application of geospatial technologies for environmental impact assessment: an indian scenario

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Application of geospatial technologiesfor environmental impact assessment:an Indian ScenarioD. R. Satapathy a , Y. B. Katpatal b & S. R. Wate aa Environmental Impact & Risk Assessment Division , NationalEnvironmental Engineering Research Institute , Nehru Marg,Nagpur 20, Indiab Department of Civil Engineering , Visvesvaraya National Instituteof Technology , Nagpur 11, IndiaPublished online: 10 Apr 2008.

To cite this article: D. R. Satapathy , Y. B. Katpatal & S. R. Wate (2008) Application of geospatialtechnologies for environmental impact assessment: an Indian Scenario, International Journal ofRemote Sensing, 29:2, 355-386, DOI: 10.1080/01431160701269002

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Application of geospatial technologies for environmental impactassessment: an Indian Scenario

D. R. SATAPATHY*{, Y. B. KATPATAL{ and S. R. WATE{

{Environmental Impact & Risk Assessment Division, National Environmental

Engineering Research Institute, Nehru Marg, Nagpur 20, India

{Department of Civil Engineering, Visvesvaraya National Institute of Technology,

Nagpur 11, India

(Received 20 October 2006; in final form 15 Jaunary 2007 )

Geospatial technology is an essential component of the Environmental Impact

Assessment (EIA) process, as environmental resources are directly affected by

changes in the shape and extent of the proposed disturbance. With the use of

spatial techniques like remote sensing, Geographical Information Systems, and

Global Positioning Systems, EIA has enhanced substantial viewing, movement,

query, and even map-making capabilities. However, one of the main challenges is

to have access to the most up-to-date and accurate geospatial data and

interpretations. With an emphasis on using geospatial data in particular, the

value of the information resource is far higher than is generally available with

text and numeric information. This paper focuses on discussing the application of

geospatial tools in environmental monitoring and the effective analysis of the

natural resources for developmental planning, policy formulation, and decision-

making. Several specific relevant applications of geospatial tools to integrate EIA

are presented in the context of an Indian scenario. Applications have included

monitoring of natural resources (air, water, land, etc.), ground-level ozone, soil

erosion, study of sea-level rise due to global warming, change-detection studies,

delineation of ecologically sensitive areas using digital-image analysis and

Geographic Information Systems. This study focuses on the possibility of using a

proposed spatial-decision-support system to conduct EIA, which should make it

possible to upload, evaluate, maintain, and report field and analytical data that

have been stored in a variety of formats.

1. Introduction

Interdisciplinary work in science has been driven in recent years at least partly by

new technologies that meet the needs of several disciplines simultaneously. India is a

country of great geographical diversity and natural resources. In spite of its wealth

of natural resources, India is still considered a developing country. An increase in

population and conflicting demands for their basic needs have led to a great portion

of the natural resources being used in an unsustainable manner. The Indian rural

environment has suffered an accelerating depletion of vegetation, leading to a

diminishing soil fertility, soil erosion, and increasingly severe drought impact due to

groundwater scarcity and basic amenities required by increasing human and animal

populations. Clearly, sound natural resource management and planning are

*Corresponding author. Email: [email protected]

International Journal of Remote Sensing

Vol. 29, No. 2, 20 January 2008, 355–386

International Journal of Remote SensingISSN 0143-1161 print/ISSN 1366-5901 online # 2008 Taylor & Francis

http://www.tandf.co.uk/journalsDOI: 10.1080/01431160701269002

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essential to tackle the aforementioned problems and to bring about sustainable

development. Therefore, environmental and natural-resource information should be

available at all levels of the public and decision-makers to support the decision-

making process and in planning for sound natural-resource management leading to

sustainable development and environmental protection for the country in general.

Many organizations have provided a wide variety of high-quality environmental

data and information in spatial format on India to improve the Environmental

Impact Assessment (EIA) studies (http://www.eicinformation.org). Since

Geographical Information Systems (GIS) provide flexibility in combining layers

of information according specifically to the user’s requirements, in analysing

environmental damage, they are mostly used for environmental monitoring and

spatial analysis of environmental data for the decision-making process. However,there is a lack of impact prediction using various modelling capabilities of GIS for

specific case studies due to the unavailability of GIS interfaces with many

environmental-modelling software packages. Thus, EIA requires a specifically

designed module which can cover all aspects of the study including anticipated

impacts and suggesting mitigation measures, important facets of EIA. A study

showing the analysis of about 20 years of total ozone content (TOC ) derived from

TOMS Nimbus-7, Meteor-3, and Earth Probe satellite observation has clearly

shown that the TOC trend over Srinagar, India is of the same order of magnitude asthat over Athens and across the Mediterannean region, keeping almost the same

seasonal behaviour (Efstathiou et al. 2003).

The activities carried out for any industrial development have impacts on surface

cover and environment. Renewable natural resources, i.e. land, water, and forests,

as well as other forms of biodiversity, which meet the basic needs for food, water,clothing, and shelter, are now deteriorating. Advanced techniques in remote sensing

and GIS in conjunction with Global Positioning Systems (GPS) can offer

environmentalists, developers, and planners the means they need for ensuring the

safety of the population, sustainable management of available resources, and

decision-making processes. Remote sensing, GIS, and GPS technologies have been

applied in many developmental projects to conduct EIA studies.

A geospatial database has been developed for environmental datasets. Some of

the indicators for environmental monitoring are rainfall, vegetation, ground-level

ozone, carbon dioxide, water-quality parameters, air-quality parameters, baseline

land use and land cover, and soil erosion potential mapping typically for mining

projects. An attempt has been made to utilize remote sensing and GIS and GPS

techniques useful for conducting environmental monitoring and assessment with

respect to:

N land-use/land-cover analysis;

N environmental change-detection studies based on multi-temporal satellite data;

N predicting vegetation-cover loss following project implementation;

N mapping soil-erosion intensity over the project area;

N mapping environmentally/ecologically sensitive areas or hotspots;

N selecting potential sites for environmental restoration measures;

N dispersion of pollutants;

N terrain models used to estimate shadow regions, slope and aspect allocation/

siting: allocation of land for different resources, etc.;

N preparing comprehensive thematic maps for planners, decision-makers, and

environmentalists.

