Flood Inundation Mapping with Supervised Classifiers: 2021 Gediz Plain Flood

Authors : Enis ARSLAN, Serkan KARTAL

Pages : 100-113

Doi:10.48123/rsgis.1220879

View : 28 | Download : 9

Publication Date : 2023-03-28

Article Type : Research Paper

Abstract :Generation of flood inundation maps is beneficial in flood risk assessment and evaluation. Flood inundation mapping can be achieved by many remote sensing techniques like change detection insert ignore into journalissuearticles values(CD); with thresholding and machine learning-based insert ignore into journalissuearticles values(ML); methods. Optical and synthetic aperture radar insert ignore into journalissuearticles values(SAR); imagery are widely used, provided by different satellite systems. This study used Sentinel-1 SAR and Sentinel-2 MSI satellite data in Google Earth Engine insert ignore into journalissuearticles values(GEE); with supervised ML algorithms. Gediz Plain, Turkey was selected as the study area, which is an agricultural area covered mostly by croplands. A flood event that occurred on February 2, 2021, was examined and flood inundation map for the study area was composed. Support Vector Machines insert ignore into journalissuearticles values(SVM);, Random Forest insert ignore into journalissuearticles values(RF); and K-Nearest Neighbor insert ignore into journalissuearticles values(KNN); ML algorithms were selected and models were trained with manually created labelled data in GEE. Also, CD was applied on after and before event SAR images in a traditional approach. RF classifier performs best in Sentinel-2 MSI imagery with 94% overall classification accuracy where KNN classifier gives 93.3% accuracy value for Sentinel-1 SAR dataset, indicating the robustness of SAR imagery for all-weather conditions.
Keywords : Taşkın haritalaması, GEE, Sınıflandırıcı, Sentinel 1, Sentinel 2