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Application of Remote Sensing and GIS tools in delineating Environmentally-Fragile-Areas (EFAs) for sustainable land use planning: A case study of Delhi Region
Dr. Amarjeet Kaur1, Venkatesh Dutta2, Dr. D. K. Chaddha3
Research Scholar, Department of Geography,
1. Head, School of Environment Management,
Guru Gobind Singh Indraprastha University,
Kashmere Gate, Delhi - 110 006. E-mail:amarjeet@ipu.edu
2. Ph.D. Student, Faculty of Policy & Planning,
TERI School of Advanced Studies, India Habitat Centre,
Lodi Road, New Delhi - 110 003. E-mail: vdutta@terischool.ac.in
3. Former Chairman, Central Ground Water Board (CGWB), Delhi.
Dr. Amarjeet Kaur1, Venkatesh Dutta2, Dr. D. K. Chaddha3
Research Scholar, Department of Geography,
1. Head, School of Environment Management,
Guru Gobind Singh Indraprastha University,
Kashmere Gate, Delhi - 110 006. E-mail:amarjeet@ipu.edu
2. Ph.D. Student, Faculty of Policy & Planning,
TERI School of Advanced Studies, India Habitat Centre,
Lodi Road, New Delhi - 110 003. E-mail: vdutta@terischool.ac.in
3. Former Chairman, Central Ground Water Board (CGWB), Delhi.
Introduction
Land is one of the prime natural resources. Urban population growth and urban-sprawl induced land use changes coupled with industrial development are resulting in unplanned use as well as misuse of land leading to conversion of useful land into wastelands. The changes of land use/land cover pattern over a time period control the pressure on land (Sengupta & Venkatachalam, 2001). The complexity of urban development is so dynamic that it calls for an immediate perspective planning of cities and towns (Sokhi & Rashid, 1999). For a sustainable use of the land it is essential that proper planning and monitoring be done. Timely and accurate information on the existing land use/land cover pattern and its spatial distribution and changes is a pre-requisite for planning, utilisation and formulation of policies and programmes for making any micro and macro-level developmental plan. Accurate, reliable and comprehensive spatio-temporal information on land use practices in a city is prerequisite for sustainable land management. Remote sensing offers cost-effective solutions to city planners data needs for both macro and micro level analysis of the land use planning leading to urban environment management. The better management and rationale use of land calls for accurate and timely changes in the dimension, nature, and spatial balance between exploitation and regeneration. GIS is best utilised for integration of various data sets to obtain a homogeneous composite land development units which helps in identifying the problem areas and suggest conservation measures (Murthy et al, 2001). In regional/urban development planning identification of types of ecologically critical areas and its orientation for future growth is important for balance land use. This type of planning will be useful to promote environmentally protected zones and other fragile land use forms. Latest information so obtained through remote sensing technology on EFAs would undoubtedly be of great value to various agencies like those concerned with land reclamation, soil conservation, and afforestation as well as for planning, selecting priority areas, allocating funds and executing reclamation methods on sound scientific basis (Kaur et al, 2001).
Study area
Delhi, the capital city of India, is one of the prime mega cities of the world. Situated on 28o30' North latitude and 77o00' East longitude, it lies at an altitude of between 700 and 1,000 feet (213 and 305 metres) and covers an area of 1,485 square kilometres. Situated on the Yamuna River (a tributary of the Ganges River) Delhi is bordered on the east by the state of Uttar Pradesh and on the north, west, and south by Haryana. The region has a tropical steppe climate. The general prevalence of continental air leads to relatively dry conditions with extremely hot summers. Monthly mean temperatures range from 14.3oC in January (minimum 3oC) to 34.5oC in June (maximum 47oC). The annual mean temperature is 25.3oC. The main seasonal climatic influence is the monsoon, typically from June to October. The mean annual rainfall total is 71.5 mm. Maximum rainfall occurs in July (211 mm). The heavy rains of the monsoon act as a "scrubber". North-westerly winds usually prevail; however, in June and July south-easterly predominate.
Land use classification
The remote sensing technology along with GIS is an ideal tool to identify, locate and map various types of lands associated with different landform units (Dhinwa, 1992; Palaniyandi and Nagarathinam, 1997; Murthy & Venkateswara, 1997; Khan et al., 1999). The timely information about the changing pattern of land use plays significant role in land use planning and sustainable land development. The mapping and monitoring of the land use/land cover requires a land use classification system. One of the most widely used data format for information extraction about the land use and land cover is the infrared False Colour Composite (FCC) image. The extraction of information from such images about ground reality is done by image interpretation for which generally three methods namely photo interpretation, spectral analysis and data integration are used. Prasad and Sinha (2002) describe the image characteristics and visual interpretation techniques of various land cover and land use categories which is summarised in the table 1.
