An Open-Source Geo-spatial Landslide High Risk Area Demarcation for Advancing Sustainable Development in Sri Lanka’s Rathnapura Divisional Secretariat Division

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🔬研究者:Offers a replicable open-source GIS methodology for landslide risk assessment in developing regions.

🏢実務担当者:Local authorities can use this open-source tool for low-cost hazard mapping and land-use planning.

🏛政策担当者:Supports evidence-based disaster risk reduction policies and SDG reporting for landslide-prone areas.

Natural hazards are environmental processes or phenomena that pose potential threats to human societies and ecosystems. In Sri Lanka, landslides constitute one of the most significant natural hazards, with approximately 20,000 km² across ten districts identified as landslide-prone. Among these, the Rathnapura District has recorded a notably high frequency of landslide occurrences. Notably, the 2017 landslide event in this district resulted in substantial impacts, affecting 92,757 individuals and causing extensive damage to property. Therefore, effective identification of high-risk zones is essential for disaster mitigation, yet local and national institutions often face challenges due to the lack of cost-effective assessment tools. This study introduces an open-source geospatial methodology, utilizing freely available satellite data, open datasets, and Quantum Geographic Information System (QGIS) software as a sustainable alternative to commercial GIS solutions. Determinant parameters for landslide susceptibility slope, elevation, topographic wetness, vegetation cover, annual average rainfall, drainage density, and drainage network were derived from literature and integrated through weighted overlay analysis. The analysis revealed that 70.37 km² of the Rathnapura District is classified as highly susceptible to landslides, with the Rathnapura Divisional Secretariat Division (DSD) exhibiting the greatest susceptibility, encompassing 18.41 km² across 46 Grama Niladhari Division (GND) boundaries. Landslide vulnerability was evaluated using physical features (road networks, building footprints from OpenStreetMap) and population census data. High and very high socio-economic vulnerability areas were concentrated within 10 Grama Niladari Divisions (GNDs). Combined susceptibility and vulnerability analysis revealed 10 GNDs as high-risk areas. This open-source geospatial approach directly contributes to Sustainable Development Goals (SDGs), particularly SDG 11 (Sustainable Cities and Communities), SDG 13 (Climate Action), and SDG 15 (Life on Land), by enabling cost-effective hazard mapping, strengthening local adaptive capacity, and supporting evidence-based land use planning. By promoting equitable access to spatial data and analytical tools, it empowers local authorities, planners, and communities to implement proactive disaster risk reduction strategies, optimize resource allocation, and ensure environmentally responsible development. The methodology promotes economic sustainability through reduced dependency on costly software, social sustainability by prioritizing at-risk populations, and environmental sustainability by guiding development away from ecologically sensitive areas. Findings provide a scientific basis for proactive, non-structural mitigation measures, fostering resilient, safe, and sustainable communities in landslide-prone regions as the initial step of disaster management cycle, which are prevention, preparedness and response activities in order to achieve an advancing sustainable development in Sri Lanka’s Rathnapura DSD.

• semanticscholar https://doi.org/10.64211/oidaijsd190424first seen 2026-06-29 08:00:04