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Application of Remote Sensing and GIS on soil erosion assessment at Bata River Basin, India

M. H. Mohamed Rinos1, S. P. Aggarwal2, Ranjith Premalal De Silva3
1 & 3Department of Agricultural Engineering, Faculty of Agriculture,
University of Peradeniya, Peradeniya, Sri Lanka.
[email protected], [email protected]

2Water Resources Division
Indian Institute of Remote Sensing, Dehradun, India.
[email protected]

Abstract
Soil erosion assessment is a capital-intensive and time-consuming exercise. A number of parametric models have been developed to predict soil erosion at drainage basins, yet Universal Soil Loss Equation (USLE) (Wischmeier and Smith, 1978) is most widely used empirical equation for estimating annual soil loss from agricultural basins. While conventional methods yield point-based information, Remote Sensing (RS) technique makes it possible to measure hydrologic parameters on spatial scales while GIS integrates the spatial analytical functionality for spatially distributed data. Some of the inputs of the model such as cover factor and to a lesser extent supporting conservation practice factor and soil erodibility factor can also be successfully derived from remotely sensed data. Further, Modified USLE (MUSLE) uses the same empirical principles as USLE. However, it includes numerous improvements, such as monthly factors, influence of profile convexity/concavity using segmentation of irregular slopes and improved empirical equations for the computation of LS factor (Foster & Wischmeier 1974, Renard et al. 1991). In this study, IRS-1D LISS III and ID Pan data were used to identify the land use status of the Bata river basin. Based on maximum likelihood classifier, the area was classified into eight land use classes namely, Dense Forest, Moderate Forest, Open Forest, Wheat, Sugarcane, Settlement, River Bed, Water Body. A 12-day intensive field checking was undertaken in order to collect ground truth information. Digital Elevation Model (DEM) of Bata river basin was created by digitizing contour lines and spot heights from the SOI toposheets at 1:50,000 scale. Modified Fournier index was used to derive parameters for modified erosivity factor. The modified LS factor map was generated from the slope and aspect map derived from the DEM. The K factor map was prepared from the soil map, which was obtained from the previous studies done at Geo-Science Division of IIRS, Dehradun. The P and C factor values were chosen based on the research findings of Central Soil and Water Conservation Research and Training Institute, Dehradun and spatial extent was introduced from land use/ cover map prepared from LISS III data. Maps covering each parameter (R, K, LS, C and P) were integrated to generate a composite map of erosion intensity based on the advanced GIS functionality. This intensity map was classified into different priority classes. Study area was further subdivided into 23 subwatersheds to identify the priority areas in terms of soil erosion intensity. Each subwatershed was analyzed individually in terms of soil type, average slope, drainage length, drainage density, drainage order, height difference, landuse/landcover and average NDVI with soil erosion to find out the dominant factor leads to higher erosion.

Introduction
Problems associated with soil erosion, movement and deposition of sediment in rivers, lakes and estuaries persist through the geologic ages in almost all parts of the earth. But the situation is aggravated in recent times with man’s increasing interventions with the environment. At present, the quality of available data is extremely uneven. Land use planning based on unreliable data can lead to costly and gross errors. Soil erosion research is a capital-intensive and time-consuming exercise. Global extrapolation on the basis of few data collected by diverse and non-standardized methods can lead to gross errors and it can also lead to costly mistakes and misjudgments on critical policy issues.

Remote sensing provides convenient solution for this problem. Further, voluminous data gathered with the help of remote sensing techniques are better handled and utilized with the help of Geographical Information Systems (GIS). In this case study, GIS functionality were extensively utilized in the preparation of erosion and natural resources inventory and their analysis for assessing soil erosion and soil conservation planning.

Scientific management of soil, water and vegetation resources on watershed basis is, very important to arrest erosion and rapid siltation in rivers, lakes and estuaries. It is, however, realized that due to financial and organizational constraints, it is not feasible to treat the entire watershed within a short time. Prioritization of watersheds on the basis of those sub-watersheds within a watershed which contribute maximum sediment yield obviously should determine our priority to evolve appropriate conservation management strategy so that maximum benefit can be derived out of any such money-time-effort making scheme.

Objectives of the Study

  • Development of a soil erosion intensity map using modified universal soil loss equation with the aid of remotely sensed data in a GIS environment, and
  • Watershed prioritization with respect to soil erosion intensity.

Study Area
The study was carried out at the basin of the Bata river (Figure 01) which is a tributary of Yamuna river. It is located between 300 25′ 3.33″ N to 300 35′ 13.71″ N latitude and 770 22′ 34.75″ E to 770 39′ 42.31″ E longitude. The maximum stretch of this region is from east to west 26.68 Km, whereas its north-south stretch is only 14.7 km. The total area drained by the river Bata being 268.6769 km2. The Bata river basin, which is bounded by the sinuous and meandering Giri river in the North and East, by the mighty Yamuna in the South-East. The Bata river basin has a sub-continental mountain type of sub-tropical monsoon climate with moderately warm to hot summers, high monsoon rains and a cool to cold winter season.

