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Soil erosion assessment in Rangitcatchment, India through a process-based model in the geospatialenvironmentHari Venkata Durga Rao Koradaa & Venkateshwar Rao Valaa

a Water Resources Group, National Remote Sensing Centre,Department of Space, Indian Space Research Organisation,Balanagar, Hyderabad, India.Accepted author version posted online: 29 Apr 2013.Publishedonline: 24 May 2013.

To cite this article: Hari Venkata Durga Rao Korada & Venkateshwar Rao Vala (2014) Soilerosion assessment in Rangit catchment, India through a process-based model in the geospatialenvironment, Geocarto International, 29:5, 507-519, DOI: 10.1080/10106049.2013.798359

To link to this article: http://dx.doi.org/10.1080/10106049.2013.798359

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Soil erosion assessment in Rangit catchment, India through aprocess-based model in the geospatial environment

Hari Venkata Durga Rao Korada* and Venkateshwar Rao Vala

Water Resources Group, National Remote Sensing Centre, Department of Space, Indian SpaceResearch Organisation, Balanagar, Hyderabad, India

(Received 19 October 2012; final version received 18 April 2013)

The accurate assessment of soil erosion in the spatial environment can give valuableinput in designing reservoirs and for preparing watershed management action plans.Rangit River in northeastern India is affected by severe soil erosion, causingreduction in its reservoir capacity. The main aim of the paper is to estimate the soilerosion in the Rangit catchment, India through a process-based soil erosion modelthat integrates remote sensing and field data in the geospatial domain, and also toidentify the problem areas for further watershed management action plans. Varioustopographic parameters that were derived from land use, soil texture and a digitalelevation model were used for the estimation of soil erosion. The Morgan, Morganand Finny model was used to compute soil erosion. Based on the drainage pattern,the catchment was divided into eight sub-catchments for detailed study. Annual soilerosion was estimated for four years from 2002 to 2005 using the correspondingyear’s meteorological data. Estimated soil erosion was compared with the field datain each year and found very good correlation.

Keywords: remote sensing; geographical information system; soil erosion; watershedmanagement; process-based model; sedimentation

1. Introduction

Soil erosion and land degradation are some of the main problems associated withwatershed management issues. Many reservoirs in India are losing their storage capacitydue to huge sediment deposits. The capacity of reservoirs has been gradually reduceddue to silting hence; sedimentation of reservoirs is of great concern to all the waterresources development projects. In some of the reservoirs, the rate of sedimentation ishigher than what has been considered at the planning stage. Many of the reservoirs inIndia are losing capacity at the rate of 0.2–1.0% annually. Recent observations havebrought to light the alarming fact that the reservoir sedimentation resulting from degra-dation of the watersheds is much higher compared to the rate that was assumed at thetime the projects were designed NRSC (2006).

Soil conservation is the only way to protect the productive lands (Durga Rao &Satish Kumar 2004). In a predominantly agricultural country like India, where droughts,famines and floods cause chronic food security, soil conservation not only increasescrop yields, but also prevents further deterioration of land. The basic principle of

*Corresponding author. Email: durgarao_khv@nrsc.gov.in

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watershed management is to use the land according to its capability and treat the landaccording to its needs for the sustainable development of the people living in that area.The land that is being used beyond its capability produces adverse effects on the envi-ronment like soil degradation in the form of erosion, ground water depletion, etc.(Durga Rao & Satish Kumar 2004).

The consequences of soil erosion will be there in both on- and off-site (Morganet al. 1984). On-site effects are particularly important on agricultural land where theredistribution of soil within a field, the loss of soil from a field, the breakdown of soilstructure, the decline in organic matter and nutrients result in a reduction of cultivablesoil depth. These effects will also influence the soil fertility also. Erosion also reducesavailable soil moisture, resulting in more drought prone conditions. The net effect is aloss of productivity which, at first; restricts what can be grown and results in increasedexpenditure on fertilisers to maintain yields but later, ultimately can lead to land aban-donment. Off-site problems result from sedimentation downstream, which reduces thecapacity of the rivers, enhances the risk of flooding, blocks irrigation canals and short-ens the design life of reservoirs. The on-site costs of erosion are necessarily borne bythe farmer although they may be passed on in part to the community in terms of higherfood prices as yield decline or land goes out of production. The prevention of soil ero-sion, which means reducing the rate of soil loss to approximately that which wouldoccur under natural conditions, relies on selecting appropriate strategies for soil conser-vation and this, in turn, requires a thorough understanding of the processes of erosion.The factors, which influence the rate of erosion, are rainfall, runoff, soil, slope, plantcover and the presence or absence of conservation measures (Morgan et al. 1984).

