This article was downloaded by: [Florida State University]On: 21 December 2014, At: 14:18Publisher: Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954 Registered office: MortimerHouse, 37-41 Mortimer Street, London W1T 3JH, UK

Geocarto InternationalPublication details, including instructions for authors and subscription information:http://www.tandfonline.com/loi/tgei20

Regional Study for Mapping the Natural ResourcesProspect & Problem Zones Using Remote Sensing andGISRajeev Kumar Jaiswal a , J Krishnamurthy b & Saumitra Mukherjee ca EOS/NNRMS, Indian Space Research Organisation Headquarters, Department of Space ,Antariksh Bhavan, New BEL Road, Bangalore, 560 094, India E-mail:b EOS/NNRMS, Indian Space Research Organisation Headquarters, Department of Space ,Antariksh Bhavan, New BEL Road, Bangalore, 560 094, India E-mail:c School of Environmental Sciences, Jawaharlal Nehru University , New Delhi, 110067,India E-mail:Published online: 02 Jan 2008.

To cite this article: Rajeev Kumar Jaiswal , J Krishnamurthy & Saumitra Mukherjee (2005) Regional Study for Mappingthe Natural Resources Prospect & Problem Zones Using Remote Sensing and GIS, Geocarto International, 20:3, 21-31, DOI:10.1080/10106040508542352

To link to this article: http://dx.doi.org/10.1080/10106040508542352

PLEASE SCROLL DOWN FOR ARTICLE

Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) containedin the publications on our platform. However, Taylor & Francis, our agents, and our licensors make norepresentations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose ofthe Content. Any opinions and views expressed in this publication are the opinions and views of the authors,and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be reliedupon and should be independently verified with primary sources of information. Taylor and Francis shallnot be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and otherliabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to orarising out of the use of the Content.

This article may be used for research, teaching, and private study purposes. Any substantial or systematicreproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in anyform to anyone is expressly forbidden. Terms & Conditions of access and use can be found at http://www.tandfonline.com/page/terms-and-conditions

21Geocarto International, Vol. 20, No. 3, September 2005 E-mail: geocarto@geocarto.comPublished by Geocarto International Centre, G.P.O. Box 4122, Hong Kong. Website: http://www.geocarto.com

Regional Study for Mapping the Natural Resources Prospect &Problem Zones Using Remote Sensing and GIS

Rajeev Kumar JaiswalEOS/NNRMS, Indian Space Research Organisation HeadquartersDepartment of Space, Antariksh BhavanNew BEL Road, Bangalore - 560 094, IndiaE-mail: rajeev@isro.gov.in

J KrishnamurthyEOS/NNRMS, Indian Space Research Organisation HeadquartersDepartment of Space, Antariksh BhavanNew BEL Road, Bangalore - 560 094, IndiaE-mail: jk_isro@yahoo.com

Saumitra MukherjeeSchool of Environmental SciencesJawaharlal Nehru UniversityNew Delhi - 110067, IndiaE-mail: dr.saumitramukherjee@usa. net

Abstract

A systematic approach of understanding the terrain characteristics at the regional level and then narrowing downto the target areas for detailed mapping not only improves the quality of information but also saves considerablytime, cost and manpower. For such activities, satellite-based remote sensing technology has become an efficient tool,due to its synoptic/multi-spectral/multi-temporaI coverage. In the present study, initially, a regional level naturalresources assessment was carried out using coarse scale satellite data and then based on the results, a representativesite was identified for detailed studies towards resources potential evaluation and to identify the areas undergoingcalamities like forest fire. This multi stage study was carried out for a part of Madhya Pradesh, India. The regionalset up of the study area was carried out on 1:250,000 scale using IRS LISS III standard FCC analog and digital data.A representative site named ‘Gorna’ sub-basin of the Son River, was selected for the detailed study at 1:50,000 scale,to mainly address the natural resources related problems of the region. The different thematic maps prepared wereintegrated in a GIS environment with suitable modeling for ground water prospect zone demarcation and forest firerisk zonation. Using the digital database of various themes, it could also be used appropriately for deriving otherdevelopmental plans. The result of the study demonstrate the utility of remote sensing technology for assessment ofnatural resources potentials at regional and local levels.

Introduction

Natural resources are those products and features of theearth, which not only provide support to life system but alsosatisfy the needs of the people. At times, these resources areoverexploited and thus result in their degradation.Management of such natural resources require knowledgenot only of their type, quantity and location, but alsoinformation about how these resources are being utilizedand impacted upon by human and other natural processes(Smyth and Yong 1998). Therefore, a periodic assessmentof natural resources through regular mapping and monitoringbecomes essential. Assessment and inventorying of natural

resources is considered as essential pre-requisite of anymanagement activity (Sebastian et al., 1985). Reliable andup-to-date information about various factors such as sizeand shape of the watershed, topography, soil, land use / landcover, drainage parameters, lithology of the area, etc. arerequired as basic information for resource management.Conventional method of resource survey, i.e. ground basedsurveying and mapping methods, while providing accurateinformation through detailed field inventorying, sufferinherent constraints. These include inadequately trainedmanpower, lack of infrastructure facilities, inaccessibility ofremote areas, non-availability of real time data and temporaldistribution of survey work, etc. At this juncture, it can be

Dow

nloa

ded

by [

Flor

ida

Stat

e U

nive

rsity

] at

14:

18 2

1 D

ecem

ber

2014

22

noted that the satellite based remote sensing technology hasbecome the most efficient tool for resources mapping andmonitoring due to their capability of viewing large areas andthe availability of data in different spectral and spatialresolution with repetitive coverage. Remote sensingtechnology has proved its potential for providing naturalresources information, even in difficult and inaccessibleterrain (Saxena et al., 1991, NRSA 2000). Apart from usingspace based remote sensing technology alone, thecombination of remote sensing with traditional ground baseddata collection technique improves natural resourcesmanagement strategies. In order to address the naturalresources related prospects/problems, integration of variousnatural resources maps are required. Therefore, naturalresources management requires not only in-depthunderstanding of concerned resources but also theinterrelationship between other allied natural resources. Forsuch purpose, the Geographic Information System (GIS)founds its place, which integrates various natural resourceslayer and attribute information and provides answers tovarious complex queries through maps, tables and charts.