Materials Used

1. Remote Sensing Data Path Row Date
a. IRS-1DLISSIII 96 50 12th Oct, 1998
b. IRS-1DLISSIII 96 50 01st Mar. 1998
c. IRS-1DPan 96 50 08th Oct, 1999
2. SOI Toposheets      
Sheet No : 53/F/6,F/7,F/10 and F/11
Scale : 1:50,000
Date Surveyed : 1965
3. Ancillary Data      
Meteorological data Station   Date
Dhaulakuan   1998-99
Paonta   1968-77
Renuka   1971-91
Nahan   1971-91
Pedological map
Soil map
  (Geoscience Division, IIRS)
(Dept. of Soil Science, Krishi Vishwavidyalaya, Palampur, 1997)

 

Land use/ cover Classification
In this study supervised classification was employed to prepare the land use/ cover map of the study area. In this study, best results were obtained from maximum likelihood classifier. Using this classifier, Bata river basin was classified into eight land use/ cover classes namely Dense Forest, Moderate Forest, Open Forest, Wheat Crop, Sugarcane, Settlement, River Bed and Water Body.

A 12-day intensive field checking effort was made in order to collect ground truth information. Initially, a rapid reconnaissance survey of the study area was carried out in order to observe the relationship between the interpreted land use/ cover, physiography and actual in the ground as well as to fix up sample sets for the survey area.

Preparation of DEM, slope map and aspect map
To create a Digital Elevation Model (DEM) of Bata river basin, contour segment map and spot-height point map were prepared by digitizing contour lines and spot-heights from the SOI topo-sheets No 53 F/6, 7, 10 and 11 (1965, 1:50,000 scale). Interpolation of this combined contour map and point map was done in ILWIS software.

Determination of factors of Modified USLE
Revised USLE – RUSLE uses the same empirical principles as USLE, however it includes numerous improvements, such as monthly factors, incorporation of the influence of profile convexity/concavity using segmentation of irregular slopes. For this study improved empirical equations were used for the computation of rainfall erosivity (R) (Fournier, 1960), topographic (LS) factor (Foster & Wischmeier, 1974) and crop management (C) factor (Lal, 1994).

Modified R factor
Fournnier (1960) developed an erosivity index for river basins. The index described as climate index C is defined as follows:

C = r2/P
where, r is the rainfall amount in the wettest month and P is the annual rainfall amount. This index was subsequently modified by FAO as follows:

Ci = 12SI=1 (ri2/P)
where, Ci is the climate index, rI is the rainfall in month i and P is the annual rainfall. This index is summed for the whole year and found to be linearly correlated with EI30 index (R) of the USLE as follows:

R = b +a*(Ci)
where, the constants a and b vary widely among different climatic zones.
Table 1 . Climate index and R factors for Bata river basin at various stations

  Dhaulakuan(97-98) Paonta(68-77) Renuka(71-91) Nahan(71-91)
Annual average 2130.60 1611.40 1082.50 1883.80
Climate index 547.91 386.20 191.34 413.06
R factor 1189.30 1055.56 894.61 1077.75

Modified LS factor
For slope < 21 %,

LS = (L/72.6)*(65.41*sin(S)+4.56*sin(S)+0.065)

For slope • 21 %,

LS = (L/22.1)0.7*(6.432*sin(S)0.79*cos(S))

where, LS = Slope length and slope steepness factor
L = Slope length (m)
S = Slope steepness (radians)

The LS factor map was created from the slope and aspect map derived from the DEM.

C Factor
For cropland, below and above ground conditions vary considerably over time. As a crop grows, increasing amounts of soil surface are protected from rainfall by canopy, while surface residue cover may decrease because of residue decomposition and tillage operations. It is important to predict Soil Loss Ratio’s (SLR) frequently for the rapidly changing soil and cropping conditions common to most cropland. Incorporating the impact of time into the model requires defining some time step over which the other effects can be assumed to remain relatively constant. Following the lead of Wischmeier and Smith (1978), this basic time unit is set at 15 days for agricultural lands.

In MUSLE, a sub-factor method is used to compute soil loss ratios as a function of five sub-factors (Laflen et. al., 1985) given as:

C = PLU*CC*SC*SR*SM

where, PLU is prior land use factor, CC is crop canopy factor, SC is surface or ground cover factor (including erosion pavement), SM is soil moisture factor and SR is surface roughness factor. The estimation of sub factor values for our conditions requires a long term experiments and considerable resource base, the crop factor values were computed by giving the weights for different cropping seasons and fallow period. C factor map was prepared from Land use/ cover map, which was prepared from supervised classification of FCC of LISS III images.

K Factor
The K factor map was prepared from the soil map, which is obtained from the previous studies done at Geo-Science Division, IIRS, Dehradun, using the values given in Tables 2.