Table 1: Land cover/land use and their image characteristics
Source: Prasad and Sinha (2002)
Methodology
Figure 1: Flowchart of simplified methodology
Collection of data and preparation of maps
The first step in a GIS project is to create a database of spatial, non-spatial and attribute information. In this study data acquired by Indian Remote Sensing satellite IRS-1C, LISS-III has been used as a primary data source for carrying out rapid reconnaissance survey. The false colour composites (FCCs) in the form of geocoded data on 1:50,000 scales have been used to generate land use/land cover map of the region. The interpretation was substantiated by IRS-1C, PAN image through carrying out field surveys for ground trusting. Apart from these data census data and Delhi Statistical Handbook (1998) were also used for a number of auxiliary information. The methodology adopted to generate the land use/land cover map is depicted in figure 1.
Data products used for the study
Table 2: Satellite data used for the study
Table 3: Ancillary data used for the study
Preparation of Thematic Maps
Thematic maps on land use/land cover were prepared based on the interpreted data using LISS III FCC, PAN, SOI toposheets and the field survey. Visual interpretation and digital analysis of satellite imagery were performed to delineate various land features.
Digitisation of Maps and Spatial Data Automation
The various thematic maps of 1:5000 scale were scanned and a raster to vector conversion was carried out. Each feature of the point, line and polygon digitised was labelled, as defined in the data dictionary. These labels were used as identifiers to link the attribute data of each feature. The digitised thematic maps then were imported to ARC/INFO as coverages. After executing 'clean' command for topology creation, the dangle node errors were removed interactively in ARCEDIT module. After all offshoot/undershoot errors have been removed, Feature Attribute Table (FAT) was created using 'build' in ARC prompt. Then each land use/land cover category was assigned a code and these codes were attached to appropriate records of FAT interactively. A Look-up-Table (LUT) was also created to assign a shade symbol for each land use/land cover category. Attribute codes for different categories were verified and additional attributes added into the features database. The theme coverage was made ready for updating into map library and also for analysis.
Results and Discussions
Table 4: Percentage of different land use categories under EFAs of NCT Delhi
Figure 2: Distribution of land use categories under EFAs
Table 5: The major categories of EFAs identified and their brief description
Sustainable land use planning
Maintenance of the productive potential of land resources and checking of land degradation is a fundamental element of sustainable land use. Land refers to not just soil but to the combined resources of terrain, water, soil and vegetation that provide the basis for land use (World Bank, 1995). Land quality is a complex attribute of land which affects its suitability for specific uses in a distinct way (FAO, 1993). Land quality needs to be assessed with respect to specific functions and types of land use. A land with poor quality is like having no land at all. Sustainable land management should enhance the economic performance of land while maintaining the quality and environmental functions of the natural resources, also preserving the cultural aspect of landscapes (LUCC Report No. 3). Data to establish a topology and characterisation of land use intensity and diversity are needed. Data that can relate the time dimension to each land use and its land cover, linked to a sustainable situation is very important.
Figure 3: The Pressure-State-Response (PSR) framework (Modified form various sources)
Land use systems in the traditional sense are complex, self-adaptive units that evolve and change in concert with external biogeochemical forces. Land use systems are, or have become human systems by virtue of the universal impacts of population growth and economic activity (Morain, 1999).Land use patterns are all governed by human resource requirements as much as by natural forces, and therefore require delicate management to achieve sustainable yields. Land issues may be grouped into three clusters. This is a loose grouping, not a classification, and some issues fall into more than one group (World Bank, 1995).
Soil and land being non-elastic resources, all the land based needs of the fast growing population has to come necessarily form limited landmass. There is, therefore, an urgent need to control land degradation and restore the locked up production in degraded lands (Ghatol and Larale, 2000). Sustainable land management necessitates precise information on the extent and spatial distribution of the different kinds of the EFAs to plan suitable reclamation or ameliorative measures consistent with the nature and intensity of the problem as well as operational ease with which reclamation/ameliorative measures can be carried out. Hence it is important to provide information on land use/land cover of a region with a special focus on EFAs, to provide early warning of adverse trends and identification of problem areas. The danger of land degradation is most apparent in areas of marginal or fragile lands, such as the semi-arid climatic zones, or soils with severe fertility constraints. Without having adequate knowledge about EFAs in a region, there is no proper foundation for policy formation and decision making on matters affecting land resources at all scales and levels. The present study was aimed at delineating EFAs so as to formulate plans for their efficient management. The findings will be of immense help in formulation of sound environmental management plan of Delhi. The methodology adopted in this study has served to highlight some challenging and relatively unexplored dimension of area-specific problem in the context of micro-level land use planning.
