Indonesia is an archipelagic country that has diverse tourism potential, one of which is the island of Bali. The island of Bali is famous for its tourism potential to the World Level, especially the potential for natural and cultural tourism that is not found in other countries. Tourist visits to the island of Bali, from various countries, always increase every year. The increase in the number of tourists has triggered the tourism industry business players to build tourism facilities such as hotels, restaurants, shopping centers, and other facilities, thereby causing an increase built-up area in the coastal tourism area. This study aims to analyze the spatial pattern of built-up land through the transformation of the built-up land index (NDBI) and correlate it with the vegetation index (NDVI, SAVI) and the water index (NDWI). Data analysis using Remote Sensing method using a cloud-based computing platform, namely Google Earth Engine (GEE), on Satellite Imagery Sentinel 2, acquisition on 2016 and 2021. The results of this study indicate that there is an increase in the pattern of built-up area in the Southern coastal area, Tourism Area. The increase in built-up area causes a decrease in the level of vegetation density and increase the level of wetness of the soil surface.
Indonesia is located right on the equator, which receives a lot of heat from the sun and rainfall. Therefore, Indonesia is prone to hydro meteorological natural disasters such as droughts, large sea waves, erosion, floods and landslides. The National Disaster Management Agency (BNPB) noted that floods are followed by landslides of the total hydro-meteorological disasters that most often occur in Indonesia. An inventory of the distribution of multi-year landslides is essential as a basis for disaster mitigation and disaster risk reduction. The research case study was carried out in an area prone to landslides around Mount Batur, Bali-Indonesia. Characteristics of areas with high rainfall and steep slopes (>45%). Detection of areas affected by landslides can be identified with multispectral remote sensing images such as Sentinel 2 Image with a spectral resolution of 13 bands and a spatial resolution ranging from 10-60 m. Data acquisition was carried out in the period 2017-2021. The Support Vector Machine (SVM) algorithm is an alternative for detecting landslide areas in this study. The result showed that the accuracy assessment of the SVM algorithm on the training and validation/testing models is more than 84%. We obtained carrying out a landslide inventory is 25.29 km2. Based on our analysis, the most extensive landslide distribution was found in Batur Village (South and Central), followed by Songan A, Sukawana, Kintamani, and Buahan Villages. This research can be used to develop the Landslide Susceptibility model so that entering the landslide inventory parameters gives good results. As well as a basis for disaster risk reduction (DRR), especially for the community, government, and tourists in this research location.
Flood disasters always hit densely populated urban areas during the rainy season. The causes of flooding that will examine in this scientific article are the condition of water infiltration into the soil. The case study was conducted in the urban area of Denpasar, Bali, Indonesia. Remote sensing data derived from various satellite images i.e., Sentinel-2 (BSI and NDVI extraction), Alos Palsar Imagery (slope extraction), CHIRPS (annual rainfall), and soil texture by laboratory analysis. Acquisition of remote sensing data using a Cloud Computing platform named Google Earth Engine (GEE). Data analysis using weighted overlay with ArcGIS 10.8 and threshold classification using natural breaks (Jenks). Denpasar City has the potential for water infiltration is good to very critical conditions. The correlation of the water infiltration map was carried out by comparing flood events in Denpasar City. The correlation results show (R2 = 0.84), (r = 0.916), (RMSE = 0.138), and p-value <0.05, these values indicate very high relation. Flood events often occur in zones with very critical water infiltration with high building density and low vegetated land cover. The condition of water infiltration critical to very critical category, spatially at the proportion of land cover vegetation < 1% and built-up area > 37%.
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