Geospatial Data Integration for the Flood Vulnerable Area Classification in Jratunseluna Watershed

Abstract

Flood is a threat that has significant impacts on communities and the environment. To improve the management of disaster risk, this research takes an integrated approach by utilizing geospatial data from various sources. The main objective of this research is to provide an integrated approach to determining flood-vulnerable area classes. This research focuses on the processing of various geospatial data such as DEM (Digital Elevation Model) imagery, Landsat 8 satellite imagery, Hydrological data based on SHuttle Elevation Derivatives at multiple Scales (HydroSHEDS) water flow accumulation imagery, and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) rainfall imagery which are used as data sources to model the flood vulnerable area classification of The Jratunseluna watershed. Landsat 8 satellite imagery is used as a source for landuse land cover (LULC) classification, it is done to score each land category to the level of ability to absorb and drain excess water, the remaining data is used to score the earth elevation, accumulated water flow, and rainfall from the area. The weights and scores are used as the basis values to create a flood-vulnerable area classification model. The result of this research is a flood-vulnerable area classification map generated from a pre-made model.