TY - BOOK ID - 134037483 TI - Advances in Remote Sensing-based Disaster Monitoring and Assessment AU - Im, Jungho AU - Park, Haemi AU - Takeuchi, Wataru PY - 2020 PB - Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute DB - UniCat KW - Research & information: general KW - wildfire KW - satellite vegetation indices KW - live fuel moisture KW - empirical model function KW - Southern California KW - chaparral ecosystem KW - forest fire KW - forest recovery KW - satellite remote sensing KW - vegetation index KW - burn index KW - gross primary production KW - South Korea KW - land subsidence KW - PS-InSAR KW - uneven settlement KW - building construction KW - Beijing urban area KW - floodplain delineation KW - inaccessible region KW - machine learning KW - flash flood KW - risk KW - LSSVM KW - China KW - Himawari-8 KW - threshold-based algorithm KW - remote sensing KW - dryness monitoring KW - soil moisture KW - NIR–Red spectral space KW - Landsat-8 KW - MODIS KW - Xinjiang province of China KW - SDE KW - PE KW - groundwater level KW - compressible sediment layer KW - tropical cyclone formation KW - WindSat KW - disaster monitoring KW - wireless sensor network KW - debris flow KW - anomaly detection KW - deep learning KW - accelerometer sensor KW - total precipitable water KW - Himawari-8 AHI KW - random forest KW - deep neural network KW - XGBoost KW - n/a KW - NIR-Red spectral space UR - https://www.unicat.be/uniCat?func=search&query=sysid:134037483 AB - Remote sensing data and techniques have been widely used for disaster monitoring and assessment. In particular, recent advances in sensor technologies and artificial intelligence-based modeling are very promising for disaster monitoring and readying responses aimed at reducing the damage caused by disasters. This book contains eleven scientific papers that have studied novel approaches applied to a range of natural disasters such as forest fire, urban land subsidence, flood, and tropical cyclones. ER -