Abstract
Abstract
Research on natural disasters requires analytical procedures that are fast, reproducible, and based on sound scientific principles. They must be accessible to institutions responsible for environmental monitoring, land-use planning, and natural disaster risk reduction. This study evaluated the practical use of publicly available geospatial data and open-source GIS tools. Two contrasting natural hazards were analyzed: the September 2024 flood in Lower Silesia, Poland, with a particular focus on the urban floodplain in Wrocław, and the December 2022 landslide in Batang Kali, Selangor State, Malaysia. The study utilized Copernicus Sentinel-2 MSI Level 2A satellite imagery, DEM data from the Copernicus program, OpenStreetMap vector layers, and spatial processing in QGIS. The methodology employed a pre- and post-event change detection scheme based on spectral indices. The Normalized Difference Water Index (NDWI) was used to map the extent of flooding, while the Normalized Difference Vegetation Index (NDVI) was used to identify vegetation disturbances associated with the landslide. To identify the areas affected by the events and assess their relationships with built-up areas, hydrographic features, and terrain morphology, raster classification, elevation analysis, vector layer overlay, and thematic mapping were used. To identify disaster-affected areas and assess their relationships with built-up areas, hydrographic features, and terrain morphology, raster classification, elevation analysis, vector layer overlay, and thematic mapping were used. The results show that the Copernicus Data Space ecosystem and QGIS software provide a robust, transparent, and cost-effective analytical environment for the preliminary assessment of natural disaster impacts. In the case of Wrocław, the NDWI index helped identify flooded areas and infrastructure potentially at risk. In the case of Batang Kali, the differentiation of the NDVI index helped identify patterns of disturbed vegetation associated with the landslide trace. The study confirms that open-source geospatial tools can support repeatable analysis of natural disasters, educational practices, and early-stage decision support for natural disaster risk reduction.