Covering 100% of the project area for natural resourcesmapping with coarse resolution satellite data and using higherresolution data only for specific areas of interest is the mostcost-effective and efficient approach (SPOT, 2003). This isalso known as the “multi-resolution solution”. Accordingly,in the present study, attempts have been made to explore thecapabilities of remote sensing techniques in assessing regionalnatural resources of parts of Madhya Pradesh, India at asmaller scale and then identify a representative test sitewithin the study area to carry out detailed natural resourcesanalysis at a larger scale. This study suggests the approachof understanding the terrain characteristics at the regionallevel and then to identify target area for detailed analysis atlarger scale. As the information about natural resources set-up for the region is already made available at regional scale,the techniques evolved for target area can be easily replicatedfor the other parts of the region. This approach saves thetime, cost and efforts for assessment of natural resources ofa large area.

Objectives

• To study natural resources at a regional level using remotelysensed data;

• To identify and demarcate a representative site for studyingnatural resources related problems of the area and generatedatabase at a larger scale using remotely sensed data andGIS;

• To demonstrate the use of natural resources database toaddress the natural resources related problems, such as,demarcation of ground water prospect zones and forestfire risk zonation in a GIS environment.

Study Area

The area selected for the study is part of Shahdol district,

Sidhi district and Surguja districts of Madhya Pradesh, Indiacovering an area of around 6300 sq. kms. The study area iscovered in Survey of India (SOI) toposheet no. 64 E on 1:250,000 scale and bounded by longitudes 81º 0' to 81º 45' Eand latitudes 23º 15'to 24º 0' N. Physiographically, the areaexpresses different types of landform units of denudationalorigin, mainly of hills and extensive pediplains spread overdifferent parts of the area. Generally, most of the hills arecovered with forests and the area is traversed by the streamnetwork of the Son river and the Banas river. The study areais shown in Figure 1.

Data

The datasets used for the study are as follows:• Indian Remote Sensing Satellite (IRS) - 1D Linear Imaging

Self Scanner III (LISS-III) photographic standard falsecolour composite (FCC) products on 1:250,000 and1:50,000 scale acquired on May 7th 1999 and thecorresponding digital data of the same date for generationof digitally enhanced data;

• Survey of India topographical sheets on 1:250,000 and1:50,000 scale for the corresponding area;

• Published geological map on 1:250,000 scale prepared byGeological Survey of India (GSI) based on field survey;

• Limited field observations collected during field checks.LISS III sensor operates in visible and NIR bands (0.52-

0.59 µm, 0.62-68 µm and 0.77-0.86µm) with a spatialresolution of 23.5m and a SWIR band (1.55-1.70 µm) withspatial resolution of 70.5 m. The digitally enhanced falsecolour composite data of the study area is illustrated inFigure 2.

Methodology

The study was carried out in three stages:(i) Geological and physiographical maps were prepared on

1:250,000 scale for assessing the natural resources ofthe region (Figure 4 a & b). Visual interpretation ofLISS III standard FCC, as well as, digitally enhancedproducts were utilised in preparation of geological andphysiographical maps in consultation with the publishedgeological map. ERDAS IMAGINE software has beenused for generation of digitally enhanced products.Considering the characteristics of the terrain, contrastenhancement and non-linear edge enhancement (Sobelfilter) have been applied to the raw data to enhancelitho boundary and landforms. Geological map has beenprepared mainly by refining the boundaries of the variouslitho-units as depicted in the published geological mapusing image characteristics of tone and texturalvariations in remote sensing data. In addition, the statusof water resources, vegetation and climate for the studyarea were also assessed. It was observed that this regionis suffering with the recurrent forest fire and scarcity ofground water.

Dow

nloa

ded

by [

Flor

ida

Stat

e U

nive

rsity

] at

14:

18 2

1 D

ecem

ber

2014

23

(ii) After understanding the natural resources set-up of theregion and the problems prevailing in the region (asdiscussed above), a representative test site named Gornasub-basin of Son river was selected for detailed study at1:50,000 scale. Considering that large-scale satellitedata could provide detailed information on ground waterprospects as well as monitoring the spatial extent ofscars of the forest fire, which would be of importantinformation for the developmental authorities, thisrepresentative test sites has been selected. A set ofthematic maps for Gorna sub-basin mainly, lithology,geological structures, landforms, soils, surface waterbodies, land use / land cover and vegetation wereprepared using digitally enhanced remote sensing dataand the thematic maps such as drainage density, slope,road network and settlements were prepared using datacollected through conventional surveys (Figure 5 a,b,c,d,e & f).

