Gis Sem 3

Analysis and estimation of deforestation using satellite imagery and GIS Introduction Forests amongst other natural resources have been degraded during the last decades continuously. The following factors are the main causes for such degradation:    

Changing of landuse from forest into pasture, agriculture and urban, as a result of population growth and general land scarcity, Cutting down of forests for timber production and wood industries, Use of the wood as a source of heat and energy in economically poor area, General degradation of forests caused by industrial growth, environmental pollution, increase in fuel consumption and global warming.

It is obvious that the most important factor in the destruction of forests is the human activities. For a better and sustainable management of such resources we need to know:   

The amount and location of deforestation, The rate and speed of deforestation, and Reasons and causes of deforestation,

The science and technologies of GIS and remote sensing could be a perfect tool for answering the above questions. Remote sensing can be the basis of fast and inexpensive data collection and the analytical capabilities of a GIS can be used for analyzing the types, location and rates of changes. The final aim in this research is to study and estimate the changes (damages) in the forest areas of Arasbaran, north-west of Iran and to evaluate the importance of different factors in this phenomenon. Study area The study area, called Arasbaran, is a mountainous area with elevation between 300 and 2700 meter above the see level. It is in the north of Azarbayejan province and very near to the Caspian sea. The area is located between 38º40´ and 39º09´ latitude and between 46º42´ and 47º03´ longitude. It covers a diversity of elevation, slope, population and landuse and includes a variety of see shore, rivers, etc. Beside the undamaged natural environment in some parts, a big part of the area has been changed by agriculture and grazing activities. This includes the thinly scattered woods, pastures, about 66 villages and differently cultivated areas. The data set used The satellite images used in this study are a Landsat TM image of 1987 and a Landsat ETM+ image of 2001 with a general resolution of about 28.5 meters. The old 1:50000 topographic maps of the Army’s Geographic Organization and the new 1:25000 digital topographic maps of the National Cartographic Center of Iran have been used for geo-referencing of the two images. The contour lines of the topographic maps are used for the generation of three maps. These maps represent values of elevation, slope and aspect in the area. Moreover, the location of the villages are extracted and used for generating the map of distance from the population centers.

Analysis and estimation of deforestation using satellite imagery and GIS Preprocessing and analysis of the satellite images Usually three types of errors occur when a satellite image is generated by the satellite sensor. The first is the sensor error. The second is the error created by the atmospheric parameters, which affect the amount of radiation received by the sensor. The third one is the geometric errors related to the curvature of the Earth surface, the Earth rotation, elevation differences, location and situation of the satellite etc. Therefore, These errors should be considered and managed before using the data:

1.Sensor errors The two images used were already corrected by their providers. Therefore, there was no need for any processing in this regard.

2.Radiometric correction The Earth atmosphere scatters the shorter wavelengths in a selective manner and this reduces the contrast of the image. The numerical value of each pixel in the image is not a realistic representation of the amount of radiation from the ground surface. These values are changed either by atmospheric absorption or by scattering throughout the atmosphere. In general, atmospheric errors are discussed in three parts: the Haze, Sunangle and Skylight errors. Atmospheric corrections are required in the following situations: 1.When we want to compare the images related to different times: When using methods such as image subtraction and image division for change detection, the effect of atmosphere on the two images related to different times are quite different. 2.When the ratio of two bands of an image is needed to be calculated, because the atmosphere has different effects on different wavelengths. 3.When we want to study spectral characteristics of different phenomena.

If we wanted to use the division or subtraction of images for determining the changes in forest landuse, then we would have to correct for the haze, sunangle and skylight errors. In our approach we compare the results of the landuse classification maps extracted from the two images. The classification of landuse can be done better and more accurate with the raw (unprocessed) images. Therefore, there was not any need for the above corrections in our images.

2.Geometric corrections The process and analysis of multi-temporal data can be done only when they are georeferenced similarly, or in another words, when they are geo-referenced to each other. Our images had to be geo-referenced to each other with an accuracy of one pixel. Otherwise, the error coming from different coordinates for similar objects in the two images can be wrongly accepted as a landuse change. In other words, with an inaccurate geo-referencing, a pixel might refer to different objects in the two images and be considered as a landuse or land cover change, which is not realistic. To prevent such a problem, in comparison of multi-temporal images, the best solution is to georeference one of the images using the available topographic maps and then geo-referencing the other images according to the first one, i.e. using image-to-image registration. In photo/image registration (geo-referencing), the most important task is the proper selection of control points, especially when there is a long time period between the map and the image. Usually, man-made features such as buildings and road intersections are a better choice for control points than the natural ones. The reasons are that they have sharper boundaries and more contrast with their surrounding. Besides, they are geometrically more stable than features such as river/stream junctions. The general rules are that we should try to select more stable features that have longer change periods and the more control points we select the more accurate our registration will be. We simply used the first order polynomial equations for geo-referencing of the images, which remove the errors related to the rotation and scaling of the image. These are: X = a0 + a1x + a2y Y = b0 + b1x + b2y

