This study presents the information on the dynamics of changes in land use/land cover (LULC) spatially and temporally related to the causes of flooding in the study area. The dynamics of LULC changes have been derived based on the classification of Landsat imagery for the period between 1990 and 2016. Terrain surface classification (TSC) was proposed as a micro-landform classification approach in this study to create flood hazard assessment and mapping that was produced based on the integration of TSC with a probability map for flood inundation, and flood depth information derived from field observation. TSC as the micro-landform classification approach was derived from SRTM30 DEM data. Multi-temporal Sentinel-1 data were used to construct a pattern of historical inundation or past flooding in the study area and also to produce the flood probability map. The results of the study indicate that the proposed flood hazard mapping (FHM) from the TSC as a micro-landform classification approach has the same pattern with the results of the integration of historical inundation or previous floods, as well as field investigations in the study area. This research will remain an important benchmark for planners, policymakers and researchers regarding spatial planning in the study area. In addition, the results can provide important input for sustainable land use plans and strategies for mitigating flood hazards.

Utilization of remote sensing in geology is based on some identification of main parameters. They were the relief or morphology, flow patterns, and lineament. So it was necessary to study extraction method based on those parameters. This study aimed to obtain lineament density zone in the Geumpang area, Aceh, associated with mineral resource potential. Information of lineament density using remote sensing data was expected to help solve the problems that arised in the activities of early exploration, the difficulty of finding the prospect areas, so that the activities of pre-exploration always required a wide area and required a long time to determine the location of mineral prospect areas, it would have a direct impact on the financial of exploration activities. The used data was Landsat 8 and DEM SRTM of 30 m. The used method was processing of shaded relief on DEM data with the azimuth angle 0o, 45o, 90o, and 135o, then the result of hill shade process was done overlay, so DEM seen from all different azimuth angles. The results of the overlay were processed using the algorithm LINE with parameters such as the radius of the filter in pixels (RADI) 60, the threshold for edge gradient (GTHR) 120, the threshold for the curve length (LTHR) 100, the threshold for line fitting error (FTHR) 3, threshold for angular (ATHR) 30, and the threshold for linking distance (DTHR) 100. Vector lineament data from LINE algorithm process then performed density analysis to obtain lineament density zoning. Results from the study showed that the area has a high density lineament associated with mineral potency, so it was useful for exploration activities to minimize the survey area.

The North Java coastal area, known as the Pantura region, is experiencing significant land subsidence, with certain areas sinking up to 10 cm per year. Pekalongan is among the most affected, with subsidence rates between 10 and 19 cm annually, mainly due to groundwater extraction, sediment compaction, and coastal erosion. Other coastal cities, like Semarang and Demak, show rates averaging 4 to 10 cm per year. This rapid subsidence is due to favorable geological conditions and ongoing urban development. This study investigates land subsidence in Pekalongan using the PS-InSAR method and dynamic visualization of time-series land cover data. PS-InSAR was applied to 45 scenes from ALOS-2 PALSAR-2 to monitor subsidence from 2014 to 2022. The results were validated with in situ subsidence benchmarks. Urban development dynamics were analyzed through land cover and land use change (LULC) and population density over the same period, using the GLC_FCS30D dataset in the GEE to detect non-natural LULC. The PS-InSAR results indicated that over 60.9% of investigation points experienced subsidence, up to 100 cm between 2014 and 2022. Ground validation showed an 83% agreement with PS-InSAR results. A statistical analysis of LULC from 2014 to 2022 did not show significant built-up area development, but the extension of salt marshes and water bodies indicated subsidence expansion. The population density reached 6873 people per square km by 2022, causing extensive groundwater use for domestic and industrial purposes, further aggravating the subsidence.

Indonesia located at the confluence of Eurasian tectonic plate, Australian tectonic plate and the Pacific tectonic plate. Therefore, Indonesia has big geothermal potential. One of the areas that has geothermal potential is Ungaran Mount. Remote sensing technology can have a role in geothermal exploration activity to map the distribution of land surface temperatures associated with geothermal manifestations. The advantages of remote sensing are able to get information without having to go directly to the field with a large area, and it takes quick, so that the information can be used as an initial reference exploration activities. This study aimed to obtain the distribution of land surface temperature as a regional analysis of geothermal potential. The method of this research was a correlation of brightness temperature (BT) Landsat 8 with land surface temperature (LST) MODIS. The results of correlation analysis showed the R2 value was equal to 0.87, it shows that between BT Landsat 8 and LST MODIS has a very high correlation. Based on Landsat 8 LST imagery correction, the average of fumarole temperature and hot spring is 240C. Fumarole and hot spring are located in dense vegetation land which has average temperature around 26.90C. Land surface temperature Landsat 8 can not be directly used to identify geothermal potential, especially in the dense vegetation area, due to the existence of dense vegetation which can absorb heat energy released by geothermal surface feature.

Berdasarkan data Pendapatan Nasional Indonesia 2017, sektor pertambangan dan penggalian mempunyai peran penting bagi Indonesia. Sektor ini menyumbangkan 7,57% pada produk domestik bruto Indonesia di tahun 2017 . Salah satu sektor pertambangan yang potensial di Indonesia adalah pertambangan mineral Timah di Pulau Bangka dan Belitung. Namun kegiatan pertambangan ini banyak menimbulkan dampak negatif dari sisi lingkungan. Salah satu upaya awal untuk menanggulangi dampak negatif terhadap lingkungan adalah melakukan identifikasi kawasan pertambangan timah secara spasial. Teknologi yang dapat membantu untuk hal ini salah satunya adalah teknologi penginderaan jauh radar. Penelitian ini menggunakan data satelit radar sentinel-1 yang diluncurkan oleh European Space Agency (ESA). Tujuan penelitian ini adalah pemanfaatan data radar Sentinel-1 untuk identifikasi kawasan pertambangan menggunakan metode Object-Base Image Analysis (OBIA). Data sentinel-1 disegmentasi menggunakan algorithma multiresolution segmentation kemudian di klasifikasi menggunakan algorithma nearest neighbor. Masukan data yang digunakan untuk proses klasifikasi dibuat menjadi dua variasi, yang pertama adalah data standar deviasi, mean, dan brightness pada masing – masing segmen di tiap band, kemudian variasi kedua adalah penambahan data tekstur berupa nilai grey level coocurance matrix (GLCM). Hasil klasifikasi menunjukan bahwa masukan data yang menggunakan data tekstur GLCM mempunyai akurasi lebih tinggi dibandingkan dengan yang tanpa data tekstur GLCM. Secara statisktik Hasil klasifikasi dengan type satu menunjukan bahwa total akurasi nya adalah sebesar 89,0 %, dengan nilai kappa sebesar 0,48 sedangkan untuk type dua menunjukan bahwa total akurasinya adalah 89,3%, dengan kappa sebesar 0,50. Hasil klasifikasi kawasan pertambangan dapat digunakan sebagai masukan awal dalam rangka identifikasi spasial kerusakan lingkungan akibat aktivitas pertambangan.