Gis Sem 9

Application of Global Positioning system in Water Vapor Estimation Global Positioning System (GPS) is a satellite based navigation system, designed to provide instantaneous position, velocity and time information on the surface of the Earth at any time in any weather condition. GPS has become an effective tool in various applications related to the earth observation. GPS radio waves are delayed due to the ionosphere and troposphere. For positioning this is the source of error, but it has been found that tropospheric delay is due to the composition of gases and water vapor present in the troposphere.GPS can be used effectively for determining the amount of total water vapor, which in turn can be used for monitoring the flood, short-term weather forecast, and various atmospheric studies. The total water vapor content can be determined by using the GPS data and formulating suitable model of the troposphere. The temperature, pressure, and the relative humidity can be measured by using precise meteorological instrument called MET3A on the surface of the earth. The integrated total water vapor content can be computed from the zenith equivalent total path delay affecting the radio signal propagation in the troposphere. The zenith total delay will be decomposed in to the zenith wet delay (ZWD) and zenith hydrostatic delay (ZHD). The ZWD is found out by subtracting ZHD, from zenith total delay (ZTD). ZHD is computed by using suitable atmospheric model. ZWD can be converted in to precipitable water vapor (PWV). The objective of this study is the integration of the GPS Receiver with meteorological instrument called the MET3A. To determine the zenith total troposphere delay using GAMIT software in near real time. The validation of the Saastamonion model for ZHD determination has been carried out. Regression model has been developed for the determination of the ZHD for IIT Bombay station using radiosonde data for year 2005.ZWD has been calculated by subtracting ZHD from the ZTD.ZWD has been converted into PWV using the function ¡Ç which is defined by the atmospheric mean temperature. The comparison is made between the observed radiosonde values and the one which are obtained using GPS data. Form the study it can be concluded that GPS methodology for the estimation of the PWV near real time solutions is possible as well as this is the more economical method for short term weather forecast over the traditional methods like radiosonde, sun photometer which are available for the estimation of the PWV.

Environmental consideration in railway route selection with GIS - Case study: Rasht-Anzali railway in Iran . Introduction Transportation network expansion is a key factor for realization of economic and social development, particularly in developing countries. Railways for having comparative merits such as high level safety and energy saving has a desirable place among different transportation modes. For particular condition of Iran, its extent and position in Middle East, sprawl of settlement and distance of growth poles, development of railway systems is vital. In conventional railway route selection, economic and technical parameters were mere determinant factors in which environmental impacts of projects were completely neglected. Minimization of environmental impact and liabilities requires consideration of environmental criteria in initial planning and railway route selection. Consideration of environmental parameters, in addition of economic and social ones by increasing required data, make more complex process of route selection in traditional mode. Emerging complexity of decision making process in new situation that involve much more data and consideration of different type of criteria, urge investigator and planner to utilize of more effective technologies such as GIS to help analyzing a great volume of data. GIS make possible to analyze and process data as quickly and precisely as possible. Over the past few years, an increasing number of investigations on GIS-base route selection

became available. Feldman et al (1995) used remotely sensed data and GIS for pipeline routing in a small section of the proposed Caspian oil pipeline. For this mean a model was developed incorporating pipeline length, topography, geology, land use, stream, wetland, road and railroad crossing to identify least cost pathway. In another study (Jacobs and Vuong, 2001) that was done in USA, GIS technology was successfully applied for routing High Speed Railway (HST) in Minnesota Province. The purpose of this study was to develop a linear facility-siting method especially for a HST route. The key factor and constraints that affect land suitability for a HST route identified by this study were soil type, slope, roads and highways crossing, wetlands, lakes and streams and urban areas. Multi Criteria Evaluation (MCE) was used to identify suitable lands. The least cost pathway was also used to create a potential zone in which to locate a route. 2. Methodology 2.1. Study area An area in the Gilan province in north of Iran was selected for this study (Figure 1). Gilan province due to having some character such as existence of agricultural land, forest, wetlands, grand rivers and sea shores is one of the very important wildlife habitats and scenic landscapes in the world and regional scale that presents fragile and vulnerable nature against development of human activities. So Rasht-Anzali railway is of national level significance. Nowadays Rasht city plays an important role as an industrial and agricultural pole of region and Anzali port is also an import-export center in the north of Iran. Environmental characteristics of Gilan attract a large number of tourists and related human activities in parallel, that all require a clean and safe transportation system.

Figure 1: Location of the study area in Iran and its Landsat color composite image 321(RGB)

2.2. Environmental parameters and data acquisition To achieve research goal, effective parameters in railway route selection must be identified. Following parameters indicate those items are considered in current study. Land cover: land cover maps provide information on general cover that may be used in railway route selection. To produce this map a subset of Landsat–7 ETM data dated on May 2000 was analyzed. Rectification of satellite data was implemented using ground control points method. To achieve a better resolution the multiespectral bands were fused with ETM-Pan. Suitable spectral transformations and color composites were performed on the satellite data. Image classification was performed using a new approach (digital and visual). The land use map checked for accuracy by field study. River: For minimization environmental impact, routes should be far from rivers. Railway construction costs will be increased to fill in or build bridges over rivers, ponds or sections of lakes too. The map of the rivers was extracted from digital topographic map at the scale of 1:25000.

