Gis Sem 8

Application of GIS/GPS/RS in Environmental Data management

GOAL/OBJECTIVE • •

The goal is to allow resource allocation and environmental management decisions to be based on up-to-date/accurate information. The objective of our department will be to provide the mechanisms that will allow sharing of information in a timely manner among other departments of the Ministry of Environment as well as government institutions concerned with environmental and natural resources issues.

It is expected in the future that: • • •

Staff capacity will be increased and institutions strengthened in order to make informed decisions regarding sustainable development; Information sharing and exchanging promotion policies will be established for concerned institutions; The availability and accessibility of environmental and natural resources data to national government agencies and international community will be enhanced.

Environmental Data/Information • •

Bio-physical data Socio-economic data

Core Dataset Preparation Requirement • • • • • • • • • • • • •

Infrastructure; Soil Class; Vegetation Cover; Air quality Measurement; Demography; Climate Zonation; Administrative Boundaries; Topography; Land Use; Geology; Major Harvesting Activities; Water Quality Measurements; Soil Analysis Samples.

Meta-Database Preparation Metadata refers to "data which describes data," including, data catalogues, data dictionaries, indexes and the like. Each map should contain the reference year for the data types used, and the source of the data. There are two forms of data representation: i) State of Data that shows the state of a resource at one point in time, ii) Change in State that shows the change in state between two different times. As data sets for different time periods are acquired, Change of State mapping should be used to enable the user to more readily assess environmental changes. On-going activities are concentrated in: • • •

Preparation, storage, maintenance, and updating of data/information, including data catalogues, data dictionaries, indexes and the like; Capacity building in GIS and database management (including training, institutional strengthening and training of trainers); and Developing a national GIS database for Environmental Assessment and State of the Environment (SoE) Reporting.

APPLICATION OF GIS IN SUPPORT OF THE COASTAL AND MARINE ENVIRONMENTAL PLANNING AND MANAGEMENT Summary of Key Issues for Coastal and Marine Waters • • • • • •

• •

Seven major issues in the coastal and marine areas of Cambodia were identified: An almost complete lack of basic infrastructure for coastal and marine environmental management; The absence of reliable data and base information from which to prepare and implement plans and projects; Severely degraded physical infrastructure: roads; irrigation systems; coastal protection dikes, etc. Continuing lack of security in some of the coastal areas; A severely inadequate legal and policy framework for coastal and marine environmental management in particular, and for government and public administration in general; Pervasive poverty; and Degradation of productive natural resources, primarily fisheries and forestry resources (including those contained in protected areas) that

results primarily from poverty, a lack of physical infrastructure; and lack of security. Changes in Condition and Protection of Existing Coastal and Marine Protected Areas Name

Area (ha)

Province

Phnom Bokor

140,000

Kampot

Kep

5,000

Kep

Ream

15,000

Kg. Som

Botum Sakor

171,250

Koh Kong

23,750

Koh Kong

27,700

Koh Kong

National Parks

Wildlife Sanctuaries Peam Krasaop Multiple Use Management Areas Dong Peng Status of Coastal Environment The principal indicators of the status of the coastal environment are suggested for the initial periods for information reporting. For all indicators it is important to know where they occur: Environmental Effects Changes in biodiversity, with particular reference to: Birds Marine fish and organisms Marine mammals Marine reptiles and amphibians Changes in ecosystem health, with particular reference to: Coral reefs Mangroves Sea-grass beds Changes in endangered/rare species, with particular reference to:

Birds Marine fish and organisms Marine mammals Marine reptiles and amphibians Changes in water quality, with particular reference to: Nutrients Heavy metals Sediment loads entering the sea Pesticides and fertilizers Organic compounds Changes in coastal land use, with particular reference to land use change and loss of vegetation cover Resource Management • • • • • • •

Changes in volume and species of the fish catch Changes in fishing effort and methods Changes in employment in fishing Changes in aquaculture production from marine and brackish water sources Changes in size of protected marine and shoreline area Trade in marine species Occurrence of spills

Socio-economic • • • • • • •

Changes in industrial activity Changes in industrial waste loads Changes in population, income and employment Changes in human waste loads Changes in agricultural activity Changes in agricultural waste loads Changes in land use

Coastal Map Development • •

When considering developing a GIS-based macro scale reporting system, one must take into account the following: Costs associated with data acquisition, data preparation, and georeferencing, not to mention updating of that data on a periodic basis;

• •

•

Data available or available in a format that meets geographical reporting are not considered all important; A single agency never has all data that would be needed in a georeferenced coastal and marine environmental information management system; and Presentation of information in map form requires a basis for locating the information in the map. Concepts used in a textural document do not readily transfer to a map in a GIS.

