ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Volume II-2/W2
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-2/W2, 207–214, 2015
https://doi.org/10.5194/isprsannals-II-2-W2-207-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-2/W2, 207–214, 2015
https://doi.org/10.5194/isprsannals-II-2-W2-207-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

  19 Oct 2015

19 Oct 2015

ESTIMATION OF ANNUAL AVERAGE SOIL LOSS, BASED ON RUSLE MODEL IN KALLAR WATERSHED, BHAVANI BASIN, TAMIL NADU, INDIA

S. Abdul Rahaman1, S. Aruchamy1, R. Jegankumar1, and S. Abdul Ajeez2 S. Abdul Rahaman et al.
  • 1Dept. of Geography, Bharathidasan University, Tiruchirappalli, Tamil Nadu, India
  • 2Research and Development, Lumina Datamatics, Chennai, Tamil Nadu, India

Keywords: Soil Erosion, Soil Loss, Erosivity, Erodability, Erosion Risk, RUSLE, Remote Sensing and GIS

Abstract. Soil erosion is a widespread environmental challenge faced in Kallar watershed nowadays. Erosion is defined as the movement of soil by water and wind, and it occurs in Kallar watershed under a wide range of land uses. Erosion by water can be dramatic during storm events, resulting in wash-outs and gullies. It can also be insidious, occurring as sheet and rill erosion during heavy rains. Most of the soil lost by water erosion is by the processes of sheet and rill erosion. Land degradation and subsequent soil erosion and sedimentation play a significant role in impairing water resources within sub watersheds, watersheds and basins. Using conventional methods to assess soil erosion risk is expensive and time consuming. A comprehensive methodology that integrates Remote sensing and Geographic Information Systems (GIS), coupled with the use of an empirical model (Revised Universal Soil Loss Equation- RUSLE) to assess risk, can identify and assess soil erosion potential and estimate the value of soil loss. GIS data layers including, rainfall erosivity (R), soil erodability (K), slope length and steepness (LS), cover management (C) and conservation practice (P) factors were computed to determine their effects on average annual soil loss in the study area. The final map of annual soil erosion shows a maximum soil loss of 398.58 t/ h-1/ y-1. Based on the result soil erosion was classified in to soil erosion severity map with five classes, very low, low, moderate, high and critical respectively. Further RUSLE factors has been broken into two categories, soil erosion susceptibility (A=RKLS), and soil erosion hazard (A=RKLSCP) have been computed. It is understood that functions of C and P are factors that can be controlled and thus can greatly reduce soil loss through management and conservational measures.