where x and y are the coordinates of a point in the first coordinate system and X and Y are its new coordinates in the new coordinate system. In this study, the ETM+ image of the year 2001 was first geo-referenced using the information in its header approximately. Then, it was geo-referenced accurately using the available 1:25000 digital maps and the digitized features of the 1:50000 maps of the area. The control points were selected using different color composites with band-combinations of 754, 432 and 543. Afterward, the TM image of 1987 was geo-referenced using the already registered TM image. For geo-referencing the 2001 image 18 control points were used initially. Every control point with an RMSE or residual error bigger than a pixel size was removed from the calculation and the process of registration was repeated with the rest of the control points. Finally, 10 points with the average error of 16.47 meters remained and were used for registration. For image-to-image registration of the 1987 image 20 control points were initially used. Finally, 6 points were removed and the image was geo-referenced using the remained 14 points with the RMSE of 18.92 meters. In view of the fact that our images are used for landuse classification, any change to the numeric value of the pixels will introduce some errors and has an undesirable effect on our classification. Therefore, to minimize this effect during geometric correction, the new values of pixels were generated using the nearest neighbor method. Assessment of deforestation by comparison of the classification maps One of the methods for change detection using satellite images is to compare the results of classification of the images. Two other methods are to calculate the division or subtraction of the two images. The main problem of these methods is that they can only define where some changes are happened. The advantage of the classified-map comparison method in to the other methods is that not only the location but also the nature and type of the changes will be determined. In other words we will define what landuse has been changed to what other. In this method, first, the images of different times are classified according to the purpose of change detection. Afterward, by overlaying the two classified images with a proper overlay condition, we can determine the location and amount of any changes we are interested in. Because our goal was to determine the deforestation, the only two classes that we considered are the forest and non-forest. The two images are classified using the Maximum-Likelihood method. By overlaying the results of classifications, the map of the occurred changes are resulted, as is shown in Figure 1. From this map, it can be realized how much of the forest have been damaged and where this has happened. In addition, the pattern and spatial distribution of the phenomenon is properly illustrated. Furthermore, it can be seen where the forest and non-forest classes have been stable and where new forest has been growing. Creation of the logistic regression model A regression model is a statistical model in which a relation between a phenomenon (a dependent variable) and some of its factors (some independent variables) will be defined based on some observations. These observations are in fact a set of values measured or observed for the dependent and independent variables. Having the model specified and calibrated, the unknown value of the phenomenon can be calculated and predicted on the basis of known values of its factors. Logistic regression models, a special type of regression models, are used when we want to study the probability of membership in two contradictory classes, such as a forest area being either stable or destroyed. It should be noted that logistic regression can be used to determine the

probability of any of the two possibilities (classes) identically. A logistic regression model is usually of the type: log it(p) = a +b1x1 + b2x2 + b3x3 , where Here, ‘p’ is the dependent variable and shows the probability of one of the two conditions. Dependent variables of ‘x1’, ‘x2’ and ‘x3’ represent the factors defining the phenomenon and ‘b1’, ‘b2’ and ‘b3’ are their coefficients: ‘a’ is the additive coefficient.

In this research, we selected a sampling set of about 5% of the pixels, from the two classes of stable forest and destroyed forests, i.e. forests that have remained forest and forests that have been changed or destroyed. The number of sample pixels is 5106 pixels in total. In these sample pixels, the parameters of elevation, slope, aspect and distance from villages are considered as independent variables and the stability of the forest as the dependent variable. As mentioned, we could use the forest destruction (deforestation) as the dependent variable and get exactly the same results. Independent variables are extracted from the relevant generated maps. The dependent variable, i.e. the forest stability, is represented by the two values of ‘0’ and ‘1’ for the sample pixels. ‘0’ represents the deforested areas and ‘1’ represents the stable forests. By introducing the sample data to the specified logistic regression model, in the first stage, the variable of distance from population centers entered to the model, improving the X2 parameter to the value 601.641. This parameter (called square of ‘chi’) is a measure for the goodness of the model; a low value for X2 means the model is suitable to the data. The effectiveness of the model in prediction of the phenomenon can be summarized in a simple table (Table 1). From the total 5106 sample pixels, 1256 pixel were changed (deforested) and 3850 pixels were unchanged forest. In every stage of the regression, all pixels are evaluated by the model and a value is predicted for each pixel. The number of both correctly and wrongly predicted pixels for each stage is shown in Table 1. In other words, in this table, the groups of deforested and unchanged forest pixels are compared with what is predicted for them by the model. From the 2nd and 3rd column of the table is clear that 12.18% of the changed (deforested) pixels and 92.47% of unchanged pixels are predicted correctly by the model. This means a total prediction-accuracy of 72.72%.

