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Integrated use of Remote
Sensing and GIS for Soil Erosion Hazard Modelling- A Case Study
S. K. Saha
Agriculture & Soils Division
Indian Institute of Remote Sensing, NRSA
Dehradun - 248 001 (U.P.), India
S. K. Saha
Agriculture & Soils Division
Indian Institute of Remote Sensing, NRSA
Dehradun - 248 001 (U.P.), India
Abstract
Assessment and inventory on soil erosion hazard are essential for formulation of effective soil conservation plans of a watershed for sustainable development. The objective of this study was to assess and map soil erosion hazard of Doon valley ( Dehradun district, Uttar Pradesh, India) following GIS based scalogram modeling approach using remote sensing (IRS-1B, LISS-I) derived physiography - soil and land cover and DEM derived terrain slope mape and ancillary data of soil characteristics and rainfall as inputs. Integrated Scalogram modeling approach resulted in sever classes of soil erosion hazard in the study area with numerical values of Erosion Hazard Index ( EHI) ranging between I ( very low hazard) to 3.5 ( very high hazard). The results indicate that satellite remote sensing and GIS techniques are indeed valuable tools for soil erosion hazard assessment by integration of soil erosion controlling soils cape, terrain and climatic parameters .
1. Introduction
Inventory on soil erosion hazard is vital for effective soil conservation plans of a watershed for sustainable development. The potential utility of remotely sends data in the forms of aerial photographs and satellite sensors data have been well recognized, in mapping and assessing landscape attributes controlling soil erosion, such as physiography, soils, landuse/ land cover, relief, soil erosion pattern ( e.g. pande et. al., 1982)
There is considerable potential for the use of GIS technology as an aid to soil erosion hazard assessment. Soil erosion hazard is most frequently assessed by using Universal soil Loss Equation ( USLE). Recently , several studies showed the potential utility of GIS technique for quantitatively assessing soil erosion hazard based on USLE predicated erosion soil loss ( Saha et al., 1991; Saha and pande, 1993; Mongkolsawat et al., 1994). Cruz (1992) developed a scalogram ( numerical grades) modelling concept and he utilized this concept for upland agriculture suitability assessment following GIS using soils and terrain parameters. A GIS based integrated modelling approach utilizing soilscape, terrain and climatic parameters controlling soil erosion is only the effective means of practical assessment of soil erosion hazard.
This study was undertaken with the objective to assess and map soil erosion hazard following GIS scalogram modelling approach using satellite remote sensing derived physiography- soil and land cover and DEM derived slope maps and ancillary data of soil characteristics and rainfall as inputs.
2. The modelling approach
In scalogram modelling approach (Cruz, 1992), an arithmetic operation was combind with the corresponding numerical weights for the main criteria and sub- criteria to generate a score that includes attributes . According to this modelling approach, soil erosion hazard was assessed using an Erosion Hazard Index ( Score) (EHI) having following arithmetic expression:
EHI ( Score) = [ XI (AI) + X2 ( A 2 ) + …….+ Xn (An) ]
/(XI+X2+…..+ Xn)
(where, XI , X2 , Xn are numerical weights of the main criteria and A1, A2, An are the numerical of sub- criteria representing X1, X2, Xn, respectively
In this study five main criteria such as rian erosivity; soil erosivity, land cover erosivity , terrain slope erosivity and soil depth erosivity with 2.5 ,2, 2.5 ,2 and 1 are considered according to relative importance with respect to soil erosion of the study area. Each main criteria has several sub-criteria depending on rainfall pattern, soils land cover and terrain variabilities in the area. Numerical weights of main criteria viz. Rain erosivity and terrain slope erosivity are taken higher ( 2.5) because these are the critical attributes affecting soil erosion in the study area. The numerical weights and values of the main criteria and sub-criteria, respectively are variable and can be chosen based on experience and experimental study results of a region. In the present study, the following Scalogram model was used for soil erosion hazard assessment -
EHI (Score) = [ 2.5 (Rain erosivity) +2(Soil erosivity)+ 2.5 (slope erosivity) +(Land cover erosivity) + 1(Soil depth erosivity)]/10
(Where, rain-erosivity; soil erosivity; and land cover erosivity ; slope erosivity and soil depth erosivituy are the numerical values of sub-criteria representing them).
