S. Sampath Kumar, K. T. Sridhar
(Peagus Software Consultabnts Pvt Ltd, Bangalore)
M. K. Chakraborty, B. K. Tewary
(Central Mining Research Institute, Dhanbad)
Geomatics tools have been used as aide to study the carrying capacity of Damodar River Basin. The basin is rich in mineral resources and has a high level of mining based industrial and economic activity. In order to facilitate future planning for the basin, it carrying capacity was studied with reference to its resources and degradation in land, water, air, noise and socioeconomic living standards.
A digital geographic database of Damodar River Basin was created and GIS analysis and modelling techniques used to answer questions related to carrying capacity of DRB. This paper specifically focuses on the GIS techniques and methodologies used to design and create the geographic database and carry out analysis and modelling of the data. The analysis/ modelling techniques identified for this study are discussed and their use in finding answers to real-life questions related to the basin are illustrated.
The Damodar River Basin (DRB), a part of the Ganges river system drained by two major river systems Damodar and Barakar, extends over the states of Bihar and West Bengal. The basin is rich in mineral resources, particularly coal, and has a high level of mining based industrial and economic activity. Several urban and industrial centres exist in the region. The result of unplanned growth in the region has led to environmental problems related to land, water, air, noise and general standard of living.
To study the current state of the basin and plan for its future growth, the Ministry of Environment and Forest, Government of India, set up a multi-institutional project with Central Mining Research Institute (CMRI), Dhanbad, as the nodal agency. Several organisations in the government and private sectors were involved at various stages of the project. The primary goal of the project is to apply carrying capacity based techniques for planning the future growth of the region. Geomatics tools such as geographical information system and image processing packages were used as aids in the study of DRB
This paper reports on one aspect of CCDRB project, namely the use of computer aided geomatics tools. More specifically this paper focuses on, and discusses, the GIS techniques and methodologies adopted to meet the needs and requirements of the study of Carrying Capacity of DRB (CCDRB).
Carrying Capacity of DRB
Sustainable development of an industralised region is dependent on the physical limits of its natural resource base and assimilation of generated residual wastes. To achieve sustainable development of a region, planning for the region must take into account the carrying capacity of the region. Carrying capacity of the region. Carrying capacity of a region may be loosely defined as the intrinsic capacity of a region to assimilate various categories of degradation and pollution such that it does not affect the sustainability of the region. A detailed discussion on carrying capcaity can be found in (CMR95, CMR97).
Planning based on carrying capacity needs to study the existing scenario of the region and its assimilative capacity. Carrying capacity of DRB was studied with reference to the following five parameters, four of which relate to the environment and the last one to the standard of living.
- Socioeconomic standards
DRB data for all the five parameters were collected by different organistaions participating in the project and GIS techniques were used to create a geographic database of DRB and analyse the geographic data for identifying hot spots and what-if scenarios.
The broad objectives for the use of geomatics tools for CCDRB project are listed below
- design a digital geographic database for DRB; identify spatial and attribute data requirements their sources
- create a digital geographic database as per the design integrating both spatial and attribute data from multiple sources
- carry out satellite image interpretation for DRB region
- identify, design and carry out geographic analysis and modelling of DRB digital data to identify hot spots and find answers for what-if scenarios
- generate hardcopy map outputs on paper media
All GIS work for the project was carried out using the indigenous GIS package GIPSY (product of Pegasus Software Consultants Pvt Ltd, India) running on Intel Pentium platform under Windows 95/ 3.1. All image processing work for the project was carried out using EASI-PACE (product of PCI Inc., Canada) running on IBM AIX workstations. Raster data obtained from image processing was vectorised and integrated with GIPSY geographic database.
Geographic Database Design
The design extent of the geographic database for DRB is based on the following broad guidelines
- The spatial extent of the geographic database must cover the Damodar River Basin falling under the states of Bihar and West Bengal.
- All spatial data spanning entire DRB area shall be at a map scale of 1:250,000.
- Maps of smaller regions within DRB, such as maps of coal fields and towns, shall be at a scale better than 250,00.
- Data from multiple sources at different scales is to be integrated in the geographic database.
- All maps spanning entire DRB area shall be geo-coded, i.e. provided with latitude/longitude values.
- The minimum administrative unit at which demographic and socioeconomic data is to be integrated shall be a block.
Based on the above guidelines the geographic database of DRB has been designed at two levels
- macro database comprising maps that span the entire area of DRB; all maps in the macro database are at a scale of 1:250,000; all maps in the macro database are geo-coded
- micro database comprising detailed maps in the micro database depend on the source data and range from 1:3000 to 1:250,000: wherever possible maps in the micro database depend on the source data and range from 1:3000 to 1:250,000: wherever possible maps in the micro database are geo-coded.