In the second stage, the elevation variable entered the model and changed the X2 parameter to the value 272.826. Having the two parameters of elevation and distance from villages together in the model, 18.55% of the changed (deforested) pixels and 93.82% of unchanged pixels were predicted correctly by the model. This stage showed a total accuracy of 75.30% in the prediction of pixels (Table 1). In the third stage, the aspect variable entered the model and caused a significant improvement in the X2 parameter, changing it into 92.681. The three parameters of elevation, distance from villages and aspect (aspect of slope) being incorporated in the model, 31.93% of the changed pixels and 93.90% of unchanged pixels were predicted correctly by the model. This resulted in a total accuracy of 78.65% in the prediction of pixels, as can be extracted from the last two columns of Table 1. Table 1 Prediction accuracy of the model in different stages of introducing variables Predicted as Predicted as changed unchanged (1st stage) (1st stage)

Predicted as Predicted as changed unchanged (2nd stage) (2nd stage)

Predicted as Predicted as changed unchanged (3rd stage) (3rd stage)

153

1103

233

1023

401

855

Unchanged 290 pixels

3560

238

3612

235

3615

Changed Pixels

After the third stage, no other independent variable could enter the model. This means that the only remained variable, i.e. slope, could not cause any significant improvement to the performance of the model and its suitability with the data. This can be either because of the irrelevance of this variable to the phenomenon, or because of its high correlation with other variables incorporated in the model. Table 2 shows the results of the calibration of the model. The model represents the phenomenon of forest-stability (as opposed to deforestation) on the basis of three factors of distance from population centers, elevation and aspect. The coefficients of these factors in the resulted model are presented in Table 2. Table 2 Coefficients of variables in the resulted regression model Factor (variable) name

Coefficient in the logistic regression model

Distance from population centers 0.0010 Elevation

0.0068

Aspect

0.0027

Constant value

- 9.8675

Conclusions and future work The following remarks and recommendations can be concluded from this study: 

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In analysis and comparison of images of different times, the error (accuracy) of georeferencing for the images should be smaller than a pixel dimension. Otherwise, the difference in the geometry and location of any feature in the two images will result in the acceptance of an unrealistic change. In this research, we first needed to classify the satellite images. The changes introduced to the pixel-values, by interpolation and during geometric correction, have an undesirable

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effect on the result of classification. To moderate this effect, the new values of pixels can be generated using the nearest neighbor method. As was expected before the research, by moving away from the population centers the stability of forests increase. This is because, in the vicinity of villages, the forests are cut down mainly with the intention of using the land for agriculture and grazing and using the wood as fuel. In areas with higher elevation forest is more stable. This is partly because in high areas the environment in general is cleaner and more intact. The higher the area, the less suitable it is for agriculture and the more difficult it is to go. The slope aspect has an important role in deforestation in this area. The inclines and hills toward south get more sunlight and therefore are more suitable for agriculture. On the other hand, the east and north directions are enjoying the humidity coming from the Caspian Sea. From the beginning, we were aware of the possible dependency among the introduced factors, but the types of dependencies were not clear. It was proved that the slope has a high correlation with other factors, mainly with distance from villages and elevation. Therefore, the importance of comprehensive studies about factors affecting a phenomenon before modeling it has been proved. There are many other factors that might be relevant to deforestation and are not covered in this study. Examples of such factors are soil type, distance from the roads and population of the villages. In studies related to landuse change between years, effort should be made to use the images of the same season and even the same month. When images are from the same month or season, the changes detected from them are more realistic and reliable.

GIS and Remote Sensing for Natural Resource Survey and Management Abstract: The socio-economic development of any country is based on land resources and water resources. Due to increase in population, these resources are over stretched often leading to resource depletion. There is need to prudently manage these delicate resources. Remote Sensing and GIS techniques can be applied effective measure to generate data and information for sustainable development. After more than 25 years of satellite-based land remote sensing experimentation and development, these technologies reached almost all sectors of Earth science application. The use of remote sensing data and derivative information has ever promise of entering into mainstream of governing at local and regional level. Global Scan Technologies, Dubai implements the latest advent in spatial mapping technologies for natural resource survey and management and have carried out various project for UAE Government Organisation. The application includes : 