Base Map Units Where specific boundaries or locations for data are not known the data or the data concerns input to the coastal zone from inland (e.g. Sediment loads of rivers) the data should be referenced to the smallest coastal administrative unit (e.g. district, county, province). The preferred "Map Reporting Unit" or MRU is for the district level. However, recognizing the practicality of scale and the logistical implications to establish a uniform level of data for reporting, it is recommended that the initial mapping system focus at the provincial level. This map unit structure will allow a convenient way to provide a local, provincial and national level of reporting with defined geographic referencing. Reference Data Below is a guide to attribute data reporting. Based Attribute Data Reference List Sample Stream-flow: Where possible list as many of the following parameters as feasible: • • • • • • • •

Name of monitoring station (more than one station may be entered) Year or record Drainage area (km²) Mean monthly flows (cubic meters per second) Peak flow (cubic meters per second) Date of peak flow Lowest flow in record (cubic meters per second) Date of lowest flow

Employment structure Numbers or percentage of people employed in main sectors (e.g. Tourism, Manufacturing, Agriculture, Forestry, Commerce, Retail, Oil industry, Fishing, Aquaculture, others) for the MRU

Endangered/rare species: List marine species for the MRU (as far as this is known): • • • • • •

mammals birds fish shellfish amphibians/reptiles Note endangered or rare species found in map unit (if none state none)

Waste handling facilities: - For solid waste - For sewerage - For industrial waste, etc.

A COASTAL AND MARINE ENVIRONMENTAL MANAGEMENT INFORMATION SYSTEM Every data set must have a metadata code, which identifies the source of the data, when the data was collected or measured or monitored or surveyed by the source organization. This is very important for all data and especially important for ecosystem and habitat data. Every map legend should have a list of metadata for the base map and reference codes to an appendix for the attribute and map data sources and data of collection. Generalized map series consist of map data and map types E.g. For Water Quality Map Data This data refers to monitoring stations within the coastal waters. The data to be shown is intended to reflect the total pollution load to the sea at the location of a monitoring station(s). The first priority would be to show the location of each monitoring site in each MRU with the parameters shown for each map below. Map Type As this data is point data point displays will be used. MAP 1: Total nutrients (sum of nitrates, nitrites and phosphates in ppm) MAP 2: Total organic compounds MAP 3: BOD COD MAP 4: Concentration of the five highest heavy metal concentrations. Select the highest of the above values for each of the following periods: - January to March inclusive

- April to June inclusive - July to September inclusive - October to December inclusive Coastal and Marine Environmental Database Development Databases used for environmental data at the Ministry of Environment are evaluated to determine whether an existing database design and/or their data could be readily used for a GIS-based coastal and marine environmental information system. While many fields are useful, the database structures must be altered to enable geo-referencing before they can be used in GIS. Database structure should be compatible whenever possible to facilitate the development of integrated data management systems (Biophysical and socio-economic databases should use Microsoft Excel, Microsoft Access, FoxPro, for example).

CONCLUSION Due to the fact that geographic information system (GIS) and remote sensing (RS) technology is very important tool for planning, management and monitoring of natural resources, the Royal Government of the Kingdom of Cambodia in pursuing its objectives of rehabilitating the country’s economy and alleviating the people’s poverty is keen to develop an integrated information system. In this matter, GIS and remote sensing technology is considered a particularly important tool for the Ministry of Environment which is charged with managing, improving and preserving the country’s environment. In order to meet the requirements for the successful application of remote sensing and GIS in sustainable development, not only should the staff of the relevant institutions be well trained, but Cambodia still lacks experience in and facilities for producing maps. Cambodia therefore continues to seek technical assistance in increasing staff capacity, institution building and other key areas to enhance its ability to improve the availability and accessibility of environment and natural resources data, and establish an information exchange network and compatible data set for environmental planning and management. The Application Study of RS and GIS Technology in Environmental Remote Sensing Investigation of She Fu -- Dongsheng Coal fields In China Lei Jiannian Gao Huijun Qiu Shaopeng Zhang Guangchao Zhang Feng (Remote Sensing Application Institute of ARSC,Xi'AN 710054 China)