References
Land is one of the prime natural resources. Urban population growth and urban-sprawl induced land use changes coupled with industrial development are resulting in unplanned use as well as misuse of land leading to conversion of useful land into wastelands. The changes of land use/land cover pattern over a time period control the pressure on land (Sengupta & Venkatachalam, 2001). The complexity of urban development is so dynamic that it calls for an immediate perspective planning of cities and towns (Sokhi & Rashid, 1999). For a sustainable use of the land it is essential that proper planning and monitoring be done. Timely and accurate information on the existing land use/land cover pattern and its spatial distribution and changes is a pre-requisite for planning, utilisation and formulation of policies and programmes for making any micro and macro-level developmental plan. Accurate, reliable and comprehensive spatio-temporal information on land use practices in a city is prerequisite for sustainable land management. Remote sensing offers cost-effective solutions to city planners data needs for both macro and micro level analysis of the land use planning leading to urban environment management. The better management and rationale use of land calls for accurate and timely changes in the dimension, nature, and spatial balance between exploitation and regeneration. GIS is best utilised for integration of various data sets to obtain a homogeneous composite land development units which helps in identifying the problem areas and suggest conservation measures (Murthy et al, 2001). In regional/urban development planning identification of types of ecologically critical areas and its orientation for future growth is important for balance land use. This type of planning will be useful to promote environmentally protected zones and other fragile land use forms. Latest information so obtained through remote sensing technology on EFAs would undoubtedly be of great value to various agencies like those concerned with land reclamation, soil conservation, and afforestation as well as for planning, selecting priority areas, allocating funds and executing reclamation methods on sound scientific basis (Kaur et al, 2001).
Study area
Delhi, the capital city of India, is one of the prime mega cities of the world. Situated on 28o30' North latitude and 77o00' East longitude, it lies at an altitude of between 700 and 1,000 feet (213 and 305 metres) and covers an area of 1,485 square kilometres. Situated on the Yamuna River (a tributary of the Ganges River) Delhi is bordered on the east by the state of Uttar Pradesh and on the north, west, and south by Haryana. The region has a tropical steppe climate. The general prevalence of continental air leads to relatively dry conditions with extremely hot summers. Monthly mean temperatures range from 14.3oC in January (minimum 3oC) to 34.5oC in June (maximum 47oC). The annual mean temperature is 25.3oC. The main seasonal climatic influence is the monsoon, typically from June to October. The mean annual rainfall total is 71.5 mm. Maximum rainfall occurs in July (211 mm). The heavy rains of the monsoon act as a "scrubber". North-westerly winds usually prevail; however, in June and July south-easterly predominate.
Land use classification
The remote sensing technology along with GIS is an ideal tool to identify, locate and map various types of lands associated with different landform units (Dhinwa, 1992; Palaniyandi and Nagarathinam, 1997; Murthy & Venkateswara, 1997; Khan et al., 1999). The timely information about the changing pattern of land use plays significant role in land use planning and sustainable land development. The mapping and monitoring of the land use/land cover requires a land use classification system. One of the most widely used data format for information extraction about the land use and land cover is the infrared False Colour Composite (FCC) image. The extraction of information from such images about ground reality is done by image interpretation for which generally three methods namely photo interpretation, spectral analysis and data integration are used. Prasad and Sinha (2002) describe the image characteristics and visual interpretation techniques of various land cover and land use categories which is summarised in the table 1.
