Oil spill is one of the most common marine environmental problems. Oil spills can be caused by leakage at oil refineries at sea or disposal of vessel waste. This event has an impact on various sectors, such as fisheries, tourism, and marine ecosystems. This study aims to determine the spectral reflectance of Sentinel-2 response to detecting oil spill on the sea. Oil identification in the sea can be made visually by looking at colored patterns at sea level. Sentinel-2 image reflectance was obtained by processing the image using the Google Earth Engine platform. The results were clipped according to the area of ​​interest and divided to get a value between 0 and 1. Bands combination is possible to identify the oil spill visually. The silvery pattern saw in the red-green-blue combination, but it is arduous to estimate its distribution because of the silvery pattern seen for thick oil. The combination of SWIR-NIR-red bands proved effective in showing the distribution of oil with a deep black pattern. Spectral measurements in the field were undertaken by taking samples in the areas of oil spills and clean water bodies. The oil layer had a lower reflectance than the clean water body. The blue band gave a high response, but the red band gave less response. In the NIR and SWIR bands, the reflectance of oil was lower than the water body. In conclusion, the SWIR - NIR - RED band combination is better used to determine oil spills due to it shows the characteristics of oil generally, either thin or thick oil.

Land subsidence had been a significant problem in DKI Jakarta and Semarang, with at least 20 kilometres of roads affected. Repairing them will require at least US $ 1 million per kilometre. Land subsidence monitoring has been carried out using terrestrial methods (GPS and levelling), which are believed to have a high degree of accuracy. The high accuracy of the terrestrial method results in a lack of precision over a large area. On the other hand, remote sensing technology as a non-terrestrial method has developed to monitor land subsidence which can produce high precision over a large area. This study aimed to test the Sentinel-1 satellite data using the Differential Interferometric Synthetic Aperture Radar (DInSAR) method in monitoring land subsidence in DKI Jakarta. DInSAR is a method in Remote Sensing that utilizes radar sensors to analyze the phase differences of a SAR data pair that have different times of capture and have been catalogued to obtain displacement along the area of collection. The results showed that the North Jakarta area experienced the highest land subsidence in the entire Jakarta area. The annual average rate from 2017-2019 is 3.4 cm. The value of 3.4 cm is the average value of all samples in the North Jakarta area. The second area where high land subsidence is West Jakarta, where the maximum amount value of subsidence is 2.8 cm. The accuracy-test results with the MONAS test point showed that the difference between field data and DInSAR results was ± 6.5 cm. The results of this research indicate that the DInSAR method is quite capable of describing land subsidence in the DKI Jakarta area with a relatively good level of precision.

Nowadays, satellite technology has developed significantly. Geodesy satellites such as Grace and Grace-FO can be used for subsurface mapping. The mapping is in the form of detection of the plate details, faults, and regional geodynamic conditions. This study aims to detect plate and faults from space geodesy using the gravity disturbance and Bouguer gravity anomaly parameter. The study area is in the Sunda Strait. Gravity disturbance is one of the gravity model parameters. Gravity disturbance is the gravitational potential of the topography expressed by the spherical harmonic model and the topographic effect by Barthelmes's calculations. Gravity disturbance can visualize subsurface conditions. Bouguer gravity anomaly is needed to get the condition on subsurface objects. This parameter visualizes subsurface conditions in the form of rocks and non-rocks. These conditions can distinguish oceanic crust and continental crust. Gravity contours are needed to obtain plate and faults predictions. The results obtained are validated patterns and shapes with plate and faults secondary data. The tolerance used in this validation is 80%. The gravity disturbance parameter obtained a value of 83% in verifying the accuracy of assessment in plate and faults detection. The Bouguer gravity disturbance parameter obtained a verification value of accuracy assessment in plate detection but 65% in faults detection. This accuracy assessment uses pattern and texture parameters in detecting the similarity of two or more images. This plate and faults detection results are more detailed and can be used for geophysical, geological, earthquake, and earth dynamics applications.

Thermal front has been widely used as a parameter for determining fishing zones. Tis study aimed to  determine  the  thermal  front  distribution  and  to  analyze  its  association  with  the  Bali  Sardinella  fishing zones  in  the  Bali  Strait.  Termal  front  generated  using  sea  surface  temperature  (SST)  from  Aqua  MODIS imagery. Meanwhile, the fishing point data of Bali Sardinella were collected to validate our analysis results. Te data were analyzed into Spatio-temporal information. Te main facts that stand out are that the thermal front  was  predominantly  found  in  the  peak  of  first (April)  and  second  (September)  transitional  season, which was the peak season for the thermal front to occur in a year. Te least of the thermal front occurred in the  South-west  monsoon.  Te  linear  relationship  was found  when  the  peak  of  thermal  front  occurrence compared to the number of catch yields. Based on matching distance analysis, the maximum distance used (twenty  kilometres  buffer)  show  36  matching  points from  101  data  compared  or  at  range  35.6%.  In conclusion, there is a linear relationship between the thermal front parameter and catch yield. It is still used to predict the fishing zone, even though the correlation is not significantly found.

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