Road: Reduction of intersection of railway with existing road, decrease construction cost and increase safety of transportation. For this aim map, of roads was produced base of the existing digital map. Cultural heritage: Cultural heritage forms identity of each society and nation. So people should try to preserve and enhance their place and conditions in the environment. To create a map about the heritage, documents and existing maps were studied. Geology: Geological studies were done for recognition of resistance of rocks underlying superficial layers. For this aim, maps of geology related to the study area digitized. Soil: Susceptible soils to erosion and unconsolidated material need much cost for rail construction than consolidated types. For this aim, a reliable soil map of the area digitized and used for producing desired classified soil map. 2.3. Evaluation of effective parameters in railway route selection In order to minimize environmental impacts and to achieve sustainable development in route construction projects, it is essential to determine the relative importance of the considered parameters. There are some methods for this aim. Selection of a method depends on the tradeoffs between ease of use, accuracy, the degree of understanding on the part the decision maker, and the theoretical foundation underlying a given method, the availability of computer software, and the way the method can be incorporated into GIS-based multicriteria decision analysis. Empirical applications suggest that the pairwise comparison method is one of the most effective techniques for spatial decision-making including GIS-based approaches (Malczewski 1999). This method was developed by Saaty (1980) in the context of the Analytical Hierarchy Process (AHP). This method involves pairwise comparisons to create a ratio matrix. As input, it takes the pairwise comparisons of the parameters and produces their relative weights as output. It is recommended to consider environmental and civil expert’s views regarding to relative priority of the parameters. In this study, it was collected from a questionnaire that has been designed for this aim. Weights of parameters calculated according to these views and prioritized in this order: cultural heritage, land covers and land use, slope, soil type, geology, rivers and roads. 2.5. Comparison of routes and optimal route selection After automatic designation of routes in GIS, each route must be evaluated in respect to environmental considerations. This lets us to choose a route, which has minimal environmental impacts. In this study, Analytical Hierarchy Process (AHP) was applied for choosing optimal alternative. Using this method conventionally designed routes and those determined by automatic method in GIS were compared and optimal route was chosen. 3. Results and conclutions The purpose of this study was to develop a tool to locate a suitable railway route between Rasht and Anzaly cities in Gilan province. The GIS approach using environmental parameters and least cost pathway analysis proved to be successful in achieving this goal. Using a GIS approach was very important in this study. GIS tools allow incorporation of digital data layers of various different scales. In this research railway routes that resulted from traditional method were compared with GIS method. Results indicate the routes that were designed with applying GIS method are more environmentally sound than traditional one. According to the results of this investigation, applying GIS technology and inclusion of environmental expert’s views are recommended to minimize environmental liabilities. The approach developed in this study is very general and it is recommended to use it as a guide for future studies.

Biodiversity threat through exotic species monitoring and management using Remotely Sensed data and GIS techniques - A case study of Banni (Kachchh) Gujarat, India

Introduction Natural grassland is a plant community in which the dominant species are perennial grasses, there are few or no shrubs and trees are absent or less in numbers. Usually associated with the dominant grasses are less abundant grass species and variety of other herbaceous plants, both annual and perennial types, which at certain times of the year give a characteristic aspect to the plant community. To be presented at 6th Annual International Conference - MAP INDIA 2003 during 28-31 January, 2003 at New delhi. Grassland is one of the numbers of seral phases of vegetation. The vegetation structure is dynamic rather than static. One ecological association follows upon, and grows in consequence of, its predecessor in a well-marked and orderly sequence. One association therefore acts as a nursery to its immediate successor. This series of successional phases, from the first to the last, is referred to as the "sere", grassland forming one characteristic phase of that sere. The development of the sere may be arrested at any given point if environmental conditions are such that further development is retarded. The sere may thus end at a sub-climax rather than at its climax stage, e.g. in semi-arid areas the natural vegetation may be steppe or open grassland with no trees of any kind. In areas of higher rainfall forest is the climax. In regions of high rainfall, the tendency to revert toward forest is particularly marked and confronts the pioneer with difficult problems of stock and pasture management. Continued understocking will allow a normal reversion, first to weeds, and then to shrubs and scrubs, habitual overgrazing will tend to weaken the sward so that the establishment of weeds is made easier. Many of the large grassland areas, such as the prairies and plains of North America, the pampas of South America, the steppes of Asia and the Veld of Africa are believed to be of great antiquity and are climax formations determined by soil and climate. Other grasslands are of more recent origin and have replaced forests that have been destroyed mainly by cutting and fire; these have been maintained largely through grazing animals. True grasslands exist in most part of the world where the rainfall is not sufficient to produce thick forest, and yet sufficiently high to prevent the creation of a desert. The great grasslands once covered nearly half of the earth's land surface, from the rolling Prairies of North America to the great Savannahs of Africa and the vast Steppes of Eurasia. Generally speaking, grassland have few, scattered, small sized trees to break the drying winds. Most of these areas go through periodic drought conditions. As a result, majority of plants of these regions lives more " in " the soil than above it. Just under the surface there is a tangled map of roots and rhizomes. Some grassroots grow down to the depths of a meter into the soil, while the tap roots of other soft stemmed plants may penetrate to five meter in their search for water and nourishment. Importance of Grassland When we talk about grassland, we have to first consider the main component, that goes to form the bulk of the grassland, that is grass. Of all, the grasses are the most important to man. All our food stuff like corn, wheat, oats, rye, barley, and rice, sugarcane are grasses. Bamboo's are also grasses. Although, the great importance of grasslands lies in providing sustenance, grasses also serve humanity in other ways. Grass may be used for building homes and furniture (walls, thatch, matting, brooms) lawns, sports fields and as components of some cosmetics and medicines. Grassland provides crucial grazing land and pastures for the domestic and migrated livestock, which forms important livelihood for majority of the population of Banni and surrounding districts. The milching capacity and overall health of the cattle, is an indicator of grassland quality. From these grasslands large quantity of forage grass is collected annually by cutting, and storing it in grass godowns for the droughts. However, free grazing can deteriorate these grasslands, for which only controlled " rotational grazing " is useful. Introduced Species :

Introduction of non-native species (also known as "alien" or "exotic" species), deliberately or accidentally, has been a major threat to biological diversity worldwide as the introduced species have often flourished at the cost of the local species. India's Biodiversity, too, has been affected by introduction of alien species. Several exotic animals and plants introduced in the Andaman and Nicobar Islands are posing a threat to the local species of fauna and flora. Animal husbandry, an occupation of majority of Indian farmers, is directly dependent upon grassland for sustenance and it contributes a significant 5 to 6% towards India's national income (The State of India's Environment 1984-85). Study Area The Banni area, as the name signifies, is a 'Banni hui' (in Hindi) meaning made up land formed by the detritus brought down and deposited predominantly by the Indus river, which was reported to flow through the Great Rann in the past. The great and the little ranns of Kachchh were the old arms of the sea in the old geological period. Due to the eruption and formation of the Allah Bund near the Kori Creek, the lands in the Great and Little ranns got blocked up and were filled up by the deposits brought down by the Indus river (Source: Notes from Animal Husbandry of Agriculture, Gujarat). Once upon a time Banni was considered the largest grassland of its kind in Asia, but has fallen upon sad times in the last decade. The Banni area under the present investigation extends over Bhuj and Nakhtrana Talukas of Kachchh Districts. It is situated on the northern border of Kachchh mainland, consisting of 45 villages. The actual area lies between North latitudes of 23o19' and 23o52' N and East longitudes of 68o56' to 70o32' E. Vegetation comprises of grassland, shrubs and legumes found naturally in the Banni area. Normally the area is covered with coarse and low perennial grasses and other non-grass species present in Banni area are as follows (Source : Banni Development Office, Bhuj, Kachchh). 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26.

