Flooding has been increasing since 2004 in Japan due to localized heavy rainfall and geographical conditions. Determining areas vulnerable to flooding as one element of flood hazard maps related to disaster management for urban development is necessary. This research integrated Remote Sensing data, the Geography Information System (GIS) method and Analytical Hierarchy Process (AHP) calculation to determine the physical flood-vulnerable area in Okazaki City. We developed this research by applying data from the Geospatial Information Authority of Japan (GSI) to generate the slope map and drainage density; AMEDAS (Automated Meteorological Data Acquisition System) from the Japan Meteorological Agency (JMA) to generate the rainfall data; Soil map from the Ministry of Land, Infrastructure, Transport and Tourism (MLIT) data; and Sentinel-2 imagery to generate the land cover map. We applied the AHP calculation for weighting pairwise the parameters by comparing five iterations of the normalized matrix. We utilized the spatial analysis tool in ArcGIS to run the pairwise comparison to adjudicate the distribution of flooding according to the AHP procedure. The percentage of relative weight was slope (43%), drainage density (20%), rainfall intensity (17%), then both infiltration rate and land cover (10%). The consistency value was reasonable: consistency index (CI-0.007) and consistency ratio (CR-0.6%). We generated high accuracy for flood vulnerability prediction; 0.88 for Probability of Detection (POD), 0.28 for Probability of False Detection (POFD), 0.44 for Critical Success Index (CSI), 1.9 for Bias, and 95 of Area under Curve (AUC). The flood vulnerability was matched to the flood inundation survey of Okazaki City in August 2008 and indicated an excellent Relative Operating Characteristic (ROC).
Several studies suggest that protected areas conserve forests because deforestation rates are lower inside than outside protected area boundaries. Such benefits may be overestimated when deforestation rates within protected areas are contrasted with rates in lands where forest conversion is sanctioned. Here, we reexamine protected area performance by disentangling the effects of land use regulations surrounding the 110,000 km 2 protected area network in Sumatra, Indonesia. We compared 1990-2000 deforestation rates across: (1) protected areas; (2) unprotected areas sanctioned for conversion; and (3) unprotected production areas where commercial logging is permitted but conversion is not. Deforestation rates were lower in protected areas than in conversion areas (Mean: −19.8%; 95% C.I.: −29.7-−10.0%; P < 0.001), but did not differ from production areas (Mean: −3.3%; 95% C.I.: −9.6-2.6%; P = 0.273). The measured protection impact of Sumatran protected areas differs with land use regulations governing unprotected lands used for comparisons. If these regulations are not considered, protected areas will appear increasingly effective as larger unprotected forested areas are sanctioned for conversion and deforested. In the 1990s, production areas were as effective as protected areas at reducing deforestation. We discuss implications of these findings for carbon conservation.
For countries in Southeast Asia that mainly rely on surface water as their water resource, changes in weather patterns and hydrological systems due to climate change will cause severely decreased water resource availability. Warm weather triggers more water use and exacerbates the extraction of water resources, which will change the operation patterns of water usage and increase demand, resulting in water scarcity. The occurrence of prolonged drought upsets the balance between water supply and demand, significantly increasing the vulnerability of regions to damaging impacts. The objectives of this study are to identify trends and determine the impacts of extreme drought events on water levels for the major important water dams in the northern part of Borneo, and to assess the risk of water insecurity for the dams. In this context, remote sensing images are used to determine the degree of risk of water insecurity in the regions. Statistical methods are used in the analysis of daily water levels and rainfall data. The findings show that water levels in dams on the North and Northeast Coasts of Borneo are greatly affected by the extreme drought climate caused by the Northeast Monsoon, with mild to the high risk recorded in terms of water insecurity, with only two of the water dams being water-secure. This study shows how climate change has affected water availability throughout the regions.
Flash floods are recurrent events around the Japan region almost every year. Torrential rain occurred around Kanto and Tohoku area due to typhoon No. 18 in September 2015. Overflowing of the Kinugawa River led to river bank collapse. Thus, the flood extended into Joso City, Ibaraki Prefecture, Japan. ALOS-2/PALSAR-2 was the fastest satellite to record this flood disaster area. A quick method to extract the flood inundation area by utilizing the ALOS-2/ PALSAR-2 image as a rapid response to the flood disaster is required. This study evaluated three methods to extract the flood immediately after the flood occurring. This study compared the extraction approaches of flooded area by unsupervised classification, supervised classification and binary/threshold of backscattering value of flood. The results show that unsupervised classification and supervised classification are overestimated. This study recommends the binarization of the backscattering value to extract the extended flood area. This method is a straight forward approach and generates a similar distribution with the field survey by using the aerial photo with high accuracy (94% of kappa coefficient). We utilized slope map which derived from DEM data to eliminate the overestimated area due to shadowing effect in SAR images.
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.