Several parametric models have been developed to predict soil erosion at drainagebasins, hill slopes and field levels. With a few exceptions, these models are based onsoil type, land use, landform, climatic and topographic information. Remote sensingand Geographical Information System (GIS) techniques make it possible to measurevarious topographic and hydraulic parameters on spatial scales. Scientific managementof soil, water and vegetation resources on watershed basis is, therefore, very importantto arrest erosion and rapid siltation in rivers, lakes and estuaries. It is, however, realizedthat due to financial and organizational constraints, it is not feasible to treat the entirewatershed within a short time. Prioritization of watersheds on the basis of those sub-watersheds within a watershed which contribute maximum sediment yield obviouslyshould determine our priority to evolve appropriate conservation management strategyso that maximum benefit can be derived out from any such scheme.

Most of the soil erosion models are of the empirical grey-box type in nature. Theyare based on defining the most important factors through the use of observation, mea-surement, experiment and statistical techniques relating them to soil loss (Morgan 1995,2001). Universal Soil Loss Equation (USLE) is a statistical model developed, modifiedand updated by Wischmeier and Smith (1978). This model gained worldwide accep-tance because of its simplicity and efficiency. The other version of the model, ModifiedUSLE by Williams and Berndt (1977), is intended to estimate sediment yield for a sin-gle event. The revised version of USLE (RUSLE) maintains the simplicity of the basicequation (USLE) with its factors broken into finer sub factors to permit more flexibilityin calculating soil loss (Yoder et al. 2001). Morgan’s soil erosion model developed byMorgan et al. (1984) to estimate the annual soil loss in hill slopes retains the simplicityof the USLE. This model encompasses some advances in understanding the soil erosionprocess. Sander et al. (1996), have extended the steady-state solutions to account forthe time variation of suspended sediment concentration during an erosion event. A very

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simple approximate analytical solution is found which agrees extremely well withexperimental data obtained from nine experiments. Laflen et al. (2004), summarizeshow well one erosion prediction computer model ‘WEPP’ performed compared tomeasured soil loss data in a range of validation studies on erosion plots and smallwatersheds. The results indicate that the model can be applied for erosion predictionand conservation planning with confidence in soil loss estimation.

Remote sensing in conjunction with the GIS has evolved as a powerful tool in esti-mating soil erosion more accurately as it considers the spatial variability of all topo-graphic parameters (Jain et al. 2001; Durga Rao & Satish Kumar 2004). As land use/land cover is the main parameter in the soil loss process, this information and itsdynamics can be derived from the satellite remote sensing data more precisely due toits synoptic coverage and high spatial and temporal resolutions. Use of satellite data isvery economical as it can be used in reconnaissance survey stage, planning, implemen-tation, management and monitoring stages of the watershed. King et al. (2005) havebriefed the synthesis of the information obtainable from remote-sensing data and how itcan be related to two significant functions of catchment hydrology, namely, the pro-cesses of production and transfer. After presenting examples of the type of informationthat can be derived from remote sensing (characterization of soil surface by differentwavelengths, temporal changes of surface states, incision and geometry of possiblewater pathways on the surface, etc.), they examine how this information can provideparameters for input into runoff and erosion models. Bou Kheir et al. (2008) generatedsoil erosion potential maps at 1:100,000 scale of the study area within Lebanon usingsoil erodibility, morphology, land use and rainfall erosivity information. They empha-sised the importance of integrating information on infiltration of rock outcrops to assesssoil erosion in Mediterranean karst landscapes. Zhang et al. (2008) discussed the theoryand methodologies of soil erosion assessment and established an implementation pro-cess according to USLE factors combined with thematic maps and DEMs. They havetested the USLE methodology in Nihe Gou catchment in Chunhua County of ShaanxiProvince, China and verified with the field data. They concluded that the computedresults have reasonable match with the field data. Sharma and Singh (1995) have usedANSWERS model for computation of soil erosion using remote sensing and GIS tools.They have demonstrated the advantages and limitations of the model.

