The subsidence of the land surface in the north coast of Java has become a national and international concern, which says that Jakarta will sink in the next few years. Remote sensing, especially with SAR data, is widely used to view the deformation and subsidence of the ground. Several studies have indeed shown a trend of land subsidence in Jakarta in recent years. This research processing uses Sentinel 1 data to obtain information related to the rate of subsidence using the Insar method, which results. The Lidar data is then used to predict inundation models in recent years to see areas below sea level. Then, a Land Use Land Cover analysis is carried out to see the use of land that will experience inundation in the future. The results show that the total area inundated in 2031 is 1393.6 ha, with the most significant area will inundated in North Jakarta, and for future potential LULC in Jakarta The largest land use land cover in Urban Area with 85 % from total LULC. And for total Potential LULC will be inundated is urban area with 1197.79 ha.
Forest Fire Danger Rating System (FDRS) developed in Indonesia is based on the Canadian Forest Fires Danger Rating System. The Meteorology, Climatology, and Geophysics Agency operates and publishes a daily Fire Weather Index system on its website as part of the FDRS. The so-called SPARTAN system is based on weather elements of rainfall, air temperature, wind speed, and humidity and does not consider soil conditions. This research aims to improve the Fire Weather Index system by adding information on land conditions. In this study, the area of interest was South Sumatera Province of Indonesia and the period of analysis was 2019. The normalized difference polarization index (NDPI) derived from the Synthetic Aperture Radar (SAR) data of the Sentinel-1 satellite and land cover changes and fire incidents derived from the optical data of the Sentinel-2 satellite are used to represent land conditions. Since NDPI shows a good correlation with the degree of soil moisture, the NDPI is considered for the soil moisture conditions. Furthermore, integrating soil moisture conditions and land cover changes into the FWI system provides better early warning information for land/forest fires. Fire hotspot data and in-situ fire information are used to validate the results. This study concludes that adding information on land conditions will provide detailed and better land/forest fire warnings.
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