Dichanthium-annulatum, (Forsk) Stapf Sporobolus helvolus (Trin) Thw. Chloris barbate, SW. Cenchrus biflorus, Roxb. Eleusine bianata Elysecarpus rugosus (legume), Wall Heylandis latebrosa (legume), DC Digitarea sanguinalis, Scop. Var Ciliaris Prain Crotolaria medicaginea, Lam. Indigofera sps. (Legume), Linn. Sida sps. (Malvaceaa) L. Malanocenchrus jacquemontii, J&S Sporobolus diander (Retz) P. Beauv Cenchrus setigerus, vahl Aristida adscensionis, L Aristida funiculata, Trin & Rupr Setaria rhachitricho, Cook Eragrostis minor and major, Host. Eragrostis trimula, Hochst. Cyprus rotundus, Linn(dupareate form) Desmostachya bipinnata (L.) Stapf Cyperus rotundus, Linn Cressa cretica (Convovulaceae), Linn Eragrostis bulbosa Kochia sps. (Polygonaceae), Roth Suaeda fruticosa

Out of the above 26 grass species first 12 species are palatable and rest of them are salt-tolerant grasses. Banni area deterioration is linked to the increasing salinity ingress, impoverishment and illiteracy of its inhabitants, a growing human and livestock population, and invasion of prosopis

juliflora, which offers quick fuelwood, but its proliferation is dangerous for the grassland, over grazing and improper management of the land. Data used The following data was used during the course of this study 1. IRS 1C/1D LISS-III data( transparencies ) of two seasons at 1:50,000 2. Banni area map prepared by Banni Development Authority and WRD/CDO joint report. 3. Ground truth data collection. GIS Database Design and Organisation for Banni The database for the Banni development plan has basically two components, Spatial and NonSpatial. The Geographic Information system (GIS) package is the core of the database for handling the two sets of data. In the present study ARC/INFO GIS package has been employed as the main tool to design, Organization, storage, retrieval, analysis and generation of cartographic outputs. Non-Spatial Data basically consisting of numeric/attributes in respect of Grassland type-code, Salinity range-code, composition and prosopis density class-code. Since it was required that a typical analysis had to be carried out for Banni Development Plan, as it was discussed with other participating agencies like Gujarat Institute of Desert Ecology (GUIDE), Animal Husbandry, Banni Development office at Bhuj. The database contents are given in TABLE - 1 . TABLE - 1 :Primary and derived themes used for banni development plan Sr. Theme No.

Type

Primary/Derived Source

Criteria

Remarks

1

Grassland/ Landcover

map

Polygon Primary

IRS 1C/1D 1998-99

-

-

2

Banni Boundary

Polygon

Primary

Banni Devp. Office

-

-

Primary

SOI toposheets and IRS 1C/1D L3 DATA

-

-

-

-

3

Roads

Line

4

Elevation

Points

Primary

SOI toposheets & limited GPS points

5

Drainage

Lines

Primary

SOI toposheets

-

-

6

Contours

Polygons Derived and lines

Elevation points

-

Tin and Lattice Model of ARC/INFO

7

Slope

Polygons Derived

Elevation Points

8

Action Plans For:

8.1 Palatable Grass

Polygons Derived

Landcover Map

Tin and Lattice Model of ARC/INFO Multiparametric

Identification & Extraction using

Mass 8.2 Weeding of Prosopis

Pure 8.3 Prosopis

8.4

Salinity range

Water 8.5 Harvesting

criterion based Analysis

GIS

Landcover Map

Multiparametric criterion based Analysis

Identification and Extraction using GIS

Polygons Derived

Landcover Map

Multiparametric criterion based Analysis

Recursive Elimination Analysis using GIS

Polygons Derived

Landcover Map

Criterion based Analysis

Identification of fertility island of Vegetation types

Polygons Derived

Slope Map & Drainage

Criterion based Analysis

Possible sites identified as per the discussion with experts

Polygons Derived

Each of the above mentioned action plans was generated using multi - parametric criterion based analysis by GIS techniques. Methodology Looking in to the typical problems of Banni grasslands and subsequent discussions held with experts, participating agencies and also with the agencies working in Banni development activity at Bhuj, it is felt that the problems of Banni can be addressed by four major action plans which are required for implementing for Banni development. They are: 1. 2. 3. 4.

Palatable good Grasslands protection and conservation Arresting Prosopis juliflora invasion into grasslands ( both Palatable and Salt tolerant ) Phase - wise removal of Prosopis in non - saline areas and Rain water harvesting for salinity leaching and increasing grass production

The methodology flow chart is given below:

Fig - 01: Schematic Representation of Methodology for Banni Development Plan

Results, Discussions and Suggestions Based on the analysis carried out the following results were found. ♣Prosopis invasion Invasion of Prosopis can be attributed to various allogenic and autogenic factors operating at spatial scales ranging from the small patch to entire landscape. Allogenic factors include climatic changes, over grazing etc. Allogenic factors operating to favor Prosopis invasion over good grasslands is an increase in the spatial and temporal heterogeneity of soil salinity, cattle droppings etc.. which promotes the invasion of Prosopis. Prosopis invasion in Banni using multi-temporal Satellite data of 1980,1985 1988 and 1998 (Normal, Drought, Normal) have been calculated. The percent of area under P.juliflora are as follows: Year Area (Ha.) % 1980 37893