356 D. R. Satapathy et al.

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To bring about development by utilizing natural resources sustainably, environ-

mental and natural resource information should be available to people and policy

decision-makers. Therefore, applications of geospatial technologies play a crucial

and inevitable role for environmental monitoring and natural-resource mapping

(Yilma 2004). Hence, a spatial decision-support system has been proposed which

can add spatial/non-spatial data, display the monitoring status graphically, and

assess impacts using modelling and analytical techniques.

2. EIA using geospatial techniques

EIA is a legislative procedure to combine a development successfully with

environmental conservation and to realize sustainable development. A crucial and

mandatory component for environmental assessment is the prediction of the likely

consequences of implementing developmental projects, designing the appropriate

preventive or mitigating measures for negative impacts, and enhancement measures

for positive impact.

Developmental projects often impact adversely on the environment.

Environmental pollution and degradation are intensified by both human activities

(anthropogenic activities) and natural occurrences (adverse climatic conditions).

Such activities as construction, mineral and natural-resource exploitation, oil and

gas exploration, and unsustainable agricultural practices affect the environment the

most (Arimoro et al. 2002). Man’s environment, which encompasses physical,

chemical, biological, and social entities, is dynamic. All these entities can be grouped

into physical and social systems (Erickson 1994).

EIA encompasses the generation of baseline information on land cover,

vegetation pattern, geomorphology, hydrogeology, drainage pattern, air, water,

and noise quality, socio-economics, etc. to assess the possible impacts and feasibility

of a project activity. The adoption of advanced technologies like remote sensing and

GIS provides accurate and synoptic spatial and temporal databases on vegetation

and land cover, surface hydrology, and aerosols for larger areas in a time- and cost-

effective way. A study of long-range persistence in global Aerosol Index (AI)

dynamics was carried out from satellite observations on different timescales using

Detrended Fluctuation Analysis (DFA) (Varotsos et al. 2006). These inputs are

useful in site selection and for assessing baseline status prior to the establishment of

project activities. In addition, it is also useful in drawing up effective Environmental

Management Plans (EMP), which includes catchment-treatment plans, compensa-

tory afforestation activities, resettlement and rehabilitation activities, land reclama-

tion and temporal monitoring of effectiveness of EMPs, etc. Satellite remote-sensing

data have been effectively used in site selection, loss of agriculture/forest lands due

to project activities, route alignment for power grids/pipe lines, ecological

monitoring of thermal power plants, assessment of mining impacts, submergence

area studies, impacts on wetlands, etc.

In the area of EIA, GIS has yielded excellent results by combining the areas of

each individual assessment case and overlapping them with satellite remote-sensing

data. Thereby, one can detect where the changes are in the landscape and vegetation

before and after development, and determine whether the results of the development

match the original proposals. In the future, aerial photography can be employed to

measure changes in landscape to prevent developments that damage the environ-

ment.

Application of geospatial technologies for environmental impact assessment 357

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The first systems evolved in the late 1960s, and by the mid-1970s they were used

for EIA. In 1972, a computerized version of the technique was used for siting power

lines and roads (Munn 1975). It is noteworthy that the so-called ‘first GIS’ (Canada

GIS or CGIS) was used for EIA in the late 1970s for the preparation of an

Environmental Impact Statement (EIS) for a dam on the river Thames (Muthusamy

and Ramalingam 2003). However, the use of GIS in the EIA process for scoping in

particular has been limited, due to the high costs in terms of time and money,

relative to the time and budgets allocated for EIA preparation. There are several

noteworthy complex, though sporadic, reports on the uses of GIS for EIA such as

using GIS in complex modelling representation techniques (Schaller 1990) or its

potential as a repository of data and cumulative impact assessment (Johnston et al.

1988).

2.1 GIS approaches in EIA that may involve screening, scoping, monitoring, andassessment in India

These include the following:

N initial environmental examination (IEE);

N monitoring and interpretation of baseline data;

N assessing impacts (especially cumulative impacts);

N identifying and analysing project alternatives (geographic location/site selec-

tion, overall design and choice of technology);

N mapping of data during monitoring and auditing;

N helping in decision-making or policy formulation;

N environmental impact auditing.

3. Geospatial tools

One of the major challenges in conducting an EIA study is to have access to the

most current and accurate geospatial data and interpretations. The geospatial data

and geographic extents are constantly changing. Further analysis must be done at

both local and regional scales with respect to different timescales. Scale effects

should be measured separately for spatial extent and spatial detail, and both

quantitatively using a GIS and qualitatively using the judgement of EIA experts

(Joao 2002). The study found that changes in scale could affect the results of EIAs.

For example, the impact significance and the number of houses affected by air

pollution from the road varied according to the scale used. The conventional EIA

procedure gives accurate results for all the aspects and contaminants, and the geo-

spatial technique can be employed for detecting only a few major pollutants, which

can be tracked by satellite remote sensing and can be represented in GIS. The geo-

spatial EIA technique also facilitates comparison of the spread of pollution at

different times and also allows for a pictorial representation of the extent of

environmental pollution over a specific period (Patil et al. 2002). Traditionally,

when utilizing GIS, data are classified into graphic data and attribute data. Graphic

data are large in quantity and contain few changes, and usually attribute data are

large in quantity and highly changeable. Therefore, these two types of data are

stored in different databases. Attribute databases are often structured relationally.