The geographic area of Damodar River Basin is covered by nine SOI 1:250,000 topo-sheets
72H, 72L, 73A, 73E, 73I, 73M, 73N, 79A, 79B
and falls between the bounding latitude and longitude values given below.
Latitude: 22deg N to 25deg N
Longitude: 84deg E to 89deg E
The macro database comprises a number of thematic layers spanning the entire DRB region and can be categorised under the five major parameters, namely land, water, air, noise and socioeconomic data, considered for CCRDB study.
The micro database comprises detailed maps of priority towns of DRB and major coal fields within DRB.
Sources for spatial data in the geographic database of DRB has been from printed maps of following agencies/publications
- Survey of India
- National Bureau of Census
- Geological Survey of India
- Damodar Valley Corporation
- National Bureau of Soil Survey
- All India Soil and Land Use Bureau
- Coal Atlas of India
- Planning Atlas: Damodar Valley
- Central Mine Planing & Design Institute Ltd.
Satellite data of National Remote Sensing Agency, ISRO, Hyderabad, from the following sources has been used in the creation of landuse maps of DRB.
- IRS 1B LISS I images of 1997
- IRS 1B LISS I images of 1990
- LANDSAT MSS images of 1984
GIS techniques for carrying capacity study of Damodar River Basin
In addition to the above sources of map data, specially prepared maps from other organistions participating in CCDRB project have also been used. Map data prepared by the following organisations has been used in the creation of DRB geographic database.
- Central Mining Research Institute, Dhanbad
- National Environmental Engineering Research Institute, Nagpur
- Centre for Inter-disciplinary Studies of Mountain & Hill Environment, University of Delhi, New Delhi.
Any qualifying attribute data in the printed maps used for creating DRB geographic database and related publications of the respective agencies has been integrated with the spatial data. Demographic data from census tracts has also been integrated at the block and town levels of DRB. In addition, project specific data collected by the following agencies has been integrated with maps of DRB.
- Central Mining Research Institute, Dhanbad
- National Environmental Engineering Research Institute, Nagpur
- Metallurgical and Engineering Consultants Ltd., Ranchi
- National Institute of Small Mines, Calcutta
- Indian School of Mines, Dhanbad
Macro Database Creation
The broad methodology used to create digital data for DRB thematic layers of the macro database is the following
- digitising of individual map sheet tiles
- construction of polygon topology wherever required
- editing of digitised themes
- association of names/attribute data with spatial features
- geo-referencing, mosaicking and edge matching of individual tiles
- clipping with DRB boundary
Steps (a) to (d) complete the creation of digital data for the individual tiles. Step (e) merges the individual tiles to create a composite thematic layer for entire DRB area.. Step (f) eliminates spatial features outside DRB area in the composite map. The final step symbolises the map data by suitable coloring and symbol association. In the case of satellite data, steps (a) to (d) above are replaced bu processing of imagery and vectoristaion of the raster output.
For land related geographic data of DRB, thematic layers under the following categories were created in the geographic database.
- Administrative Units: Districts and Blocks
- Transport Network
- Landuse Land Capability
The total area of DRB computed from the digital boundary is 23,370.97 square kms and spans across two states, Bihar and West Bengal. Digital data at two levels of administrative units, namely districts and block, were created for DRB. Table 1 below lists the districts of DRB and area values for each district within DRB.
Block boundaries are not available in SOI 1:250,000 sheets but can be found in maps published as part of District/Census handbooks. These maps are not geo-coded and are not at a scale of 250,000, but at varying scales depending upon the map. Block boundaries from district/census handbooks were digitised and geo-referenced to the district boundary map. DRB has a total of 99 blocks from the districts of Bihar and West Bengal.
The transport network of DRB comprises national highways, metalled and unmetalled roads from SOI publications and railway lines, both broad and metre gauges.
|District||State||Area within DRB
Soil slope of NBSS was digitised and slope classes as per NBSS standards assigned to the polygons
Landuse data of DRB is a major input data for study of land degradation, identification of hot spots and formulation of ameliorative steps for the region. As DRB is an area rich in mines and is highly industralised, landuse over the years is essential for change analysis of forests, mines and built-up areas in the region, Survey of India 1:250,000 sheets were printed in the early seventiesand the surveying was possibly done several years before that. As current landuse is essential for the study of land, satellite imagery was used to determine the landuse of the region. To facilitate trend analysis, it was decided to create landuse layers for the current priod, early nineties, early eighties and early seventies.