Abstract The regional invesgation and evaluation quality becomes global focus with the conception of continued development accepted universally .In 1998,Remote Sensing Application Institute of

ARSC completed the investigation of ShenFu - Dongsheng coal fields which was implemented by means of remote sensing and GIS technique .The project provided the scientific evidence as a successful attempt regarding the policy of both reasonable coal exploitation and environment protection. The investigation of environment quality was owed to TM image interpretation combining with field investigation and ground reflective wave test. The analysis of environment quality was based upon multi-period remote sensing interpretation and routine inspection data. Remote sensing information and routine information were conducted by quality synthetic evaluation system attributing to GIS Application which enabled coal field environment quality synthetic evaluation to come true and the proposals to put forward concerning coal exploitation environment protection and management. TM5,4,3 wave synthetic image obtained in 1987 and 1996 with the enlargement at the scale of 1:1000 by means of mosaic proof -reading benefits the field investigation assuring indoor mapping with accuracy. Not only does the image provide the vision of landforms, vegetation, earth's surface ingredients, coal mining, transport and other large-scale engineering projects but also it makes a good response to air pollution aroused by some polluting sources as small coal pit, coke pit without exception of building, power, and iron-smelting enterprises. The investigation of environment quality begins with primary interpretation. It conducts the investigation in typical area as the first procedure and establishes the interpretation sign as an environment factor on TM image. Then the investigation perfect the interpretation sign expanding to the whole area.6 maps of environment quality covering 33,000 km2 respectively at the scale of 1:100,000 are implemented by environmental interpretations and investigation with the application of MICROSTATION geologic mapping software pack. The spatial distributing features were accurately represented by the forms of vegetation desertization,soil erosion,surface water pollution,air pollution,solid waste and other elements. Through the interpretation of multi-period remote sensing image and the comparative analysis of routine data,the environment factor develops as a trend of less soil desertization after 1987. Severe and average desertization reduced 28.5%. Soil erosion remarkably lessened. In 80's,river silt discharge decreased 56.18% compared with 50's and 60's. Environment quality synthetic evaluation system is based upon layer analysis theory raised by American operational researcher T.L.santy and GIS technology. The evaluation cannot be conducted by manual work because of its large range and huge data. However,it is the compute with ARC/INFO3.4 software that devotes to the establishment of managing system ranging from digital mapping of environment factor,the calculation of weight in different layers,imitation and environment factor data synthetic processing to the display of synthetic evaluation achievements. The total 24 maps were completed in the investigation. The coal field environment quality is classified as the degree of 3 class 9 in the first ration accordingly. They represent the characteristics of environment quality spatial distribution. These characteristics indicate that the environment quality in whole area is poor,the good quality part merely takes 16.61% of whole area; the framework of environment quality manifests south superior to morth and west superior to east. As coal mining construction is developing rapidly ,environment quality has worsened severely in middle are.

Estimation of Emission changes about green house effect gasses by landcover changes using remote sensing and GIS in Sumatra Island, Indonesia