3. The study area and data used
The study area constitutes Doon Valley of dehradun district Uttar Pradesh and lies between 77 35' to 78 19' E longitudes and 29 57' 30' to 30 30' 00' N latitudes approximately. It has sub-tropical climate with mean annual rainfall varying from 1600 mm ( hills and piedmont plains area) TO 2000 TO 2200 mm (mountainous areas) and mean annual temperature of 24 c. the soil moisture and temperature regimes are characterized by udic and Hypeerthermic and Thermic ( in mountains), respectively.
The various types of data used in this study are: multitemporal satellite (IRS-1A LISS I) FCCS; Survey of India Topographical maps ( 1:250,000 and 1:50,000scales), agro meteorological data of rainfall and air temperature recorded at meteorological stations and field soil and land use surveys and laboratory analysed soil data of organic matter soil texture of soil samples collected from soil scape units of the area.
4. Methodogy
The various steps of the methodology adop0ted in this study:
6. Conclusions
This study demonstrates that satellite remote sensing and GIS techniques are indeed valuable tools in assessment and mapping of soil erosion hazard following scalogram modeling approach by integration of soil erosion controlling soilscape terrain and climatic parameters.
Acknowledgement
The author express his sincere thanks to director NRSA Hyderabad : Dean, IIRS, and Head, Agriculture & Soils division, IIRS, Dehradun, India for providing necessary facilities and encouragement during the course of this study.
References
Assessment and inventory on soil erosion hazard are essential for formulation of effective soil conservation plans of a watershed for sustainable development. The objective of this study was to assess and map soil erosion hazard of Doon valley ( Dehradun district, Uttar Pradesh, India) following GIS based scalogram modeling approach using remote sensing (IRS-1B, LISS-I) derived physiography - soil and land cover and DEM derived terrain slope mape and ancillary data of soil characteristics and rainfall as inputs. Integrated Scalogram modeling approach resulted in sever classes of soil erosion hazard in the study area with numerical values of Erosion Hazard Index ( EHI) ranging between I ( very low hazard) to 3.5 ( very high hazard). The results indicate that satellite remote sensing and GIS techniques are indeed valuable tools for soil erosion hazard assessment by integration of soil erosion controlling soils cape, terrain and climatic parameters .
1. Introduction
Inventory on soil erosion hazard is vital for effective soil conservation plans of a watershed for sustainable development. The potential utility of remotely sends data in the forms of aerial photographs and satellite sensors data have been well recognized, in mapping and assessing landscape attributes controlling soil erosion, such as physiography, soils, landuse/ land cover, relief, soil erosion pattern ( e.g. pande et. al., 1982)
There is considerable potential for the use of GIS technology as an aid to soil erosion hazard assessment. Soil erosion hazard is most frequently assessed by using Universal soil Loss Equation ( USLE). Recently , several studies showed the potential utility of GIS technique for quantitatively assessing soil erosion hazard based on USLE predicated erosion soil loss ( Saha et al., 1991; Saha and pande, 1993; Mongkolsawat et al., 1994). Cruz (1992) developed a scalogram ( numerical grades) modelling concept and he utilized this concept for upland agriculture suitability assessment following GIS using soils and terrain parameters. A GIS based integrated modelling approach utilizing soilscape, terrain and climatic parameters controlling soil erosion is only the effective means of practical assessment of soil erosion hazard.
This study was undertaken with the objective to assess and map soil erosion hazard following GIS scalogram modelling approach using satellite remote sensing derived physiography- soil and land cover and DEM derived slope maps and ancillary data of soil characteristics and rainfall as inputs.