Lithology & geological structures, landforms, landuse/land cover and vegetation map has been preparedbased on the visual interpretation of standard FCC ofIRS 1D LISS III and digitally enhanced products. Astandard interpretation key such as tone, texture, etc.has been utilised while interpreting the photographicproducts. Vegetation type map has been prepared bydelineating forest type and other vegetated areaoutside the forest area. For soils map preparation, atentative pre-field image analytical legend wasformed. Based on all the soil mapping units presentin the study area, one profile / minipit has beenevacuated in each class. The morphologicalcharacteristics of each class were examined in detail.In course of the fieldwork, relationship between imageinterpretation units and soil classes was progressivelyestablished and the pre-field interpretation map wastransformed into the soil map. Surface water bodiesand the drainage networks extracted from the Surveyof India (SOI) toposheets and the satellite data. Allthe stream courses in the basin were traced and streamordering was carried out using the system of Strahler(1964). The total streams lengths were measuredwhich was used for calculating and preparing thedrainage density for all the microwatersheds of Gornasub-basin. SOI toposheets on 1:50,000 scale with20m contour interval was used for delineatingdifferent slope categories. The vertical drop has beenmeasured from the contour intervals and the horizontaldistance has been estimated by multiplying the mapdistance with the scale factor. Close spaced contourson the map have higher percentage slope as comparedto sparse contours in the same space. Hence densityof contours on the map has been used for preparingthe slope map. Createbuffer utility of ARC/INFO hasbeen used to create zones around the settlementlocations in order to obtain the settlement buffermap.

(iii) The potentials of such natural resources databases havebeen demonstrated for addressing natural resourcesprospects/problems. The relevant maps were selectedand integrated in GIS environs (ESRI 1989). A suitablemodel for demarcating ground water prospect zonesand forest fire risk zonation was adopted.

The methodology adopted in the study is illustrated in aflow chart in Figure 3.

Natural Resources Assessment using RemoteSensing Data

A systematic approach for understanding the terrain atthe regional scale was carried out for, (i) Geological set-up(ii) Geological structures (iii) Physiography (iv) Vegetationstatus and (v) Water resources. The details are describebelow:

Figure 1 Location of the study area for (i) regional study (ii) detailedstudy

Figure 2 Standard False Colour Composite of IRS 1D LISS III of thestudy area. Test site is shown in yellow colour

Dow

nloa

ded

by [

Flor

ida

Stat

e U

nive

rsity

] at

14:

18 2

1 D

ecem

ber

2014

24

Geological set-upThe study area is geologically represented by sandstone,

shale, limestone, coal seams, basalt, dolerite andconglomerate. Different rock types present in the area can bestratigraphically categorised into Bijawar Group, VindhyanSupergroup, Gondwana Supergroup, Lameta beds andDeccan Trap (GSI 1981).

The Deccan traps are made up principally of basic volcanicrocks of basaltic and doleritic composition. In the study area,Deccan traps are sporadically distributed mainly in the formof linear patches. On the satellite image, this rock type isseen with distinct grey colour with rough texture. Thederivatives of Deccan trap rock are the black soils, which areseen on the satellite image as dark grey tone with smoothtexture. A very small portion of south-western part of thestudy area is constituted by lameta bed which consists oflimestone with subordinate sandstone and clays. SupraBarakar Group consists of thick series of clay and beds ofsandstone, which belongs to Middle Gondwana Super Group.It constitutes largest part of the study area. Barakar Formationis one among the four formations of Damuda Group, whichis named after Barakar river and comprises mainly ofsandstone, shales and coal seams. The Vindhyan Supergrouprocks, constituting Semri Group commences with a basalconglomerate bed constitutes only in the north-western partof the study area. The Bijawar group, is mainly composed oflimestone and is seen in the FCC image by its distinct lightgrey tone with fine texture in the north-western part.

The geological map of the study area prepared usingremote sensing data in consultation with the publishedgeological map (GSI 1981) is shown in Figure 4a. The majorlithologic units could be easily demarcated from the satelliteimage due to their distinct tonal and textural variations.Though some of the litho-units could not be mapped onremotely sensed data, as what was shown in the geologicalmap of GSI, by and large the refinement of the lithologicboundary of Deccan Trap, Supra Barakar Group, DamudaGroup, Semri Group and Bijawar Group could be made dueto their characteristic tonal and textural variations. Thegeological map of the study area prepared from remotelysensed data, by and large, provided the spatial distribution ofdifferent lithologic rocks, which are required forunderstanding the regional geological set-up of the studyarea.

Geological structuresA lineament is a mapable linear or curvilinear feature of a

surface whose parts align in a straight or slightly curvingrelationship that may be the expression of a fault or other lineof weakness. The surface features that make a lineament maybe geomorphic or tonal. Straight stream valleys and alignedsegments of valleys are typical geomorphic expressions oflineaments. A tonal lineament may be a straight boundarybetween contrasting tones or a stripe against a background ofcontrasting tone. Differences in vegetation, moisture contentand soil or rock compositions account for most tonal contrasts

(Sabins 1987). Lineaments can play a major role in identifyingsuitable sites for ground water exploitation/artificial rechargeof ground water because water can percolate and travel throughthem even up to several kms.

Lineaments have been visually interpreted from thesatellite data as shown in Figure 4a. As many as 70 lineamentshave been mapped which can be grouped into two categoriesviz., (i) NW-SE trending lineaments and (ii) E-W trendinglineaments. NW-SE lineaments are mainly following thestream course and all the river/streams are controlled bylineaments. E-W lineaments are sporadically distributed inthe study area.

Physiographic set-upBased on terrain characteristics, the area under study can

be broadly classified into five distinct physiographic units,viz., plains characterised by black soils and linear in patternis occupying the middle and lower part of the study area(near to river / streams), plains with thick vegetation arefound at lower half and north-western part of the area (mainlyforest areas situated at plains), very high relief hills withthick vegetation are present at few pockets of the study area(the upper portion of the hills), gently undulating terrainwith or no vegetation constitutes the largest part (mainly acultural / habitation area with agricultural lands) and highlyundulating terrains with thick vegetation constitutes secondlargest part (mainly low lying and gently sloping hill area).The physiographic map of the study area prepared fromremote sensing data is shown in Figure 4b.

Surface water resourcesThe study area falls under sub-tropical monsoon climate.

