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Land cover change is a prevalent thing in Indonesia. This phenomenon often causes deforestation rates to continue to increase every year, which can cause various natural disasters. This study will look at changes in land cover, make land cover prediction models, and see the relationship between land cover changes and the flood disaster that occurred in Banjarmasin City and its surroundings. Remote sensing is used to see changes in land cover from year to year with GlobeLand30 satellite imagery. Satellite imagery processing is carried out using the Cellular Automata – Markov Chain method to see the land cover prediction. The results show that the most significant land cover change from 2000 to 2020 is experienced by built-up land and forests, while in 2030, forests are predicted to experience deforestation of 356 km2 from 2020. The deforestation will cause catastrophic flooding in 2021, where flooding extends to areas that are not estimated to be high flood hazards, with 111 flood points located in the plantation area.

Palabuhanratu Bay is a location in the southern part of Java Island with a high lobster population. Based on field observation, the lobster population in Palabuhanratu Bay is dominated by Panulirus homarus (green sand lobster), Panulirus versicolor (bamboo lobster), Panulirus penicillatus (black lobster), and Panulirus ornatus (pearl lobster). This study aimed to develop a spatial model using satellite-derived data to predict potential lobster harvest grounds in Palabuhanratu Bay. The Earth observational satellite data used were multispectral Landsat 8-SR imagery, and information about chlorophyll-a, salinity, total suspended solids (TSSs), sea surface temperature (SST), and distance from the coastline was extracted. Multiple linear regression was applied to build the prediction model, which was validated using 10-fold cross-validation. The result of the lobster harvest prediction model agreed with the root-mean-square error (RMSE) and adjusted R2 values of 0.326 and 0.708, respectively. The distribution of lobsters was strong at the following preferred ranges: chlorophyll-a: 1.1–1.7 mg/m3, salinity: 20.2–23.7 ppt, TSS: 40–56.4 mg/L, SST: 29.5–29.9 °C, and distance from the coastline: 500–4700 m. In this study, the habitats of four species of lobsters and their relationships with satellite-derived parameters were evaluated.

The environmental damage in Kabupaten Bandung is critical. To solve the environmental problems, the integrated spatial data of the physical environment is required. Land system data as the integrated spatial data which describes the characteristics of land are useful for the analysis and evaluation of the environmental conditions. The objective of the study is to describe the characteristics of the physical environment of Kabupaten Bandung based on the land system map for the input of decision-making in achieving sustainable development. The study used the spatial analysis and descriptive analytical method. The land system data was obtained from the Badan Informasi Geospaasial (BIG). The results show that the landforms in Kabupaten Bandung are dominated by volcanic and fluvial landforms. The volcanic landforms has morphology dominated by mountainous areas with the slope steepness more than 45% and the soil types of andosols. The land capability of these mountainous areas belongs to grade 7 (for conservation areas). The fluvial landforms are lacustrine plains, which are prone to flood due to inundation. From this study, it is concluded that the land utilization in Kabupaten Bandung, particularly in mountainous areas, is not in accordance with the land carrying capacity. To achieve the sustainable development, the land utilization should consider the regulation of spatial planning, by which the lands with slope steepness more than 40% are categorized as protected areas. Keywords: Land system, land characteristics, landform, suistanable development

Remote sensing and geographic information systems can be applied to extract coastal and marine parameters related to the identification of possible data types, approaches and algorithms as a quick solution in water quality assessment. The purpose of this research are to find the suitable algorithms of salinity and total suspended solid for Palabuhanratu Bay and see the performance of Sentinel-2 satellite image in implementing algorithms based on Landsat satellite image. This study applies several algorithms to extract the estimated salinity and total solid suspended values from the Landsat 8 and Sentinel-2 satellite image using Google Earth Engine. The results of the analysis show algorithms that are suitable for implementation in the waters of Palabuhanratu Bay are the Cilamaya algorithm for estimating salinity values, and the Budhiman algorithm for estimating the total suspended solid value. Sentinel-2 satellite image has a good performance for implementing algorithms that built on Landsat image. So, the algorithm that build on Landsat image can be used to detect salinity and TSS in Sentinel-2 image.

Night time light may indicate that a region is well developed. The phenomena can DNB satellite imagery. The amount of reflected light can be attributed to many population activities and indicates the number of inhabitants in a region. Based on the purpose of this research is to analyze the dynamics of population growth in Java in 2000 and 2015 based on the brightness level of light at night. The method be seen from the sheer reflection of light at night that is received by the VIIRS-used is by a classification method of K-Means and a descriptive analysis. The results showed that as intensity level higher, the number of population is also higher. High-intensity categories generally occur in the provincial capital, and there are new cities that are developing and entering the high-intensity category as well.

Pantai merupakan suatu kawasan peralihan atau pertemuan antara darat dan laut. Pada Kabupaten Gianyar, Bali membentang laut sepanjang selatan Pulau Bali yang merupakan daerah yang berbatasan langsung dengan wilayah pesisir. Tentunya hal tersebut tidak lepas dari adanya dinamika perubahan pada fisik pantai yang disebabkan seperti pengikisan daratan oleh air laut (abrasi) maupun adanya angkutan sedimen dari darat (akresi) yang pada umumnya menjadi sorotan terhadap perubahan garis pantai. Untuk itu diperlukan penelitian guna mengetahui besarnya perubahan yang terjadi sepanjang garis pantai tahun 2002 sampai 2017 sehingga menghasilkan peta perubahan garis pantai. Metode yang digunakan adalah menggunakan interpretasi ratio pada kanal SWIR dan hijau pada citra Landsat 7 dan Landsat 8 ditambah dengan melakukan klasifikasi, dapat dilakukan untuk mengidentifikasi garis pantai beserta menganalisis besarnya perubahan yang terjadi. Hasil analisis tumpang susun identifikasi garis pantai di Kabupaten Gianyar menunjukkan luas pesisir pada tahun 2002 sebesar 42,441 km 2 dan pada tahun 2017 sebesar 42,285 km 2 dimana terjadi abrasi sebesar 0,195 km 2 yang diakibatkan oleh faktor alam yaitu pesisir Kabupaten Gianyar berada di zona laut lepas.