Advances in Remote Sensing-based Disaster Monitoring and Assessment

• Otros Autores: Im, Jungho (Editor), Park, Haemi (Editor), Takeuchi, Wataru (Editor)
• Formato: Electrónico Capítulo de libro
• Lenguaje: inglés
• Publicado: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2020
• Materias: 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
• Acceso en línea: 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 d...