# Advances in Remote Sensing-Based Disaster Monitoring and Assessment
## 1 Overview of Contributions
The choice of model input variable and model verification were carefully conducted and high risk areas were identified through the risk analysis. [PERSON] et al. [6] developed a forest fire detection model using geostationary satellite images, Himawari-8 AHI, over South Korea. The model consisted of thresholding, random forest machine learning, and post-processing. In South Korea, wildfires frequently occur at a small scale. For this reason, accurate and rapid forest fire detection using high spatial and temporal resolution satellite data is crucial. However, existing approaches have several critical limitations including a very high false alarm rate. The three-step fire detection model proposed in this study focused on maintaining a high probability of detection (>90%) without increasing a false alarm rate (i.e., significant reduction of a false alarm rate when compared to the existing approaches). The proposed model was validated with real fire events, resulting in a good performance even for small scale fires. [PERSON] et al. [7] proposed a new dryness monitoring indicator, the ratio dryness monitoring index (RDMI). Surface dryness monitoring is important to assess water deficiency as a disaster to harm human lives and ecosystems. The RDMI was developed using distances from the \"Edges on the triangle\" on the near-infrared (NIR) and Red reflectance feature space since the NIR and Red wavelengths are closely related to moisture and vegetation. In particular, defining wet and dry edges using NIR and Red reflectance is a novel component when compared to existing surface dryness indices. The proposed approach was demonstrated in Xinjiang, China, where the biggest desert in Asia is located. The results showed a conspicuous agreement with the distribution of landcover types. [PERSON] et al. [8] combined two SAR data, Envisat ASAR and Radarsat-2, with the PS-In SAR method to capture the temporal patterns of land subsidence and demonstrated the stage of land subsidence in terms of temporal evolution in the east of the Beijing Plain in China, which is known as an area that has largely subsided. A permutation entropy method was used to reverse the temporal evolution pattern of land subsidence. The rate of subsidence results from the SAR timeseries was validated with in situ data resulting in high accuracy (R\({}^{2}\) = 0.94). The time-series of land subsidence showed uneven patterns and agreed well with the decreasing pattern of groundwater, although the subsidence would progress along with the geological conditions. Finally, the overexploitation of groundwater was considered as the main cause of land subsidence from this temporal analysis. Tropical cyclones (TCs) are one of the most risky disasters in terms of casualties and economic losses. However, the determination of TC initiation still requires human interpretation. Several studies have been conducted to automate the process of identifying whether a TC will develop. [PERSON] et al. (2018) developed an automatic TC initiation detection model with machine learning (ML) approaches and compared those methods using four metrics: heat rate, false alarm rate, Peirce skill score, and lead time. The ocean surface wind and precipitation from WindSat were used to build three ML-based models--decision trees (DT), random forest (RF), and support vector machine (SVM)--and linear discriminant analysis (LDA) as a conventional model. Both cases of developing and non-developing tropical disturbances from the Joint Typhoon Warning Center (JTWC) best track were collected to train the models. The results of all accuracy metrics showed a higher performance for the ML models than for the LDA model. In particular, the ML models were able to detect TC initiation 26-30 h before a TC was diagnosed as a tropical depression, which was 5-9 h earlier than the detection by LDA. [PERSON] et al. (2010) proposed an original monitoring system for detecting debris flow by building a wireless accelerometer network and evaluated it over a mountainous area in Japan.