Reliable information about the spatial distribution of surface waters is critically important in various scientific disciplines. Synthetic Aperture Radar (SAR) is an effective way to detect floods and monitor water bodies over large areas. Sentinel-1 is a new available SAR and its spatial resolution and short temporal baselines have the potential to facilitate the monitoring of surface water changes, which are dynamic in space and time. While several methods and tools for flood detection and surface water extraction already exist, they often comprise a significant manual user interaction and do not specifically target the exploitation of Sentinel-1 data. The existing methods commonly rely on thresholding at the level of individual pixels, ignoring the correlation among nearby pixels. Thus, in this paper, we propose a fully automatic processing chain for rapid flood and surface water mapping with smooth labeling based on Sentinel-1 amplitude data. The method is applied to three different sites submitted to recent flooding events. The quantitative evaluation shows relevant results with overall accuracies of more than 98% and F-measure values ranging from 0.64 to 0.92. These results are encouraging and the first step to proposing operational image chain processing to help end-users quickly map flooding events or surface waters.

COASTLINE CHANGES DETECTION USING SENTINEL-1 SATELLITE IMAGERY IN SURABAYA, EAST JAVA, INDONESIA

During the recent years, maritime surveillance has been receiving a growing interest. Ship detection and identification are parts of maritime surveillance in order to dealing with illegal fishery, maritime traffic, sea border activity, or oil spill detection and monitoring. Nowadays, Synthetic Aperture Radar (SAR) as one of active remote sensing technology provide signals to penetrate cloud, can be advantage to be used in tropical region with the intention to monitor sea objects on the sea surface from the space. The availability of Sentinel-1 as SAR imaging mission, providing continuous all-weather, day-and-night imagery, makes it ideal for precise cueing and location of ship activities at sea. Utilization of CFAR (Constant False Alarm Rate) algorithm provided by SNAP (Sentinel Application Platform) software from ESA show rapid detection of ship in the study areas (Madura Strait and Lamong Gulf). Compared with manual ship extraction method, it gives sufficient results.

Estimation of Biomass and Carbon Stock Using NDVI from Multispectral Camera in the Revegetation Area of PT Berau Coal

Oil spill phenomena, particularly in the West Side of Java Sea, occur due to the dense oil industry and maritime activities causing potential vulnerability to oil pollution. Rapid detection of oil spill distribution needs to be conducted to minimize the resulting impacts. By developing an early detection method for oil spills in the Western Java Sea using Synthetic Aperture Radar (SAR) technology from Sentinel-1A Satellite using SNAP software with an Adaptive Threshold approach. The detection method is based on the principle that oil causes the sea surface to become calm, resulting in a drastic reduction in radar wave reflection values. Research results show oil spill detection in June 2023 with an area reaching 73,823 km² and an accuracy level of 93,75% based on confusion matrix validation. This research also integrates windfield analysis to support radar image interpretation, with wind speed estimation results of 1-12 m/s and dominant direction toward northwest to north. Windfield data was validated using BMKG reanalysis data and Copernicus Marine My Ocean Pro. The developed method is superior to optical imagery in terms of detection visualization and object classification capability within the spill area. The findings of this research provide important contributions to the development of effective monitoring and response systems to protect marine ecosystems, and can serve as a basis for planning environmental impact mitigation from oil spills in the region.