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Landuse / Landcover study- Visual and digital interpretation of satellite imageries are implemented to prepare pre-field map based on spectral/tone/texture homogeneity. Prefield interpreted map and digitally enhanced satellite data is used on the ground to identify different land use and land cover classes and to generate geodatabase for the land use and land cover. Vegetation Mapping- The classification system is open ended and is based on globally followed principles of vegetation classification using Climate/ Physiognomy / floristics. Based on the spatial extent, separability using remote sensing and ground information on vegetation distribution a geodatabase has been generated for Vegetation theme. Soil Mapping - The satellite data is interpreted based on photo-elements like tone, texture, size, shape, pattern, aspect, association etc. The first step in image-interpretation is the delineation of landscape units. The discrimination of landscape units and soil mapping units is based on lithology, relief, drainage pattern, natural vegetation, and

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sometimes land use along with the associated image elements . Field Survey is executed proceeding the preliminary interpretation of satellite imagery. The soil mapping units is evaluated for different land capability classes following USDA land capability classification based on soil, topography etc., limitations Geology and Hydrogeology mapping- The first step in generation of Geology and Hydrogeology Map is the Preliminary interpretation of satellite imageries for demarcation of Lithological boundaries, lineament ,other structural feature interpretation and characterization of various geomorphic units.

Extensive field survey has been taken to verify the pre-field interpretation data and finally the lithology, geomorphology and structural maps are integrated to finalize the map and to generate Geodatabase for Geology and Hydrogeology Themes. Introduction : Global Scan Technologies, Dubai is implementing the latest and cost effective solution for Thematic Mapping in United Arab Emirates and Worldwide. The thematic mapping services includes Geology, Geomorphology, Hydrogeology, Vegetation, Soil and Land use Land cover studies. The following article enumerates such a study carried out for the part of Middle East Region. A brief methodology for execution of this project is explained as follows: Methodology Input data The satellite data of the study area are procured from IRS-P6, LISS-III & LISS-IV and has been used for Geology, Geomorphological, Soil, Vegetation and Land use Land cover studies. Published soil maps, topographic maps, climatic data etc. are also collected and used as collateral data. Data Processing The IRS P6 satellite data were geo-referenced and suitable Image enhancements are applied to facilitate the delineation and interpretation of different thematic information. Data Interpretation Visual and digital interpretation methods were used to prepare pre-field interpreted map. The satellite data is interpreted based on photo elements like tone, texture, size, shape, pattern, aspect, association etc. These pre-field interpreted maps and digitally enhanced satellite data are used on the ground to identify different elements of various themes. Field Verification and Data Collection Suitable field sampling designs in terms of line transects/ quadrants are used to assess the interpreted elements and relate with satellite data. The field data collections are aided by GPS in order to locate the ground verification points on the image and for further incorporation of details. For the all the sample collection and field points visited attribute information on vegetation, geomorphologic, soil and topographic parameters are also collected. The detailed soil-site study was undertaken in each soil-mapping unit by general traversing and by collecting surface soil, minipit and soil profile observations at intervals depending on soil variability The sample points were decided based on the geological / Geomorphological / soil heterogeneity mapped from the satellite data. Finalization of Maps Based on the pre-field interpretation, ground truth verification and available secondary information final maps were prepared in 1: 25000 scales. Towards this both visual and digital approaches are conjunctively used.

Project Flow Chart

Themes Specifications: Land use Land cover The land use and land cover map is prepared using RESOURCESAT LISS IV satellite data. The classification scheme was designed keeping in view of the management practices addressing each land use/ land cover parcel, amenability of these parcels for identification/mapping in LISS IV dataset. Under the Level-I classification, Built up, Cultivated areas, Woody vegetation, Grasslands, Wastelands, Wetlands and Water bodies were segregated. In addition subclasses of Level-I LULC classes observed based on spectral satellite data and were evaluated on the ground, to characterize them as information classes. All the LULC classes were visually interpreted based on tone/texture, contextual and ground information. The major information class in the study area is ‘built up areas’ was consisting of urban and rural fringe landscapes and the urban built up areas were dominated by residential, mixed residential and industrial areas. The rural landscape essentially consisted of settlements, camel camps and cultivated areas. Statistical data of community wise built up area was generated and analyzed.

