at 0,79-0,89µm. Linear Mixing Model The Linear Mixing Model approach assumes that the spectrum measured by a sensor is a linear combination of the spectra of all components within the pixel. For solving the LMM, Lagrange method and optimization technique were used. This method was developed for assumed n components in a pixel. The matematic model of LMM can be expressed as shown in equations (1-2).

R1

a11 x1  a12 x 2  "  a1n x n  e1

R2

a 21 x1  a 22 x 2  "  a 2 n x n  e2

! !! Rm a m1 x1  a m 2 x 2  "  a mn x n  em m

f ( x)

¦ e2i

x1  x 2  x3

n

i 1

j 1

¦ ( Ri  ¦ aij x j ) 2 o min,

i 1

subject to:

m

1,

x1 t 0, x 2 t 0, x3 t 0! x n t 0 , where Ri is measured satellite sensor response for a pixel in spectral band i

ai j is spectral response of mixture component, j, for spectral band i x j is proportion of mixture component, j, for a pixel ei is the error term for spectral band i

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(1)

(2)

1998

1999

0% 20%

40% 2000

2001

60%

2002

2003

Figure 3 The saxaul percentage images over years 1998-2003

80%

100%

Conclution and Discussion In the derived fraction images, each pixel is associated with percentage values from 0 to 100 for Saxaul. The study focused on applying LMM as a sub pixel classification. The saxaul forest is unique brush plant, therefore the saxual forest should be taken into consideration to investigate and protect as a tool for combating desertification. The linear mixing model based on the Optimization Method, which we have concentrated on, may be applicable to many forest types, and leads to a particularly simple mathematical description of the generation for signal of a given mixture. Application of the method to higher resolution data to test its potential was hindered in this present study, by the lack of multi–temporal high resolution data set. Further work should test the robustness of the approach adopted here, when applied to large areas by using multi-temporal data to detect saxaul forest changes resulting from desertification in Gobi. We would expect mixtures of vegetation types to be present within a pixel, even with high-resolution data from Landsat TM and the SPOT High Resolution Visible Imaging System(HRV). VI. ACKNOWLEDGMENTS The authors are also grateful to SPOT/VEGETATION for supplying data. We thank our colleague Matthew Tyburski for comments and help in English. 1.1. References 1. Adams, J. B., and Smith, M. O, 1986, Spectral mixture modelling: a new analysis of rock and soil types on the Viking Lander 1 Site. Journal of Geophysical Research, 91, 8098–8112 2. Adams,J.B.,and Adams,J.D.,1984, Geologic mapping using Landsat MSS and TM images:removing vegetation by modeling spectral mixtures.Proceedings of the Third Thematic Conference on Remote Sensing for Experimental Geology,Colorado Springs,Colorado,(Michigan:ERIM), pp.615-622. 3. Bastin, L., 1997, Comparison of fuzzy c-means classification, linear mixture modelling and MLC probabilities as tools for unmixing coarse pixels. International Journal of Remote Sensing, 18, 3629–3648. 4. Cross, A.M., Settle, J.J., Drake, N.A., and Paivinen, R.T.M., 1991, Subpixel measurement of tropical forest cover using AVHRR data. International Journal of Remote Sensing, 6, 1159-1177. - 141 -

