Reprint

Remote Sensing of Natural Hazards

Edited by
August 2022
314 pages
  • ISBN978-3-0365-4308-6 (Hardback)
  • ISBN978-3-0365-4307-9 (PDF)

This book is a reprint of the Special Issue Remote Sensing of Natural Hazards that was published in

Engineering
Environmental & Earth Sciences
Summary

Each year, natural hazards such as earthquakes, cyclones, flooding, landslides, wildfires, avalanches, volcanic eruption, extreme temperatures, storm surges, drought, etc., result in widespread loss of life, livelihood, and critical infrastructure globally. With the unprecedented growth of the human population, largescale development activities, and changes to the natural environment, the frequency and intensity of extreme natural events and consequent impacts are expected to increase in the future.Technological interventions provide essential provisions for the prevention and mitigation of natural hazards. The data obtained through remote sensing systems with varied spatial, spectral, and temporal resolutions particularly provide prospects for furthering knowledge on spatiotemporal patterns and forecasting of natural hazards. The collection of data using earth observation systems has been valuable for alleviating the adverse effects of natural hazards, especially with their near real-time capabilities for tracking extreme natural events. Remote sensing systems from different platforms also serve as an important decision-support tool for devising response strategies, coordinating rescue operations, and making damage and loss estimations.With these in mind, this book seeks original contributions to the advanced applications of remote sensing and geographic information systems (GIS) techniques in understanding various dimensions of natural hazards through new theory, data products, and robust approaches.

Format
  • Hardback
License
© 2022 by the authors; CC BY-NC-ND license
Keywords
sequential estimation; InSAR time series; groundwater; land subsidence and rebound; earthquake; rapid mapping; damage assessment; deep learning; convolutional neural networks; ordinal regression; aerial image; landslide; machine learning models; remote sensing; ensemble models; validation; ice storm; forest ecosystems; disaster impact; post-disaster recovery; remote sensing; ice jam; snowmelt; flood mapping; monitoring and prediction; VIIRS; ABI; NUAE; flash flood; BRT; CART; naive Bayes tree; geohydrological model; landslide susceptibility; Bangladesh; digital elevation model; random forest; modified frequency ratio; logistic regression; automatic landslide detection; OBIA; PBA; random forests; supervised classification; landslides; remote sensing; uncertainty; K-Nearest Neighbor; Multi-Layer Perceptron; Random Forest; Support Vector Machine; agriculture; drought; NDVI; MODIS; remote sensing; Bangladesh; landslide deformation; InSAR; reservoir water level; Sentinel-1; Three Gorges Reservoir area (China); peri-urbanization; urban growth boundary demarcation; climate change; climate migrants; natural hazards; flooding; land use and land cover; night-time light data; Dhaka; Bangladesh