Their techniques and implementation are well established. To meet the requirements


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of environmental protection management, the graphic database is divided into three

categories: vector data, satellite data, and GPS data

3.1 Remote sensing

This is the scientific technology that can be used to measure and monitor important

biophysical characteristics and human activities on earth (Jensen 2000). Managing

the earth’s natural resources and planning future developments are important for

acquiring accurate spatial information through GIS to create important layers of

biophysical, land use/land cover and socio-economic conditions in a GIS database

derived from analysis of remotely sensed data (Star et al. 1991). Remote sensing can

be used to identify and isolate regional to sub regional objects/factors of significance

in an EIA in a time- and cost-effective manner

Remotely sensed data are collected using a passive or active remote-sensing

system. However, satellite imageries integrated with other data sets are more

accurate in delineating land use/land cover. Among the most widely used

environmental-monitoring systems will be remote sensing of the earth’s oceans,

land, and atmosphere. Remote-sensing instruments mounted on orbiting satellites

or aircraft produce map-like images and gather other non-pictorial data about the

environment, including land use, vegetation cover, water, temperature, and air

pollution. Remote observations of the biological effective solar ultraviolet

irradiance, made using instrumentation flown on a Falcon aircraft over the entire

area of Greece, from the sea up to the tropopause level obtained in the frame of

radiation field in the troposphere and access to aircraft have shown that an average

increase of about 7.2¡1.2% km21, which is in close agreement with the theoretically

derived value (Varotsos et al. 2001a).

3.2 GIS

The ability to combine maps with associated tabular data distinguishes GIS from

other information systems and makes it valuable for a wide range of uses for public

and private organizations in the assessment of status, explaining events and changes,

predicting outcomes, developing plans, and monitoring activities in the natural

resources and environmental fields. Atmospheric turbidity in conjunction with the

trends of solar radiation components reflects rapid urbanization and industrializa-

tion (Jacovides et al. 1994). Recent Internet techniques have been developed with

exceptional progress, thus showing that GIS opens up special opportunities and

prospects in the area of sustainable development. The above-mentioned information

can be stored on the Internet. The World Bank has strongly endorsed GIS as a tool

for EIA (Hassan and Kjorven 1993, World Bank 1993, Eedy 1995, Hassan 1995).

3.2.1 Advantages of GIS in EIA. These are as follows:

N provides a systematic approach for the collection of environmental informa-

tion;

N makes the data used in EIA preparation accessible to all decision-makers;

N facilitates the analysis of environmental impacts, which otherwise might be

undetected or ignored due to analytical difficulties or high costs;

N increases the compatibility and comparability of diverse data sets;

N reduces the overall costs and institutional overlap in collection and manage-

ment of environmental information;


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N it is easy to update the GIS database;

N it is easy to produce illustrative maps, tables, and other analysis results (World

Bank 1993).

Public Participation GIS (PPGIS) or Community-Integrated GIS (CIGIS) aims at

increasing the number and diversity of people who are capable of participating in

spatial decision-making, i.e.:

N not entirely based on ‘expert knowledge’;

N assumes that local knowledge is valuable;

N increases the access base to digital data and spatial information;

N incorporates socially differentiated landscape perceptions;

N can be used to integrate GIS and multimedia;

N enables more democratic spatial decision-making.

3.2.2 Typical uses of GIS in a specific case study of EIA. These are as follows:

N describing the distribution and condition of natural resources and areas of

concern;

N identifying the nature, sources, magnitude, and location of environmental

stresses in an ecosystem;

N describing the current and potential level of exposure to a given stress in an

ecosystem;

N assessing the response of an ecosystem to existing and potential stresses;

N assisting in the evaluation of risks to an ecosystem from exposure to various

stresses;

N Habitat Suitability Index: habitat quality for wildlife population has a spatial

component across large geographic areas (Lai et al. 2000). Habitat Suitability

Index (HSI) models have been widely used to document the quality and quantity

of available habitat for a specific wildlife species. In impact assessment, HSI

represents the best long-term evaluator of the overall project (Eedy 1995).

3.3 GPS

The utility of GPS in the field of environmental monitoring is immensely useful. It

involves receiving GPS satellite data at any time under any climate at location with a

good communication channel and calculating the position coordinates of the

receiver. GPS is a method of rapid measurement. It can be applied to different

spatial positions of important landmarks, such as factories, industrial sites, disaster

areas, water-supply districts, etc. (www.epa.gov.tw/ENGLISH/offices/L/egis.htm).

GPS applications in natural-resource management include inventory and mapping

of soils, vegetation types, threatened endangered species, lake and stream

boundaries, and wildlife habitats. The habitat suitability for these species can serve

as an indicator for the zoning of protection areas and a better integration of species

protection with landscape planning at local and sub-regional levels (Weiers et al.

2004). GPS has been used to aid in damage assessment after natural disasters such as

fires, floods, and earthquakes. GPS has also been used to map ecologically sensitive

sites and for infrastructure (streets, highways, and utilities) mapping, management,

and planning for future growth as a partial fulfillment to the EIA study.


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The data obtained by GIS can be used not only by the environmental authorities

but also by the public and regulatory agencies. The public may well become

accustomed to using the Internet to obtain environmental information from GIS

(www.esri.com). The integration of environmental datasets with geospatial

technologies to assess environmental quality can be achieved through the following

steps, which will be demonstrated with case studies (figure 1).

4. Application of geospatial technologies for different EIA studies

Increasingly, GIS is used as the organizational framework for environmental

management and monitoring programmes through the services providing spatial

analysis and modelling, database development, customized GIS design and

implementation, Internet map services, and map layout and production. Similarly,

remote-sensing data can provide valuable information to complement traditional

ground-based environmental assessment and monitoring sources through the

Figure 1. Flow chart showing the integration of environmental datasets with geospatialtechnologies to assess environmental quality.


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services providing digital-image processing, spatial analysis, and field validation.

The following case studies have been conducted using an image analysis system

(IAS) and GIS software, viz. ESRI ArcGIS ArcInfo and ArcView 8.3, ArcSDE,

MapInfo, ArcView 3.x and PCI Geomatica 10.0 and Global Positioning System

Garmin e-Trex Legend.

4.1 Base map

Base maps refer to basic topographic maps, which show basic information or

general characteristics of the mapping area without emphasis on any particular

factors. The main considerations for base maps are scale, accuracy, and data

coverage including rivers, roads, and administrative boundaries. Currently, two

widely used topographic maps in India are the 1 : 25 000/1 : 50 000 basic topographic

maps provided by Survey of India. With the GIS functions of spatial analysis, such

as overlay analysis and buffer analysis, and functions of handling attribute data, the

GIS-based map overlay method can assess road environmental impacts in large and

complicated areas, by overlaying various environmental vulnerability grade maps

and distribution maps of impact extent coefficient (Li et al. 1999).