The main objective of the study is to estimate the soil erosion in the Rangit catch-ment, India through a process based soil erosion model, by integrating remote sensingand field data in the geospatial domain, and also to identify the problem areas for fur-ther watershed management action plans. Considering the methods, models, databaseused in the earlier studies, and considering the available spatial and non-spatial data ofthe Rangit catchment, Morgan, Morgan and Finny model which is a process based dis-tributed modal was used in the study to compute soil erosion more accurately. TheMorgan, Morgan and Finny model retains the concept of erosion assessment of RUSLEand integrates the overland flow capacity in computing the soil erosion in the presentstudy.

2. Soil erosion modelling approach

Morgan et al. (1984) developed a model to predict the soil loss from the field size areason hill slopes. The model was compiled by bringing together the results of research bygeomorphologists and agricultural engineers. It separates the soil erosion process intowater phase and sediment phase. In sediment phase, rate of detachment will be

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computed by raindrop impact and eroded soil carrying capacity by runoff will be com-puted in overland flow phase. The process of splash transport and detachment by runoffis ignored. Thus, the sediment phase consists of two productive equations, one for therate of splash detachment and other for the transport capacity of overland flow.

The model compares the prediction of soil detachment by rain splash, transportcapacity of runoff and assigns the lower value as the annual rate of soil loss there bydenoting whether detachment or transport is the limiting factor. The predictionsobtained by the model are most sensitive to changes in annual rainfall and soil parame-ters. So, annual rainfall data and soil data at a large scale is required to get betterresults. The kinetic energy in the water phase is purely dependent on rainfall. Rate ofsoil detachment mostly depends on the kinetic energy of the rain and soil type. Thus,good information on rainfall data and soils are required for the successful prediction ofsoil loss. Modal description in sediment and water phases are discussed below.

2.1. Water phase

This phase consists of estimation of kinetic energy of the rainfall exerted on the soiland the estimation of the volume of overland flow.

2.2. Kinetic energy

Kinetic energy (E) can be calculated from annual rainfall data and knowing the inten-sity of the erosive rain. This can be given in J/m2 and can be expressed as;

E ¼ R ð11:9þ 8:7 log IÞ ð1Þ

where R=Annual rainfall in mm, I= Intensity of erosive rain in mm/h (25 for tropicalclimates)

2.3. Overland flow

The overland flow (Q) in mm, can be calculated from the average annual rainfall, num-ber of rainy days in the year, land use type and soil type of the area. It can be mathe-matically expressed as;

Q ¼ R expð�RC=RoÞ ð2Þ

where R =Annual rainfall in mm; Rc = 1000.MS.BD.RD (Et/Eo)0.5, MS=Soil moisture

content at field capacity 1/3 bar tension (% w/w), BD=Bulk density of top layer(Mg/m3), RD=Topsoil root depth in metres defined as depth of soil from the surfaceto stony impermeable layer; to the base of the A-horizon; to the dominant root base orto the 1.0m which ever is shallowest, Ro =R/Rn, Rn =Number of rainy days andEt/Eo = Ratio of actual and potential evapotranspiration.

2.4. Sediment phase

This is the second phase of the Morgan’s model, it includes the estimation of the rateof detachment by raindrop impact and transport capacity of the overland flow.

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2.5. Rate of detachment

The rate of soil detachment by raindrop impact (F) depends on the kinetic energy ofthe rainfall and soil detachability index. The rate of detachment is expressed in kg/m2

as;

F ¼ KðEe�AaÞb � 10�3 ð3Þ

where K =Soil detachability Index (g/J) and A=Percentage of rainfall contributing topermanent interception and stream flow.