Vegetation The vegetation cover map is generated using IRS Resourcesat LISS III & IV. The vegetation in the study area is regulated by desert climate, seasonality, physiographic, geomorphologic and soil regimes. The vegetation is broadly demarcated into natural and managed vegetation. The natural vegetation mainly consisted of formations of Mangroves, Prosopis, Leptadenia. The managed vegetation mainly consisting of avenue plantations, grasslands, lawns, golf courses and palm /mixed plantations. Phyto-sociological analysis was carried out after collecting sufficient number of sample data from the natural vegetated areas. The vegetation mainly mangroves, Prosopis, Laptadenia are further stratified into dense and open canopy density classes. Further different categories of vegetation under each of the community has been extracted and analyzed to understand the percentage of vegetation present to that of vacant land. Such information on spatial distribution in qualitative and quantitative terms would be useful in further exploring and analyzing the aspects of biodiversity and ecological conservation Soils The soil is mapped using remote sensing satellite data IRS- P6 LISS IV. The soils of the study area were classified upto series level and their association’s level as per the Keys to Soil Taxonomy (Soil survey staff, 2003). . Essentially soil survey is a study and mapping of soils in the field. It is the systematic examination, description, classification and mapping of soils of an area and it comprises of a group of interlinked operations involving      

Preliminary visual interpretation of satellite data Fieldwork to study important characteristics of soils and associated land characteristics such as landform, natural vegetation, slope etc. Laboratory analysis to support and supplement the field observations. Correlation and classification of soils into defined taxonomic units. Mapping of soils - that is establishing and drawing soil boundaries of different kinds of soils on standard geographical base map. Generation of Geo-database for Soil

Geology The geo-referenced satellite digital data was used to carry out ‘on screen’ vectorization of geological parameters. Basically three vector layers were generated in. The first vector consists of geological structure attributes with length based classification second vector consists of geomorphic attributes and the third vector consists of broad litho logical map. In the case of image processing, spatial and spectral domain enhancement was carried out using ENVI software. The following steps were involved:1. Satellite data has been be geo-referenced with the available map sheets. 2. LISS-3 / Liss-4 and AWIFS data were be acquired for the entire study area 3. LISS-3 was used for regional assessments and LISS-4 data was used for detail assessments. 4. These data sets were co registered with other collateral data sets by taking common Ground Control points (GCP). 5. The satellite data was enhanced both in spectral and spatial domain. 6. A optimised image was generated for visual / Onscreen interpretation. 7. The existing geological map was not available for the area and hence using geomorphic analysis, field and published literature a broad level lithological map was prepared. 8. The geological structure map was prepared with mainly on type of lineament with emphasis on length, Faults and thrusts

9. The geomorphological map was prepared with emphasize on genetic classification of landforms. The major group are coastal landforms, aeolian landforms, and structural landforms. 10. A pre-field map was prepared using satellite data 11. Ground validation was carried out with emphasis on selective ground checks 12. The ground observation was incorporated at appropriate places to finalize post field map 13. All the three themes have been integrated in GIS environment to generate hydrogeomorphology map. Conclusion The generated theme can be implemented for further planning of the urban and rural area .The action plan report can be created using the Geodata database and total decision support system can be developed to depict location and type of action / control measures recommended for sustainable development plan of Natural Resource. Zonal and Community wise Soil resource development plan, Water resource development plan, Vegetation resource development plan, Land use and Land cover plan can be incorporated using statistics of the personal Geodatase of the respective Theme.

A Comparative Analysis of Vegetation Indices: - A study on Mumbai city, India. In this study an attempt was made to determine the areas under vegetation in the city of Mumbai. The different vegetation indices (VI) RATIO, NDVI, PVI1, PVI3, WDVI were compared. The VI images were run through a Principal Component Analysis. The data used for the study was IRS 1C LISS 3 remote sensing data from December 2003. It was found that Sanjay Gandhi National Park shows high vegetation in all the indices. From the image histograms, NDVI was found to have a higher contrast. From the statistical data obtained after Principal Component Analysis, it was found that PVI1 has a high correlation with Component 1. PVI3 had a high correlation with Component 2. Two areas of Mumbai were studied in the VI images, IIT Powai Gymkhana and Kanheri caves. The VI values for dense vegetation cover and rock/soil and grass in these two areas were compared. It was found that NDVI and RATIO show a higher separation in values.

Remote Sensing protects ancient forests Svetetogorsk takes the lead 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?

