Cloud-native RUSLE modeling for soil erosion risk assessment and sustainable land management in volcanic highland ecosystems
DOI:
https://doi.org/10.30862/inornatus.v6i1.1241Keywords:
Cloud-based modeling, ecosystem degradation, RUSLE, soil erosionAbstract
This study develops and evaluates a cloud-native implementation of the RUSLE using the Google Earth Engine platform to enhance accessibility and analytical efficiency in soil erosion modeling. The proposed framework integrates satellite-based precipitation data, digital soil information, vegetation-cover indicators, terrain analysis, and user-defined conservation-practice scenarios to generate erosion risk maps and simulate mitigation interventions. The system integrates satellite-based precipitation data, digital soil properties, and terrain analysis to automatically generate spatial erosion risk maps and simulate conservation interventions. A case study conducted in the Batu–Malang–Lumajang highland region of East Java demonstrates that the model effectively identifies critical erosion hotspots and enables real-time simulation of mitigation practices such as terracing. Results indicate a substantial reduction in high-risk erosion zones, with decreases exceeding 90% under conservation scenarios. Although the analysis does not directly measure water-quality or sediment-load parameters, the erosion-risk outputs provide an indirect basis for understanding potential sediment-related environmental pressure in vulnerable watersheds. The findings show that cloud-based environmental modeling can significantly improve environmental impact assessment, support sustainable land-use planning, and provide scalable tools for ecosystem protection in vulnerable landscapes.
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