2.6. Transport capacity

The transport capacity due to overland flow (G) is dependent on the volume of overlandflow (Q), crop cover management factor and the topography of the area. This can beexpressed in kg/m2 as;

G ¼ CQdsinðSÞ � 10�3 ð4Þ

where C =Crop cover management factor and S=Steepness of slope expressed in slopeangle.

The final soil erosion loss will be the minimum of F and G. The model will analyseeach and every pixel in F and G grids and the output value of erosion for each pixelwill be the minimum of corresponding pixel in these two grids.

3. The study area

Rangit River is one of the major tributaries of the river Teesta in Western Sikkim,which originates in Talung glacier and has many tributaries like Rothang Chu,Rimbichu, Kaletchu and the little Rangit. The Rangit River in its early reaches flowsthrough very deep valleys and steep slopes till it joins Rothangchu, which originatesfrom Rothang glaciers and is one of the major tributaries of River Rangit. The rivercourse up to Legship is filled with small pebbles and boulders whereas; below this theriverbed consists of small- to medium-sized cobbles and sand. River Rangit joins RiverTeesta near Melli, a border town in Sikkim. Topography of the Rangit catchment isvery steep. The catchment area of the River Rangit at dam site is 971.3 Sq km and ele-vation ranges from 6700m on upstream part of catchment to 608m at Rangit dam site.The permanent snow line in the catchment is at the elevation of 4500m having thecatchment area of 267 Sq km, which is 23% of the total catchment. The average annualrainfall is 2200mm which spreads from May to October. The run off at the dam site isquite low during the months of February and March and very high during July toSeptember. The river experiences severe soil erosion and yields a lot of sediment at thedam site due to its topographic and climatic conditions. The geographical location ofthe study area is shown in the Figure 1.

4. Data and methodology

Various thematic layers with necessary attributes required for soil erosion assessmentwere prepared in the GIS environment by integrating remote sensing satellite data, soiltextural data, topographic data, climatic data and other field data.

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4.1. Basin boundary

Basin boundary of the catchment up to the dam site was delineated using the latest ver-sion of SRTM digital elevation model (DEM) using automated delineation method.Topographic elevations and drainage network were taken as a basis for demarcating thebasin boundary. The delineated boundary was updated using the satellite data of theIndian Remote Sensing (IRS) P6 LISS-III sensor. All the thematic layers were extractedto the boundary extent for further analysis.

4.2. Land use/land cover

This is one of the important thematic layers for computation of soil erosion. Landcover type, its rooting depth and its management factor plays an important role inthe soil erosion assessment. Land use/land cover map of the Rangit watershed wasprepared using IRS P6 Advanced Widefield Sensor data after applying differentimage processing and classification techniques coupled with the limited ground truth.Attribute raster maps for A, Et, Eo and C were prepared using the land use mapand the corresponding attribute information and these maps were used in the modelfor the computation of soil erosion.

Figure 1. Geographical location of the study area (IRS P6, LISS III image).

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4.3. DEM and slopes

SRTM DEM of the study area was used in delineating basin and sub-basins using auto-mated algorithms after computing flow direction and flow accumulation grids. A slopegrid derived from the DEM was used in computing transport capacity of overland flowin the catchment and subsequently in erosion estimation.

4.4. Soils

Soil textural information is very important in computing erosion rate. Soil erodabilitydepends upon bulk density of the soil, top soil rooting depth and soil moisture content.A soil textural map of the study area was prepared in GIS domain at 1:500,000 scaleusing soil maps of the National Bureau of Soil Sciences & Land use Planning (NBSS& LUP), India and the corresponding soil textural information. Attribute raster maps forMS, BD and K were prepared using the corresponding attribute information of theseparameters and the soil textural map.

4.5. Field data

Rainfall data from the field for the period 2002–2005 was obtained and analysed.Temporal variation of rainfall has been considered in the computation of soil erosion.Sediment yield data at the Rangit dam site were collected for the same period and usedin the model validation.

4.6. Landslide map

A landslide map prepared by Geosciences Division of NRSC was used in the study foranalysis with soil erosion map. IRS P6, LISS-IV (multi spectral) data of resolution

Figure 2. Land use/land cover.

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5.8m. was used in preparing the landslide map after applying different image process-ing techniques. Limited field check was carried out in the study area for identifying thelandslide zones.

