Advances in Remote Sensing-based Disaster Monitoring and Assessment

• Other Authors: Im, Jungho (Editor), Park, Haemi (Editor), Takeuchi, Wataru (Editor)
• Format: Electronic Book Chapter
• Language: English
• Published: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2020
• Subjects: Research & information: general; wildfire; satellite vegetation indices; live fuel moisture; empirical model function; Southern California; chaparral ecosystem; forest fire; forest recovery; satellite remote sensing; vegetation index; burn index; gross primary production; South Korea; land subsidence; PS-InSAR; uneven settlement; building construction; Beijing urban area; floodplain delineation; inaccessible region; machine learning; flash flood; risk; LSSVM; China; Himawari-8; threshold-based algorithm; remote sensing; dryness monitoring; soil moisture; NIR-Red spectral space; Landsat-8; MODIS; Xinjiang province of China; SDE; PE; groundwater level; compressible sediment layer; tropical cyclone formation; WindSat; disaster monitoring; wireless sensor network; debris flow; anomaly detection; deep learning; accelerometer sensor; total precipitable water; Himawari-8 AHI; random forest; deep neural network; XGBoost; n/a
• Online Access: DOAB: download the publication; DOAB: description of the publication

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.