Remote Sensing of Natural Hazards

• Autore: Ahmed Bayes
• Pubblicazione: Basel, 2022
• Descrizione fisica: 1 online resource (314 p.)
• Soggetto topico: Geography; Research and information: general
• Soggetto non controllato: ABI; aerial image; agriculture; automatic landslide detection; Bangladesh; BRT; CART; climate change; climate migrants; convolutional neural networks; damage assessment; deep learning; Dhaka; digital elevation model; disaster impact; drought; earthquake; ensemble models; flash flood; flood mapping; flooding; forest ecosystems; geohydrological model; groundwater; ice jam; ice storm; InSAR; InSAR time series; K-Nearest Neighbor; land subsidence and rebound; land use and land cover; landslide; landslide deformation; landslide susceptibility; landslides; logistic regression; machine learning models; modified frequency ratio; MODIS; monitoring and prediction; Multi-Layer Perceptron; naive Bayes tree; natural hazards; NDVI; night-time light data; NUAE; OBIA; ordinal regression; PBA; peri-urbanization; post-disaster recovery; random forest; Random Forest; random forests; rapid mapping; remote sensing; reservoir water level; Sentinel-1; sequential estimation; snowmelt; supervised classification; Support Vector Machine; Three Gorges Reservoir area (China); uncertainty; urban growth boundary demarcation; validation; VIIRS
• Persona (resp. second.): AlamAkhtar; AhmedBayes
• Sommario/riassunto: 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.
• Titolo autorizzato: Remote Sensing of Natural Hazards
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910595070103321
• Lo trovi qui: Univ. Federico II