4.1.1 Method used. The base map was prepared by scanning the toposheets of

1 : 50 000 scales at a good resolution published by Survey of India, Government of

India. Then, the scanned maps were geocoded with the help of image-processing

software and then georeferenced to a required projection system with required

ellipsoid. Then, different layers like road, river, railway, etc. were digitized with the

help of GIS software. A Case Study of Generating Base Map for Chandrapur

district Mining Area is presented in (figure 2).

4.2 Environmental monitoring data

Environmental monitoring of water resources, including streams, lakes, estuaries

and near-shore marine habitats through sampling and analysis of data collected

from water, sediment, fish and other biota (animals and plants), air-quality data,

noise data, etc. utilizing a combination of statistical analysis, weight of evidence and

expert knowledge, quantitative analytical data, and trend analysis to assess

environmental impacts and levels of compliance can be integrated with GIS

technologies to complement traditional environmental management. Monitoring

environmental data is one of the major sources of data for EIA, which can be

integrated with GIS. A typical heavy-metal concentration monitoring in water

bodies is presented here. Various sampling locations have been selected with the help

of GPS based on the source of pollution. The heavy-metal concentration of samples

has been added as attribute data with the help of GIS software. A case study of

representing heavy-metal concentrations using GIS is presented (figure 3).

4.3 Thematic maps

Thematic maps are usually based on base maps, on which a thematic phenomenon

has been added. The main purpose of a thematic map is to emphasize the factor of

interest, e.g. data layers created from the distribution of air-quality surveying data.

A study of diurnal variation of surface ozone at Athens using a re-evaluated historic

record of surface ozone mixing ratios, measured at the National Observatory of


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Athens for the period 1901–1940, using De James colometric papers can be

represented as different themes (Cartalis and Varotsos 1994, Varotsos et al. 2001b).

Thematic maps are prepared by adding different layers to the area of interest. A case

study of prediction of ground-level ozone concentration using inverse distance

weight interpolation (IDW) in Chandrapur district is presented in figure 4.

4.4 Other attribute and statistical data

Other attribute data include social, political, and economic information. These

data can be acquired officially through the national administrative

information-management network. The use of other attribute and statistical data

will increase public confidence in environmental information.

Figure 2. Base map for Chandrapur district, Maharashtra.


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4.5 Land use/land cover mapping

Land management, where anthropogenic pressures are increasing at the expense of

safety and natural resources, requires rapid and accurate mapping of land

Figure 3. Drainage network with heavy-metal concentration monitoring in Chandrapurdistrict, Maharashtra.


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disturbances (Bobrowsky 2002). Monitoring of general land-cover changes or more

specific themes such as land degradation is also an important aspect of ‘change’ of

the land surface. Land management and land-use planning are related to one

another, and land management often follows development under the guidance of

land-use plans. Land-use planning is an important environmental issue, as sound

land-use planning is essential for economic development, for avoiding conflicts

between land uses, and for maintaining a high quality of life in communities (Jensen

2000).

4.5.1 Spatial-data acquisition and assembly. At the core of a landscape approach

to monitoring ecological status and trends is the basic need for quantifiable and

consistent spatial data of biophysical characteristics (e.g. land use and land cover)

on national, regional, and local scales. Multiple-date landscape data permit

calculation of landscape indicators at different time intervals, and changes in these

values can then be interpreted with regards to potential changes in natural-resource

conditions.

Land-use and land-cover data are often derived from some type of overhead,

remotely sensed imagery such as aerial photographs and digital satellite data.

Various classifications of land use and land cover are derived from imagery based on

manual interpretations or a variety of digital-processing techniques, depending on

the application. Remote-sensing data and resources also provide valuable

environmental monitoring services such as change detection, topographic analysis,

and various types of mapping, indicator development, and analytical support to

environmental regulatory programmes.

4.5.2 Supervised statistical classification. There are several types of statistics-based

supervised classification algorithms. Some of the more popular ones are (in

Figure 4. Three-dimensional model of the interpolated surface for the GLO ozone data.Ground-level ozone concentration in Chandrapur district, Maharashtra.


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increasing complexity); parallelepiped, minimum distance, maximum likelihood,

and mahalanobis distance. With supervised statistical classification algorithms, the

analyst must first locate and define samples in the image of each class that are

required for the final map. These samples are called training areas. Once a sufficient

number of training areas are selected, the supervised classification is then run. The

algorithm then compares each pixel in the image with the different training areas to

determine which training area is most ‘similar’ to the pixel in the image. Once the

most ‘similar’ training area is found, the image pixel is labelled with the respective

land-cover class. The two primary reasons for visiting the area that is being mapped

are to collect data that can be used to train the algorithm or the interpreter and to

collect data that can be used to evaluate the land-cover map and estimate the

accuracy of the individual classes (a process called validation). In the needs of

assessment, some thought should be given to the required accuracy of the final map.

The accuracy can refer to either spatial accuracy or thematic accuracy. Spatial

accuracy is directly related to the base information that is used. If it is using satellite

imagery, the spatial accuracy of the final map will be dictated by the spatial accuracy

of the satellite image that was used to create the map.

4.5.3 Accuracy assessment. Thematic accuracy specifies how well individual

mapped classes match what is on the ground. For example, if the map indicates

that there is forest at a particular location, what is the actual land-cover type? Error

is inherent to the mapping process, and determining an acceptable level of error

requires careful thought. Factors affecting accuracy include: the quality and type of

the base data, the experience of the analyst, the analyst’s familiarity with the area

being classified, and the level of detail of the classification scheme. A case study of

land-use/land-cover analysis is presented in figure 5, and the inventory of land-use/

land-cover status is presented in table 1.