IRS data (1997 and 1990) and LANDSAT data (1984) were used to create classified landuse covers of DRB using image processing techniques. The raster landuse maps were vectorised and integrated with DRB geographic database. Landuse layer digitised from SOI 1:250,000 and District Planning series map was used for early seventies layer of Landuse.
Soil themes for depth, texture and erosion were created using soil series maps at 1:250,000 scale from National Bureau of Soil Survey, Soil pH and soil chemical values for nitrogen (N), phosphorous (P) and potassium (K) were measured at sampled locations by CMRI.
Geological quadrangle maps published by Geological Survey of India, and geology/mineral maps of Bihar and West Bengal were used to create a geology layer for DRB. The geomorphology map of DRB was created from 1,000,000 scale map published in the Planning Atlas of DRB.
The agro-ecology zones map of DRB was created from an NBSS publication of agro-ecological sub-regions.
Data about locations of medicinal plants, cultural heritage sites, tourist spots and elephant migration corridors in DRB was collected by Centre for Inter-Disciplinary Studies of Mountain &Hill Environment, Delhi University. This data was geo-referenced and integrated in the geographic database of DRB.
For water related geographic data of DRB, thematic layers under the following categorises were created in the geogrpahic database.
- Waterbodies: rivers, lakes and reservoirs
- Flood Zones
- Ground Water: Quantity and Quality
- Surface Water Quality
A total of forty watershed areas are found in DRB Ground water quantity data from Central Ground Water Board was available block wise: values provided include ground water draft and balance. Ground water quality data (water hardness, chloride, etc.) was measured at selected well locations [CMR95, CMR97].
Surface water quality was measured at a number of locations along the river network DRB [CMR97]. The locations of the measurement points along the river network were identified and introduced as point features in the waterbodies theme.
A number of air quality monitoring stations were set up within DRB by MECON and ISM for measuring air quality values of SPM, SO2and NOx [CMR97]. Lat/Long values for the measurement locations were provided by MECON and ISM. Based on these values, point features with relevant air quality data were added to DRB boundary themes to create an air quality monitoring station layer for DRB.
Traffic noise values were measured by CMRI and ISM at a number locations within DRB [CMR97]. The noise measurement locations were identified on the road network of DRB.
Data For socioeconomic standards data of DRB, thematic layers under the following categories were created in the geographic database.
- Power Network
- Solid Waste
- Quality of Life Index
Demography data from Census tracts of 1991 at urban and rural block levels for population, literacy and population density were integrated with block maps of DRB.
Data for the following amenities were integrated with block maps of DRB. For each type of amenity multiple parameters related to amenity were considered; for example, education parameters considered include number of primary schools, middle schools, training schools and other schools in a block.
- Post & Telegraph
Data for solid wastes generated in the year 1991 at selected mahor towns within DRB was collected and projections made for the years 2001, 2011 and 2021 [cmr97].
The quality of life was assessed by surveying a selected set of DRB blocks in Bihar and West Bengal by CMRI and National Institute of Small Mines. Based on the survey data collected by these two organisations, a subjective index and objective index were determined for the sampled blocks. QOL indices were provided as numerical values for the surveyed blocks [CMR97]. These values were associated attribute data with the block map of DRB.
Micro Database Creation
He micro database contains detailed maps at scales ranging from 3960 to 250,000 for selected areas of DRB. The region is rich in coal deposits and mining activity is carried out in seven major coal fields within DRB. As coal mining is an important activity in DRB, the micro database contains maps of seven major coal fields of DRB.
- East Bokaro
- West Bokaro
- North Kamapura
- South Kamapura
For each coal field a polygonal theme comprising coal deposit areas, built-up areas, etc., and a linear features theme comprising roads, lineaments, etc., have been created as themes for some of the coal fields based on source maps from Central Mine Planning & Design Institute Ltd.
Town maps of priority towns of DRB, namely Dhanbad, Bokaro and Raniganj, and are also included in the micro database.
The goal of geographic analysis and modelling is to find answers to physical issues related to the application: in this study, to identify hot spots in DRB and depict scenarios for hypothetical what-if questions. Analysis and modelling of geographic data may be on either of the two data domains integrated by GIS systems, namely spatial or geometric data and attribute data domains. In our discussion on geographic analysis and modelling we exclude geographic querying [SMM94] operations supported by GIS systems.
The effectiveness of a GIS in solving application problems is highly dependent on the techniques used and the kind of operations supported by the GIS package. In addition to conventional vector overlay operations, a number of other techniques can be used for analysing and modelling geographic data. For the CCDRB project a number of GIS techniques were identified and used to address issues related to the basin. The following techniques were used in this study.
