Integrated SAR and Optical Approach for Multi-Temporal Flood Mapping in Bandar Lampung
Abstrak
Flooding is one of the most frequent hydrometeorological disasters in urban coastal regions, including Bandar Lampung, Indonesia. This study aims to analyze the spatial and temporal distribution of flood inundation in 2017, 2019, and 2022 using Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical imagery. Flood detection was conducted using the Normalized Difference Water Index (NDWI) derived from Sentinel-2 and backscatter coefficient thresholding from Sentinel-1 data. The results indicate that Rajabasa, Panjang, and Kedamaian sub-districts experienced recurring flood events, with Rajabasa showing the largest increase in inundation area, reaching approximately 2.2 km² in 2022. The integration of SAR and optical imagery improves flood detection reliability under varying weather conditions. These findings provide important geospatial information to support disaster mitigation planning and sustainable urban development in Bandar Lampung.
Keywords: flood mapping; multi-temporal analysis; sentinel-1; sentinel-2; ndwi; bandar lampung
##plugins.generic.usageStats.downloads##
Referensi
BNPB. (2022). Indonesian Disaster Risk Index 2022. National Disaster Management Agency of Indonesia.
Clement, M. A., Kilsby, C. G., & Moore, P. (2018). Multi-temporal synthetic aperture radar flood mapping. Remote Sensing of Environment, 204, 248–262. https://doi.org/10.1016/j.rse.2017.10.030
Huang, C., Chen, Y., & Wu, J. (2018). DEM-based flood inundation mapping. Water, 10(11), 1520. https://doi.org/10.3390/w10111520
IPCC. (2021). Climate Change 2021: The Physical Science Basis. Cambridge University Press.
Kuenzer, C., & Dech, S. (2013). Thermal Infrared Remote Sensing: Sensors, Methods, Applications. Springer.
Li, Y., Martinis, S., Wieland, M., & Schlaffer, S. (2019). Urban flood mapping using Sentinel-1 SAR data. Remote Sensing, 11(7), 786. https://doi.org/10.3390/rs11070786
Martinis, S., Kersten, J., & Twele, A. (2015). A fully automated TerraSAR-X based flood service. ISPRS Journal of Photogrammetry and Remote Sensing, 104, 203–212. https://doi.org/10.1016/j.isprsjprs.2014.07.014
Mason, D. C., Speck, R., Devereux, B., Schumann, G. J. P., Neal, J. C., & Bates, P. D. (2010). Flood detection in urban areas using TerraSAR-X. IEEE Transactions on Geoscience and Remote Sensing, 48(2), 882–894. https://doi.org/10.1109/TGRS.2009.2029236
McFeeters, S. K. (1996). The use of the normalized difference water index (NDWI). International Journal of Remote Sensing, 17(7), 1425–1432. https://doi.org/10.1080/01431169608948714
Pekel, J. F., Cottam, A., Gorelick, N., & Belward, A. S. (2016). High-resolution mapping of global surface water. Nature, 540, 418–422. https://doi.org/10.1038/nature20584
Rahman, M. S., Di, L., & Yu, E. (2019). Remote sensing-based flood mapping in urban areas: Progress and challenges. Remote Sensing, 11(13), 1589. https://doi.org/10.3390/rs11131589
Schumann, G. J. P., & Moller, D. K. (2015). Microwave remote sensing of flood inundation. Physics and Chemistry of the Earth, 83–84, 84–95. https://doi.org/10.1016/j.pce.2015.03.006
Twele, A., Cao, W., Plank, S., & Martinis, S. (2016). Sentinel-1-based flood mapping: A fully automated processing chain. International Journal of Remote Sensing, 37(13), 2990–3004. https://doi.org/10.1080/01431161.2016.1177488
Xu, H. (2006). Modification of normalized difference water index (NDWI) to enhance open water features. International Journal of Remote Sensing, 27(14), 3025–3033. https://doi.org/10.1080/01431160600589179
Brakenridge, G. R., Nghiem, S. V., Anderson, E., & Mic, R. (2007). Orbital microwave measurement of river discharge and flood extent. Water Resources Research, 43(4). https://doi.org/10.1029/2006WR005238
