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<p>Surface dynamics are pivotal in shaping the Earth’s topography, which is convoluted with various geo-hazards such as mass movements and soil erosion processes. These hazards hold broader significance, leading to considerable environmental and socio-economic impacts due to their nature. To minimize their impact, it is important to map vulnerable zones using the most advanced geospatial technologies. The current study investigates potential Surface Area Gradation (SAG) and Surface Erosion Mapping (SEM) using a mass loss identification approach and empirical soil erosion modeling -Revised Universal Soil Loss Equation (RUSLE) in the Upper Bhagirathi Basin (UBB) for a short-term range of years 2020–2021. The SAG algorithm detects surface accretion and erosion patterns using Sentinel-1 GRD datasets, while the SEM employs the Geospatial dataset to estimate annual soil erosion. The study also examines topographic elevation changes concerning Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), and Soil Moisture Index (SMI), which were selected due to their strong influence on vegetation dynamics, surface moisture retention, and thermal behavior -factors closely linked to surface degradation and erosion processes. The model effectively monitors environmental and surface dynamics, identifying significant erosion zones along the right bank of the Bhagirathi River, primarily driven by erosive tributaries originating from glaciated terrains. The observed consistency between SAG and SEM outputs highlights their potential as landslide predictors. SEM-based vulnerability classes were distributed as follows: Very Slight to Very Severe, ranging from 46.11%, 20.10%, 9.50%, 5.90%, 11.50%, and 6.96%, respectively. In contrast, SAG analysis classified the area into depletion (38.58%) and gain (61.42%) subcategories. Observations depict variations in NDVI, SMI, and LST, coupled with topographic features, influence terrain fragility and erosion susceptibility. Ultimately, local high-altitude dynamics and geologically weak landscapes govern erosion rates. SEM and SAG techniques effectively delineate erosion-prone zones, enhance the understanding of surface geodynamics, and provide spatial insights that support early warning systems and hazard mitigation strategies in the region.</p>

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Geohazard linkages with surface geodynamics: a short-term temporal topographic analysis of mass movement and gradation patterns in the upper bhagirathi basin

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月26日

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  • Harish Khali,
  • Rajat Subhra Chatterjee,
  • Kishan Singh Rawat

摘要

Surface dynamics are pivotal in shaping the Earth’s topography, which is convoluted with various geo-hazards such as mass movements and soil erosion processes. These hazards hold broader significance, leading to considerable environmental and socio-economic impacts due to their nature. To minimize their impact, it is important to map vulnerable zones using the most advanced geospatial technologies. The current study investigates potential Surface Area Gradation (SAG) and Surface Erosion Mapping (SEM) using a mass loss identification approach and empirical soil erosion modeling -Revised Universal Soil Loss Equation (RUSLE) in the Upper Bhagirathi Basin (UBB) for a short-term range of years 2020–2021. The SAG algorithm detects surface accretion and erosion patterns using Sentinel-1 GRD datasets, while the SEM employs the Geospatial dataset to estimate annual soil erosion. The study also examines topographic elevation changes concerning Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), and Soil Moisture Index (SMI), which were selected due to their strong influence on vegetation dynamics, surface moisture retention, and thermal behavior -factors closely linked to surface degradation and erosion processes. The model effectively monitors environmental and surface dynamics, identifying significant erosion zones along the right bank of the Bhagirathi River, primarily driven by erosive tributaries originating from glaciated terrains. The observed consistency between SAG and SEM outputs highlights their potential as landslide predictors. SEM-based vulnerability classes were distributed as follows: Very Slight to Very Severe, ranging from 46.11%, 20.10%, 9.50%, 5.90%, 11.50%, and 6.96%, respectively. In contrast, SAG analysis classified the area into depletion (38.58%) and gain (61.42%) subcategories. Observations depict variations in NDVI, SMI, and LST, coupled with topographic features, influence terrain fragility and erosion susceptibility. Ultimately, local high-altitude dynamics and geologically weak landscapes govern erosion rates. SEM and SAG techniques effectively delineate erosion-prone zones, enhance the understanding of surface geodynamics, and provide spatial insights that support early warning systems and hazard mitigation strategies in the region.