Introduction Deforestation, conversion of forest into non-forest land cover, especially in tropical forest area has been an international concern. It was estimated that tropical forest was deforested by 6 – 16.8 million hectares per year (Grainger, 1993; Barbier et. all., 1991; Myers, 1994). Since forest hold the most carbon in terrestrial ecosystem, such changes give significant impact on the net increase of atmospheric carbon. In addition, land cover changes results in greenhouse effect gases (GHG:CO2, N2O, CH4) dynamics. GHG emission of soil surface is influenced by several factors such as land cover types, climatic factors, biological factors and physical environment factors. Emission measurements usually are conducted at a point location, therefore problem arise when emission estimation will be used for scaling up into a broader areas. The research aimed at the development of Spatial database to assist the regional estimation of aboveground carbon stock loss and soil surface GHG emission changes caused by land cover changes using GIS and Remote Sensing. As a case study land cover change between 1992 and 1995 of Pasir mayang area and 1986 to 1992 of Jambi Province, Indonesia will be evaluated. Research method Development GHG database at Pasir mayang area Using LANDSAT/TM data, we determined land cover of Pasir mayang area in 1992 and 1995. Pasir mayang area is 33km (east to west) and 20km (north to south) and is listed as Fig. 1. In the area, estimation of total above ground carbon stock is calculated by multiplying the value of ha by total area of each land cover. The same method was applied for calculating the total emission of GHG. Development GHG database at Jambi Province The study area is located in Jambi Province, between 0° 45’ and 20° 45’ latitude south; 101° and 104° 55’ longitude east (Fig. 1). The total area is 48,715 sq. km. It ranges from swampy coastal plains in the east to more than 1,000 meters above the sea level in the western part. According to statistical data, in 1995 the population of Jambi was 2.18 Million and has increased more than two fold compared to 1971 data (Bappeda Jambi 1995 and 1988). The research is initiated by the development land cover maps, and followed field measurement. Spatial database (land cover) construction was conducted in Forest Ecology and Remote Sensing Lab. of Regional Center for Tropical Biology (BIOTROP), and Remote Sensing Research Unit of National Institute of Agro-environmental Sciences, Japan. Field measurements (above ground biomass, and GHG flux) were conducted by BIOTROP, Impact Center of South East Asia and National Institute of Agro-environmental Sciences, Japan. Land cover map construction Spatial database of Land cover were developed based on land cover maps in 1986 and 1992 at scale 1 : 250,000 published by BIOTROP. These two maps were made based on visual interpretation of LANDSAT and SPOT. Bio-mass estimation (Aboveground carbon stock) Weight of sample components of the tree i.e. timbers, stems, branches, twigs, leaves and roots of primary forest, secondary forest and logged over forest were estimated by using equation developed by Kira and Iwata (1989). Tree biomass for one hectare plot was calculated by multiplying biomass of each tree with the number of tree per hectare. To get aboveground carbon stock the biomass weight was multiplied by factor of 0.5. Soil GHG flux measurement Flux of carbon dioxide, nitrous oxide and methane of soil surface were measured at various land cover types in order to obtain the estimates of GHG emissions by the ground survey group of our project.

Fig. 1 Pasir mayang area and Jambi Province

Result and Discussion Developed GHG database at Pasir mayang area The land cover maps of Pasir mayang area in 1993 and 1995 were indicated as Fig. 2. Estimation of above ground carbon stock is calculated by multiplying the unite value by total area of each land cover using the land cover maps of Pasir mayang area in 1993 and 1995. The same method was applied for calculating the total emission of GHG. These results are shown as table 1 and table 2. Logged forest was the most dominant land-cover in Pasir mayang, followed by rubber and secondary vegetation (rubber jungle), fallow land (bush/shrubs), grassland and bare land (clear cut area) (Table 1).

Fig. 2 Land cover maps of Pasir mayang area and changes in 1993 and 1995

Table 1 Land cover and above ground carbon stock changes in Pasir Mayang between 1993 – 1995 Land cover

Carbon stock per ha (ton/ha)

Area in 1993 (ha)

Total above ground Carbon stock in 1993 (ton)

Area in 1995 (ha)

Total above ground Carbon stock in 1995 (ton)

Logged forest

155.2

68,529.5

10,634,270.75

63,235.5

9,812,758.4

Bush/Shrubs

15.0

10,224.8

153,372.0

10,450.3

156,754.5

Rubber and sec. Vegetation.