- GIS theme operations
- Formula application
- Qualitative analysis
- Change detection analysis
- Socioeconomic data models
- Multi-parameter analysis
- Presentation charts
Analysis and modelling operations involve multiple steps creating data for the next step. Due to the size of data, operations can be time consuming.
The above techniques of geographic analysis and modelling employed in this project are explained below briefly.
GIS theme Operations
A GIS supports a number of operations on thematic layers for combining, extracting and transforming map data. In vector GIS packages these operations work on both spatial and attribute data domains. The operations are well defined geometric and topological operations which are also given a semantics in the attribute domain [SMM94]. Due to the evolutionary nature of GIS systems, the attribute domain semantics is somewhat ad-hoc. For some of the theme based operations which combine thematic layers, the input layers used for the operations must be geo-referenced. GIS theme operations are sometimes referred to as spatial modelling or overlays [Bur86].
For example, a map of degraded land can be generated by merging land parcels that have a certain degree of erosion, flooding status and are salt affected. Using extraction and transformation operations forest cover within coal fields can be identified from data generated for the whole basin.
An attribute column value in the data tables of the geographic database may be computed by a mathematical formula using some of the other data columns in the table. For example, the watershed wise ground water recharge can be computed as a formula based on area value and average rainfall of a land parcel.
A typical method used in GIS modelling is to compute numerical values for each spatial feature in a theme and classify the numerical values on an interval basis. For example, the literacy levels of blocks may be ranked on a five point scale of values very good, good, average, poor and very poor based on numerical values of literacy for each block. This method converts quantitative data to qualitative data depends on the domain knowledge of the expert choosing the intervals for quality classification. A number of statistical techniques can be used for the classification [Bur86].
Change Detection Analysis
An important use of a GIS is to identify temporal changes in spatial data. The temporal factor may be based on past, current and future scenario data in the geographic database. For example change in forest cover over decades can be detected subject to availability of forests cover data for decadal periods. Change detection analysis is done using spatial modelling operations such as theme combination, extraction and polygon aggregation.
Socioeconomic Data Models Socioeconomic data modelling primarily works on attribute data and the result of the modelling is associated with a suitable spatial feature. In modelling of socioeconomic data it is necessary to calculate a functional hierarchy of settlements [Sha81] in the study area so that they may be compared to assess the relative socioeconomic development of the settlements chosen for the study [WGDP]. A composite functionality index is determined for each unit of settlement [SAC92, SMJ96] as a comparative index. The development index may itself be defined on multiple parameters such as availability of power, transport facilities, communication facilities, etc.
Influence Zone ModelsInfluence zone models convert spatial data in point, line or polygonal form to polygonal areas representing influence zones. Examples of such influence zone modelling techniques are constructed of buffer corridors, generation of voronoi diagrams (Thiessen polygon) [Aur91] from a triangulation model and isolines generation from a triangulation model. A buffer corridor around a mining area can approximate the likely spread of mining or building activities; a voronoi diagram constructed on rainfall data measured at point locations can depict the average rainfall collected around the measuring station.
Multi-Parameter Analysis A recurrent theme in GIS modelling is multi-parameter analysis. A number of GIS applications need to consider multiple parameters to identify suitable areas that meet the required goal. For example, to identify agriculturally suitable areas parameters such as land capability, soil slope, geomorphology, etc. need to be taken in to account. Subjectivity in the definition of relative importance of the multiple parameters for the goal is inevitable.
Multi-parameter analysis may be done by one domain experts who examine the values for the parameters considered and arrive at a relative scale of suitability. The importance matrix method of Saaty [Saa80] for multi-prameter analysis is a technique to isolate the subjectivity in the analysis and has been used in [SAC92, SMJ96].
Presentation Charts Presentation charts such as pies, bars, graphs, etc., provide an easily understandable graphical method of depicting tabular data. In a GIS environment such charts can be spatially associated, or geo-referenced, with features in a map. For example the crop production of all districts can be presented as pie charts superimposed on each districts of the map.
Sections 7.1 to 7.5 list the issues of DRB taken up for geographic modelling and analysis and the GIS modelling/analysis techniques adopted for each issue. Due to paucity of space we do not elaborate on the methodology for each issue. More details can be found in [Peg98].
Analysis carried out on land related data of the macro database addresses the following questions.
