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Application of Remote Sensing and GIS on soil erosion assessment at Bata River Basin, India
Modified LS factor
For
slope < 21 %,
LS =
(L/72.6)*(65.41*sin(S)+4.56*sin(S)+0.065)
For slope ³ 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.
Table 2
. K values for different soil textures
| 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.
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