35.5

6,541.8

232,233.9

11,414.3

405,207.7

Grass land

6.0

3,156.5

18,939.0

3,468.3

20,809.8

Bare land

0.0

Total in 1993

986.3

0

870.5

0

89,438.9

11,038,815.7

89,438.9

10,395,530.4

Note: Above ground biomass was estimate using allometric equation, conducted by Biotrop

Table 2 Soil green house gas emission changes of Pasir Mayang between 1993 and 1995 Land-cover

Carbon dioxide (ton/hour)

Nitrous oxide (kg/hour)

Methane (kg/hour)

1993

1995

1993

1995

1993

1995

Logged forest

241.4

222.8

7.343

6.776

-9819.3

-90607.7

Fallow land

59.4

60.7

2.041

2.086

-4.5

-4.6

Rubber and sec. vegetation

31.0

54.0

1.328

2.317

-1.3

-2.2

Grassland

19.1

20.9

0.347

0.381

0.0

0.0

Bareland

6.1

5.4

0.326

0.117

-73.2

-64.6

Total

357.0

363.8

11.194

11.679

-9898.3

-9132.1

Note : Calculation was made based on mean value of 10 months (10 time) measurement conducted The measurements were made in Jan., Feb., Mar., June, July, Aug., Sep., Oct., Nov. and Dec

Between 1993-1995, logged forest area decreased of about 5,300 ha, while rubber jungle and fallow land increased 4,872 ha and 225 ha, respectively. Due to this, above ground carbon stock of the area decreased from 11.1 million ton to 10.4 million ton, or have loss of about 0.7 million ton. Table 2 summarized the GHG emission of soil in 1993 and 1995. Comparison of the total GHG flux of the two period time studies based on land-cover have showed that there was an increase flux of nitrous oxide and carbon dioxide and absorption reduction of methane. Developed GHG database at Jambi Province area in Sumatra Land cover changes Land cover patterns in 1986 and 1992 is presented in Table 3. Proportion of primary forest decreased from 33.9% in 1986 to 25.8% in 1992. Fallow lands (shrubs) decreased from 19.3% to 12.5% in 1992. Further analysis of each land cover types is presented in Fig. 3. It shows that about 24% of primary forest area were converted into logged forest, shrubs (fallow lands), cash crop plantation, cultivated and settlement areas. About 30% of logged forest were converted into shrubs, cash crop plantation, a mixture of cultivated and settlements. Table 3 Land cover and above ground bio-mass changes between 1986 and 1992 1986

1992

LAND COVER

Area (sq. km)

% of total area

Total carbon (106 ton)

Area (sq. km)

% of total area

Total carbon (106 ton)

Primary forest

16521.20

33.91

416.89

12569.86

25.80

317.19

Secondary forest

0.00

0.00

0.00

1274.34

2.62

7.40

Logged forest

10022.39

20.57

155.53

12448.65

25.55

193.18

Fallow land

9401.68

19.30

14.10

6072.66

12.47

9.11

Grassland

535.99

1.10

0.32

523.19

1.07

0.31

Bare land

3.67

0.01

0.00

3.67

0.01

0.00

Cash crops plantation

912.78

1.87

2.56

3303.17

6.78

9.25

Paddy field

1002.78

2.06

0.75

649.16

1.33

0.49

Upland field

0.00

0.00

0.00

235.84

0.48

0.18

Cultivated lands and Secondary Vegetation

7036.29

14.44

24.97

7933.39

16.29

28.16

Cultivated lands and Settlement

1339.84

2.75

0.50

1630.68

3.35

0.61

Urban area

0.00

0.00

0.00

132.17

0.27

0.00

Water surface/lake

42.41

0.09

0.00

42.27

0.09

0.00

No data

1896.60

3.89

-

1896.6

3.89

-

Total

48715.65

100.00

615.62

48715.65

100.00

565.88

Note: Above ground biomass was estimate using allometric equation, conducted by BIOTROP