9.85

1985 35046

9.11

1988 76786

19.96

1998 118675

31.23

Looking at the present scenario and comparing with our own previous studies, it is very interesting to calculate the future trend of invasion in the Banni (W) area. To do the trend analysis of Prosopis invasion in Banni, Kachchh, we have considered an imperial equation, which had

been validated by using 1988,1998 & 1999 Satellite data. Based on that equation we have predicted P-invasion in Banni upto 2020.The results are as follows: Year Area (ha.) %area 1998 118799.40 31.2630 2005 149275.78 39.2831 2010 171017.48 45.0046 2015 192736.38 50.7201 2020 214432.71 56.4297 This shows very alarming situation for Banni Grasslands. By the year 2020 more than 56% of the total geographical area of Banni would be under prosopis and destroys entire bio-diversity, and grassland eco-system of the area, if proper controlling measures were not taken immediately. One of the measures suggested was Mass weeding of Prosopis immediately after post monsoon so as to control its invasion in new areas where good and palatable grass is growing at present. Fig-02 shows spatial distribution of areas for mass weeding of P.juliflora immediately after the post-monsoon. ♣Phase - wise removal of prosopis Looking at the stock of Prosopis in Banni, it is very essential to remove (uprooting or cut and burn with kerosene) from non - saline areas. But, removing entire Prosopis at once may cause ecological problems. So, it is suggested to remove Prosopis phase - wise (may be 1kmX1km plots) starting from matured patches to complete with in Four or Five years. Fig-03 shows priority areas for phase-wise removal of P.juliflora from pure patches of prosopis and prosopis invading in good grasslands. ♣Protecting and Controlling open Grasslands It is observed from the analysis that, if the rainfall is normal, there is a good grass growth in nonsaline patches of land in Banni. Because of over grazing by domestic and migrated cattle from surrounding districts and also from Rajasthan, the grass is getting exhausted before it fully grows to a particular stage. To overcome this problem it is essential to protect open (uncontrolled) grasslands to arrest the entry of cattle freely from all sides of the patches. So, it is suggested to fence these patches. Moreover, it is required to do furrowing in these lands for moisture retaining and thereby better production in the subsequent years. Salinity ingress 

Trenching This is another major problem in Banni. Salinity ingress is approximately about 4% per year in the region. To control this ingress to some extent it is suggested to dig a trench around good grasslands, which helps in leaching out salinity and arrest cattle entry to some extent. Fig - 04 depicts the length of trenching and fencing required and the cost may be calculated based on local labor and material charges.



Ghaduli - Santalpur It is also suggested that the road which is planned to construct between Ghaduli Santalpur (via. Khavda - Katwndh - Dolaveera - Amrapur - Bela and Madhutra) must be completed having sluice gate opening only one side to arrest salinity ingress into Banni and other areas.

♣Rain water harvesting Leaching out salinity atleast Five to Eight inches from the surface will help grass to grow in low saline areas of Banni. This is possible by allowing fresh water (rain water) to flow over saline areas. For this purpose rain water harvesting is necessary. So, the possible locations were suggested. However, it is very much required to study "What type of structure ?, How much capacity etc.. " for implementation. Fig - 05 shows the topography of the area and Fig - 06 shows the status of drainage pattern in the area and suggested rainwater-harvesting structures. Constraints using GIS There are some intricate problems in implementation of GIS in Environmental studies in India. The problems posed with our Indian scenario are 1) Non availability of properly spatial data 2) Lack of proper infrastructure with the Government bodies 3) Meager skilled Manpower in the government planning and development departments 4) GIS softwares being more costly. Some of the probable solutions are 1) Availability of map data in a centralised facility 2) Awareness and increasing the skills proficiency in GIS in government and private sector. 3) Increasing the infrastructure facilities to cope up with the latest technologies and 4) Supplementing the Environmental planning division with adequate funds Conclusion In summary, GIS technology will continue to play a vital role in environmental system management. GIS becomes the primary repository of information that can be quickly accessed and viewed when required. GIS is becoming more suitable for emergency operations and is integrating tools that allow real-time display of information. Rapid access to information, safety, efficiency, and better resource management decisions can be made with the use of GIS. GIS technology can provide critical information at the need of the hour to take the remedial measures in no time as effective as possible.

Application of Remote Sensing and Geographical Inforamation System (GIS) in Forest Survey in Nepal Abstract Nepal is mountainous and land-lucked country with an area of 147,181 km2. the theme of the study of the woodyvegetation forest and shrub) covers of Nepal is analyzed to obtain the quick and reliable information by using remote sensing and GIS technology. The forest inventory of the Terai (the southern belt of the country) districts of Nepal was assessed with Landsat satellite imagery. Some districts were assessed by district forest inventory and the remaining hilly areas were assessed by aerial photo interpretation. The result were combined from the three different types of forest inventories in order to get an estimate of the woody vegetation cover of the country. The current study of the woody vegetation cover of Nepal in 1992/1996 was 39.6% of the total area (forest 29% and shrub 10.6%). The result given by the land Resources Mapping Project(LRMP) in 1978/79 was 42.7% (forest 38% and 4.7%). The woody vegetation cover is decreased by 3.1 % within the period of 1978/79 to 1992//1996. The result shown only the different are the proportion of forest in the woody vegetation have declined and the proportion of the shrub has increased. The woody vegetation cover of the Terai belt of Nepal was obtained 41% according to the to the satellite image analysis of 1990/91 images District survey for some districts done in 1994 was 50.5% and the remaining hilly areas done in 1996 were 37%. The analysis of the current study using photo point sampling showed that the woody vegetation cover in the hilly areas declined from 56% in 19978/79 to 37.1% in 1992/1996. It showed that the

