Advances in Remote Sensing-based Disaster Monitoring and Assessment

• Format: eBook
• Language: English
• Published: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2020
• Subjects: accelerometer sensor; anomaly detection; Beijing urban area; building construction; burn index; chaparral ecosystem; China; compressible sediment layer; debris flow; deep learning; deep neural network; disaster monitoring; dryness monitoring; empirical model function; flash flood; floodplain delineation; forest fire; forest recovery; gross primary production; groundwater level; Himawari-8; Himawari-8 AHI; inaccessible region; land subsidence; Landsat-8; live fuel moisture; LSSVM; machine learning; MODIS; n/a; NIR–Red spectral space; PS-InSAR; random forest; Reference, Information and Interdisciplinary subjects; remote sensing; Research and information: general; risk; satellite remote sensing; satellite vegetation indices; SDE; soil moisture; South Korea; Southern California; threshold-based algorithm; total precipitable water; tropical cyclone formation; uneven settlement; vegetation index; wildfire; WindSat; wireless sensor network; XGBoost; Xinjiang province of China
• ISBN: 3039433229; 9783039433223; 3039433237; 9783039433230
• DOI: 10.3390/books978-3-03943-323-0

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.