The tract experiences rainfall under the influence of southwestmonsoon from the middle of June to end of September. Theaverage of 25 years annual rainfall recorded at Shahdol is111 mm. About 86% of rainfall occur during the rainyseason while 10% and 4% of the annual rainfall occursduring winter and summer seasons respectively.

The middle and southern sectors of the study area isdrained by the Son river, Chundi and Kunuk tributary. Banasriver is located at NE part of the area. The drainage pattern inthe area is mostly dendritic to sub-dendritic and the drainagedensity is low to moderate. Most of the tributary streams godry during summer but there may be flash flooding duringthe rainy season. A major surface water body is seen at thenorth-western part of the study area. The study area is proneto drought.

Vegetation resourcesRemote sensing data and limited field observation have

been utilised for studying the vegetation status of the areaincluding the scars of the forest fire. As it can be seen fromFigure 2, about 30% of the study area is covered under forestcover (red tone with mottled texture), mostly on hilly regions,in the southwestern, eastern and northeastern part of thestudy area. About 50% of the forest area is covered by Sal

Dow

nloa

ded

by [

Flor

ida

Stat

e U

nive

rsity

] at

14:

18 2

1 D

ecem

ber

2014

25

forest. Besides this, mixed deciduous forest is met within allthe ranges. The tonal and textural characteristics of the imagehave helped in differentiating the vegetation covered withsingle dominant forest and vegetation covered with mixeddeciduous forest. The major part of the plain areas is coveredunder agricultural crops. The scars of the forest fire are clearlyvisible due to its distinct image characteristics (dark greyishpatches). The natural vegetation of the area is essentiallyarboreal. Under a subtropical climate with moderately highrainfall and a good soil depth, the different higher and lowerplant species of the area grow luxuriantly and the vegetationcover is generally rich. But much of the natural vegetation hassuffered degradation in recent years under biotic interferenceand has been replaced by shrubs, bushes, meadows andcultivated fields. Many gaps have appeared within the forests,which can be seen in the satellite image as grey tone withcoarse texture. Grazing is generally heavy due to highaccessibility of the area. The detailed study has revealed thatthe ecosystem is under the process of destruction (Jaiswal etal., 1999).

Natural Resources Evaluation

Considering the fact that the resources managementrequires not only the in-depth understanding of the resourcesconcerned but also the interrelationship between otherallied natural resources, the different maps prepared andthe information gathered have been interrelated with eachother. The interrelation provided clue for understandingthe terrain characteristics. To cite an example, theinterrelation of geological map with physiographic maprevealed the following:

• Areas characterised by the Basaltic / doleritic rocks arehaving plains with black soil area;

• Areas characterised by Limestone / Shale rocks showsundulating terrain without vegetation;

• Areas characterised by Sandstone / Shale rocks arecovered with thick vegetation, either on plains or hills.

It has been understood that the problems related tonatural resources of this area are water scarcity and forestfire, which needs immediate attention. The ground waterconditions in major parts of the area are reported as poor tomoderate. The ground level forest fire is the regularphenomenon being encountered in this region. Thesenecessitate the generation of database of natural resources.Once the database is ready, it could be utilised for generatinga decision support system in a GIS environment.

Considering that high-resolution satellite data couldprovide detailed information on ground water prospects aswell as monitoring the spatial extent of scars of the forestfire, which would be of importance for the developmentalauthorities, a representative test site within this area namelyGorna sub-basin of Son river was selected. This site liesbetween latitudes 23º 28' to 23º 38' N and longitudes 81º17' to 81º 32' E (Figure 1). The geographical area of thebasin is about 135 sq. kms. As part of generation of databaseof natural resources and ancillary

information, various thematic maps were prepared. Theselected set of thematic maps is shown in Figure 5. Fordemonstration purpose, the ground water potential zonesmapping and forest fire risk zones demarcation has beencarried out for Gorna sub-basin, which is briefly describedin the following sections.

Ground Water Prospect Zone MappingThe occurrence and movement of ground water in an

area is governed by several factors such as topography,lithology, geological structures, depth of weathering,extent of fractures, secondary porosity, slope, drainagepattern, landforms, land use/land cover, climatic conditionsand interrelationship between these factors (Roy 1991,Greenbaum 1992, Mukherjee 1996). In additionquantitative morphometric parameters of the drainagebasin also play a major role in evaluating the hydrologicparameters, which in turn helps to understand the groundwater situation (Krishnamurthy and Srinivas 1995). In

Figure 3 Methodology adopted for regional resources assessment andprospect/problem mapping

Dow

nloa

ded

by [

Flor

ida

Stat

e U

nive

rsity

] at

14:

18 2

1 D

ecem

ber

2014

26

Figure 4 (a) Geological map and (b) Physiographical map of the study area prepared from IRS 1D LISS IIIimage.

Figure 5 Thematic maps prepared usding remotesensing and conventional data a)Geological map, b) Landforms map, c)Soil type map, d)Slope map, e) Vegetationmap and f) Settlement buffer map.

Dow

nloa

ded

by [

Flor

ida

Stat

e U

nive

rsity

] at

14:

18 2

1 D

ecem

ber

2014

27

general, the conventional methods of exploration do notalways takes into account of diverse factors that control theoccurrence and movement of ground water. The regions orlocations selected by these methods are therefore not asreliable as they should be in reality (Murthy 2000). Hence,remote sensing has come handy in the search forgroundwater prospect zones as it provides the recent spatialdisposition of basic information on geology, landforms,soils, land use/land cover, surface water bodies etc. atfaster and reliable mode with less cost and manpower(Krishnamurthy et al., 1996, Reddy et al., 1996). Sincedelineation of ground water prospect zones is based on thecombined role being played by various factors, it isnecessary to use GIS.

