# Advances in Remote Sensing-Based Disaster Monitoring and Assessment
## 1 Overview of Contributions  
[PERSON] et al. [1] modeled live fuel moisture (LFM) using the enhanced vegetation index (EVI) of the moderate resolution imaging spectroradiometer (MODIS). The LFM is a conventional index for indicating the danger level of wildfires. Linear models between EVI and other meteorological factors and in situ LFM observations in California were developed in the study. There was a stronger relationship between LFM and EVI when ancillary meteorological predictors were considered together when compared to the model that only used the EVI. It was confirmed that the temporal discrepancy between in situ measurements and satellite data has substantial impact on the accuracy of LFM estimation. Furthermore, the spatial consistency between the in situ and satellite-based datasets were examined. The proposed method was tested with the Coby fire that occurred in January 2014 in California, USA. The fire ignition point and the burnt area were well matched with the place where the LFM showed under 60%, which was considered as highly dangerous for wildfires. [PERSON] et al. [2] investigated the usefulness of satellite-based burned ratios and vegetation indices to explore post-fire recovery processes. Normalized burned ratio (NBR) and the difference between pre- and post-fire NBRs were calculated using a MODIS product (i.e., MOD09 collection 6) of Terra. The burned ratio of wildfire not only affects the loss of carbon resource, but also the carbon assimilation ratio. For that, the gross primary production (GPP) of MODIS (MOD17A2H) was additionally compared to monitor the post-fire recovery processes. These metrics were able to visualize the phenomena of forest recovery in South Korea, which experienced a severe fire event in 2004. [PERSON] et al. [3] investigated the relationship between urban structures and land subsidence using the Envisat advanced synthetic aperture radar (ASAR) and TerraSAR-X high resolution SAR data. In Beijing, an intensively developed urban area, the high-rise building areas showed significant land subsidence when compared to the areas of low-rise buildings. The permanent scatter interferometric synthetic aperture radar (PS-InSAR) technique was harmonized with high resolution SAR data and in situ observations to reveal the mechanisms of land subsidence under the urban areas. The novelty of this study lies in the block scale analysis with the advantage of using high resolution SAR. [PERSON] and [PERSON] [4] simulated flood damage areas (FDAs) in North Korea by taking advantage of satellite-based information derived from inaccessible areas. Expert-based multiple remote sensing and GIS approaches were chosen for the delineation of flood inundated areas (FIAs) referenced to visible Google Earth high resolution imagery. Sentinel-1 radar images were used to detect the FIAs. The stream flows along the geomorphology were modeled by the Geomorphon model. The originality of this study was included in the model selection by using multiple combinations of input variables. Finally, the most robust model was able to delineate FDAs, which agreed well with the damage information in the reports provided by the North Korean government. [PERSON] et al. [5] established a flash flood risk model in Yunnan Province in China, a typical flood-prone area. Unlike typical floods, flash floods are known to be highly risky, making it difficult for people to evacuate their residences. The model was developed using satellite-based meteorological, topographical, hydrological, and anthropological indices as the input factors affecting flash floods by using an artificial intelligence algorithm, named the least squares support vector machine (LSSVM). The highest model performance in terms of accuracy was achieved by the LSSVM with a radial basis function (RBF) kernel. In particular, the curve number in the topographical factors was the most contributing factor to the flash flood risk model.