2. The modelling approach
In scalogram modelling approach (Cruz, 1992), an arithmetic operation was combind with the corresponding numerical weights for the main criteria and sub- criteria to generate a score that includes attributes . According to this modelling approach, soil erosion hazard was assessed using an Erosion Hazard Index ( Score) (EHI) having following arithmetic expression:
(where, XI , X2 , Xn are numerical weights of the main criteria and A1, A2, An are the numerical of sub- criteria representing X1, X2, Xn, respectively
In this study five main criteria such as rian erosivity; soil erosivity, land cover erosivity , terrain slope erosivity and soil depth erosivity with 2.5 ,2, 2.5 ,2 and 1 are considered according to relative importance with respect to soil erosion of the study area. Each main criteria has several sub-criteria depending on rainfall pattern, soils land cover and terrain variabilities in the area. Numerical weights of main criteria viz. Rain erosivity and terrain slope erosivity are taken higher ( 2.5) because these are the critical attributes affecting soil erosion in the study area. The numerical weights and values of the main criteria and sub-criteria, respectively are variable and can be chosen based on experience and experimental study results of a region. In the present study, the following Scalogram model was used for soil erosion hazard assessment -
EHI (Score) = [ 2.5 (Rain erosivity) +2(Soil erosivity)+ 2.5 (slope erosivity) +(Land cover erosivity) + 1(Soil depth erosivity)]/10
(Where, rain-erosivity; soil erosivity; and land cover erosivity ; slope erosivity and soil depth erosivituy are the numerical values of sub-criteria representing them).
3. The study area and data used
The study area constitutes Doon Valley of dehradun district Uttar Pradesh and lies between 77 35' to 78 19' E longitudes and 29 57' 30' to 30 30' 00' N latitudes approximately. It has sub-tropical climate with mean annual rainfall varying from 1600 mm ( hills and piedmont plains area) TO 2000 TO 2200 mm (mountainous areas) and mean annual temperature of 24 c. the soil moisture and temperature regimes are characterized by udic and Hypeerthermic and Thermic ( in mountains), respectively.
The various types of data used in this study are: multitemporal satellite (IRS-1A LISS I) FCCS; Survey of India Topographical maps ( 1:250,000 and 1:50,000scales), agro meteorological data of rainfall and air temperature recorded at meteorological stations and field soil and land use surveys and laboratory analysed soil data of organic matter soil texture of soil samples collected from soil scape units of the area.
4. Methodogy
The various steps of the methodology adop0ted in this study:
- preparation of small scale hypsography- soil map: this was accomplished by visual interpretation of multi-temporal FCCS of IRS- LISS-I based on image elements supplemented with terrain information on hypsography landuse, drainage pattern et. The final soil map was prepared by associating soil composition for each satellite inter preted soilscape unites details of methodology are given by shaha & pande ,1993.
- preparation of landuse / land cover map: landuse/ land cover map with level -I and leel-II land cover classes of the area was prepared by visual analysis of satellite FCCs based on image characteristics supplemented with judicious field check.
- generation of terrain slops map: the terrain slope map was created from DEM ( Digital Elevation Model) following elevation matrix filtering technique and reclassification. DEM was generated gy digitizing contour lines from topographical maps and rasterisation and isoline interpolation using ILWIS ( Integrated land and water information system) GIS package.
- Creation of digital data based of thematic maps: The digital data bases of physiography soils and land cover were created by digirtizing these maps. PC based ILWIS GIS package version 1.41 was used for digitizing and geo- processing's.
- Creation of soil erosion hazard fators data bases : Soil erosivity and soil depth erosivity factors map layers were generated by linking attributes data of soil Erodibillty factor and soil depth soil mapping units digital soil map data ------and reclassification using GIS. The K valaes of each soilscaope units was determined using field observed and laboratory estimated soil characteristics such as texture structure organic matter content and the perme ability of surfaces soils associated with each soilscape units and by following the momogram given by wishmeier et al. 1971. the rain erosivity map layer was generated by GIS aided spatial interpolation of point rainfall data. Slope erosivity and land cover erosivity map layers were prepared by GIS aided reclassification of DEM derived slope map and satellite date derived and digitized land cover map respectively.
- Soil erosion hazard assessment and mapping: Soil Erosion Hazard Index (EHI) was computed by integration of soil erosivity factors layers using spatial modeling module of ILWIS GIS package,. Finally, soil erosion hazard map was generated by reclassification of EHI map using GIS.
- Soils, soils and soil depth erosivity; Ten soilscape units
representing Himalya mountain siwealik hills piedmont plains and terrace
are delineated and mapped in the study area using satellite data.