vegetation was decreased by 19.1%. The forest coverage in the Terai belt of the country was obtained by applying the remote sensing and GIS tools. The results showed that the woody vegetation are decreased by 15% or 1.3% per year during the period 12 year from 1978/79 to 1990/91. The methodology used in the Terai can not be used in the hilly area of the country due to slopes, shades and highly inaccuracy of the available data so far. However, remote sensing and GIS tools are being used only for the preparation of woody vegetation cover maps of the hilly areas to some extent for planners and decision-makers. Introduction Nepal is a mountainous and land-lucked country with an area 147,181 km2. The country is bounded by China to the North and India to the South, East and West and located between 80% 4'E to 88%. 12'E longtitudes and 26o 22'n to 30o 27'N latitudes. The country is covered by 39.6% of the woody vegetation (forest and shrub). The theme of the study of woody vegetation cover of the country is studied by using remote sensing and GIS tools to obtain quick and reliable information. Remote Sensing and GIS tools have been used successfully for studying the forest resources of the country. Since 1990, the Forest Survey Division (FSD) under the forest Research and survey center (FORESC) and the forest Resource Information system (FRIS) Projected have been carried out the forest inventory-both at the district level as well as the national level using the latest aerial photographs and remotely sensed data together with the available topographic and land use maps. Forest Resources of the Country Forest play a significant role in the livelihood of the rural people as they are highly depended on the forest resources. The forest resources of the country should be, therefore, well managed in a scientific way to meet of ever increasing population on the sustainable basis. In the contest of hilly country Nepal, forest is one of the most important resources for the rural development. Most of rural people live in forest or near the forest and are dependent for fuel-wood, fodder, timber and generate income from forest to maintain their daily needs. On one side the forest is decreasing and deteriorating and on the other side creating environment problems. Forest, therefore, are the most important resources, and an extremely important component of the environment and plays a vital role in the improvement of the socio-economic condition of the rural people as well as in conserving the natural resource of the country. The current study of the woody vegetation cover of the country in 1992/1996 was 39.6% of the total area (forest 29% and shrub 10.6%) Development of Remote Sensing and GIS in Nepal Releasing importance tool for the information of the forest resources according to the need of the objective for the management remote sensing was established in 1981 in the name of National Remote Sensing Centre (NRSC) as an autonomous institution through the joint co-operation between HMG/N and USAID. It has been working as a multi-disciplinary work together for generating useful information to apply the technology for the national development works. The Centre had been worked as a focal point for RS activities conceming Natural Resources. New aerial photographs of the country were taken for the preparation of the forest maps as well as carrying out the forest inventory in the field. In July 1989, the NRSC is merged under the Forest Research and Survey Centre as a Remote Sensing Section. In the last two decades, the main activity of Remote Sensing Section of FSD is to collect forest resources data using conventional and modern techniques using remote sensing and GIS tools. It is providing the information to al offices under the MFSC for sustainable management and development of forest resources of Nepal. Since 1990, the FSD of FORESC and the FRIS have been carrying out the National level forest

inventory. FRIS Project of FINNIDA Government is providing technical as well as financial support to the FSD OF FORESC. The object of the project is to develop and establish a FRIS that has to provide relevant up-to-data information of forest resources of Nepal for the contest of development planning. Two phases of the project had been completed and next third phase continuing till Mid-July 1999. The focus of development planning in any country is to fulfill the social and human aspiration of its people, meeting the essential requirements of living raising income levels and improving their quality of life. The focus of development planning in any country is to fulfill the social and human aspiration of its people, meeting the essential requirements of living raising income levels and improving their quality of life. Remote Sensing and GIS have been providing for development since 1964/65 from the aerial photographs of 1963/64 for mapping and monitoring of forest. Now, it has become very popular and handy tools for resources monitoring, evaluation and planning to meet the demand of ever increasing population on the sustainable basis. Methodology used in the Country Three different types of forest inventories were used to determine at the national level to estimate the woody vegetation cover in Nepal. A part of Terai districts were assessed with Landsat TM satellite imagery. A minimum of 10% canopy closure is often used a s a criteria for separating forested from non-forested land. Based on satellite imagery, it was not possible to define a certain canopy closure for limiting forest from non-forest. Therefore, a threshold value of the combination of Landsat bands (NDVI value) was chosen as a proxy. Normalized difference Vegetation Index (NDVI) these holding is applied for separating forested land from non-forested land in the satellite imageries. NDVI is used elsewhere to be highly correction with green biomass making it a viable tool for the identification of forest. Rectification and cloud correction are done in the satellite imageries with the help of photos LRMP land use maps. The methodology used in the Terai can not be used in the hilly area of the country due to slopes and shades and highly inaccuracy of the available data. Some districts were assessed by district forest inventories. The forest cover for these districts were determined using a traditional method of aerial photos interpretation and forest mapping with field checking. The inventory method was stratified random sampling using forest types, stand sizes and stocking classes as stratification variables. The stratification was done on aerial photography. The remaining hilly districts were assessed by aerial photo interpretation. A grid of sampling points (3.66 km x 3.66 km) for photos interpretation was first drown on the topographic maps of the Indian Survey. The grid points were selected from the points where the coordinate lines of the topographic cross. Location of the points was done with the help of easily recongnized ground features. The latest panchromatic black and white contact photos of the scale 1:50,000 were obtained from the Topographic Mapping Project. The photos were inspected with a mirror sterescope. Land use was determined at all points on the photos. Application of Remote Sensing and GIS in Forest Survey in Nepal Remote Sensing and GIS tools are used to study various natural resources. They have been using successfully in the forest resources in Nepal. The woody vegetation cover includes all the categories of forest (closed and open, natural and planted) and shrub lands. Agricultural land are not included even having growing plenty of trees on them. The woody vegetation is the joint figure of the forest and shrub because there is not clear boundary between forest and shrub. Remote sensing and GIS are used in various fields like monitoring of forest resources, watershed

management, flooding, road network, urban and rural development, mining, hydrology, meteorology, irrigation and hydropower and yield forecasting so on. Sine 1980, National Remote Sensing Centre (NRSC) had applied RS and GIS for the deforestation for sustainable development, possibility of hydropower and irrigation development, management and planning of natural resources, estimation of forest areas on natural bases and yield forecasting study etc.. After merging the NRSC under the FORESC as a Remote Sensing Section that has been taken the collection of information in the forest sector such as: • • • • •

To prepare the reports on woody vegetation cover and maps using satellite image. To prepare the various thematic maps of districts using GIS technology. To classify the forests of Disticts using satellite image. To prepare the statistics and database maps in relation to forestry and To study the existing forest condition of districts using GIS technology from satellite image.