Contamination of Urban India Environment by Hazardous Industries Abstract Rapid industrialization, urbanization and development of transport network have added impetus to economic development at the cost of environment. Although such development is integral to economic growth problem lies in their unfettered proliferation in India, leading to severe environmental degradation particularly since 1970. Metropolitan region often act as nodes for concentration of economic and political power resulting in rapid changes in land use / land cover in their neighborhood indicating environmental degradation. Sectoral shift in land use, i.e., from agriculture to urbanization or industrial use are significant indicating decimating role of agriculture in the region and hardship to rural people. Ill-planned industrialization has caused large-scale contamination of natural resources, viz., surface and ground water and soils. Scattered location of hazardous chemical industries in urban areas and meager availability of proper waste management system in Hyderabad, Banglaore, Chennai and Delhi, are primary cause of nonpoint source pollution in these urban centres. ARCGIS was used in tandem with satellite data (IRS - 1D - LISS III& PAN merged data) to map location of hazardous industries in these urban areas and estimate the spread and direction of flow of contaminants. The pattern and extent of contamination of soil and water was mapped and quantified to facilitate undertaking of remediation plans. Introduction Industrialization has provided livelihood and opportunities to millions in urban India. However, it has also brought in its wake problem of waste disposal, contamination of environment – air, soil, surface water bodies and ground water aquifer etc. which have resulted in contamination hazard imperiling human beings, livestock and plant life. Lack of proper planning in siting of industrial units, inadequate development of infrastructure, and lack of waste management facility etc., have precipitated this debacle, turning most of them into environmental flashpoints. Urgent measures for amelioration, waste management, recycling, waste minimisation, punitive action against defaulters etc., would facilitate halting of damage to ensure recovery (Biswas, 1997). To achieve this objective, an inventory of contaminated sites around hazardous chemical industries was prepared. Firstly, hazardous industries and their locations were listed. Subsequently, the study areas were prioritized based on extent of contamination. Later these sites were studied using satellite data from IRS –1D LISS-III & PAN for assessing change in NDVI. ARCGIS was employed to overlay and analyze soil, geology, drainage pattern, water bodies, ground water aquifers, slope, etc. to estimate pollution hazard and extent of contamination. This strategy facilitated the environmental audit besides assisting in physical and fiscal planning for initiating ameliorative measures. Industries producing inorganic chemicals, fertilizers, dyes, paints, pharmaceuticals and battery were identified as hazardous as their waste is non-degradable and tedious to recycle (GPCB & CPCB 1997). Concentration of these industries in various parts of India, were identified based on the lists provided by various State Pollution Control Board. The present study illustrates the methodology of environmental audit of four metropolitan areas in India – Hyderabad, Banglore, Chennai and Delhi using satellite data and GIS. Study Areas An area of 3062 km2 around Bangalore extending between 12° 55’ - 13° 15’ N and 77° 15’ - 77° 45’ E as depicted by SOI toposheet no. 57G/ 6, 8, 57H/5, 6, 10,12, 13 and 14 was studied. The region comprises of shallow gravelly soils with steep slope inducing severe erosion and also deep well-drained soils (100 cm) suitable for agriculture which makes it highly vulnerable to serious ground water contamination. Around Chennai over 3041 km2 extending from 12° 30’- 13° 15’ N and 79° 45’- 800 30’E (SOI toposheet no. 66C/4, 8, 66D/1,2 and 57P/13) was studied. The region has predominantly alluvial soils with large patches of red, black and laterite soil which are fertile

and highly valuable for paddy cultivation; chemical contamination of the region would have severe repercussions. An area of 2760 km2 lying between 77° 00’-77° 30’ E and 280 15’ – 29° 00’ N (SOI topo sheet no. 53H/1, 2, 3 and 6) around Delhi was studied. Alluvial soil and shallow water table owing to R. Yamuna make the region highly vulnerable to widespread chemical contamination. Around Hyderabad an area of 2934 km2 extending between 78° - 78° 45’ E and 17° 15’ - 17° 45’ N (SOI toposheet no. 56 K/2, 6, 7 and 11) was studied. The study area belongs to Deccan plateau and its undulating terrain with red and mixed red soils spotted with numerous water bodies is invaluable to the rural and urban areas and vulnerable to contamination hazard (Kausalya Ramachandran, 2001). Material & method Baseline information on land use / land cover of the regions for 1971, were generated from SOI toposheet and compared with IRS-1D LISS-III & PAN data of March 2001 using Land Use classification given by NRSA (1987). Land use and land cover maps of the four study areas were analyzed in ARCGIS and change detection indicating degradation of resource quality was performed. Using slope information the four study areas were delineated into watersheds for assessing flow-path of pollutants and spread of contamination in soil and surface water around the factory site. Normalized differential vegetation index (NDVI) was used to observe vigour of vegetative growth to correlate the impact of contamination. To assess water quality in various surface water bodies, characteristic reflectance curves were generated to establish a relationship between pollution status of a water body and its spectral digital values. Discussion Land Use alterations and Land Cover modifications Alteration and modification of land use and land cover are indicative of processes of change that may lead to positive impact on development or denote resource degradation. Study of these resources using satellite data on a temporal basis would yield information on impact trends. For an insight in this aspect, satellite data from IRS 1D (LISS - III & PAN) were interpreted with older sets of data from IRS –1A / 1B, LANDSAT –TM in conjunction with SOI toposheet to reveal trends in change. Based on this analysis it was found that Hyderabad metropolitan region had changed drastically. While in 1971, agriculture was dominant accounting for 62.6 % area with open scrub, settlement and water bodies accounting for 15, 5.7 and 5.6 % respectively. However, by March 2000 agriculture had lost its preeminence to urbanisation and built-up area accounted for 43% of the study area (Purnend et al 2001). There was a corresponding decrease in area under open scrub (4.2%) and water bodies (2%) and the number of water bodies decreased from 1271 to 960 during the corresponding period. Besides this, catchment of major water bodies in the region, viz., Himayat Sagar, Osman Sagar and Hussain Sagar were severely encroached upon adversely affecting inflow and decreasing dilution potential (Fig.1).