All the parametric maps such as kinetic energy of rainfall, top soil rooting depth,bulk density of soil, crop cover management factor, ratio of actual to potential evapo-transpiration and soil moisture storage capacity and field data were integrated and ana-lysed, and fed into the model setup. Final soil erosion at each pixel level was computedusing the model equations in distributed modelling environment. Soil erosion in eachsub-catchment was computed and analysed. The erosion map was overlaid with the landuse, slope, soils, and landslide map and analysed thoroughly to identify the reasons forsevere erosion.

Soil erosion for the years 2002–2005 (four years) was computed using the samemodel parameters and by changing the climatic data. The complete process wasrepeated for all the four years. The computed erosion was validated with the field mea-surements in the corresponding year.

5. Results and discussions

From the land use land cover map prepared from the IRS P6–LISS-III data, it can beinferred that forest is the predominant land cover in the study area. Agricultural landexists in the downstream side of the catchment and snow cover on the ridges of the

Figure 3. Soil textures.

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catchment. Interception by the vegetation ‘A’ is high in forest land and nil in the snowcovered area. Land use/land cover of the catchment is shown in the Figure 2.

Soil maps obtained from NBSS & LUP were converted into digital form and a soiltextural map was prepared with the help of soil map and the attributes of soil texture.Soil textural map is shown in the Figure 3. Main textural classes in this area are loamyskeletal, course loamy and fine loamy. In the study area, bulk density of top soil variesfrom 0.8 to 1.4 Tonnes/m3. Soil moisture content at field capacity varies from 0.15 to0.6 (% w/w), where as top soil root depth varies from 3 to 10 cm. Detachability indexof the soil present in the study area ranges between 0.1 and 0.4 (g/J). Slopes were cal-culated using the DEM of the study area. Slopes in this area are very steep; nearly 88%of the total catchment area falls in the category of more than 35% slope. As a result ofthese steep slopes, the erosion rate is high and soil will be carried away very fast to thedownstream side.

Figure 4. Soil erosion rate (classified) in the Rangit catchment during 2005.

Table 1. Aerial extent of soil erosion in the Rangit catchment during 2005.

Rate of soil erosion (T/Ha/year)⁄ Aerial extent (Ha)

1 No erosion (<5T/Ha/Y) 56551.52 Slight erosion (5–10T/Ha/Y) 15245.23 Moderate erosion (10–20T/Ha/Y) 6210.54 Strong erosion (20–40 T/Ha/Y) 3909.85 Severe erosion (>40T/Ha/Y) 7665.9

⁄T/Ha/Y: Tonne/hectare/year.

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With the parametric maps created from the land use, soils, slopes and field data, thetransport capacity due to overland flow (G) and the rate of soil detachment was calcu-lated using the model setup. The minimum of these two (F and G) is the final soil ero-sion in the catchment. Erosion in permanent snow area (75.46 sq km.) was notcalculated. Soil erosion at each pixel level has been computed during the four years(2002–2005). For better interpretation, soil erosion rate grids were further classified intovarious classes based on Food and Agriculture Organization guidelines (FAO 2006) andlimited field check. Rate of erosion and its aerial extent during the year 2005 is shownin the Figure 4 and Table 1. From this, it can be concluded that the average rate of soilerosion in the Rangit catchment during 2005 was moderate (13.6 T/Ha/Y). The erosionrate in the up-stream portion of the catchment was high compared to other areas; it maybe due to the presence of less vegetation density in steep slopes.

As discussed earlier, the total catchment area was divided into eight sub-watersheds.The weighted value of annual soil erosion (Tonnes/ha) was calculated in each sub-watershed by overlaying soil erosion map with the sub-watershed map and shown inthe Figure 5. Sub-watershed wise topographic parameters (slope), land use, soil textureand soil erosion rate were estimated and analysed to understand the reasons for higherosion rate in the down-stream side. These parameters are shown in the Table 2.

Figure 5. Soil erosion in each sub-watershed (T/Ha/year) during 2005.

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Erosion rate in the sub-watersheds C, E, and F is less due to presence of dense forestcover and less slopes.