Thematic maps of lithology, landforms, soils, land use/land cover, lineaments and drainage density and slope classeswere chosen for ground water prospect mapping purpose.The characteristics with respect to ground water controllingfeatures is detailed below:(i) Lithology & geological structures - Based on the

interpretation, the sub-basin has been classified intofour lithological classes, viz., Dolerite, Basalt, Sandstoneintermixed with Clay and Sandstone intermixed withShale. Sandstone intermixed with shale is relativelymore permeable compared to sandstone intermixed withclay, hence sandstone intermixed with shale iscategorized as ‘excellent’ ground water prospect zones.Sandstone intermixed with clay is categorized as ‘good’.Basalt rocks and its derived products exhibit fine-grainedtexture, hence are classified as ‘moderate’. Doleritesare primarily hard and compact in nature; ground watermovement is difficult in this rock type and henceassigned as ‘poor’ category. Areas around the lineamentsand intersection of lineaments are considered to be thefavorable sites for accumulation of ground water due toweaker horizon. Zone around lineament 500 m (250 mon either side) are classified as ‘excellent’ ground waterprospect zones.

(ii) Landforms - The different types of landforms presentcan be generally grouped into (i) valley fills (ii) buriedpediplains (iii) denudational hills (iv) ridges. On thebasis of thickness and composition of weathered mantle,the pediment plains have been classified into BuriedPediplain (Shallow), Buried Pediplain (Moderate) andBuried Pediplain covered with black soils. The specificcharacteristic of each of the landform mentioned abovevary greatly in terms of shape, size, dimension, thicknessof the overburden material, permeability, porosity, etc.,depends on the underlying rock type, structural control,climate and vegetative cover. Valley fill zones are thestream course with accumulation of highly porous andpermeable alluvial materials hence are characterized as‘excellent’. As buried pediplain are gently undulatingplains covered with weathered materials that arefavorable for ground water accumulation, hence assigned‘excellent’ to ‘moderate’, depending upon the thicknessof the material as well as the soil cover. Denudational

hills are characterized by highly sloping topographyand high surface runoff, therefore are categorized as‘moderate’. Linear ridges are the linear surface exposurecharacterized by hard rocks and acts as run-off zoneand hence are categorized as ‘poor’.

(iii) Soils - Ten different types of soils have been delineatedon the basis of hydrological conditions. Soil type 8 and10 are hydrologically depicting deep, sandy loamsurface, moderately well drained and shallow watertable. Therefore, these units have been classified under‘excellent’ category. Type 9 is having the sameproperties as Type 8 & 10, but has deeper and gentlesurfaces, hence this has been given more weightageunder the ‘excellent’ category. Type 2 consists ofgravelly sandy loam and moderately deep on stronglysloping surface, is classified under ‘good’ category.Type 1 and 3 consists of dolerite dykes associated withgravelly sandy clay loam on ridges having steep slopes.This type has been assigned as ‘moderate’. SimilarlyType 4, 5 & 7 are characterized by shallow, well-drained, sandy clay loam to gravelly clay loam ongently sloping surfaces and have been assigned as‘moderate’. Type 6 depicts moderately deep, welldrained and severally eroded, hence has been categorizedunder ‘poor’ category.

(iv) Slope - On the basis of slope percentage, the study areahas been classified into six slope classes. The class 1 isassigned under ‘excellent’ category, due to nearly flatterrain and optimal infiltration rate. Class 2 is categorizedas ‘good’ due to slightly undulating topography withmaximum percolation or partly runoff. Class 3 is havingrunoff relatively high with a small amount of infiltrationand is kept under the ‘moderate’ category. Classes 4, 5and 6 assigned as ‘poor’ due to the presence of steeplysloping topography and high surface run-off.

(v) Drainage density - The drainage density calculation hasbeen done for all the microwatersheds of Gorna sub-basin (ranges from 0.34 to 2.70) and accordingly thedrainage density map has been prepared. It has beenobserved that the low drainage density favors highground water percolation and accumulation. Based onthe drainage density of the sub-basin, it has been groupedin three classes, < 1.0 km/km2, 1 .0-2.0 km/km2 and > 2.0 km/km2 . Accordingly, class 1, class 2 and class 3have been assigned to the excellent, good and poorcategories respectively.

(vi) Land use/land cover - The sub-basin consists of cropland,fallow land, plantation, forested area, land with scrubsand land without scrubs. From the landuse point ofview, croplands are the ‘excellent’ site for ground waterexploration. Agricultural lands, which are not presentlybeing used, are classified as fallow lands and hencecategorized under ‘good’. Though forest and plantationfalls under good ground water prospects, the same hasbeen purposefully categorized under ‘poor’ class,keeping in view that these areas are generally notpermitted for any activity within the area. Lands without

Dow

nloa

ded

by [

Flor

ida

Stat

e U

nive

rsity

] at

14:

18 2

1 D

ecem

ber

2014

28

scrub have been rated lower than land with scrub sincevegetation cover promotes percolation.

The characteristics of ground water controlling featuresfor natural resources database used for this study are given inTable 1.

The logical reasoning was adopted for categorization andweight assignment for each of the thematic features has beendone. By integrating the various thematic maps in the GISenvironment with suitable model developed exclusively forthis purpose (Jaiswal et al., 2003), the ground waterprospective zones have been demarcated (Figure 6a). Initiallyeach one of the polygons in the final thematic layer wasqualitatively visualized into one of the categories: (i)Excellent, (ii) Good, (iii) Moderate and (iv) Poor in terms oftheir importance with respect to ground water occurrence.Then suitable weights were assigned to each thematic featureafter considering their characteristics. Knowledge basedweight assignment was done for each feature and they wereintegrated and analyzed using weighted aggregation method(ESRI 1988 and 1989). In this method, the total weights ofthe final integrated polygons were derived as sum or productof the weights assigned to the different layers according totheir suitability. Finally, the ground water prospect zonemap was generated.