Fig. 3 Land cover Changes from 1986 to 1992

Aboveground carbon stock changes Aboveground carbon content estimation of each land cover was calculated by multiplying the area of each land cover with carbon stock per unit area. Table 3 has showed the changes of aboveground carbon due to land cover changes. Total above ground carbon stock decrease from 6.16 x 108 ton in 1986 to 5.66 x 108 ton in 1992 or loss of about 0.50 x 108 ton within 6 years equal to 8.3 millions ton per year. The loss of aboveground carbon was mainly came from primary forest conversion. IPCC have divided the loss of aboveground carbon content into on site and offsite release. These two categories were classified further into direct burning (fuel wood and slash and burn agricultural) and decomposition process release of unburned biomass (Houhton et.al., 1996). Thus the amount of carbon and GHG released to the atmosphere were depended on these processes. Estimation of the amount carbon and GHG release need yearly basis time series of spatial data and the information on commercial wood and fuel wood harvest, and burning efficiency data of each land cover type. Soil Greenhouse gas emission changes GHG flux of soil varies depend on type the site condition and season. The comparisons below were made based on flux measurement conducted in November 1997 in several sites of Jambi Province. The calculation results of total flux based on 1986 and 1992 land cover data for major land cover presented in Table 4. Comparison of the total GHG flux of the two periods of time studies could not be performed since there are still no information on GHG flux of soil surface under cash crops plantation and secondary forest. However, it seems that the conversion of natural forest will cause on the decrease of methane gas absorption and induce the increase of nitrous oxide and carbon dioxide flux emission. Landuse/Land cover class

Table 4 Greenhouse gases flux changes between 1986 and 1992 Total flux of CO2 (mg/day) Total flux of N2O (mg/day) Total flux of CH4 (mg/day) 1986

1992

1986

1992

1986

1992

Primary forest

1.69x1014

1.28x1014

3.2x109

2.43x109

-1.45x1010

-1.10x1010

Secondary forest

0.00

5.75x1012

0.00

7.03x108

0.00

-3.36x109

Logged forest

1.24x1014

1.54x1014

2.48 x109

3.08x109

-1.04x1010

-1.29x1010

Fallow land

1.31x1014

8.46x1013

4.51 x109

2.91x109

-1.00x1010

-6.47x109

7.76x1012

7.58x1012

1.42 x108

1.38x108

0.00

0.00

10

10

Grassland Bare land Cash crops plantation* Paddy field

2.44x10

2.44x10

5.67x10

5

5

5.67x10

-6.25x10

5

-6.25x105

1.87x1013

6.75x1013

4.68x108

1.69x109

0.00

0.00

-

-

9.63x107

6.23x107

7.22x108

4.67x108

Upland field Cultivated land and Secondary vegetation Cultivated lands and Settlement Total flux

0.00

2.41x1012

0.00

4.03x107

0.00

0.00

8.00x1013

9.02x1013

3.43 x109

3.87x109

-3.33x109

-3.75x109

6.85x1012

8.33x1012

1.16 x108

1.41x108

0.00

0.00

536.5x1012

548.3x1012

14.4 x109

15.1x109

-37.5x109

-37.0x109

Note : calculations were made based on field measurement in November 1997, conducted by IC-SEA * : assumed flux of CH4 and CO2 of cash crops plantation are equal upland, while flux of N2O is equal to three times of upland field flux due to intensive fertilizer application.

Spectral Model of Water Quality parameters Yin Qui, Shu Xiaozhou, Kuang Dingbo Shanghai Institue of Technology Physics, Chinese Academy of sciences