4.6 Analysis of impacts of land-use change on vegetation using spatial techniques

There are several different methods of change detection to assess the change in

biophysical characteristics. In this study, an attempt was made to determine the

change in vegetation cover using change-detection approaches, viz. traditional post-

classification and NDVI analysis (Lunetta and Elvidge 1999). With the land-use and

land-cover change resulting from the afforestation, agricultural improvement,

natural vegetation growth, and shifting cultivation, there is a requirement to develop

a database for rapid and cost-effective mapping and monitoring of extensive areas

of land to determine their conservation value (Griffiths et al. 1980). Interference of

shifting cultivation, intensification of agriculture activities, and changes in cropping

patterns contribute to conversion of fallow land to forest land and barren land to

open forest.

Post-classification analysis is a simple method for change detection which involves

the classification of each of the images independently, followed by a comparison of the

corresponding pixel (thematic) labels to identify areas where change has occurred.

Results can then be displayed in a change matrix or a change map. Change studies can

also be carried out with the analysis of normalized differential vegetation index.

Unfortunately, every error in the individual data classification map will also be

present in the final change-detection map. Therefore, it is very important that the

individual classification maps used in the post-classification change-detection

method be as accurate as possible. The post-classification comparison method


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Figure 5. False-colour composite and land-use/land-cover status in Pamban Island, India.


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provides complete change information reducing the impact of atmospheric and

environmental differences to a minimum and makes it possible to classify imageries

recorded at different time periods. Post-classification comparison involves the

classification of each of the images shown independently, followed by a comparison

of the corresponding pixels to identify areas where a change has occurred. Post-

classification comparisons of derived thematic maps go beyond simple change

detection and attempt to quantify the different types of change. The degree of success

depends upon the reliability of the maps with the image classification (Shalaby and

Tateishi 2007). Based on the two classification maps, a post-classification comparison

was performed, and a change correlation matrix obtained using the overlay technique

for two classified maps. The results are displayed in a change matrix (table 2). Using

the matrix, the changes in land cover in the study area can be analysed. The diagonal

matrix indicates no change in corresponding land-use\land-cover classes for two

different years. The upper diagonal matrix indicates the changed land covers for the

present year, converted from different land-use\land-cover classes in the previous

year. The lower diagonal matrix indicates the changed land covers for the previous

year, converted from different land-use\land-cover classes in the present year.

4.6.1 Method used. The method adopted is shown in figure 6.

4.6.2 Mapping temporal changes of vegetation using the DNDVI technique. The

vegetation index differencing technique was used to analyse the change in vegetation

(green) versus non-vegetation (non-green) with the two temporal datasets. NDVI is

based on the principle of spectral difference based on strong vegetation absorbance

in the red and strong reflectance in the near-infrared part of the spectrum. The

difference in NDVI is useful in distinguishing between the increase and decrease in

vegetation cover based on the following equation (Ramachandra and Kumar 2004).

The procedure for change detection using DNDVI is shown in figure 7.

DNDVI~ IR{Rð Þ= IRzRð Þt2{ IR{Rð Þ= IRzRð Þt1: ð1Þ

t1 and t2 in the equation denote the two different dates, where t1 denotes 27

February 1997 and t2 15 February 2004. Similarly, DNDVI was also calculated for

different time periods: 3 May 1993–21 May 2004, 27 February 1997–15 February

2004, and 3 May 1993–27 February 1997 (figure 8).

Table 1. Inventory of land-use/land-cover status in Pamban Island, based on satellite datafrom May 2002.

Category

Inventory by IRS 1D LISS III + PAN May 2002

Area (ha) Percentage of total

Vegetation cover 1 717.835 7.438Vegetation cover 2 2758.664 28.585Fringe vegetation 866.136 8.974Turbid water 460.603 4.772Mud flats 97.185 1.012Sand bar 1261.951 13.076Fallow land 776.699 8.048Shrub 804.056 8.331Barren sandy 427.754 4.432Degraded land 1479.719 15.332Total 9650.602 100


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Table 2. Comparison matrix of land-use/land-cover change analysis in and around Karlapat Bauxite Plateau.

Change detectionfor 1993 to 1997

Land use/land cover for February 1997

Denseforest

Openforest

Fallowland

Bare soil/sand Barren land

Cropland

Land with/withoutscrub

Waterbodies

River/streams Settlement

Landuse/landcoverforMay1993

Dense forest 386.304 175.91 94.188 66.699 22.190 7.86355 31.298 0.00922 0.11405 0.0000Open forest 76.4916 50.318 6.9546 9.5322 4.5250 2.44627 7.7961 0.00115 0.02765 0.0000Fallow land 120.586 108.45 151.68 63.755 94.495 13.3810 87.046 0.08986 0.47981 0.0241Bare soil/

sand0.06451 0.9463 1.4993 29.871 41.904 6.14765 39.237 0.01555 0.75954 0.4377

Barren land 3.51763 17.595 38.453 43.720 209.15 50.6931 73.423 0.35597 15.4857 1.9768Crop land 17.4585 72.047 16.570 11.411 12.289 13.6535 28.211 0.00403 0.23328 0.0092Land with/

withoutscrub

15.2968 65.844 38.511 27.425 70.269 32.9137 111.89 0.49306 6.46733 0.1042

Water bodies 0.12557 0.1157 0.3450 0.0616 0.2160 0.08640 0.0812 0.37382 0.61747 0.0000River/

streams0.00000 0.0000 0.0000 0.0270 0.0000 0.00000 0.0000 0.00000 24.7345 0.0000

Settlement 0.00000 0.0005 0.0115 0.0000 0.0622 0.01786 0.0063 0.00000 0.01152 5.5451

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4.7 Aquaculture facility assessment and mangrove mapping

The ecological and commercial problems of shrimp culture are largely related to the

removal of mangroves. Practically in all aquaculture systems, operators cut the

mangrove forest and construct dykes, with sluice gates to retain water. The original

tidal regime, which is essential for the survival of mangrove tree species, is totally

disturbed (Rajitha et al. 2007). Areas of environmental monitoring of aquaculture

facilities and ecological sensitive mangrove were conducted for the Godavari

Estuary, Andhra Pradesh. The mangrove formations of Godavari estuary are

formed due to silting over many centuries. The estuary covers an area of 62 000 ha, of

which dense Coringa mangrove forest spreads over 6600 ha. Satellite sensor data were

used to detect changes in the mangrove cover for a period of 12 years (1992–2004)

Figure 6. Flow chart for the study approach for post-classification comparison.