The output of ground water prospect map shows fourclasses, viz., Excellent, Good, Moderate and Poor. The groundwater prospect zone map generated through this model forGorna sub-basin was verified with the ground reality toascertain the validity of the model developed. The verificationshowed that the ground water prospect zones demarcatedthrough the model are in agreement with the field observation(Jaiswal et al., 2003).

Forest Fire Risk MappingForest fire is the recurrent problem in the study area

(Khan et al., 1992). Satellite data plays a vital role inidentification and mapping of forest fires, find out thefrequency with which different vegetation type/zones isaffected and GIS can be used effectively to combinedifferent forest fire causing factors for demarcating theforest fire risk zone map (Fernandez et al., 1997, Koutsiasand Karteris 2000). Fire proneness of any area depends onmany factors such as, vegetation type / density, humidityof the area, vicinity to settlements, distances from roadsand the host of others (Chuvieco and Congalton 1989, Royet al., 1991, Jain et al., 1996). The prominent among thepossible factors leading to accidental fires in the Gornasub-basin are vegetation type, slope, distance from roadsand vicinity to settlements (Jaiswal et al., 2002). Theselected thematic maps used from the resources databaseand their characteristics with respect to forest fire is detailedbelow:(i) Vegetation type - Vegetation of the area has been

classified into eight forest types and three non-forestclasses. Dry and dense vegetation especially bambooconstitutes major portion of the forests of Gorna sub-basin, which is more susceptible to fire.

(ii) Slope - It is an important physiographic factor, which isrelated to wind behaviours and hence affects the fireproneness of the area. Fire travels most rapidly up-slopes and least rapidly down-slopes. The major portionof the forest of Gorna sub-basin is located on the hillshaving steep slopes, which helps in spread of fire.

(iii) Distance from roads - The man, animal and vehicularmovement and activities on the road provide enoughscope for accidental / manmade fire. Nearer the roads,more would be the chance of the fire. Hence bufferzones of 100m, 200m, 300m and 400m intervalshave been created around the roads. The Gorna sub-basin forests are traversed by all types of roads givingthe way for local people (leaves collectors) andgraziers to become the cause of forest fire. Leavesselling are main source of income of many people ofthis area. They carelessly throw the matchsticks andburning ends of cigarette and are the major cause offorest fire.

(iv) Vicinity to settlements - The areas near to the habitats /settlements are more prone to fire since the habitation /cultural practices of the inhabitants can lead to accidentalfire. In the present study area, very few settlementlocations falls inside the forest, but still it may be thecause of forest fire. The corridors of 1000m, 2000m and3000m perimeter were created around the settlementlocation, digitised as polygon data.

The input information on forest fire influencing parametersis in descriptive form and reveals the parameters favoringthe fire risk. In order to achieve effective conclusions throughcomputation and other mathematical operations in the GISanalysis, the descriptive information needs to be convertedinto forest fire risk index or ratings. The vegetation typeswere rated on a 1-10 scale. During analysis, vegetation wasgiven the highest weightage because even though the fireenvironment may be favorable, forest fire cannot occurunless there is inflammable material. Each class of foresttype was rated as per the composition of species in thoseclasses. Slope, which does not necessarily influence theprobability of an ignition but has a strong influence on thebehavior of fire, was assigned second highest weight. Thedifferent slope classes were rated according to the sensibilityof the fire spread after the ignition of fire. Accessibility tohuman activity is a key variable for predicting the probabilityof an ignition, though it does not influence the behavior of afire. The proximity factor was assigned the third level weight,as the influence of anthropogenic actions is also the causefor initiation of the forest fires. Roads inside the forest arethe possible routes for promenades. Therefore, road variableswere assigned equal weights.

The zone in close proximity of the settlement area wasassigned a higher rating. The risk factor decreases fartherfrom these features. After understanding the behaviour withrespect to forest fire risk, the different classes were givensuitable ratings according to their fire sensitivity amongother classes in the same thematic layer.

Forest fire risk zones were delineated through subjective

Dow

nloa

ded

by [

Flor

ida

Stat

e U

nive

rsity

] at

14:

18 2

1 D

ecem

ber

2014

29

General characteristicsAlong with streams, low lying plain areaFlat to gently undulating plain with moderate overburden and less vegetation

Flat to gently undulating plain of vast aerial extent with shallow over burden, goodvegetationVast aerial extent, black soil area, generally habitation/agricultural lands

Gently dipping, undulating hills with moderate to high relief developed over GondwanaSandstonesLinear ridges, doleritic rocksFlat area, agricultural land (vegetation is not visible due to harvested presently)

Mainly forest area, hills with thick vegetation

Black soil area, generallyhabitation/agricultural landLinear dykesShallow, dark reddish brown, gravelly sandy clay loam surface and subsurface withstones and outcrops on surface having 15-25% slope on elongated dykes, well drained,severely erodedModerately deep, reddish brown, gravelly sandy loam surface and subsurface havinglithic contact with stones and rock outcrop on surface having 10-15% slope, welldrained, severely erodedShallow, reddish brown, gravelly sandy loam surface and subsurface having lithiccontact with stones and rock outcrop on surface with steep slopes on hills, well drained,severely eroded on upper portion of the hillsShallow, reddish brown to dusky red, sandy clay loam surface and subsurface withstones and rock outcrops on 3-5% lower slopes on sedimentary hills, well drained,severely erodedShallow, yellowish red, gravelly clay loam having lithic contact with stones and rockoutcrops on surface on 3-8% slope, well drained, severely erodedModerately deep, very dark grey to very dark greyish brown, clay throughout withsurface and subsurface cracks on 1-3% slope, moderately well drained, severely erodedShallow, reddish brown, sandy loam surface and subsurface with rock outcrops onsurface having 1-3% slope on moderate pediplain surface, well drained, severely erodedDeep, yellowish brown to brown, loamy sand surface to sandy clay loam subsurface on1 - 5% slope on shallow pediplain surface, moderately drained and erodedVery deep, brown to strong brown, sandy loam surface to sandy clay loam subsurface on1-3% slope on moderate pediplain surface, moderately well drained and erodedDeep, greyish brown, sandy loam surface on 1-3% valley, aquic condition, slighterosion, moderately well drained and erodedVery gently slopingGently slopingModerately slopingStrongly slopingModerately steep to steep slopingVery steep slopingFavours for high ground water percolation and accumulationFavours for less ground water percolation and accumulationUnfavourable for ground water percolation and accumulationAll the crops (Rabi, Kharif)Agricultural land temporarily left un-croppedWasteland with scrubsBarren areasPlantation outside the forestNotified boundary of the forestWeak horizons