Fig. 1 Change in Land Use/ Land cover and decrease in agriculture (1971 - 2000) in 30 years

Similarly, Bangalore metropolitan region witnessed tremendous change in land use and land cover owing to urbanisation and industrialisation. While agriculture was predominant in 1971 (64.82 %), wasteland, built-up area and water bodies occupied 11.8, 5.4 and 3.6 % of the study area, respectively. By March 2000 built-up area increased to 13.3 % of the study area while wasteland and water bodies shrank to 8.6 % and 4.1% respectively. In Chennai, population density increased tremendously during the corresponding period. In 1971 agriculture was dominant with 57.4 % of the study area while open scrub, built-up land and water bodies occupied 7.2, 10.1and 12.8 %. However by March 2001 agriculture was restricted to 43.5 % while built-up area increased to 20.6%. There was a decline in area under open scrub, water body, reserved forest and mud flats along the coastline. There has been a massive population explosion in Delhi during this period. It increased from 4 million in 1970 and 13.7 million in 2001 which was accompanied with massive growth in industrial activities. However due to absence of any zoning regulation or a comprehensive plan for urban development, most industries were located haphazardly leading to overcrowding, traffic bottlenecks and unhealthy living conditions as many industries were located in residential areas. In 1970 agriculture was predominant in 61.2 % area while built-up land accounted for only 16.8 %. However, by March 2001, built up area doubled to 34.2 % and several large chunks of agricultural land were moved to other sectoral uses causing massive displacement of rural population and forced migration. Table 1 illustrates the trends in land use and land cover in these four metropolises. Table 1: Change in land use in the metros between 1971 & 2000 (area in km2 ) Banglore

Hyderabad

Chennai

Class

1971

1971

1971

Agriculture

1984.4 905.3

1839.2 881.1

Vineyard

3.7

5.5

7.9

14.8

Built-up land

165.6

408.0

168.4

625.1

2001

2001

Delhi 2001

1971

2001

1747.2 1324.0 16 87.8

1188.8

306.5

973.4

625.2

436.7

Reserve Forest

320.0

328.2

Plantation

116.7

469.7

Water body

111.3

124.8

166.2

Scrub land

359.9

263.3

Stony waste Total Area

235.7

233.9

178.6

168.5

15.8

164.3

360.1

23.1< /td> 78.5

141.3

389.5

354.7

56.1

150.9

438.7

934.4

219.4

170.0

374.3

299.8

81.0

106.4

10.6

6.1

22.6

12.1

3061.6 2504.6 2937.1 2937.0 3016.1 3008.6 2616.4

Land conversion - 557.0

-0.1

-7.5

15.7

2719.2

+ 102.8

Table 2 depicts the environmental scorecard with reference to trends in land cover changes and quality of portents of environment, namely, productivity potential, biodiversity, water quality and quantity and organic carbon (OC) content in soil. Non-point source pollution owing to industrialization To assess contamination in soil, water and general environment, the study area were delineated into watersheds to specify the direction of contaminant flow and its hazard potential. Bangalore region was demarcated into 10 watersheds with Suvarnamukhi Halla watershed being the largest (155.2 km2 ) with a maximum number of hazardous industries, i.e., 44 industrial units including 19 of paints and 22 of pharmaceuticals. Spectral reflectance from 37 water bodies spread in the region were assessed and 10 of them were found to be polluted (Fig. 2).