| Land Cover/land use | Image Characteristics |
| 1. Settlements | Light grey clustering with particular patterns for the urban area. There may be brownish maroon patches for in between vegetation. For the rural settlement there occur no particular patterns of such image characteristics. |
| 2. Agriculture | Identify rabbi if the month of data acquisition is January or February or March and colour is brown red. For the kharif crops same characteristics in image occur if the image data are acquired in the month of September, October or November.(b)Fallow land is identified by light grey colour within cropped area (red colour).(c) Plantation occurs as brownish maroon patches. |
| 3. Forest (a) Dense forests (b) Degraded forest (c) Forest blank (d) Forest plantation |
Dense forests are identified by dark red colour patterns. In the case of degraded forest the dark red colour patterns contain small brown or white patches. The blanks in the forest show creamy patches in the dark red/ background. Forest plantations are identified by dark red colour sign of particular pattern. |
| 4. Waste Land (a) Muddy water logging (b) Clear water logging (c) Temporary water logging (d) Permanent water logging (e) Marshy area water logging (f) Gullied land (g) Land with scrub (h) Land without scrub (i) Sandy area |
Muddy water logging occurs as blackish or deep blue spots while clear water logging area is identified by dark/bright blue patches. Comparing the images of rainy season and out of rainy season identifies temporary and Permanent water logging. Marshy area is recognized as a sign of vegetation (red/pink spots) in the water logged (blackish blue/bright blue) area. Gullied land occurs as white/grey spot. The image of land with scrub contains white patches in the land area. Sandy area is classified as bright white coloration along the course of river. |
| 5. Water bodies (a) River/stream (b) Canal (c) Lake/ Reservoirs (d) Embankments |
River/stream is identified as long non-linear path coloured with dark blue/ bright blue line in white background. Canals are identified as line segments sign of water. Lake/reservoirs are identified as patterns along the river. Embankment occurs as light grey structure along the river. |
| 6. Others | Grasslands are identified as uneven appearance characterized by red (light to medium grey tones) snow is identified as white patches on the hills. |
Methodology
Figure 1: Flowchart of simplified methodology
Collection of data and preparation of maps
The first step in a GIS project is to create a database of spatial, non-spatial and attribute information. In this study data acquired by Indian Remote Sensing satellite IRS-1C, LISS-III has been used as a primary data source for carrying out rapid reconnaissance survey. The false colour composites (FCCs) in the form of geocoded data on 1:50,000 scales have been used to generate land use/land cover map of the region. The interpretation was substantiated by IRS-1C, PAN image through carrying out field surveys for ground trusting. Apart from these data census data and Delhi Statistical Handbook (1998) were also used for a number of auxiliary information. The methodology adopted to generate the land use/land cover map is depicted in figure 1.
Data products used for the study
| Satellite | Sensor | Scale | Spatial resolution | Path/row |
| IRS-1C (1999) | PAN (Panchromatic) | 1:25,000 | 5.8 m | 56/60 |
| RS-1C (1999) | LISS-III | 1:50,000 | 23.6 m | 56/60 |
Table 3: Ancillary data used for the study
| Toposheet | Year of publication | Scale | Source |
| 53 H/1 | 1977 | 1:50,000 | Survey of India |
| 53 H/2 | 1977 | 1:50,000 | Survey of India |
| 53 H/3 | 1977 | 1:50,000 | Survey of India |
| 53 H/6 | 1977 | 1:50,000 | Survey of India |
| 53 H/7 | 1977 | 1:50,000 | Survey of India |
| 53 D/13 | 1977 | 1:50,000 | Survey of India |
| 53 D/14 | 1977 | 1:50,000 | Survey of India |
Preparation of Thematic Maps
Thematic maps on land use/land cover were prepared based on the interpreted data using LISS III FCC, PAN, SOI toposheets and the field survey. Visual interpretation and digital analysis of satellite imagery were performed to delineate various land features.
Digitisation of Maps and Spatial Data Automation
The various thematic maps of 1:5000 scale were scanned and a raster to vector conversion was carried out. Each feature of the point, line and polygon digitised was labelled, as defined in the data dictionary. These labels were used as identifiers to link the attribute data of each feature. The digitised thematic maps then were imported to ARC/INFO as coverages. After executing 'clean' command for topology creation, the dangle node errors were removed interactively in ARCEDIT module. After all offshoot/undershoot errors have been removed, Feature Attribute Table (FAT) was created using 'build' in ARC prompt. Then each land use/land cover category was assigned a code and these codes were attached to appropriate records of FAT interactively. A Look-up-Table (LUT) was also created to assign a shade symbol for each land use/land cover category. Attribute codes for different categories were verified and additional attributes added into the features database. The theme coverage was made ready for updating into map library and also for analysis.