Table 1 Characteristics of various ground water controlling features in Gorna sub-basin

ThemeLandforms

Lithology

Soils

Slope

DrainageDensity

L a n d u s e /Land cover

Lineament

Class1. Valleys Fills2 . Bur ied Pedipla in(Moderate)3 . Bur ied Pedipla in(Shallow)4. Bur ied Pedipla incovered with black soils5. Denudational Hills

6. Ridges/Dykes1. Sandstone intermixedwith Shale2. Sandstone intermixedwith Clay3. Basalt

4. DoleriteType 1

Type 2

Type 3

Type 4

Type 5

Type 6

Type 7

Type 8

Type 9

Type 10

1. Class 1 (0-3%)2. Class 2 (3-5%)3. Class 3 (5-10%)4. Class 4 (10-15%)5. Class 5 (15-35%)6. Class 6 (> 35%)1. < 1.0 km/sq. km2. 1.0 - 2.0 km/sq. km3. > 3.0 km/sq. km1. Crop land2. Fallow land3. Land with scrub4. Land without scrub5. Plantation6. ForestLineaments

1

2

3

4

5

6

7

Dow

nloa

ded

by [

Flor

ida

Stat

e U

nive

rsity

] at

14:

18 2

1 D

ecem

ber

2014

30

weights assigned to interpret all the units of the layers according to theirvulnerability or inducing for forest fire and integrated using the followingequation in the GIS (Figure 6b):

FR = 10 Fi=1-11 + 2 Hj=1-4 + 2 Rk=1-4 + 3 Sl=1-6

Where FR is the numerical index of fire risk, F is the vegetation variable(with 1 -11 classes), H indicates proximity to human habitation (with 1-4 classes), R is road factor (with 1-4 classes) and S indicates slope factor(with 1-6 classes). The superscripts i, j, k, l indicate subclasses based onimportance in determining the fire risk.

Four categories of forest fire risk ranging from very high to low werederived automatically. Almost 30% of the Gorna sub-basin was predictedto be under very high and high-risk zones. The evolved GIS basedforest fire risk model of the study area was found to be in strongagreement with actual fire affected sites (Jaiswal et al. 2002). Suchmaps will be helpful for the forest department officials in preventingand minimising fire risk activities with in the forest and taking properaction when fire breaks out. The area shown under different ‘fire prone’zones are those areas where fire can occur due to man’s unintentionalactivities and which could certainly be averted by taking precautionarymeasures. Such a map would help plan the main roads, subsidiary

roads, inspection paths etc., leading to a reliablecommunication and transport system toefficiently fight with the small and big forestfires.

Other Developmental ActivitiesAs a demonstrative study, demarcation of

ground water prospect zones and forest fire riskzones mapping were attempted using relevantthematic maps from the database of thematiclayers generated for detailed study. However, asthe spatial data of the thematic layers are availablein digital format, it would be possible to usethem with additional data, if required, and withappropriate modelling for various otherdevelopmental activities, viz., identification ofsuitable sites for artificial recharge structures;soil resources conservation in the catchmentsarea; rural drinking water supply; land suitabilityanalysis; monitoring of forest cover using recentsatellite data, etc.

Conclusion

The present study has been carried out withthe main objective to understand the terraincharacteristics at the regional level and thenperform the detailed analysis for the smallrepresentative area, prepare a geo-scientificdatabase, which can be used for the integratedanalysis for the problem/prospect of the region.This approach provides not only the higher levelof information content but also saves time, costand efforts. The study has demonstrated theusefulness / application potentials of remotesensing techniques in assessing the naturalresources by preparation of thematic maps, viz.,land use / land cover, landforms, lithology,lineament, soils etc. The study also illustrates theefficiency of remote sensing and GIS incombination with field observations in groundwater exploration activities and fire risk zonemapping. The study ultimately suggest the needfor generating the spatial database of variousnatural resources themes and using them forvarious developmental activities with appropriateGIS modeling.

Acknowledgements

The authors are grateful to Dr V Jayaraman,Director and Mr Mukund Rao, Dy. Director,Earth Observation Systems Programme Office,Ind ian Space Resea rch Organ i sa t ionHeadquarters, Bangalore for providing valuablesupport during the analysis of the present work.

Figure 6 a) Ground water prospect zone map, b) Forest fire risk zone map.

Dow

nloa

ded

by [

Flor

ida

Stat

e U

nive

rsity

] at

14:

18 2

1 D

ecem

ber

2014

31

References

Chuvieco, E., and Congalton, R.G., (1989) Application of RemoteSensing and Geographical Information System to Forest Fire HazardMapping. Remote Sensing for Environment, 29: 147-159.

ESRI, Inc., (1988) User Guide ARC/INFO, The Geographic InformationSystem Software. Redlands, CA, USA.

