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The drought that occurred in Indramayu Regency was caused by a shift of the beginning season and a long dry season which affected the availability of water storage for plants. Indramayu Regency is one of the rice centers in West Java with 56% of its area is rice fields. But in recent years rice productivity has been reduced due to drought. The Indramayu District Agriculture Office noted that in 2012, 2015 and 2018 paddy fields. The purpose of this study was to determine the distribution of 2012, 2015 and 2018 wetland agricultural drought areas and their relationship with rainfall in Indramayu Regency. The VHI drought index (Vegetation Health Index) is used to determine the pattern of distribution of the drought area of agricultural land. VHI is a combination of VCI (Vegetation Condition Index) and TCI (Temperature Condition Index) derived from NDVI data processing (Normalized Difference Vegetation Index), LST (Land Surface Temperature) of Landsat 7 and 8 images. The processing results of the VHI index show the distribution of drought levels no drought to extreme drought, where in 2012, 2015 and 2018 the distribution of drought in agricultural land has the same pattern, which is dominated by the coastal areas of Indramayu Regency due to the influence of less rainfall. While the level of mild drought is in the western and center regions of Indramayu Regency.

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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.