The land Resources Mapping Project maps (LRMP) of the scale 1:50,000 based on the aerial photos of the year 1978/79 are used for digitizing the boundary of the study area. The boundaries from the LRMP land use maps are used to transform into digital form, and then other details are added into it by using other maps such as Indian Topographic maps of the scale 1:63,360. Results The woody vegetation cover of the Terai belt of the country was obtained 41% according to the satellite image analysis of 1990/91 images. District survey for some districts done in 1994 was 50% and the remaining hilly areas done in 1996 were 37%. The combining results of there different types of the woody vegetation cover of the country in 1992/1996 was estimated 39.6% of the total area (forest 29% and shrub 10.6). the results given by the Land Resource Mapping Project (LRMP) in 1978/79 was 42.7% (forest 38% and shrub 4.7%) and Master plan was given (forest 37.4% and shrub 4.8%). The woody vegetation cover is decreased by 3.1 % during the period between 1978/79 to 1929/79 to 1992/1996. the results shown only the different are the proportion of forest in the woody vegetation cover have declined and the proportion of the shrub has increased. Table 1. Percentage of the woody vegetation cover of the total area of the country comparing with previous studies. Category LRMP(1978/79) Master Plan(1985/86) Current NationalForest Inventory(1990/1996) Forest

38.0%

37.4%

29.0%

Shrub

4.7%

4.8%

10.6%

Total

42.7%

42.2%

39.6%

The total area of the Terai belt calculated from digital LRMP maps is 3.4 mill.ha (34,000 sq km), out of which 1.4 mill. Ha (41%) is covered by forest. The average rate of deforestation for the total area of the plain has been 15% or the rate of 1.3 per year. Table 2. Change of woody vegetation in the Terai belt Category

1978/79 1990/91

Terai belt(plain area) 64300

Change

545900 -99400 15%

20 districts Some of the districts (7 districts) were done by the traditional method of the aerial photo interpretation and forest mapping to determine the woody vegetation cover. The woody vegetation cover some districts Are given in the following table Table 3 Woody vegetation cover by the District Forest Survey(1994). Category

Total Area (ha)

District Forest Inventory (7 districts)

1284200

Woody Vegetation cover Area (ha)

%

592065

50

The remaining hilly area (48 districts) were done by the sampling point in 1996. The following table will show the changes of the vegetation cover in the hilly area. Table 4. Change of the forest and shrub (woody vegetation) cover in the Hilly areas by Sampling Point. Woody Vegetation Cover (ha)

Category

Total Area (ha)

LRMP(1978/79)

National Forest Inventory (1992/96)

Change Area (ha)

Hilly Area 48 Districts

10608700

5948272 (56%)

3930998 (37%)

2017274 (19%)

The analysis of the current using photo sampling showed that the woody vegetation cover in the hilly areas declined from 56% in 1978/79 to 37% in 1992/1996. It showed that the woody vegetation was decreased by 19%. The Terai belt of the country was obtained by applying the remote sensing and GIS tools. The results showed that the woody vegetation area decreased by 15% or 1.3% per year during the period of 12 years form 1978/79 to 1990/91. Conclusion Remote Sensing and GIS tools are very useful for studying the forest resources and its change. Maps and data are the important tools for the organization of resources where man's requirement and activities are continually increasing and changing. Maps with other necessary information as required of the objective must be updated by the RS and GIS technology to meet new needs. Therefore, RS is playing a vital role in effective and efficient mapping and monitoring on the natural resources. Integrated of GIS with remotely sensed data has added newer dimension of remote sensing application. After merging the NRSC under FSD of FORESE as a Section which mostly focusing in the forest resources. The study of the forest resources in the country, result of the analysis of the woody vegetation cover is only to attempt to illustrate the present status of the woody resources and their past development. Special emphasis has been put on describing the result of the latest photo interpretation analysis including a change study.

Estimating Carbon-fixation in India based on Remote Sensing Data Abstract The energy emitted by sun and captured by the terrestrial vegetation through the process of carbon-fixation governs the energy fluxes on the earth. Carbon-fixation, thus, is an important parameter in the study of different biological processes. Reflectance based data from the earth observing remote sensing satellites provide vital information regarding terrestrial vegetation.

Efforts were made in the present study to estimate carbon-fixation over Indian territory using Production Efficiency Model (PEM). PEM used to evaluate Net Primary Productivity (NPP) requires decomposition of productivity into independent parameters involved in the production built up process. In the first attempt of its kind, most of the important parameters used in the PEM WERE derived from the satellite observations. NASA/NOAA Pathfinder AVHRR Land (PAL) 10 day composted NDVI data with a spatial resolution of 8 km was used from the study. The study area was divided into agricultural and natural vegetation areas based on the NDVI-climatological technique developed by Hoda and Dye (1995). The NDVI for the years 1987, 1988,1989 were used to estimate fraction of PAR absorbed (FAPAR) based on the relationship Fapar = -0.31 +1.9*NDVI provided by the SAIL model. Incident PAR (IPAR) data set for India was extracted from the monthly global IPAR data set already generated using UV reflectivity data from Nimbus Total Ozone Mapping Spectrometer (TOMS). The IPAR data when combined with the fAPAR data, provided absorbed PAR(APAR). APAR was subsequently cumbered to NPP using the mean PAR conversion efficiency values of 2.07 and 0.64 g/M joules for agricultural and non-agricultural vegetations, respectively, calculated based on literature. Annual and interracial variations in carbon-fixation in India have also been discussed. 1. Introduction Productivity is the rate of atmospheric carbon uptake by vegetation through the process of photosynthesis. Built up of productivity is a complex phenomenon which is a culmination of many temporal plant processes. Recent methods to evaluate NPP involves decomposition of productivity into independent parameters such as incoming solar radiation, radiation absorption efficiency and conversion efficiency of absorbed radiation into organic matter (Kumar and Monteith, 1981). The models developed in these studies are an advancement over the statistical models properly accounting for various steps in the productivity built up process. Goward et. Al. (1985) showed that vegetation indices, such as Normalized Difference Vegeta-tion Index (NDVI) are related to net primary production (NPP, g m-2 year1). Monteith (1987) suggested that NPP under non-stressed conditions is linearly related to the amount of Photosynthetically active radiation (PAR, MJ m-2) that is abosrbed by green foliage (APAR, MJ m-2). Further, Kumar and Monteith (1981) showed how the fraction of PAR absorbed (fAPAR) relates to the ratio of red reflectance ® to near infrared (NIR). Asrar et.al. (1984) subsequently related the NDVI to the Fapar; hence NDVI may be used to estimate NPP at global scale. Eck and Dye (1991) describged a simple, physically based, satellite remote sensing method for estimating IPAR that uses ultraviolet (UV) reflectivity data from the Nimubus Total Ozone Mapping Sepectrometer (TOMS). Subsequently, Dye and Gward (1993) also created a global APAR image using spectral reflectance measurements from the NOAA-7 AVHRR and TOMS data. Hunt (1994) suggested that global estimates of NPP based on vegetation indices should include a classification among established forest, young forest and non-forest ecosystems to account for difference in _. To address this problem, Hooda and Dye (1995) developed an automated technique for the identification of agricultural areas using NDVI-climatological modeling. One of the major problem in the NPP estimation is the finding of representative values of PAR Conversion Efficiency (e) for various vegetation types as it changes with the type of vegetation, temperature, water availability and metabolic type of the plant (C3 or C4 type). Prince 1991 and Rumy et. Al. (1994) searched though the literature and listed e values for various vegetation and ecosystem types. Prince and Goward (1995) used the PEM for global productivity studies. In the present study the carbon-fixation in India has been studied using all the PEM parameters derived from remote sensing data. 2. Data Used