5. Cross, A. M., Settle, J. J., Drake, N. A., and Paivinen, R. T. M., 1991, Subpixel measurement of tropical forest cover using AVHRR data. International Journal of Remote Sensing, 12, 1119–1129. 6. DeFries, R. S., Hansen, M., Steininger, M., Dubayah, R., Sohlberg, R., and Townshend, J., 1997, Subpixel forest cover in central Africa from multisensor, multitemporal data. Remote Sensing of Environment, 60, 228–246. 7. DeFries, R. S., and Townshend, J. R. G., 1994, NDVI-derived land cover classification at a global scale. International Journal of Remote Sensing, 15, 3567–3586. 8. Gillespie, A.R.,Smith,M.O.,Adams,J.B.,Willis,S.C.,Fischer,A.F.III,and Sabol, D.E., 1990, Interpretation of residuals images: spectral mixture analysis of AVIRIS images, Owens Valley, California. Proceedings of the Airborne Science Workshop: AVIRIS, JPL, Pasadena,CA, (JPL Publication 90-54),pp.243-270. 9. Hlavka, C. A., and Spanner, M. A., 1995, Unmixing AVHR imagery to assess clearcuts and forest regrowth in Oregon. IEEE Transactions on Geoscience and Remote Sensing, 33, 788–795. 10. Holben, B. N., and Shimabukuro, Y. E., 1993, Linear mixing model applied to coarse spatial resolution data from multispectral satellite sensors. International Journal of Remote Sensing, 14, 2231–2240. 11. Quarmby, N.A., Townshend, J.R.G., Settle, J.J., White, K.H., Milnes, M., Hindle,T.L., and Silleos, N., 1992, Linear mixture modeling applied to AVHRR data for crop estimation. International Journal of Remote Sensing, 13, 415-425. 12. Shimabukuro, Y.E., and Smith, J.A., 1991, The least-squares mixing models to generate fraction images derived from remote sensing multispectral data. I.E.E.E. Transactions on Geoscience and Remote Sensing, GE-29, 16-20 13. Shimabukuro, Y. E., Holben, B. N., and Tucker, C. J., 1994, Fraction images derived from NOAA AVHRR data for studying the deforestation of the Brazilian Amazon. International Journal of Remote Sensing, 15, 517–520. 14. Smith,M.O.,Johnson, P.E., and Adams, J.B., 1985, Quantitative determination of mineral types and abundances from reflectance spectra using principal component analysis. Journal of Geophysical Research,90, 792-804. 15. Shimabukuro, Y.E., 1987, Shade images derived from linear mixing models of multispectral measurements of forested areas. Ph.D. Dissertation, Colorado State University, Fort Collins,CO. 16. Tsolmon R, 2003 “ Methodology to Estimate Coverage and Biomass of Boreal Forests using Satellite Data” Ph.D. Dissertation, Center Environmental Remote Sensing, Chiba University, Japan 17. Vasiliev,O.V., 1996,Optimization Methods, 1 st edition (Atlanta: World Federation Publishers).

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Dust and Sandstorm Monitoring of Mongolia Using NOAA AVHRR data L.Ochirkhuyag1; R.Tsolmon2; S.Khudulmur3; J.Sumyasuren3; L.Natsagdorj4; D.Jugder4 1

The Wildlife Conservation Society Mongolia Program Amar str-3, Internom bookstore building 3rd floor, Ulaanbaatar, Mongolia [email protected]; [email protected]; 2 Laboratory of Remote sensing NUM-ITC-UNESCO, National University of Mongolia Ikh surguuliin gudamj, Ulaanbaatar 210646a, Mongolia [email protected]; 3 Information and Computer Center, National Remote Sensing Center Juulchiny street-5, Ulaanbaatar 210646, Mongolia [email protected], [email protected] 4 Institutes of Meteorology and Hydrology Juulchiny street-5, Ulaanbaatar 210646, Mongolia [email protected]; [email protected] Abstract: Dust and sandstorms are a common meteorological phenomenon in the Gobi desert of Mongolia like in the Taklamakan Desert of northwest China, the Sahara Desert of northern Africa and other arid and semiarid regions. The brightness temperature channels 4, 5 of the NOAA AVHRR data and meteorological station data were used for the dust and sandstorm mapping in the Gobi area of Mongolia. The NOAA/AVHRR thermal infrared bands difference with combination GIS layers were carried out for the mapping this study. The result shows that the dust and sandstorm map can be achieved from the thermal bands of the satellite.

Introduction Each year from March to May, it is observed that the dust and sandstorm, which occurred in the Gobi desert of Mongolia and the Taklamakan Desert of northwest China, flies to the north pacific archipelago and the west coast of America in addition to East Asia area. The dust and sandstorm study is useful in the application of meteorological field. For example, the air turbidity increases with dust storm occurrence and hence affects the radiation budget. It is also important to assess dust storm impact on climate changes in Asia as well as the world. Mongolia is landlocked country, has continental arid climate with four different seasons. When the phenomenon of dust and sandstorms continue over several days in the Gobi Desert, Mongolian nomadic herders refer to it as the “Ugalz”. Dust and sandstorms disrupt human life and economic activities and result in soil erosion. Dust storms have some negative consequences; they delay and reduce pasture yields for livestock, sands move and affect roads, settlements and villages, which can become enshroud with sands (L.Natsagdorj and D.Jugder). One study showed that more than 70% of the pastureland area of Mongolia is under desertification, 22.1% is strongly overgrazed, and sand movement covers the 7.9 million-hectare pastureland area (Jigjidsuren and Oyuntsetseg, 1998). The distribution of the number of days with dust storms is shown in Fig. 1. The number of days with dust storm is less than 5 days over the Khangai, Khuvusgul and Khentei mountainous areas of Mongolia, 10-17 days over the area of Great Lakes, and 20-37 days over the desert and the semi-desert areas in Mongolia.