Results and Discussions
| EFAs | Area | |
| Ha | % | |
| Water logged | 1315.31 | 0.89 |
| Back swamp/marshy | 1037.36 | 0.70 |
| Rocky area with sparse vegetation | 3480.30 | 2.34 |
| Mine pits | 0032.12 | 0.02 |
| Underutilised vacant land | 4312.22 | 2.90 |
| Dumping site | 0136.68 | 0.09 |
| Flood plain with vegetation | 3464.89 | 2.33 |
| Flood plain without vegetation | 0999.55 | 0.67 |
| Sand bar | 0281.19 | 0.19 |
| Undulating land without scrub | 1898.44 | 1.28 |
| Undulating land with scrub | 0846.93 | 0.57 |
| Eroded land | 1111.67 | 0.75 |
| Fly ash dumping sites | 0029.94 | 0.20 |
| TOTAL | 18946.60 | 12.93 |
Figure 2: Distribution of land use categories under EFAs
Table 5: The major categories of EFAs identified and their brief description
| EFAs | Brief descriptions |
| Rocky area with sparse vegetation | Rocky exposures of varying lithology with sparse vegetation, comprises 2.34% of the total geographic area, the land requires afforestation and watershed development practices. |
| Water logged/back swamp and marshy land | It is a land where the water is at or near the surface and water stands for most of the year. These lands usually occupy topographically low lying areas but the lands with surface water bodies like lakes, ponds and tanks do not fall under this category. It comprises 1.59% of the total geographical area. These areas can be used as potential sites for aquaculture technology. Some waterlogged areas may be developed into fish ponds with slight improvement. This plan of developing of waterlogged area into aquaculture farming will not only clean the waterlogged and polluted areas but will also provide self-employment to a large poor population inhabiting in the slum areas. |
| Undulating land with/without scrub | These lands are mostly occupied by relatively higher topography like uplands or high grounds with or without scrub. These exclude hilly and mountainous terrain. These comprise 1.85% of the total geographic area. The area without scrub is eroded, unproductive land, which can be put to afforestation. |
| Eroded land | It covers 0.75% of the total geographical area. In these areas soil has been eroded and development of degraded land has taken place due to large scale deforestation. Afforestation should be immediately done in severely eroded land and grazing should be restricted in the newly afforested land. |
| Dumping sites | It comprises of garbage and fly-ash dumping site with a total area of 136.68 ha or 0.09% of the total geographic area. The practice of fly-ash disposal on the flood plain area should be stopped immediately and vegetation on the existing fly-ash dumps should be undertaken to prevent the spread of the fly-ash on the flood plain areas. |
| Sand bars | These are the areas which have stabilised accumulations of sand in situ or transported in riverine or inland areas. It is present either in the river beds or in the flood area of the river. It comprises 281.19 ha or 0.19% of the total geographic area. The partially stabilised sandy area in the Yamuna bank can act as an excellent source for cultivation of high cost luxurious fruit like strawberry etc. |
| Flood plain | The dominant land use in this area is underutilised agriculture and other land uses such as vegetation grasslands, scrubs, forest plantation, water logged/marshy lands, fly ash deposition etc. The rich organic content, excellent sources of irrigation water and fertile soil render this area useful for horticulture and floriculture plantations (except during 2 to 3 monsoon months). The vegetated area on the flood plain should be improved and the vegetation should be characteristic of the flood plains, preventing erosion on the stream banks and filtering the pollution. The landmass in the flood areas should be stopped for littering, solid waste disposal including fly ash. |
Sustainable land use planning
Maintenance of the productive potential of land resources and checking of land degradation is a fundamental element of sustainable land use. Land refers to not just soil but to the combined resources of terrain, water, soil and vegetation that provide the basis for land use (World Bank, 1995). Land quality is a complex attribute of land which affects its suitability for specific uses in a distinct way (FAO, 1993). Land quality needs to be assessed with respect to specific functions and types of land use. A land with poor quality is like having no land at all. Sustainable land management should enhance the economic performance of land while maintaining the quality and environmental functions of the natural resources, also preserving the cultural aspect of landscapes (LUCC Report No. 3). Data to establish a topology and characterisation of land use intensity and diversity are needed. Data that can relate the time dimension to each land use and its land cover, linked to a sustainable situation is very important.
