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).

No abstract

Vegetation mapping provides important information for understanding ecological condition through calculation of vegetation density. It based on vegetation indices developed through algorithms of a mathematical model within the visible and near-infrared reflectance bands. The index is an estimate of either leaf density per species or vegetation types, respectively. This study aimed to evaluate those indices and find the best algorithm using Sentinel-2 satellite image. Twenty four algorithms of vegetation indices were analyzed for mangrove density mapping, i.e., BR, GNDVI BR, GR, SAVI, MSAVI, NDRE, NDVI, NDVI2, NDWI, NNIP, PSRI, RR, RVI, VIRE, SVI, VIRRE, MTV1, MTVI2, RDVI, VARI, VI green, MSR, and TVI. During pre-processing stage, a digital number of a Sentinel-2 image was converted into radiance and reflectance value. The analysis resulted in three algorithms that provide the highest accuracy, i.e., NDVI (normalized difference vegetation indices) with exponential regression approach, RVI (Ratio Vegetation indices) with the exponential approach and NDVI (normalized difference vegetation indices) with a polynomial approach. The mangrove biomass spatial modeling NDVI with exponential regression approach (RMSE = 89 kg) showed the average of each pixel (10x10m) was 0.97 ton / 100 m2. Total mangrove biomass for above ground and underground vegetation in the study area was 22,365.6 tons. Sentinel-2 satellite image may best use one of those three algorithms, especially applied for mangrove vegetation.

No abstract