Assessment of Soil Erosion in the Western Ghats Region of Kannur using the RUSLE Model and Remote Sensing Data

Panthadi Sai Nath*, Suyash Pratap Singh**, Tushar Kumar***, Yash Karan Luthra****, Gajalakshmi K.*****
*-***** Department of Civil Engineering, R.V. College of Engineering, Bengaluru, Karnataka, India.
Periodicity:April - June'2025
DOI : https://doi.org/10.26634/jce.15.2.22140

Abstract

Soil erosion is a critical factor undermining the ecological resilience and farm productivity of the Western Ghats, particularly in regions like Kannur, Kerala, where high rainfall intensity and steep terrain prevail. This research estimates future soil erosion for the years 2025 and 2026 using the Revised Universal Soil Loss Equation (RUSLE) model, integrated with GIS and remote sensing technologies. The analysis incorporates key RUSLE factors including rainfall erosivity (R), soil erodibility (K), slope length and steepness (LS), land cover (C), and conservation practices (P) to simulate spatial patterns of erosion across the landscape. The predicted annual soil loss for 2025 and 2026 is estimated to average 16.67 t/ha/year and 17.24 t/ha/year respectively, with spatial variation indicating erosion severity ranging from slight to highly severe. The LS and C factors were identified as the most influential contributors to soil erosion, especially in hilly regions with sparse vegetation. The resulting erosion risk maps classify the area from least to most severely erosive zones, aiding in the prioritization of areas that require urgent soil conservation interventions. The insights from this study provide valuable inputs for sustainable land management and targeted conservation planning in the ecologically sensitive Western Ghats.

Keywords

Soil Erosion, RUSLE Model, GIS, Remote Sensing, Western Ghats, Slope Analysis.

How to Cite this Article?

Nath, P. S., Singh, S. P., Kumar, T., Luthra, Y. K., and Gajalakshmi, K. (2025). Assessment of Soil Erosion in the Western Ghats Region of Kannur using the RUSLE Model and Remote Sensing Data.i-manager’s Journal on Civil Engineering, 15(2), 9-17. https://doi.org/10.26634/jce.15.2.22140

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