Comparison of Geomatics Approach and Mathematical Model in Assessment of Soil Erosion Prone Areas – Kolli Hills, Namakkal District – Tamilnadu, India

Abstract

Soil erosion assessment is a capital-intensive and time-consuming exercise. A number of parametric models have been developed to predict soil erosion prone zone at structural terrains, yet Universal Soil Loss Equation (USLE) is most widely used empirical equation for estimating annual soil Loss. 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. In this study, Land sat ETM data was used to identify the land use status of Kolli hills region. Based on level of acquaintance effects of visual interpretation keys and through the digital image processing techniques the study region is classified into seven land use classes’ namely wet crop, dry crop, fallow land, land with scrub and without scrub and water body. The base line studies were delineated from SOI Toposheet at 1:50,000 scale. A simple approach of rank sum method is tried to define the value of erosion risk prone zone areas in the study region then the assessment of soil erosion level in the study region is defined by applying USLE based approach. 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 published soil map – by Soil Survey committee of geological survey of India. 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 Land sat ETM 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.