Lake Taihu, in Changjiang Delta Area, is the third largest plain fresh lake of China. In recent years, the water quality of Taibu descends year by year. Especially, the north area of Taihu is in obvious nutritive state. In this paper, according to the field measurement and the Landsat TM Data, the relations between water surface reflected spectra and water pollutant concentration and the change of water quality from 1986 to 1998 about Taihu are studied. Two field experiments are made. The first is in Nov. 1997 with 21 measurement points distributed in whole Taihu, and the second is in Aug. 1998 with 13 measurement points distributed in the north areea of Taihu. Two water quality parameters, chlorophyll-a (Chl) and suspended substance (SS), are analyzed. According, to the ratio of reflected spectra from water surface and that from a standard white plate measured by GER-1500, an ultra-spectra instrument with 346 channels from 350nm wavelength to 900nm wavelength, the reflectivity (diffusion reflectivity) spectra of water surface are determined. BY statistical regression, the ultra and multi-spectral models about water quality parameters are established for different pollutant concentration ranges. The relation model between reflectivity and Chl and SS is established for every GER -1500 channel. The relation models between the reflectivity of one channel or the reflectivity differences/ratio of two channels and the water quality parameters are established for remote sensing channels of several existing satellite and a set of hypothetical water quality remote sensing channels. The ultra-Spectral Model of Water Quality Parameters for GER-1500 Channels. The scattering effect of chlorophyll can be expressed by the linear item Chl. The absorption effect of chlorophyll can be expressed by the linear item In(Chl+1). The linear item SS(for SS < 100mg/L) or the loglinear item In(SS+1) can express the effect of suspended substance. The scattering effect of chlorophyll is mainly reflected at wavelengths larger than 740nm. The absorption effect of chlorophyll is mainly reflected at 400~520nm and 575~690nm wavelengths. The scattering effect of suspended substance is reflected at 680 820nm wavelengths most obviously. If S<100mg/L, the scattering of S will affect of Chlorophyll will also reflected at wavelengths in the vicinity of 350nm and 550nm. The Multi-Spectral Model of Water Quality Parameters for TM Channels. For samples of Chl=24~500ug/L and S=0~100mh/L, Scan not be retrieved by the reflectivity, reflectivity difference or reflectivity ratio of any TM channels. If the single channel model is applied, TM4 is the most suitable channel for Chl retrieving and the corresponding model, TM4 =C0+C1*Chl with a related coefficient 0.86. if the double channel model is applied, every combination of TM channels except the combination of TM1 and TM3 (which are the absorption channels of Ch1) can reflect the effect of Ch1, in which the combinations of TM4 and TM3, TM2 or TM1 have relatively good correlation with Ch1. The related coefficients of TM4 - (TM1, TM2 or TM3)= C0+C1*Ch1 are not smaller than 0.85 and the related coefficients of TM4/ (TM1, TM2 or TM3)= C0+C1*Chl are not smaller than 0.80. For samples of Ch1=0~24ug/L and SS=0~200nm/L, Ch1 can not be retrieved by any TM channels. TM4 and TM3 have log-linear correlations with S to a

certain extent, a and the related coefficients are 0.57 and 0.49 respectively. Some double channel combinations can reflect the effect Ch1. the combination of the absorption channels of Ch1, TM1 and TM3, can retrieve SS fairly good. The related coefficients of TM3-TM1=Co+C1*In(SS+1) is 0.85 and the related coefficient of TM3/TM1=Co+C1*in(Ch1+1)+C2*In(SS+1) is 0.90 with FCh1=10.45 and FSS=47.20. For samples of Ch1=0~500ug/L and SS=0~200mg/L, the reflectivity of every TM channels are related with Ch1 and SS, in which the relation of reflectivity with S has a long -linear form. The combination of the near infrared channel TM4 and the visible channel, TM1 TM2 or TM3, can filter the effect of SS and retrieve Ch1 fairly good with a related coefficients not smaller than 0.84. The combination of the green channel TM2 and the absorption channel of Ch1, TM1 or TM3 is related with Ch1. In addition, the combination of TM2 and TM3 is related with SS. the difference of the absorption channels of Ch1, TM3 and TM1, can filter the effect of Ch1 and reflect the effect of SS. The data retrieving steps from Landsat TM data to water quality parameters are (1). The change of grey frequency (f) with grey (N) is analyzed for every TM channels. If d2f/d2N attains maximum and when the grey of the reflectivity of atmosphere layer to the direct sunlight at the top of atmosphere (Rsun) is set to 1;(2). The relations between the direct transmittance, the diffusion transmittance and the diffuse reflectivity of atmosphere layer at different directions (up and down) and Rsun are established by an atmospheric Radiative transfer theory, which is used to determine the reflectivity of different TM channels at water surface from TM data; (3). The concentration of Ch1 and that of S are retrieved by the reflectivity about channel TM1 TM3 and TM4 at water surface and the multi-spectral model of water quality with TM4/TM3=C0+C1*Ch1 and TM3/TM1=C0+C1*S; (4). The change of concentration frequency (f) with concentration (N) for Vh1 and that for S are analyzed respectively. If d2f/d2N attains maximum and åNO "1% when N=J<0, the concentration is adjusted from N to N+J. As an example the change of water quality about Taihu is determined by the Landsat TM data on 1986.07.25, 1997.06.29, 1997.05.04 and 1998.08.11.