Landslide Potential Detection Model Using rdNDVI and the GEE Platform in Leuwiliang District, Bogor

Abstract

Landslide is among the most frequent natural disasters in Indonesia, especially in regions characterized by steep slopes and high rainfall. This study analyzes the potential for landslides in Leuwiliang District, Bogor Regency, using the Relative Difference Normalized Difference Vegetation Index (rdNDVI) and the Google Earth Engine (GEE) platform. Sentinel-2A imagery with a 10-meter spatial resolution was used to calculate rdNDVI values from pre- and post-event periods (2020–2023). Slope data derived from Digital Elevation Models (DEM) were integrated to identify areas exceeding a 10% slope threshold, categorized as high-risk zones. The rdNDVI analysis revealed that Karehkel Village had the largest landslide-prone area (40.06 ha), while Leuwiliang Village had the smallest (20.88 ha). Validation using field survey data in 2025 showed an accuracy of 78% for a slope threshold of 10%. The resulting WebGIS system provides interactive visualization for disaster risk mapping and supports decision-making for local mitigation planning. The combination of rdNDVI and GEE demonstrates the potential of cloud-based remote sensing for rapid and scalable landslide detection. Future work should include additional parameters such as rainfall intensity and soil moisture to enhance prediction accuracy.