Erosion rate per year was computed using the Morgan, Morgan and Finny modelfor the years 2002–2004 also assuming the same topographic parameters. All the inter-mediate layers required for computation of soil erosion in each year were created in

Table 2. Sub-watershed wise topographic, land use, soil texture and erosion rate scenario.

Sub-watNo.

Area(Ha)

Averageerosion

T/Ha/Year

Av.slope%

Dominant land use class(% of Sub-watershed area) Dominant soil texture

A 13079.4 29.9 69.8 Evergreen/Semi-evergreen(34%); Other wastelands(21%); Snow covered/glacial area (20%)

Loamy skeletal

B 18412.9 23.5 74.7 Evergreen/Semi-evergreen(50%); Shrub forest/degraded forest (17%);Other wastelands (15%)

Loamy skeletal

C 16025.6 6.8 77.3 Evergreen/Semi-evergreen(73%); Deciduous forest(8%); Shrub forest/degraded forest (7%)

Coarse loam andloamy skeletal

D 15093.2 12.2 74.4 Evergreen/Semi-evergreen(59%); Deciduous forest(12%); Other wastelands(7%)

Loamy skeletal andcoarse loam

E 16172.1 4.7 72.1 Evergreen/Semi-evergreen(63%); Shrub forest/degraded forest (16%);Deciduous forest (12%)

Coarse loam and fineloam

F 7903.2 6.1 70.8 Evergreen/Semi-evergreen(70%); Deciduous forest(23%); Kharif crop land(5%)

Loamy skeletal andfine loam

G 3474.1 19.7 63.6 Kharif crop land(42%)–terraced; Deciduousforest (40%); Evergreen/semi-evergreen (15%)

Fine loam and loamyskeletal

H 6968.0 12.0 70.0 Kharif crop land(36%)–terraced; Evergreen/semi-evergreen (32%);Deciduous forest (23%)

Fine loam and coarseloam

⁄T/Ha/Y: Tonne/hectare/year; Sub-wat: Sub-watershed; Av: Average

Table 3. Observed and computed sediment load in the Rangit catchment.

Year Rainfall (mm) Computed sediment (MT) Observed sediment (MT)

2002 1228 0.691 0.752003 2472 1.703 6.342004 2239 1.260 1.002005 2118 1.307 1.28

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GIS environment. Rainfall and number of rainy days in each year was taken into con-sideration while computing the erosion in the catchment area. Measured sediment loadin each year at the dam site was collected from the field authorities. Estimated erosionand observed erosion in each year are compared and shown in the Table 3. FromTable 3, it can be inferred that the computed sediment load is matching with theobserved values, except in the year 2003. The reason for high erosion during 2003 maybe due to high intensity of rainfall events or landslides.

6. Conclusions

From the computed spatial erosion rate, it can be inferred that the rate of soil erosion inthe Rangit catchment is moderate. Erosion in the up-stream portion of the catchment isvery high; it may be due to presence of less vegetation density in steep slopes. It isfound that erosion is less where dense forest cover exists. Erosion rate in the sub-water-sheds C, E and F is less due to presence of dense forest cover. Computed sediment loadis matching very well with the observed values except in the year 2003. From thisstudy, it can be inferred that the process-based models are realistic and useful for soilerosion assessment. The reason for high erosion during 2003 may be due to high inten-sity of rainfall events or landslides. The Morgan, Morgan and Finny model does notsimulate the effect of landslides (exact erosion due to landslide) in the catchment as thelandslides are localized phenomenon. To study the localized effects such as landslides,etc. studies at better scale may be required with sufficient field data. The present studygives the overall idea for implementing the watershed management practices to controlerosion and landslide.

AcknowledgementsThe authors sincerely acknowledge the support, encouragement and guidance provided by Dr V.K. Dadhwal, Director, NRSC. The authors greatly acknowledge the guidance, encouragement andsupport provided by Dr. V Raghavaswamy, DD-RSA, NRSC and Er G. Behera, former DD, RSGIS AA, NRSC. The authors acknowledge Dr T. Ravishankar and Dr K. Srinivas Dr of NRSCfor their cooperation and suggestions provided.

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