Reprint
Advances in Remote Sensing-based Disaster Monitoring and Assessment
Edited by
November 2020
232 pages
- ISBN978-3-03943-322-3 (Hardback)
- ISBN978-3-03943-323-0 (PDF)
This is a Reprint of the Special Issue Advances in Remote Sensing-based Disaster Monitoring and Assessment that was published in
Engineering
Environmental & Earth Sciences
Summary
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.
Format
- Hardback
License and Copyright
© 2021 by the authors; CC BY license
Keywords
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; forest fire; Himawari-8; threshold-based algorithm; machine learning; remote sensing; dryness monitoring; soil moisture; NIR–Red spectral space; Landsat-8; MODIS; Xinjiang province of China; land subsidence; SDE; PE; groundwater level; compressible sediment layer; tropical cyclone formation; WindSat; machine learning; disaster monitoring; wireless sensor network; debris flow; anomaly detection; machine learning; deep learning; accelerometer sensor; total precipitable water; Himawari-8 AHI; machine learning; random forest; deep neural network; XGBoost; n/a