Linking Visual Soil Erosion Indicators with FRN-¹³⁷Cs-Derived Soil Erosion Rates for Rapid Field-Based Soil Erosion Assessment in the Himalayan Region
DOI:
https://doi.org/10.53550/Keywords:
Visual soil erosion indicators, Radioisotopes, Rapid field-based estimation, Rate of soil loss, HimalayasAbstract
Soil erosion assessment in the Himalayas is constrained by complex topography, intense rainfall, high spatial variability, and limited field accessibility, making conventional measurement approaches difficult to implement. Although erosion models are widely used, their reliability is often limited due to insufficient field validation. This study addresses this gap by integrating visual soil erosion indicators with Cesium-137 tracing-derived erosion rates to develop a practical, field-based assessment framework. A systematic field survey was conducted in which key erosion indicators, including sheet erosion, rills, pedestals, exposed roots, and surface coarse fragments, were recorded at sampling locations. Concurrently, soil samples were collected using a random sampling strategy for 137Cs analysis to quantify net soil erosion rates. Each sampling point was assigned a visually interpreted erosion class and compared with corresponding 137Cs-derived erosion magnitudes. Agreement between observed and measured classes was evaluated using the Kappa coefficient. The results showed a strong overall agreement (Kappa = 0.70), indicating the robustness of the approach. High classification accuracy (84-86%) was achieved for extreme classes (no erosion and severe erosion), while moderate classes showed comparatively lower agreement (66-69%), reflecting the difficulty in distinguishing intermediate erosion levels in the field. Soil quality parameters further supported these results, showing a consistent decline with increasing erosion severity. Forest and grassland areas exhibited higher soil organic carbon and better structural stability, whereas barren lands showed depleted organic matter, higher bulk density, and overall degraded soil conditions. The study establishes a validated visual indicator-erosion rate framework that enables rapid, low-cost, and instrument-free estimation of soil erosion severity in mountainous regions. This approach provides a practical tool for soil erosion assessment, field validation of erosion models and for training machine learning models, and supports decision-making by researchers, policymakers, and land managers in data-scarce and resource-constrained environments.
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