ESRI, Inc., (1989) ARC/INFO Training Course Class Material Vol 1.Redlands, CA, USA.

Fernandez, A., Illera, P., and Casanova, J.L., (1997) Automatic Mappingof Surfaces Affected by Forest Fires in Spain using AVHRR NDVIComposite Image Data. Remote Sensing for Environment, 60: 153-162.

Greenbaum, D., (1992) Structural influences on the occurrence ofground water in SE Zimbabwe. In: Wright, E.P., and Burges, W.G.,“Hydrology of crystalline basement aquifers in Africa” Pp. 77-85.Geological Society Special Publication-66.

GSI, (1981) Sohagpur Geological Quadrangle Map, Madhya Pradesh,Geological Survey of India (GSI), Map Printing Division,Hyderabad, India.

Koutsias, N., and Karteris, M., (2000) Burnet Area Mapping usingLogistic Regression Modeling of a Single Post-Fire Landsat-5Thematic Mapper Image. International Journal of Remote Sensing,21: 673-687. ,

Jain, A., Ravan, S.A., Singh, R.K., Das, K.K., and Roy, P.S., (1996)Forest Fire Risk Modelling using Remote Sensing and GIS. CurrentScience, 70 (10): 928933.

Jaiswal, R.K, Mukherjee, S., Krishnamurthy, J., and Saxena, R., (2003)Role of Remote Sensing and GIS Techniques for Generation ofGround Water Prospect Zones towards Rural Development – AnApproach. International Journal of Remote Sensing, 24 (5), 993-1008.

Jaiswal, R.K., Mukherjee, S., Raju, K.D., and Saxena, R., (2002)Forest Fire Risk Zone Mapping through Satellite Imagery &Geographical Information System. International Journal of AppliedEarth Observation and Geoinformation, Elsevier Publications, 4,1 -10.

Jaiswal, R.K., Saxena, R. and Mukherjee, S., (1999) Application ofRemote Sensing Technology in Change Detection A case study ofa Part of Shahdol District, Madhya Pradesh. Journal of IndianSociety of Remote Sensing, 27 (2): (123-128).

Khan, S., Bokil, S., and Khare, N., (1992) Mapping of Fire AffectedAreas in Bandhogarh National Park, Shahdol district, MadhyaPradesh using IRS LISS II data. In: Karale, R.L., “Natural ResourcesManagement - A new perspective”, Indian Space ResearchOrganisation, Bangalore, India. Pp. 325330.

Krishnamurthy, J., and Srinivas, G., (1995) Role of Geological andGeomorphological Factors in Ground Water Exploration: A studyusing IRS LISS data. International Journal of Remote Sensing, 16(14): 25952618.

Krishnamurthy, J., Venkatesa K. N., Jayaraman, V., and Manivel, M.,(1996) An Approach to Demarcate Ground Water Potential Zonesthrough Remote Sensing and Geographic Information System.International Journal of Remote Sensing, 17 (10):1867-1884.

Mukherjee, S., (1996) Targeting Saline Aquifer by Remote Sensingand Geophysical Methods in a part of Hamirpur-Kanpur, India.Journal of Hydrology. 19 (1): 5364.

Murthy, K.S.R., (2000) Ground Water Potential in a Semi-Arid Regionof Andhra Pradesh - A Geographical Information System Approach.International Journal of Remote Sensing, 21 (9): 1867-1884.

NRSA, (2000) Rajiv Gandhi National Drinking Water Mission:Technical guidelines for preparation of ground water prospectsmaps. NRSA, Department of Space, Hyderabad, India, NRSA/AD/GG/GD/TR: 1/2000.

Reddy, P.R., Vinod, K., and Seshadri, K., (1996) Use of IRS-1C datain Groundwater Studies. Current Science, 70 (7): 600-605.

Roy, A.K., (1991) Hydromorphogeology for Ground Water Targetingand Development in Dehradun Valley, U.P. In: Gupta, P.N., andRoy, A.K., “Mountain Resource Management and Remote Sensing”Surya Publications, Dehradun.

Roy, P.S., Ranganath, B.K., Diwakar, P.G., Vohra, T.P.S., Bhan, S.K.,Singh, I.J., and Pandhan, V.C., (1991) Tropical Forest Type Mappingand Monitoring using Remote Sensing. International Journal ofRemote Sensing, 12 (111): 22052228.

Sabins, F.F. Jr., (1987) Remote Sensing Principles and Interpretation.Second Edition, W.H. Freeman and Company, New York. U.S.A.

Saxena, K.G., Kothyari, B.P., and Rao, K.S., (1991) Remote Sensingfor Management of Biological Resources; A Rational Outlook forthe Himalaya. In. Gupta, P.N., and Roy, A.K., “Mountain ResourcesManagement and Remote Sensing”, Surya Publication, Dehradun.

Sebastian, M., Jayaraman, V., and Chandrasekher, M. G., (1995)Space technology applications for sustainable development ofwatersheds. Indian Space Research Organisation, Bangalore, India.ISRO-HQ-TR: 104/1995.

Smyth, R.E., and Yong, M.D., (1998) Costing Soil Erosion: A StatewideApproach. Land Degradation Development, 9: 513-527.

SPOT, (2003) The international magazine of Spot image: How tochoose imagery, Spot Image's Corporate CommunicationsDepartment, Toulouse, France, Nº 35/1st Semester.

Strahler, AN, (1964) Quantitative geomorphology of drainage basinsand channel networks. In: Handbook of Applied Hydrology (Ed.:V.T. Chow), McGrawHill, New York. U.S.A. pp. 439-476.

Dow

nloa

ded

by [

Flor

ida

Stat

e U

nive

rsity

] at

14:

18 2

1 D

ecem

ber

2014