Figure 3: The Pressure-State-Response (PSR) framework (Modified form various sources)
Land use systems in the traditional sense are complex, self-adaptive units that evolve and change in concert with external biogeochemical forces. Land use systems are, or have become human systems by virtue of the universal impacts of population growth and economic activity (Morain, 1999).Land use patterns are all governed by human resource requirements as much as by natural forces, and therefore require delicate management to achieve sustainable yields. Land issues may be grouped into three clusters. This is a loose grouping, not a classification, and some issues fall into more than one group (World Bank, 1995).
| 1. Inappropriate land use systems | Land use is poorly matched to the land units on which they are practised, i.e., yields are low or economic returns marginal |
| 2. Land degradation | Lowering in the capacity of land for agricultural production or its potential for environmental management, i.e., lowering of land quality |
| 3. Inadequacies in the policy environment for land users | Pricing policies (taxes or subsidies) for e.g., a high fertiliser subsidy encouraging inefficient use, land tenure legislation, common property regimes etc. |
Soil and land being non-elastic resources, all the land based needs of the fast growing population has to come necessarily form limited landmass. There is, therefore, an urgent need to control land degradation and restore the locked up production in degraded lands (Ghatol and Larale, 2000). Sustainable land management necessitates precise information on the extent and spatial distribution of the different kinds of the EFAs to plan suitable reclamation or ameliorative measures consistent with the nature and intensity of the problem as well as operational ease with which reclamation/ameliorative measures can be carried out. Hence it is important to provide information on land use/land cover of a region with a special focus on EFAs, to provide early warning of adverse trends and identification of problem areas. The danger of land degradation is most apparent in areas of marginal or fragile lands, such as the semi-arid climatic zones, or soils with severe fertility constraints. Without having adequate knowledge about EFAs in a region, there is no proper foundation for policy formation and decision making on matters affecting land resources at all scales and levels. The present study was aimed at delineating EFAs so as to formulate plans for their efficient management. The findings will be of immense help in formulation of sound environmental management plan of Delhi. The methodology adopted in this study has served to highlight some challenging and relatively unexplored dimension of area-specific problem in the context of micro-level land use planning.
References
- Sengupta, S., and Venkatachalam, P., (2001) Assessment of pressure on land in an urban area using remote sensing and GIS, A. P., In Srinivas, M.G. (ed.) Remote Sensing applications, Narosa Publishing House, New Delhi.
- Sokhi, B.S., and Rashid, S.M., (1999) Remote sensing of urban environment, Manak Publications Ltd., Delhi.
- Murthy, J. N. A., Reddy, P. R. K., Pateriya, B., and Kumar, M.N., (2001) Integrated study for sustainable development using remote sensing and GIS techniques- a case study for part of Musi catchment, Rangareddy District, A. P., In Srinivas, M.G. (ed.) Remote Sensing applications, Narosa Publishing House, New Delhi.
- Kaur, A., Banger, S.K., and Jain, N., (2001) Study of environmentally fragile areas of Delhi using remote sensing/GIS technology, Indian J. Environ. & Ecoplan. 5(1):207-212.
- Dhinwa, P.S., (1992) Land use change analysis of Bharatpur district using GIS, Journal of the Indian Society of Remote Sensing, 20(4).
- Palaniyandi, M., and Nagarathinam, V., (1997) Land use/land cover mapping and change detection using space born data, Journal of the Society of Remote Sensing, 25:27-33.
- Murthy, K.S.R., and Venkateswar, R. V., (1997) Temporal studies of land use/land cover in Varah river basin, Andhra Pradesh, Journal of the Indian Society of Remote Sensing, 25:153-154.
- Khan, S., Bhuvneswari, B., and Quereshi, M., (1999) Land use/land cover mapping and change detection using remote sensing and GIS: a case study of Jamuna and its environs, Socio-economic development record, 6:32-33.
- Prasad, R., and Sinha, A. K., (2002) IRS-1B based FCC image interpretation for land use and land cover mapping: An expert system approach,
- Morain, S., (1999), GIS solutions in natural resource management: balancing the technical-political equation, Onworld Press, USA.
- Ghatol, S.G., and Karale, R.L., (2000) Assessment of degraded lands of Vidarbha region using remotely sensed data, Journal of the Indian Society of Remote Sensing, Vol. 28, No. 2 & 3, 2000.
- World Bank (1995) Land quality indicators, World Bank Discussion Paper No. 315.
- FAO (1993) Guidelines for land-use planning, FAO Development Series 1, FAO, Rome.
- LUCC Data Requirement Workshop: Survey of needs, gaps and priorities on data for land use/land cover change research, LUCC Report Series No.3, Organised by IGBP/IHDP-LUCC and IGBP-DIS, Barcelona, Spain, 11-14 Nov. 1997.