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Journal : International Journal for Disaster and Development Interface

Exploring Google Earth Engine for Flood Detection (A Case Study in Bandung City) Ruuhulhaq, Muhammad Saiful; Rohman, Arif; Shalih, Osmar
International Journal for Disaster and Development Interface Vol. 5 No. 1 (2025): April 2025
Publisher : Amcolabora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53824/ijddi.v5i1.93

Abstract

Climate change has increased the frequency and severity of urban flooding worldwide. This research utilizes Google Earth Engine (GEE) and Sentinel-1 synthetic aperture radar (SAR) imagery to detect, map, and analyze flood inundation in Bandung City, Indonesia, from 2014 to 2023. Our workflow combines radiometric calibration, Lee speckle filtering, and Otsu thresholding implemented through GEE's JavaScript API to delineate flooded areas in all weather conditions at 30 m resolution. The results show clear spatial and temporal fluctuations in inundation levels, with peaks in flood coverage in 2016, 2020 and 2021. This approach identifies recurring inundation points and supports targeted disaster management interventions such as prioritized drainage improvements, flood forecasting systems, and nature-based solutions to improve flood resilience in Bandung City by rapidly processing a decade of data in the cloud.
Utilization of Sentinel-2 Imagery for Water Quality Analysis: A Case Study of Saguling Reservoir Ruuhulhaq, Muhammad Saiful; Setianingrum, Sarah
International Journal for Disaster and Development Interface Vol. 5 No. 2 (2025): October 2025
Publisher : Amcolabora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53824/ijddi.v5i2.71

Abstract

Water resources are essential for human life and are one of the main indicators of regional development success. Saguling Reservoir is one of the reservoirs located in West Java that plays a crucial role in various aspects of the lives of the surrounding community. This study aims to determine the distribution of Total Suspended Solids in Saguling Reservoir and to assess turbidity levels using the Normalized Difference Turbidity Index. This study utilizes the Total Suspended Solids (TSS) model by Huizeng Liu using Sentinel-2 imagery data. Based on the research findings, Saguling Reservoir has three classes of Total Suspended Solids (TSS): low (902.91 hectares), moderate (1830.47 hectares), and high (592.78 hectares). Meanwhile, the turbidity levels in Saguling Reservoir are very low (212.84 hectares), moderate (2649.94 hectares), and high (457 hectares).