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Fig.1. Number of days with dust storms observed in Mongolia.

The highest frequency of dust storms is over the three areas in the Gobi Desert in Mongolia, such as the south side of the Altai Mountain and around Ulaan-nuur Lake and Zamiin-Uud. It should be noted that the distribution of dust storms has coincided well with the distribution of strong wind (L.Natsagdorj, 1982; D.Jugder, 1999) and soil conditions. Previously, we mentioned how to obtain the number of dusty days. The distribution of dusty days is shown in Fig. 2. We can see in the figure that the number of dusty days is less than 10 over the Khangai, the Khentei and the Khuvusgul mountainous areas and 61 to 124 over the Great Lakes hollow and the Gobi Desert area. It is 91 to 120 days over the Gobi on the south side of the Altai Mountain, and about 80 days surrounding the Arts Bogd Mountain. The highest occurrence of dusty days is around the Mongol Els area.

Fig.2. Number of dusty days observed in Mongolia.

We attempt to monitor dust and sandstorm using NOAA satellite data. For this purpose, we used brightness temperature differences in the thermal infrared bands in NOAA/AVHRR sensor data, the 2006 spring season are observed and received at the NOAA data receiving station of National Remote Sensing Center (NRSC). Approach The approach based on the split-window method. First we have done pre- processing for the raw NOAA/AVHRR data and Level 3 data. The NOAA/AVHRR has five bands: x Band 1, visible (VIS, 0.59-0.68mm) x Band 2, near-infrared (NIR, 0.73-1.10mm) x Band 3, mid-infrared (MIR, 3.55- 3.93mm) x Bands 4 and 5, thermal-infrared (TIR, 10.3- 11.3mm and 11.5-12.5mm). (Fig.3)

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Fig.3. NOAA/AVHRR bands Secondly, the geometric correction was applied using the PCI Geomatica software. Third the difference of the thermal bands (brightness temperature) was calculated. This algorithm is based on the split-window method. (Kinoshita, 1997) Band _ difference = ch4-ch5 (1) The last procedure was that the derived map (Fig.4) using equation 1 was overlied with GIS vectors with meteorological station data. The dust and sandstorm is described by yellow and orange color (Fig.4). Result and discussion In spring, dust and sandstorm occurs frequently in the Gobi Desert, Southern Mongolia. From the given approach we produced a dust and sandstorm map for the period March.6 to March.9, 2006. (Fig.4) A GIS layer on dust and sandstorm (Fig.4) describes the weather component such as wind speed and direction at the meteorological station. Mapping of the dust and sandstorm with GIS layers is effective for dust and sandstorm monitoring. The basic properties from thermal bands for NOAA/AVHRR were used for the detection of the dust and sandstorm . Further quantitative studies using NOAA/AVHRR are important for the comparative study with other satellites (MODIS, TOMS etc) and ground observation data such as Lidar and numerical simulation based on the meteorological model.

a) March.6, 2006

b) March.7, 2006

c) March.8, 2006

d) March.9, 2006

Fig.4. Dust and sandstorm map of March.6-9, 2006

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Reference 1. Jigjidsuren and S. Oyuntsetseg, 1998: Pastureland utilization problems and ecosystem. Ecological sustainable development. Ulaanbaatar, No.2, p.206-212. 2. Naoko Iino and Kisei Kinoshita, Properties of AVHRR images Asia dust in April 1997. Satellite Imagery of Asian Dust Events. Sep,2001, Kagoshima. p.23-28 3. L.Natsagdorj, 1982: Atmospheric circulation and dangerous weather phenomenon over the territory of Mongolia. Publication of Hydro-Meteorological Research Institute of Mongolia, No. 6, Ulaanbaatar, p. 300. 4. L.Natsagdorj and D. Jugder, 1992: Statistics method for prediction of dust storm over the Gobi and steppe area in Mongolia in spring. Scientific report, Ulaanbaatar, p. 83. 5. L. Natsagdorj and D. Jugder, 1992: Dust storm in the Mongolian Gobi. Proceeding of the Symposium on Global Change and the Gobi Desert, Ulaanbaatar, p25-40. 6. L.Natsagdorj and D. Jugder, 1993: Dust Storms in Gobian Zone of Mongolia. The First PRCMongolia Workshop on Climate Change in Arid and Semi-arid Region over the Central Asia, May 8-11, 1993, p99-104, Beijing. 7. J.Sumyasuren, Large Area Characterization of Dust Aerosols Properties usiong Remote Sensing Satellite Data. Post-graduate diploam thesis, Apr, 2005, Ahmedabad

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