Broadly at order level the Entisols Inceptisols, Mollisols and Alfisols
soils ae found in different soilscape units of the area ranged from 0.32
to 0.45. fine textured soils (fine loamy coarse loamy /Fine loamy) high
(3,k= 0.40 to 0.45) soil erosivity ( fig. 1 (b). Upper piedmonts plains
and mountains and hills have medium (2 k= 0.35 to 0.45) and low ( 1.k =
0.33 to 0.35 soil erosivity respectively because of dominance of coarse
textured soils ( coarse loamy: :Loamy skeletal/ coarse loamy) (Gig.1 (b)
).
Figure 1 Erosion Hazard Factors (Doom Valley, Dehradun)
- Landuse/land cover and erosivity : The various landuse/ land cover types identified and mapped using satellite are: Low intensity and high intensity culotivated lands dense forest degraded forest open forest and barren / open scrub. Dense forest and high intensity cultivated lands have low (1) erosivity because of soil erosion protecting good canopy cover. Degraded forestry and low intensity cultivated land have medium erosivity (2) and very high (4) erosivity, respectively (Fig. 1 (c)) becausse these lands are prone to high soil erosion.
- Rain and terrain slope erosivity : The study area consists of three
distinct zones of rain erosivity - very high (3, rainfall : 2000 to 2200
mm); high (2 rainfall : 1800 mm) to 2000 mm) and medium ( 1, rainfall :
1600 mm to 1800mm)(Fig 1 (a)). Mountain region of the area is under high
to very high rain erosivity because of higher rainfall. DEM derived
terrain slope erosivity map (Fig 2. (d) indicates that the area has four
classes of slope erosivity : Low (1 slope : 1-2 % & 2-4%) medium (2,
slope: 5-10%) high (3 slope: 10-15% and very high (4 slope: >25).
Figure 2 Soil Erosion Hazard Map (Doon Valley Dehradun)
- Soil erosion hazard assessment : GIS based the scalogram modeling approach resulted in sefen soil erosion hazard classes in the study area ( Fig2.) with numerical scores of Erosion hazard Indes (EHI) ranging between I ( very low hazard) to 3.5 9very high hard). The data pertaining to Fig.2 indicate that in the study area 23.1%,28.6%, 29.5%, 7.1%,5.1% areas have been found under very low to low: moderately low, moderate to moderately high and very high soil erosion hazard classes, respectively.
6. Conclusions
This study demonstrates that satellite remote sensing and GIS techniques are indeed valuable tools in assessment and mapping of soil erosion hazard following scalogram modeling approach by integration of soil erosion controlling soilscape terrain and climatic parameters.
Acknowledgement
The author express his sincere thanks to director NRSA Hyderabad : Dean, IIRS, and Head, Agriculture & Soils division, IIRS, Dehradun, India for providing necessary facilities and encouragement during the course of this study.
References
- Cruz, R.A.D. (1992). The determination of suitable upland agricultural areas using GIS technology. Asian pacific Remote Sensing J. , 5; 123-132.
- Mongkolsawat, c., Thirangoon, p. and sriwongsa, s. ( 1994). Soil erosion mapping with Universal soil loss Equation and GIS proc. 15th Asian conf.Rem. Sens., Bangalore,C-1-1 to C-1-6
- Opena, E.T. (1992). The applications of GIS to soil erosion susceptibility mapping. Asian pacific Rem. Sens. J., 5 (1): 117-222.
- Pande, L.M. ; prasad, j.; saha , S.K. and subrahmanyam, C. (1992) . revies of remote sensing application to soils and agriculture. Proc. silver Jublee Seminar, IIRS, Dehradun, 57-70 pp.
- Saha, S.K. and pande, L.M. (1993). Integrated approach to wards soil erosion inventory for environmental conservation, using satellite and agro- meteorological datal Asian Pacific Rem/ sens. J. 5 (2) : 21-28.
- Saha, S.K. Kudrat M. and oceania- Environmental change monitoring (Ed. Shunji Murai) Pub. Asian Association of Remote Sensing , 369-372 pp.
- Wischmeier, w,h, Johnson, C.B. and Cross , B.V. (1971). A soil erodibility monograph for farmland and construction sites. J. Soil and Water Conservation ,26: 189-193.