Textural class Organic matter content (%)
0.5 2.0 4.0
Fine sand
Very fine sand
Loamy sand Loamy ver Very fine sand
Sandy loam
Very fine sandy loam Silt loam
Clay loam
Silty clay loam
Clay
0.16
0.42
0.12
0.44
0.27
0.47
0.48
0.28
0.37
0.25
0.14
0.36
0.10
0.38
0.24
0.41
0.42
0.25
0.32
0.23
0.10
0.28
0.08
0.30
0.19
0.33
0.33
0.21
0.26
0.19

Conservation Practice (P) Factor
P factor map was prepared from Landuse/landcover map, which was prepared from supervised classification of FCC of LISS III images, using the values given in Tables 3 and 4. The P factor values were chosen based on the research findings of Central Soil and Water Conservation Research and Training Institute, Dehradun.

Table 3. P values for different conservation practices

Slope (%) Contour Strip Terrace
0-1 0.80
1-2 0.60 0.30
12-18 0.80 0.40 0.16
18-24 0.90 0.45 0.16
2-7 0.50 0.25 0.10
7-12 0.60 0.30 0.12

Table 4. P factor values for different landuse/landcover

Landuse/landcover P factor
Barren land 1.00
Sugar caner 0.12
Wheat 0.10
Dense forest 0.80
fallow land 1.00
Moderately dense forest 0.80
Open forest 0.80
River bed 1.00

Preparation of Erosion Intensity Map
All the factor maps of R, K, LS, C and P (Fig. 02) were integrated to generate a composite map of erosion intensity. This intensity map was classified into five priority classes. Study area was further subdevided into 23 subwatersheds to find out the priority in terms of soil erosion intensity. Each subwatershed was analyzed individually in terms of soil type, average slope, drainage length, drainage density, drainage order, height difference, land use/ cover and average NDVI with soil erosion to find out the dominant factor leads to higher erosion. A summary of the methodology adapted for the present study is shown in Fig. 03.

Conclusions
Annual average soil loss for the Bata river basin is 40.12 tones/ha and barren lands are contributing much for this soil loss (215.81 tones/ha/year).

Wheat / Paddy and Sugarcane covers mainly flat land on lower elevations yielding a soil loss of 22.1 – 31.17 tones/ha/year.

Areas of 22.74 and 13.61 km2 falling under very high and high priority classes respectively for whole Bata river basin. These areas should be prioratized for immediate conservation measures.

Areas of 2.49, 2.21, 2.41 and 2.35 km2 of sub watersheds 10, 16, 22 and 23 respectively are falling under very high priority class and should be considered for conservation measures urgently.

In general, it is clear from the results of this study that modified USLE is a powerful model for the qualitative as well as quantitative assessment of soil erosion intensity for the conservation management.

Multi-temporal, multi-sensor and multi-spectral remote sensing data have provided valuable and very important factors like C and P for this study. Since, the crop cover is a powerful weapon to reduce the direct impact of rainfall on soil particles, it can be recommended that all barren lands in Bata river basin be converted to agricultural land or forest plantations through proper land reclamation measures.

GIS has given a very useful environment to undertake the task of data compilation and analysis within a short period at very high resolution.

IRS-1D pan data and GPS data can be used for updating the age-old Survey of India topo-sheets, which is the prime source of data for the Digital Elevation Model and Geo-coding of images.

References

  • D. P. Shrestha, S. K. Saha (1997), “Soil Erosion Modelling”, ILWIS application guide.
  • Glenn O. Schwab et. al., (1981), “Soil and Water Conservation Engineering”, Third Edition, Oxford and IBH Publishing Co. Pvt. Ltd., New Delhi.
  • Gurmel Singh, C. Venkataramanan, G. Sastry, B. P. Joshi (1996), “Manual of Soil and Water Conservation Practices”, Oxford and IBH Publishing Co. Pvt. Ltd., New Delhi.
  • K. G. Renard, G. R. Foster, G. A. Weesies (1994), “Predicting Soil Erosion by Water – A Guide to Conservation Planning with the Revised Universal Soil Loss Equation”.
  • Lilliesand and Keifer (1994), “Remote Sensing and Image Interpretation”.
  • Morgan, R. P. C., Morgan, D. D. V. and Finney, H. J. (1984), “A Predictive Model for the Assessment of Soil Erosion Risk”, J. Agric. Engng. Res., 30:245-253.
  • Proceedings of UN-ESCAP/ISRO Science Symposium on “Space Technology for Improving Quality of Life in Developing Countries: A Perspective for the Next Millennium”, November 15-17, 1999.
  • R. Lal (1994), “Soil Erosion Research Methods”, Second Edition, Soil and Water Conservation Society, Columbus.
  • V. V. N. Murthy (1982), “Land and Water Management Engineering”, India.


Fig 01 – Location Map

   

   

Fig 02 – Modified USLE Factor Maps (R, K, LS, C and P)

Fig. 03 – Attached separately as Figure3.doc.