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The rate of population growth in Sukabumi Regency continues to grow, along with the increasing need for food. This population growth, combined with the constant changes in land cover can reduce the productivity of environment in providing natural capital for food availability. Therefore, this study aimed to examine the condition of ecosystem service provision for a decade in Sukabumi Regency due to changes in land cover. In general, the efficient use of remote sensing method and geographic information systems to monitor ecosystem services had received widespread recognition. Following this scenario, the current study used geospatial analysis with dasymetric method which was integrated with supply and demand formulas for ecosystem services provision, food availability, and remote sensing. Geographic information system was also used for land cover interpretation data. The results showed that three districts in Sukabumi Regency, namely Cicurug, Cibadak, and Cicantayan, had "exceeded" condition when the environmental condition already passed the threshold or were unable to support population's needs. Meanwhile, the other districts have "not exceeded" condition, when the environmental conditions were still able to fulfill the needs of population. Finally, the changes in agricultural land cover had a significant influence on the condition of ecosystem services. [ABSTRACT FROM AUTHOR]

Indonesia's land-use and land-cover change (LULCC) is a global concern. The relocation plan of the capital city of Indonesia to East Kalimantan will be becoming an environmental issue. Knowing the latest land cover change modeling and prediction research is essential for fundamental knowledge in spatial planning and policies for regional development. Five articles related to integrated technology of geographic information systems (GIS) and remote sensing for spatial modeling were reviewed and compared using nine variables: title, journal (ranks), keywords, objectives, data sources, variables, location, method, and main findings. The results show that the variables that significantly affect LULCC are height, slope, distance from the road, and distance from the built-up area. The artificial neural network-based cellular automata (ANN-CA) method could be the best approach to model the LULCC. Furthermore, by the current availability of global multi-temporal and multi-sensor remote sensing data, the LULCC modeling study can be limitless. [ABSTRACT FROM AUTHOR]

Landslides are a common type of disaster in Indonesia, especially in steepslope areas. The landslide process can be well understood by measuring the surface deformation. Currently, there are no practical solutions for measuring surface deformation at landslide locations other than field surveys in the Pacitan Regency. We apply LiCSBAS, to identify surface deformation in several landslide locations in a specific non-urban area with mixed topographical features. LiCSBAS is a module that utilizes data from the project of looking inside the continent from space (LiCS), using the new small baseline area subset (NSBAS) method. This study utilizes the leaf area index (LAI) to validate the ability of LiCSBAS to detect surface deformation values at landslide locations. The study succeeded in identifying surface deformations at 100 landslide locations, with deformation values ranging from 15.1 to 10.9 millimeters per year. Most of the landslide locations are closely related to volcanic rocks and volcanic sediments on slopes of 30-35°. The NSBAS method in the LiCSBAS module can reduce gaps error in the sentinel-1 image network. However, the utilization of the C-band at a pixel size of 100 meters made surface deformation only well detectable in a large open landslide area. [ABSTRACT FROM AUTHOR]

This paper presents the paddy field classification model using the approach based on periodic plant life cycle events and how these elevations in climate as well as habitat factors, such as elevation. The data used are MODIS-Terra two tiles of H28v09 and H29v09 of 2016, consist of 46 series of 8-daily data, with 500 meter resolution in Java region. The paddy field classification method based on the phenological model is done by Maximum Likelihood on the transformed annual multi-temporal image of the reflectance data, index data, and the combination of reflectance and index data. The results of the study showed that, with the reference of the Paddy Field Map from the Ministry of Agriculture (MoA), the overall accuracies of the paddy field classification results using the combination of reflectance and index data provide the highest (85.4%) among the reflectance data (83.5%) and index data (81.7%). The accuracy levels were varied; these depend on the slope and the types of paddy fields. Paddy fields on the slopes of 0-2% could be well identified by MODIS-Terra data, whereas it was difficult to identify the paddy fields on the slope >2%. Rain-fed lowland paddy field type has a lower user accuracy than irrigated paddy fields. This study also performed correlation (r2) between the analysis results and the statistical data based on district and provincial boundaries were >0.85 and >0.99 respectively. These correlations were much higher than the previous study results, which reached 0.49-0.65 (hilly-flat areas of county-level), and 0.80-0.88 (hilly-flat areas of provincial level) for China, and reached 0.44 for Indonesia. [ABSTRACT FROM AUTHOR]