- Identify the change in forest cover over decadal periods-change detection
- Identify the growth of mining areas over decadal periods-change detection
- Identify the growth of built-up areas over decadal periods-change detection
- Determine degraded land parcels in DRB-GIS theme operations
- Identify areas in DRB where immediate afforestation work is to be taken up-GIS theme operations
- Identify areas in DRB where afforestation work is to be taken up in the near future-GIS theme operations
- Delineate biologically rich areas in DRB which need to be conserved-GIS theme operations,
- Determine the suitability of land parcels in DRB for agriculture-GIS theme operations, qualitative analysis, multi-parameter analysis
- Determine degraded land within DRB areas currently available for agriculture-GIS theme operations, qualitative analysis, multi-parameter analysis.
- Determine the spatial spread of mining areas for the year 2001 and 2020-influence zones
Analysis carried out on water related data of the macro database addresses the following questions.
- Determine ground water recharge in DRB region on a watershed basis-influence zones, GIS theme operations, formula application
- On a watershed basis compute the usage of ground water with reference to recharge-formula application
- On a watershed basis compute the usage of ground water with reference to total availability-formula application
- On a watershed basis compute the recharge with reference to total availability-formula application
- Determine ground water level for the entire DRB region-influence zones
- Determine ground water hardness for the entire DRB region- influence zones
- Compute isolines of ground water chloride content for FRB-influence zones
- Identify the change in the shape of the river network over two decades-change detection
Air quality related data in the macro database was analysed to answer the questions listed below. All analysis for air quality data is based on a fifteen minute grid of DRB and Oak Ridge Air Quality Index (ORAQI) computed by CMRI and NEERI [CMR95, CMR97]
- Identify air pollution hot spots in DRB using computed values of averages and maximum Oak Ridge Air Quality Index-formula application, GIS theme operations
- Determine isopleths for air pollution using computed values of average and maximum Oak Ridge Air Quality Index-formula application, influence zones
Socioeconomic Data in the macro database was analysed to answer the following questions.
- Determine and grade the developemnt status of DRB blocks with reference to each of the seven types of amenity data considered in the macro database-socioeconomic data model, formula application, qualitative analysis
- Determine the overall amenities developemnt status of DRB blocks taking into account all seven types of amenities-multi-parameter analysis, qualitative analysis
- Identify the available analysis urban manpower resource in blocks of DRB-multi-parameter analysis, qualitative analysis
- Identify the available rural manpower resource in blocks of DRB-multi-parameter analysis, qualitative analysis
- Delineate the quality of life in blocks of DRB-formula application, qualitative analysis
- Identify the major crop areas, cropping pattern, irrigation and animal resources in districts of DRB-presentation charts
- Select core areas in DRB for industrial and economic development-GIS theme operations, multi-parameter analysis, qualitative analysis
For the seven major coal fields of DRB, a detailesd analysis was carried pout to answer the following questions.
- Determine the change in mining activities between 1990 to 1997 for each coal field-change detection
- Identify the encroachment of mining activities on forest covers of 1990 and 1997-change detection
- Determine the relationship between growth of built-up areas and mining activities for 1990 and 1997-change detection
- Determine the suitability of land parcels within each coal field for agriculture-GIS theme operations, multi-parameter analysis, qualitative analysis
- Identify currently available areas within each coal field for agriculture-GIS theme operations, qualitative analysis
- Identify the spatial extent of projected mining activities for the year 2020-influence zones
The use of geomatics tools for studying the carrying capacity of a large river basin, Damodar River Basin, rich in mining and industrial activities has been outlined. A number of GIS analysis and modelling, techniques have been identified and their use illustrated with reference to the carrying capacity of the basin.
The availability of suitable base geographic data is essential for the application of the techniques discussed here. The geographic database of DRB was designed keeping in view the requirements of the envisaged modelling and analysis. GIS techniques discussed in this paper have been drawn from multiple disciplines and have been found adequate to address complex issues related to DRB.
The primary focus of this paper has been on the GIS techniques used in this project and results related to the basin can be found in [Peg98, CMR97] and further reports on the project. Results of the geographic modelling and analysis are being further studied for a comprehensive report on DRB.
We thank Dr. S. C. Maudgal, Senior Advisor, Ministry of Environment and Forests, for his motivational leadership to the overall CCDRB project; Dr. T. N. Singh, Director, CMRI, and Prof B B Dhar, BHU & former Director, CMRI, for their interest, guidance and support to this project, Dr. C. V. Chalapati Rao, NEERI, Nagpur, for a number of discussions on identifying the basin related issues to be addressed for GIS modelling/analysis. We thank all our colleagues at CMRI, Pegasus and other participating agencies for their work and assistance at various stages of the CCDRB project.
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