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(table 3, figure 9). It was found that an area of about 1250 ha of mangroves was

destroyed by anthropogenic interference such as aquaculture, tree felling, etc. The

increase in the mangrove front towards the coast was also delineated using remote-

sensing data. The major advantages of remote-sensing data is in monitoring changes

periodically. The combination of moderate- and high-resolution data provided

detailed coastal land-use maps for implementing coastal regulation measures. It was

found that the mangrove’s spectral response/digital number (DN) values are much

lower than those of non-mangrove vegetation such as plantations and paddy fields in

the SWIR band. By taking this as an advantage, spectral data were utilized for clear

demarcation of mangroves from nearby paddy fields and other vegetation. Since

habitat loss and fragmentation are major threats to biodiversity, EIA and strategic

environmental assessment are essential instruments used in physical planning to

address such problems (Gontier et al. 2006). Simpson’s diversity index (SDI), which is

a measure of biodiversity, was found to be 0.09, thus indicating mangrove dominance.

Ecological parameters such as mud-flats/swamps, mangrove cover alterations, and

biodiversity status have been studied in detail for 12 years. Highly potential

environmentally sensitive areas as categorized in table 4 require immediate conserva-

tion and development. Overall, simple and viable measures are suggested, based on

multi-spectral data to sustain this sensitive coastal ecology useful for public

administration, regulatory agencies, and policymakers.

4.8 Assessment of vegetation cover mapping in and around Nagpur Urban city usingthe NDVI technique

Cities are a magnet for the growth of social, economic, and political development of

the country. They hold the power for the development of large-scale and small-scale

industries, educational institutions, administrative offices, public and commercial

establishments, etc. In turn, they attract more migration from the rural areas to the

Figure 7. Flow chart for change detection using the NDVI technique.


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urban areas, or shifting from one urban centre to another urban centre. Thus, the

population density is increasing in certain pockets of the cities, and so vegetation

cover mapping in and around a city is important. A typical vegetation cover

mapping around Nagpur city is estimated using the NDVI technique (table 5,

figure 10). Nearly all satellite vegetation indices employ this difference formula to

quantify the density of plant growth on the earth. The NDVI is the difference

Figure 8. Change maps using the NDVI technique for Karlapat Bauxite plateau, Orissa.


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Table 3. Land-use/land-cover change detection based on the satellite data from 1992 and2004 in Godavari Estuary.

Category

Inventory by IRS 1BLISS II March 1992

Inventory by IRS P6LISS III January 2004

Change inarea (ha)Area (ha)

Percentageof total Area (ha)

Percentageof total

Settlement 6084.97 2.63 9801.8 4.23 ( + )3716.832 Mangrove Dense 4428.30 1.91 8169.5 3.53 ( + )3741.23 Mangrove Sparse 12 977.7 4.60 7982.9 3.44 (2)4994.84 Crop land 52 449.6 23.63 44 941 19.39 (2)7508.65 Fallow land 27 125.1 11.70 10 750 4.64 (2)16 375.16 Plantation 11 571.2 4.99 11 362 4.90 (2)209.27 Barren land 5471.71 2.36 14 030 6.05 ( + )8558.298 Land with/without

Scrub8498.52 3.66 17 016 7.34 ( + )8517.48

9 Bare Soil/Sand 3795.59 1.63 4557.7 1.96 ( + )762.1110 Deep water 64 293.7 27.741 75 984 32.78 ( + )11 690.311 Shallow water 24 207.3 10.44 22 609 9.76 (2)1598.312 Aquaculture 827.626 0.35 2970.4 1.28 ( + )2142.7713 Mud flat 475.891 0.20 1586.4 0.685 ( + )1110.51

Total 231 763 100.00 231 763 231 763

Table 4. Priority-based category environmentally sensitive area for conservation.

Potential area for land-resource development Suggested optimal land use Priority for development

Crop land in Kharif/Rabiseason

Intensive agriculture, agro-horticulture

High priority

Mangroves Regeneration, protection andconservation of mangroves asa biosphere and wildlife reserve

High priority

Sandy area Development of shelter belts ofplantations to protect from seaerosion

High priority

Table 5. Inventory of different vegetation covers in and around Nagpur City.

Sr. no.Land-use/land-cover

classes NDVI range Area (ha)Area as a

percentage (%)

1 Non-vegetation 21 to 0.0977 20 659.04 58.792 Vegetation 1 0.0977–0.1445 6115.29 17.403 Vegetation 2 0.1446–0.2073 5222.61 14.864 Vegetation 3 0.2074–0.2382 1325.04 3.774 Vegetation 4 0.2383–1 1825.05 5.19

Total 35 137.03 100


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Figure 9. Comparision of the ecologically sensitive mangrove spread in Godavari Esturary,India.

Figure 10. Vegetation cover mapping using the NDVI technique in Nagpur city.


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between near-infrared (NIR, channel 2) and visible (VIS, channel 1) reflectance

values normalized over the sum of channels 1 and 2 ((NIR – VIS)/(NIR + VIS)). The

NDVI equation produces values in the range of –1.0 to 1.0, where increasing

positive values indicate increasing green vegetation, and negative values indicate

non-vegetated surface features such as water, barren land, ice, and snow or clouds.

Written mathematically, the formula is:

NDVI~ NIR{REDð Þ= NIRzREDð Þ: ð2Þ

Calculations of NDVI for a given pixel always result in a number that ranges from

21 to + 1; however, no green leaves give a value close to zero. A zero means no

vegetation, and a value close to + 1 (0.8–0.9) indicates the highest possible density of

green leaves. The methodology adopted is shown in figure 11.

