Rapid Flood Mapping Using Statistical Sampling Threshold Based on Sentinel-1 Imagery in the Barito Watershed, South Kalimantan Province, Indonesia

Muhammad Priyatna (Research Center for Remote Sensing Technology, Research Organization for Aeronautics and Space, National Research and Innovation Agency, East Jakarta, 13710, Indonesia)
Muhammad Rokhis Khomarudin (Research Center for Remote Sensing Technology, Research Organization for Aeronautics and Space, National Research and Innovation Agency, East Jakarta, 13710, Indonesia)
Sastra Kusuma Wijaya (Physics Department, Faculty of Mathematics and Natural Sciences, University of Indonesia, Depok, 16424, Indonesia)
Fajar Yulianto (Research Center for Remote Sensing Technology, Research Organization for Aeronautics and Space, National Research and Innovation Agency, East Jakarta, 13710, Indonesia)
Gatot Nugroho (Research Center for Remote Sensing Technology, Research Organization for Aeronautics and Space, National Research and Innovation Agency, East Jakarta, 13710, Indonesia)
Pingkan Mayestika Afgatiani (Research Center for Remote Sensing Technology, Research Organization for Aeronautics and Space, National Research and Innovation Agency, East Jakarta, 13710, Indonesia)
Anisa Rarasati (Research Center for Remote Sensing Technology, Research Organization for Aeronautics and Space, National Research and Innovation Agency, East Jakarta, 13710, Indonesia)
Muhammad Arfin Hussein (Instrumentation Electronics Study Program, Indonesian Nuclear Technology Polytechnic, Yogyakarta, Indonesia)

Journal of Engineering and Technological Sciences
Vol. 55 No. 1 (2023)

Flood disasters occur frequently in Indonesia and can cause property damage and even death. This research aimed to provide rapid flood mapping based on remote sensing data by using a cloud platform. In this study, the Google Earth Engine cloud platform was used to quickly detect major floods in the Barito watershed in South Kalimantan province, Indonesia. The data used in this study were Sentinel-1 images before and after the flood event, and surface reflectance of Sentinel-2 images available on the Google Earth Engine platform. Flooding is detected using the threshold method. In this study, we determined the threshold using the Otsu method and statistical sampling thresholds (SST). Four SST scenarios were used in this study, combining the mean and standard deviation of the difference backscatter of Sentinel-1 images. The results of this study showed that the second SST scenario could classify floods with the highest accuracy of 73.2%. The inundation area determined by this method was 4,504.33 km2. The first, third and fourth SST scenarios and the Otsu method could reduce the flood load with an overall accuracy of 48.37%, 43.79%, 55.5% and 68.63%, respectively. The SST scenario is considered to be a reasonably good method for rapid flood detection using Sentinel-1 satellite imagery. This rapid detection method can be applied to other areas to detect flooding. This information can be quickly produced to help stakeholders determine appropriate flood management strategies.