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<p>Soil erosion represents a critical environmental challenge that threatens agricultural productivity, ecosystem stability, and water resource sustainability, particularly in the fragile hill ecosystems of the eastern Himalaya. This study presents a comprehensive spatial assessment of soil erosion in the Lower Tuirial watershed, Mizoram, India, through the integration of Remote Sensing (RS), Geographic Information Systems (GIS), Revised Universal Soil Loss Equation (RUSLE) model and Analytic Hierarchy Process (AHP). The analysis utilized multi-source geospatial datasets, including Sentinel-2&#xa0;A imagery, Advanced Land Observing Satellite (ALOS) Phased Array-type L-band Synthetic Aperture Radar (PALSAR) Digital Elevation Model (DEM 12.5&#xa0;m), soil texture maps, and 30-year rainfall records (1993–2023), to generate key RUSLE parameters such as rainfall erosivity (r), soil erodibility (k), slope length and steepness (ls), cover management (c), and support practice (p). Parameter weighting and prioritization were refined through the AHP, and model performance was validated using Receiver Operating Characteristic (ROC) analysis. Results indicate that the annual soil loss in the watershed ranges from 0 to 576.36 t ha⁻¹ yr⁻¹, with approximately 69.15% of the area experiencing low erosion (&lt; 50 t ha⁻¹ yr⁻¹), while 5.55% falls under the severe category (&gt; 90 t ha⁻¹ yr⁻¹), predominantly in zones of shifting cultivation and steep terrain. Among the RUSLE parameters, rainfall erosivity (weight = 0.48) emerged as the most influential factor, followed by topographic (ls) conditions (0.26). Sensitivity analysis identified the cover management (c) and support practice (p) factors as key determinants of spatial erosion variability. The model achieved an AUC value of 0.783, indicating good predictive performance. The findings highlight that vegetation degradation and unsustainable land management practices exacerbate erosion in this humid tropical watershed, while dense forest cover substantially mitigates it. This study underscores the efficacy of integrating RUSLE-GIS modeling with multi-criteria analysis for spatial erosion risk mapping, providing a robust framework for targeted soil conservation and sustainable watershed management in mountainous regions of the.</p>

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Assessment of soil erosion dynamics in the Tropical-Himalayan monsoon region of the lower tuirial watershed using integrated RS-GIS, RUSLE and AHP models

  • 期刊论文

  • 发表日期：

    2026年1月14日

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  • Imanuel Lawmchullova,
  • Lal Rinkimi

摘要

Soil erosion represents a critical environmental challenge that threatens agricultural productivity, ecosystem stability, and water resource sustainability, particularly in the fragile hill ecosystems of the eastern Himalaya. This study presents a comprehensive spatial assessment of soil erosion in the Lower Tuirial watershed, Mizoram, India, through the integration of Remote Sensing (RS), Geographic Information Systems (GIS), Revised Universal Soil Loss Equation (RUSLE) model and Analytic Hierarchy Process (AHP). The analysis utilized multi-source geospatial datasets, including Sentinel-2 A imagery, Advanced Land Observing Satellite (ALOS) Phased Array-type L-band Synthetic Aperture Radar (PALSAR) Digital Elevation Model (DEM 12.5 m), soil texture maps, and 30-year rainfall records (1993–2023), to generate key RUSLE parameters such as rainfall erosivity (r), soil erodibility (k), slope length and steepness (ls), cover management (c), and support practice (p). Parameter weighting and prioritization were refined through the AHP, and model performance was validated using Receiver Operating Characteristic (ROC) analysis. Results indicate that the annual soil loss in the watershed ranges from 0 to 576.36 t ha⁻¹ yr⁻¹, with approximately 69.15% of the area experiencing low erosion (< 50 t ha⁻¹ yr⁻¹), while 5.55% falls under the severe category (> 90 t ha⁻¹ yr⁻¹), predominantly in zones of shifting cultivation and steep terrain. Among the RUSLE parameters, rainfall erosivity (weight = 0.48) emerged as the most influential factor, followed by topographic (ls) conditions (0.26). Sensitivity analysis identified the cover management (c) and support practice (p) factors as key determinants of spatial erosion variability. The model achieved an AUC value of 0.783, indicating good predictive performance. The findings highlight that vegetation degradation and unsustainable land management practices exacerbate erosion in this humid tropical watershed, while dense forest cover substantially mitigates it. This study underscores the efficacy of integrating RUSLE-GIS modeling with multi-criteria analysis for spatial erosion risk mapping, providing a robust framework for targeted soil conservation and sustainable watershed management in mountainous regions of the.