4.9 Environmental studies related to exploration, mine development, and vegetationcover loss assessment subsequent to project implementation

The bauxite deposit is strip-mined and thus will require careful restoration. As per

the proposed set-up/implementation of the project, 972 ha of vegetation cover will

be affected. The estimated loss in vegetation cover includes both mine sites and

proposed alumina refineries, as well as township areas, treatment plants, etc. Also,

vegetation cover includes a small patch of forest (380 ha), crop land, and shrubs

(table 6). The estimation is based on the spatial analysis of the satellite imageries,

viz. IRS P6 LISS III of 15 February 2004 for proposed activities (figure 12).

Figure 11. Methodology adopted for vegetation-cover mapping.


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4.10 Assessment of the impacts of enhanced sea-level rise on the Indian coast

Research into the environment utilizes GIS and remote-sensing technologies in

several significant ways. Most notable is research into global climatic and

hydrological change. The impact of global warming-induced sea-level rises due to

thermal expansion of near surface ocean water has great significance in India due to

its extensive low-lying and densely populated coastal zones. The USGS DEM for

India was used for the study. The most hazardous areas were identified for 1 m

above sea level by stretching the image between 0 and 1 (figure 13). The eastern coast

of India is largely vulnerable to a rise of 1 m sea level, but the western coast of India

is also vulnerable. States susceptible to this include Orissa, West Bengal, Andhra

Pradesh, Tamilnadu on the east coast, and Gujarat on the west coast of India.

4.11 Assessment of soil-erosion intensity for Chandrapur mining areas

The continents are eroding at an average rate that is in the order of 0.1 mm y21

(1 mm y2151 km Ma21). The present-day rate is approximately double that two

centuries ago. Erosion is largely caused by streams. The use of multi-temporal

remote-sensing images in support of an environmental modelling analysis in a

geographic information system (GIS) environment leading to the identification of a

variety of long-term interactions between land, resources, and the built environment

has been a highly promising approach in recent years (Ning et al. 2006). Estimates of

the sources of modern sediments to the world’s oceans are as follows: rivers 96%

fluvial, eolian erosion 1%, glaciers 1%, and coastal erosion 1% (www.soiler-

osion.html). Generally, the sediment sources are steep foothills, agriculture,

construction, and channel erosions (Trimble 1997). Most of the first three sources

are generated and transported by surface runoff. Channel erosion is related to

channel stability. Failure to control or prevent the loss of soil will result in reduced

crop-based production potential, lower surface water quality, and damaged

drainage networks (Rodda et al 2001). In locating sites that are currently being

eroded at high rates or that have the potential to, ideally soil conservation practices

can be implemented in attempts to lower erosion rates in these areas. Environmental

issues are always spatially orientated. Many computer models have been developed

to predict soil erosion. Erosion-risk analysis can be recommended to determine the

value of natural landscape taking into account the characteristics of the

environment and the nature of the dam construction as a development demand

(Sahin and Kurum 2002). The database of information required by these tools is

Table 6. Proposed estimated vegetation cover loss subsequent to project implementation.

S. no. Proposed plans Total area (ha)Estimated vegetation

cover loss (ha)

1 Ash pond 184.32 29.2032 Bauxite mine area 1811.175 450.8353 Alumina plant 960.077 164.2184 Red mud plant 302.054 54.3755 Railway shed 115.948 28.9156 Township 346.406 168.8837 Water-treatment plant 346.406 16.7628 Conveyor belt (indirectly affect) 116.236 62.035

Total 972


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relatively complex. GIS can handle these spatial data very easily and efficiently. GIS

is a spatial decision-making tool that can assist in the manipulation, organization,

calculation, and integration of multiple soil erosion factors (Jen Bell and Jen

Bryant). In this study, an attempt was made to estimate soil erosion in a mining

area. The Universal Soil Loss Equation (USLE) is an erosion model designed to

predict the long-term average soil losses from specific field areas in specific cropping

and management systems. The model takes into account the major factors that

influence soil erosion. These include rainfall patterns, soil types, slope steepness, and

Figure 13. Sea-level rise prediction up to 1-m elevation along the Indian coast.

Figure 12. Estimation of vegetation cover loss subsequent to project implementation.


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management and conservation practices. Each of these components is included in

the USLE.

A~R|K|LS|C|P, ð3Þ

where A5average annual soil loss; R5rainfall and runoff erosivity index; K5soil

erodibility factor; L5slope length factor; S5slope steepness factor; C5cover

management factor; and P5conservation practice factor.

A typical soil erosion potential area is shown in figure 14.

Figure 14. Map depicting the soil erosion potential in Chandrapr district, Maharashtra.


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4.12 SLOPE stability and digital elevation model for topographical studies

The slope angle (usually called the slope) greatly affects the relative magnitude of

driving forces on slopes. As the angle of a potential slip plane increases, the driving

force also increases, keeping other factors equal. (Keller 1996). The slope map is

derived from the interpolated elevation data. The grid-based DEM has been

generated by the TOPOGRID command for the area (figure 15). Cell-based grid

generation in geo-processing system is integrated with ARC/INFO. TOPOGRID is

a hydrological correct grid of elevation from point, line, and polygon overages. To

create the DEM, contours were first digitized from existing 1 : 50 000 topographic

sheets. These sheets have a 10 m and 20 m contour interval. The contour data were

then input to software that interpolates contours to create a dense matrix of

Figure 15. Map depicting the slope stability in Chandrapur district, Maharashtra.


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elevation at 24-m resolution and co-register to the landuse data. During

interpolation spot height, waterbodies, and river/stream factors were also

considered. The TOPOGRID command is an interpolation method specifically

designed for the creation of hydrologically correct digital elevation models (DEMs)

from comparatively small but well selected elevation and stream coverages. This is

based on the ANUDEM program developed by Michael Hutchinson (1988, 1989).