2.1 NDVI data NASA/NOAA Pathfinder AVHRR Land (PAL) 10 days composted NDVI data set for year 1987, 1988, and 1989 was procured from the Goddard Distributed Active Archive Center (DAAC), USA. To generate composites data set, 10 consecutive days of data are combined, taking the observation for each 8 km bin from the data with the fewest clouds and atmospheric contaminants as identified by the highest NDVI value. There are three composites per month for each year of data. The composting technique fairly removes the cloud contamination from the data to use in climatic modeling studies (Agbu and James, 1994). The data is available on Goode's Equal Area Projection. 2.2 IPAR data Global IPAR data set generated by Dye (1995) using UV reflectivity data from Nimbus TOMS sensor through the method of Eck and Dye (1991) was used for the present study. This TOMS IPAR data set consists of monthly average estimates, at a spatial resolution of 1o*1o degree from 66oN to 66oS latitude. The data for the Indian region was extracted from the global data set and interpolated to match the 8 km resolution of NDVI data. 3. Methodology 3.1 Separation of agricultural and natural vegetation The NDVI-climatological modeling technique developed by Hooda and Dye (1995) was used for the separation of agriculture of agricultural and natural vegetation areas. 3.2 Productivity Estimation Productivity estimation was done as described in the following steps: 3.2.1 Estimation of Fraction of IPAR absorbed by vegetation (Fapar) The spectral vegetation index measurements produced by calculating the NDVI have been shown, empirically and theoretically, to be related to fAPAR in vegetation canopies (Ruimy. et.al., 1994). Although there are several possible limitations to such an inference, it does appear that an approximation of this fAPAR can be derived from the NDVI (Myneni and Williams, 1994). Ruimy et.al. (1994), after an extensive search through the literature, tabulated various relationships between Fapar and NDVI developed by different workers. For the present study relationship based on SAIL model simulation was used which is represented as: fAPAR = -0.31 + 1.33 *NDVI • The 10 day composited NDVI data was first averaged to give average monthly NDVI for al the three years. Calibrations for negative values on land in the NDVI data were made in way to set the bare soil Fapar TO zero. This calibration required a uniform enhancement of 0.1 NDVI units in the data. From average monthly NDVI, fAPAR for each month was calculated using the above equation. 3.2.2 Estimation of Absorbed Photosynthetically Active Radiations (APAR) The APAR calculations required IPAR and fAPAR Data sets for India. Monthly fAPAR data set of India for the three years was generated as described in the previous step. The monthly Indian IPAR data extracted from TOMS global data set of Dye (1995) was combined with the respective fAPAR data to give monthly APAR in MJ m-2. 3.2.3 Estimation of Absorbed Photosynthetically Active Radiations (APAR) The APAR calculations required IPAR and fAPAR data sets for India. Monthly fAPAR data set of India for the three years was generated as described in the previous step. The monthly Indian IPAR data extracted from TOMS global data set of Dye (1995) was

combined with the respective fAPAR data to give monthly APAR in MJ m-2.

•

3.2.4 Estimation of NPP, biomass and Carbon-fixation The conversion of APAR into productivity requires conversion efficiencies of APAR into dry matter (e) of various crops. Since we have a map differentiating the area into agricultural and natural vegetation, mean conversion efficiency values for the two types of vegetation are required for use in the mode. Ruimy et. Al. (194) conducted an extensive literature survey and tabulated the e values for different types of vegetation reported by different workers. But most of the workers reported e values in terms of above ground dry matter only. To overcome this problem they also searched through the literature to estimate a mean ratio of below ground NPP to above ground NPP and based on this factor they calculated the e values for different agricultural and forest vegetation. From this data we calculated the weighted average e values for cultivated and forested areas in India which were found to be 2.07 and 0.644 g dry matter (above ground and below ground ) MJ-1, respectively. These values were used for converting the APAR into NPP and subsequently to biomass in the present study. The average value of carbon in the vegetative dry matter has been reported to be about 0.45 per cent which was used to estimate carbon-fixation from the estimated biomass.

4.Results and Discussions 4.1 Interannual variations in Carbon-fixation The monthly biomass and carbon fixed in the Indian territory during the years 1987, 1988 and 1989, as calculated using the above steps, are shown in table 1. For all the three years, there seems to be a general trend of change in monthly biomass generation and C-fixation. The biomass and cosequently C-fixation, starts building up in the month of January and reaches its peak in the month of February/March and then drops suddenly in April. The biomass generation and C-fixation remains low in the summer months of May and June. This again starts building up in July/August and reaches its peak in September/October and the falls suddenly in November. Thus, we observed two peaks of carbon-fixation; first in the months of Feb/March and second in the month on October/November. Whereas, two low C-fixation peaks are also observed in the months of April and November. These high and low peaks of carbon-fixation clearly corresponds with the two crop growing seasons in India. The winter crops are sown in the month of November/December and they reach their peak vegetative state in the month of Feb./March and are harvested in the month of April. The agricultural lands generally remain fellow during the summer months of May and June, corresponding to low rates of C-fixation. The summer crops are sown in the months of June/July after the onset of monsoon and they rearch their peak vegetative stage in the month of Sept./Oct. before harvesting in November. Sowing of winter seson crops starts in the end of November or December and therefore, the biomass remains low during these months. Thus, this interannual variation in the rates of C-fixation indicated that agricultural C-fixation plays a major part in the total terrestrial biomass production in India as more than 45 per cent of the total geographical area in India is under cultivation. Table 1. Estimates of total biomass and carbon-fixation in India. Biomass(million tons)

Carbon-fixation (million tons)

1987

1988

1989

1987

1988

1989

Jan.

