Landslide is a frequent disaster in Indonesia that is affected by several factors. Cisolok, Cikakak, Pelabuhanratu, and Simpenan are Sub-district that has physical characteristics which can increase the potential of landslides in the area. Therefore, mitigation efforts by potential landslide mapping are needed in the research area. In this research landslide potential map was made by using 3 methods: SMORPH (slope morphology), Index Storie, and SINMAP (stability index mapping). This study is aim to knew the differentiation of spatial pattern of the landslide potential areas by SMOPRH, SINMAP, and Index Storie methods. Spatial analysis was implemented by overlay technique between landslide potential area with landslide location. The result reveal that the research area was dominated by high potential based on the SMORPH method, low by SINMAP, and moderate by Index Storie. The result also reveal that 33% of the total research area has different potential and 7% has the same potential in the results of all three methods. Where areas with high potential in all three methods was distributed in the northern of the study area. The SMORPH method has 19,951 Ha of high potential area and there are 34 landslides in it, SINMAP 2,568 Ha with 32 landlsides, and Index Storie has 4,684 Ha with 21 landslide. The result also explain that in addition to thbeside of slope gradient factor, landuse change factor has a very big affecting for the occurrence of landslide in the research area. Mapping of landslide potential areas in this research is may practically be applied for the regional planning and development of infrastructures in the area.

At the present time university can be called "small cities" due to their size of area, population and various kinds of activities. Bogor Agricultural University is a campus that can represent a city in smaller scope with a high variety of land cover. Further, the variation of land cover will affect the surface temperature variation. This study aims to determine spatial pattern of land surface temperature variation and relationship with land cover and also the changes. The data used in this study generated from Landsat 8 imagery and field surveys, then analyze using spatial and statistical analysis tools. The results show temperature has a spatial pattern associated with the land cover. Where the highest temperature tends to be located in the central region in the form of a built-up area and the lowest temperature tends to be located in northern region in the form of forest area. The highest increase in temperature tends to appear in the area that shows changes from vegetation to built-up area. Moreover, this study also found that this phenomenon only appears with temperature value were 7ºC greater than the increase in temperature on a similar land cover. Finally, this study proves that the higher vegetation density will create a lower temperature of land surface, while the higher building density creates a higher land surface temperature.

Sukabumi District located in Southern West Java known as a region that has diverse natural characteristics, however, it is vulnerable to disasters, especially landslides. Moreover, this study focuses on Cisolok District because this region always occurred landslides every year due to topography aspect. The aim of this study is to analyze the influence of geomorphology to landslide-prone area in Cisolok District to reduce landslides. This study used overlay analysis for geomorphology mapping, while the Frequency Ratio (FR) method used for landslide-prone area mapping. Several physical variables used in this study such as slope, elevation, lithology, geological structure, road network, stream network, land use, soil type, rainfall, and landslide location. The result shows that the study areas have diverse geomorphology units dominated by volcanic slope with steep topography. While landslide-prone area consist of four classes : namely 17,03% low, 62,05% medium, 14,4% high, and 6,51% very high. Variety of landslide vulnerability in study area influenced by terrain form, land genesis, and geomorphic process.

Pariaman City is one of cities in Indonesia that has a very high incidence of earthquakes both on land and under the sea. This is caused the Pariaman City region is directly adjacent to the Indian Ocean which is the convergence for two tectonic plates, namely the Eurasian Plate and the Indo-Australian Plate. One of these plates goes down into the other plate then it happens the subduction. Subduction earthquakes that result from convergence two plates very active in generating tsunami waves. This study aims to analyze the spatial dynamics model for tsunami prone areas in Pariaman City by using the Cellular Automata-Markov Chains (CA-MC) method, this method is used to modeling tsunami prone areas in Pariaman City in 2030 based on driving factors that given to models. Driving factors used in this study are elevation, slope, distance from the coastline, distance from the road, and distance from the river. CA-MC presents land cover changes depend on neighboring cells. After the model is generated, then analyzed based on Pariaman City spatial plan in 2030 to be compared. To obtain tsunami prone areas, the prediction model for 2030 land cover is overlaid with tsunami hazard. The results showed that from 2018 to 2030, there was an increase in tsunami prone areas with low, medium and high classes in settlements areas.

This study focuses on the assessment of flood-vulnerable areas in the Minraleng watershed, Maros Regency, where the area experiences floods every year. Spatial analysis in the Geographic Information System (GIS) environment has been applied to estimate flood-vulnerable zones using six relevant physical factors, such as rainfall intensity, slope, Elevation, distance from the rivers, land use and soil type. The relative importance of physical factors has been compared in paired matrices to obtain weight values using the Spatial Multi-Criteria Evaluation (SMCE) method. The result showed that the areas located in Camba sub-district had the high vulnerability. The region with a high and very high vulnerability to flood were spread with an area of 436 ha (0,84 %) and 6.168 ha (11.8%).