4.13 Seismic hazard map

Geospatial tools can be used to facilitate measurement, mapping, monitoring, and

modelling of a variety of data types related to natural phenomena. The specific GIS

application in the field of Risk Assessment is hazard mapping to show earthquake,

landslide, flood, or fire hazards. Seismic zonation mapping is also a vital part of the

natural hazard mapping. A seismic zonation map has been prepared for the

Chandrapur district of Maharashtra (figure 16). This map is prepared based upon adistrict resource map published by Geological Survey of India.

5. Proposed spatial decision-support system to conduct EIA

Planning and management are based on a generic problem-solving process which

begins with problem definition and description, involves various forms of analysis

which might include simulation and modelling, and moves to prediction and then to

prescription or design, which often involves the evaluation of alternative solutions to

the problem. Decision characterizes every stage of this process, while the process ofimplementation of the chosen plan or policy involves this sequence once again. The

proposed structure of SDSS to conduct EIA can be described as follows.

5.1 Data source

Development of database management system (DBMS) from various external data

acquisition systems, provide the input data for the specific application. The data

include both spatial and non-spatial data. Spatial data include remote-sensing data,

toposheets, infrastructure details, environmental zonal maps (air and water quality),natural hazards, soil maps, etc., and non-spatial data mainly include data such as

other monitoring attribute data.

5.2 Analytical modelling

Analytical modelling capabilities are often a part of GIS. The analytical results

should be incorporated as a spatial input to this system with which exchange of

information is required. For example, computer-simulation models may provide

quantitative estimates of air, noise, and water-quality parameters. A numericalevaluation model has been developed to analyse eco-environmental problems in

mountainous regions using remote sensing (RS) and GIS. The spatial principal-

component analysis method is used to determine the variables and their weights (Li

et al. 2006). A review of numerical modelling has been published on the variability

of ozone concentration in the troposphere and can be integrated with GIS to form a

component of a decision-support system (Kondratyev and Varotsos 2001a, 2001b)

5.3 Knowledge base

Expert knowledge plays a vital role in taking decision for which multi-criteria

analysis (MCA) algorithm can be developed using certain programs.


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5.4 DSS models

Different decision schemes can be generated based upon various domain-specific

criteria and knowledgebase.

5.5 Graphical user interface

The graphical user interface will help developers and environmentalists to make

decisions with the help of a graphical display and tabular reporting capabilities with

control and selection options (figure 17).

A spatial decision-support system for environmental planning and EIA should be

feasible for uploading, evaluating, maintaining and reporting field and analytical

Figure 16. Natural-hazard map for Chandrapur district, Maharashtra.


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data stored in various formats. Once data are uploaded into the system database,

it should be possible to process the data using a variety of query, reporting,

graphical, and statistical tools to determine the most cost-effective methods for

achieving complete regulatory compliance. The system can combine the hands-on

knowledge of environmental-management experts and software-development

professionals with an ease of use that allows the data to be managed quickly and

cost-effectively.

6. Discussion

The amount of environmental data gathered from remote-sensing satellites and

many other sources is overwhelming. Each year, as new systems go into operation,

the data stream increases and becomes more complex. To assist in environmental

monitoring and management decision-making, these data must be available to

researchers in a timely way. Over the past 20 years, sophisticated computer-based

information systems have evolved that have great potential to help in developing

management strategies for sustainable development and environmental protection.

Moreover, GIS allows policymakers to easily visualize problems in relation to

Figure 17. Flow chart for integration of the graphical user interface.


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existing health and social services and the natural environment and so more

effectively target resources. Geospatial tools are very useful in generating new

information and in studying environmental change over time supported by raster

GIS which are used for comparison, statistics, and presentation of findings. Visual

interpretation is normally used for overviews. Supervised digital classification is

only used in smaller areas, and in such cases the field data are sufficient for

calibration and generalization of findings. EIAs are necessary tools for the planning,

evaluation, and monitoring of sustainable development of the environment.

Therefore, EIA needs to be carried out on all proposed projects in India so that

as infrastructure and economic development are enhanced, they will not be to the

detriment of the environment. GIS technology is a very useful method for carrying

out standard EIA. It affords an easy and effective way of assessing impacts of

projects on the environment, and also provides easy-to-read maps, which could be

readily studied by policy formulators. Although developments have been broadly

based across many divergent disciplines, there is still much work required to develop

techniques suited to natural-resource management and environmental monitoring,

refine techniques, improve the accuracy of output, and demonstrate and implement

work in operational systems. The low cost of the imagery is an obvious advantage

for natural resource managers, particularly in developing countries. Urban planners

will find the imagery to be of great interest. Another potential useful application is

the use of this imagery for map making and updating which is slow and expensive,

and a number of agencies have been using satellite imagery for developing basic

information. Perhaps the greatest advantage of rapid delivery of images is for

checking and controlling human activities and impacts. This will allow users to

monitor new developments, as well as design methods to assess whether

environments are degrading as a result of resource utilization.

7. Conclusion

Geospatial tool being an inevitable tool in conducting EIA is a good practice in

making decisions about policies, plans, and implementing projects. Geospatial data

collection, sharing, and access is a problem in India from a cost point of view. From

the previous study, it can inferred that traditional EIA using geospatial tools could

be a successful process in considering certain componenets like baseline information,

anticipated impacts and mitigation measures, and formulating management plans

for subsequent development. GIS and use of a spatial decision-support system to

conduct EIA need to be accelerated to lead decision-makers to make decisions for

environmental clearance in a time- and cost-effective way. Geospatial tools are

immensely useful for spatial data collection, processing, and analysis, and providing

access to spatial information in various development sectors such as mining,

transport, petroleum exploration, tourism, hydroelectric power projects, etc. A

geospatial tool integrated as a decision-making tool in the decision-making process

is of vital importance in India. The proposed spatial decision-support system can

bridge the gap between the traditional way of conducting EIA and the EIA using

geospatial techniques.

Acknowledgements

We express our gratitude to S. Devotta, The Director, National Environmental

Engineering Research Institute, Nagpur, India for his encouragement and support.


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We sincerely acknowledge our colleagues, who had put efforts in working for

various projects and providing useful suggestions.

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application of geospatial technologies for environmental impact assessment: an indian scenario

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