64.71

86.39

123.75

29.12

38.88

55.69

Feb.

101.03 91.03

127.35

45.46

40.96

57.31

Mar.

84.40

108.31

37.98

39.61

48.74

Months

88.02

Apr.

45.71

35.79

74.71

20.57

16.11

33.62

May.

63.51

53.89

53.62

28.58

24.25

24.13

Jun.

56.78

49.41

46.55

25.55

22.23

20.95

Jul.

42.99

37.61

67.61

19.35

16.92

30.42

Aug.

79.85

90.79

104.35

35.93

40.86

46.96

Sep.

127.28 157.25 182.13

57.93

70.76

81.96

Oct.

149.10 165.10 224.50

67.10

74.30

101.03

Nov.

95.89

131.94 134.15

43.15

74.30

60.37

Dec.

83.30

110.35 97.17

37.49

49.37

43.73

425.68

575.26

Annual 878.77 945.96 1278.35 395.45 •

4.2 Annual variations in Carbon-fixation Total biomass generation of 878.77, 945.96, and 12778.35 million tons was estimated in India for the years 1987, 1988 and 1989, respectively with a corresponding annual Cfixation of 395.45, 425.68 and 181.16 million tons for same years (Table 1). The performance of monsoon is the single most important factor effecting the growth of vegetation and consequenly monsoon is the single most important factor effecting the growth of vegetation and consequenly agricultural productivity in India. A study in the behavior of Indian monsoon showed that it was normal for the year 1989. But it showed a negative anomaly during 1987 and a positive anomaly during 1988 causing drought and floods in the two years, respectively. Therefore, the growth of vegetation and agricultural productivity was reduced in both the years. This interannual anomaly in carbon-fixation could also be described through the present methodology using PEM. The highest annual carbon-fixation of 575.26 million tons was estimated for the year 1989 as compared to 425.68 and 395.45 million tons for the years 1988 and 1987, respectively.

•

4. Conclusions Based upon the present study it could be concluded that the PEM cvan be used to make fairly correct estimates of biomass and carbon-fixation on a regional and global basis. Most of the parameters used in the PEM can be derived from the remotely sensed data. The model was also able to describe the annual and interannual variations in biomass production and carbon-fixation in the region. Therefore, the technique developed in the present study based upon the use of remote sensing data seems to have a great potential for making quick and accurate estimates of biomass and carbon-fixation over a large region

GIS model to assess Chennai city’s environmental performance, using green-cover as the parameters. •

Chennai city is the capital of Tamil Nadu, located in the Southeastern India. The average population growth of the city is 25% per decade that recurrently reduces the greencovered area. Exceptionally, during the post economic liberalization period, i.e. between the years 1997-2001, the city lost up to 99% of its green covered areas at some parts. Subsequently, the Chennai city started to experience wide range of environmental issues, like urban heat island, pollution and ground water depletion, etc. Though other factors are also reason for that, the receding green-covers mainly lowers the urban system's self

• • • •

• •

rejuvenation capacity. In other words, the diminishing green-covers simplified many aspects of the natural process, thus ultimately affected the Chennai city's environmental performance. To appraise this sensitive association between the green-cover and city's environmental performance, a GIS model has been developed by adapting the Chennai city as the case study area. The model is evolved, using the three sets of the green-cover services, namely the air quality amelioration, the hydrological process regulation and the micro-climatic amelioration. Through this model, the correlation between the Chennai city's green-cover change and its environmental performance change is appraised. The output confirms the positive relationship between per capita green cover modification and the Chennai city's environmental performance change. The result also shows that the Chennai city's environmental performance is reduced drastically across the city between the years 1997- 2001, at some parts to the degree of 38% is reduced

Application of Remote Sensing and GIS techniques in monitoring the vegetation patterns in Keibul Lamjao National park, Manipur, India IRS-1C digital data play an important role in monitoring the vegetation changes in the Keibul Lamjao National Park which is situated on a floating biomass (Phumdi) within the southern part of Loktak lake which is hosting the endangered species of Brow Antlered deer. It is being reduced in its census from the last few decades. Geographically the floating biomass encompasses an area of about 42 sq. km., which is inaccessible throughout the year. The thickness of the biomass is from 0.5 to 1.5 metres. The present studies deal with the generation of a vegetation map and monitoring the temporal changes (1984-1999) in vegetation to assess the possible reasons for the deterioration of National Park. The detailed vegetation pattern are delineated in 11 classes of vegetation from luxuriant grown grasses to dry grasses to decipher the micro level changes in the ecological conditions of the area. The effluents from south to the lake push the biomass towards north and wind speed in lean season pushes back to the south and the human interference appear to be main causes besides increased siltation from surrounding catchment to the lake. There are numerous factors which are a so responsible for the decrease (presently 32 sq. km.) in the geographical biomass such as agricultural encroachment and pisciculture practices in the peripheral areas of the biomass.

Remote Sensing protects ancient forests Here is an example on how satellite imageries influence a policy decision. New maps produced by Greenpeace and The Biodiversity Conservation Centre showing the decline in ancient forests in Russia based on satellite images have prompted the Svetogorsk pulp and paper mill, a major paper producer in Russia to phase out the use of ancient forest wood in its production entirely. The mill owned by a Swedish company, Tetra Laval, produces 180,000 tonnes of printing paper per year. 70% of the production is exported to Europe and America. The Svetogorsk Mill, located in the Leningrad region will introduce a major provision in its policy to ensure that no wood from ancient forests is processed. The information provided by Greenpeace GIS mapping project is startling. At a glance, anyone can see that ancient forests in Western Russia are getting smaller and more fragmented every day. Greenpeace has used the Remote Sensing technology and onthe-ground verification to produce detailed maps showing forest areas down to 20,000 ha in size. It is planning to provide this information to companies using wood products in Russia and Europe so that they can demand ancient forest-free products from their supplier. The decision of the Svetogorsk mill shows that it is possible to protect these forests and continue industrial production. In fact, there is in an urgent need to carry out such studies using GIS to give a graphical presentation to fast reducing forest areas of India and also for the industries to respect the needs of environment and understand the

importance of forest. Are industries in India ready to take initiatives on the lines similar to Svetogorsk?.