Contamination of Urban India Environment by Hazardous Industries

Fig. 2 Soil types around hazardous industrial nodes in Banglore region

In Chennai region, watersheds of Thiruvallur, Avadi, Ennore, Manali, Kanchipuram Alltur, Marai Malainagar, Nandambakkam, Pallavaram and Sriperumbudur are endangered owing to contamination. However the situation is grave at Avadi and Ennore in Thiruvallur district. In Delhi region, watersheds of Kirtinagar and Okhla were studied. While Okhla faces contamination hazard from pharmaceutical units, Kirtinagar is being contaminated by Dye industry. The other two industrial areas which threaten the Delhi environment are Gurgaon and Sonepat. Spectral reflectance from water bodies in the red and infrared bands are indicative of the water quality as they would be nearer to one another or even overlap in case of polluted water with DN in red band ranging from 40 -50 and in IR between 40 -55. Reflectance curves from Yamuna and Hindon river indicated that they were polluted. In Hyderabad region there are 159 industrial units producing hazardous substances. Although MOEF – GOI guideline restrict locating any polluting industry within 15 km of a fresh water body, in case of Patancheru - Gaddapothram – Bolaram industrial area, R. Nakkavagu, a principal tributary of R. Manjra drains the area. Nakkavagu is located within 5 km from Patancheru IDA and although the slope is < 1%, the sediment load and contaminant flow poses serious hazard to Manjira water supply system. Katedhan IDA located towards the south of Hyderabad is another contaminated area. The Balanagar - Jeedimetla - Kukutpalli IDA which drains into Hussain Sagar is also highly polluted and poses serious contamination hazard to the groundwater in residential colonies at the lower end. Estimation of spatial extent of contaminated area using GIS It was possible to delineate and map contaminated sites along the water courses and around the factory sites using GIS. The contaminated streams were identified and buffer zonation was performed. Buffers of 100, 200, 500 m on either side of the stream / drainage channel were drawn (Fig.3). Factory sites were identified as nodes and buffering with lateral distances of 500,

1000, 2000 and 5000 m were drawn. Distances for such analysis have been recommended by CPCB which were adhered to. In Chennai region the Avadi watershed with an area of 110 km2 is the largest and most contaminated site. Around Delhi, Kirtinagar and Sonepat watersheds with small areas of over 20 km2 each were found to be contaminated. Around Hyderabad, the Patancheru IDA with an area of over 726 km2 is the largest industrial and also the most contaminated area (Table 3).

Fig 3. Buffer zoning to estimate extent of contamination

Table.3: Spatial extent of contaminated area in four metros Location

Watershed area (km2)

Stream buffer area (km2)

Node buffer area (km2)

100m 200m 500m 500m 1000m 2000m 5000m Width Banglore region (comprising of 10 watersheds)

996.4

58.2

65.0

119.1 25.3

64.5

179.7

501.3

Chennai region ( with 8 watersheds)

181.3

28.5

23.1

50.8

6.0

14.4

35.0

58.8

Delhi region (with 4 small watersheds)

49.2

1.3

1.3

1.2

10.1

16.01

11.6

10.8

Hyderabad region (with 12 watersheds)

2546.3

86.7

75.4

191.9 26.1

75

259.1

881.3

Pollution potential of hazardous industries were also assessed based on the medium that would be contaminated by the industry namely, air, water, soil or through dumping of solid waste on or beneath the surface. Table 4 illustrates the hazard potential of diverse industries in the numerous industrial areas around Hyderabad. Table 4: Pollution potential of hazardous industries

Location

District

Type of Industry Chemical Pharmaceutical Paint

Balanagar

Hyderabad A1,W1,S1

Sanathnagar Jeedimetla Medchal

Petrochemical Dyes

A1,W1,S1

A1,W1,S1 A1,W1,S1 Ranga Reddy

A1,W1,S1 A1,W1,S1

A1,W1,S1

A1,W1,S1

Kukatpally

A1,W1,S1 A1,W1,S1

Kattedan

A1,W1,S1

Patancheru

A1,W1,S1 A1,W1,S1

Bollaram

A1,W1,S1 A1,W1,S1

Gaddapotharam

A1,W1,S1 A1,W1,S1

A1,W1,S1 A1,W1,S1

A1,W1,S1

&nsbp;

A1 High, A2 Medium, A3 Low - Air pollution W1 High, W2 Medium, W3 Low - Surface water & ground water pollution S1 High, S2 Medium, S3 Low - Solid waste pollution Source: APPCB, Hyderabad & CPCB (1997)

Conclusion Evidently non-point source pollution is a growing menace in urban areas in India especially in the fast growing metros of Delhi, Chennai, Hyderabad and Banglore. Industrial development must be undertaken in strict adherence to the Land Use Plan of a region and siting of hazardous industries must be undertaken with due care. There is an urgent necessity to evolve mechanisms to reduce waste generation and to recycle waste more efficiently in order to protect the environment. GIS and Remote Sensing are effective tools to study and analyse environment degradation. While GIS provides a comprehensive tool for assessing the impact of pollution both from point- as well as non-point sources, satellites provide real-time and temporal data on state of environment which can be used for such study. A combination of both techniques is useful for environmental auditing.