Kuningan Regency is one of the districts in West Java Province with a high rate of landslide events, especially in the southern part where its hilly landscape is dominated with a steep slope. Due to its vulnerability because of cracked soil after the landslide events, landslide locations in Kuningan Regency might only be safe if surveyed using the unmanned aerial vehicle (UAV). Therefore, in this study, DJI Phantom 4 Pro was flown to capture images of landslide locations with a 10 cm spatial resolution. Image processing was conducted to generate Orthophoto and Digital Surface Model (DSM) to give information about the direction and area of landslides. Two sub-districts where landslides occurred, namely Darma and Selajambe, were chosen for landslide mapping using UAV. Results show that in Darma Sub-district, the landslide area is approximately 7,026 m2, while in Selajambe Sub-district is around 8,699 m2. The study results are very useful to analyze the factors affecting landslide events such as slope.
Indonesia is a country with vast agricultural lands. However, agricultural land production in Indonesia is threatened by climate change and land use change. One of phenomenon from climate change that threatened agricultural land is El Nino. El Nino can cause long-lasting drought. In 2018, Indonesia Ministry of Agriculture predicted that in the middle of 2019, El Nino would occur in Indonesia. Magetan Regency is one of the regencies in East Java Province that has a high level of vulnerability to drought. Magetan Regency ranked 36 in Indonesia as a Drought-Prone Regency. Drought in the agricultural sector is a drought which is one of the direct impacts of the climate change phenomenon. This research aims to identify dry areas in agricultural land using Normalized Difference Drought Index and Landsat 8 Imagery with acquisition month August 2017 (Normal Year) and 2019 (El Nino Year). The results of data processing revealed that the dry areas in 2019 are wider than dry areas in 2017 with extent area of 3350.26 ha in 2017 and 9237.28 ha in 2019.
Landslide is challenging to detect and predict. Settlements are not always located on ideal land according to designated areas, such as settlements in a landslide area. Ci Manuk Upstream watershed, Garut District, West Java Province, has critical land due to human intervention. The environment is more sensitive to the components in the environmental system, so when it rains, landslide disasters are prone to happen. This study aims to predict and analyze the vulnerability settlement of landslide in Ci Manuk Upstream watershed, Garut District, West Java Province. The research method is done gradually, beginning with using Stability Index Mapping (SINMAP) method to generate a landslide potential area, then using a spatial analysis method to determine the landslide-prone area and finally assessing the vulnerability of the settlements in the landslide area. The results show that 43.88% of the total area is a potential landslide area, then 19.04% is a high landslide-prone area, and 30.80% is the vulnerability of the settlement areas in the high landslide area. This study also shows that the landslide potential area is dominant at slope >25%, dystrudept soil type, and rainfall rate of >3500 mm/year. The landslide-prone settlement areas tend to be in the landslide zone of SINMAP modeling, and vulnerable settlements have tended to be south of the watershed because of the low quality of buildings and high population densities.
Indonesia National Board for Disaster Management (BNPB) said that in 2002-2009 the drought was the second most frequent intensity after the flood disaster. Drought is a condition where a region experiences a lack of water. If this drought occurs in the agricultural area, it will certainly affect the plants that grow. One of the potential detection methods for drought can be using remote sensing. Temperature Vegetation Dryness Index (TVDI) is one method to detect drought potential with two parameters, vegetation index, and temperature. Through the triangle method, there is a relationship between the vegetation index and temperature represented by linear equations. These linear equations are used to calculate TVDI values. This study uses data Landsat 8 image and paddy fields. The objective of this research is how the distribution and the area of drought based on the TVDI algorithm. The TVDI with dry classes diffuse in the central and western parts, it is also the TVDI with the largest area about 155.82 km2 or 44% of the total. TVDI with wet classes can be seen in the western part of the region and the smallest area about 32.94 km2 or 9% of the total.
Many countries in the world have various kinds of problems, such as disasters. Indonesia is one of the countries with high area and population affected by landslides, such as in several regions in Indonesia, namely Bogor Districts. The affected population can be caused by the increasing number of residents and built-up areas, as well as the low level of public knowledge about landslides. The high population affected can describe the level of vulnerability and capacity that exists in the community, such as in South Babakan Madang Subdistrict. The density of the population and the built-up area which includes settlements, public facilities and community knowledge related to landslides that are different in 3 Countryside namely Bojong Koneng, Cijayanti, and Karang Tengah make the people in South Babakan Madang Subdistrict vulnerable to landslides. The modified scoring method with reference to Perka BNPB No. 02 of 2012 can be used to determine the level of vulnerability and capacity of the community. The results show that South Babakan Madang Subdistrict is dominated by moderate vulnerability with an index between 0.34 - 0.67. The capacity in the South Babakan Madang Subdistrict tends to be homogeneous in the low capacity class with an index < 0.34.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.