Remote Sensing for Natural Hazards Assessment and Control

Each year, natural hazards, such as earthquakes, landslides, avalanches, tsunamis, floods, wildfires, severe storms, and drought, , affect humans worldwide, resulting in deaths, suffering, and economic losses. According to insurance broker Aon, 2010–2019 was the worst decade on record for economic l...

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Médium:	E-kniha
Jazyk:	angličtina
Vydáno:	Basel MDPI - Multidisciplinary Digital Publishing Institute 2023
Témata:	acoustic gravity waves Asaoka method ASTER Australia block adjustment burn severity burned burned area burnt area monitoring casualty prediction climate zones convolutional neural network coseismic effects deep learning deformation detection digital image correlation DInSAR displacement mapping earth observation earthquake El Niño ENSO exponential model extreme climate events field line resonance fires flash floods freeze–thaw processes Gaofen-2 geographic information system Geographic Information System (GIS) glacier hazards glacier instability glacier mass balance glacier surface energy glacier surface velocity ground deformation monitoring ground motion identification Guangdong Province hazard hazard chain heavy rainfall hillslope erosion ice avalanches identification image partition importance assessment InSAR Interferometric synthetic aperture radar (InSAR) Iran k-Nearest Neighbor land subsidence Landsat 8 landslides lithosphere-magnetosphere coupling Longchuan County machine learning algorithm mapping Mathematics and Science meta-heuristics mid-resolution sensors MODIS morphological operator MT-InSAR natural hazards optical flow permafrost phase correlation Physics PRISMA Qilian Mountains rainfall erosivity Random Forest reclaimed land Reference, Information and Interdisciplinary subjects relief remote sensing Research and information: general risk assessment rock mass strength rockfall source areas rockfall susceptibility RUSLE SAR offset tracking satellite imagery Sentinel 2 Sentinel-1A/B settlement prediction shallow landslides slope angle spatial distribution spatial division spatiotemporal pattern mining stacking support vector regression susceptibility assessment suspended sediment detection Sydney tailing dam risk management Terra time series analysis time series image stack time-series InSAR TRIGRS model turbidity Turpan–Hami basin UAS visual analysis vulnerability wide-area deformation wildfire wildfires Yan’an city
ISBN:	9783036568331, 3036568336, 3036568328, 9783036568324
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Popis
Shrnutí:	Each year, natural hazards, such as earthquakes, landslides, avalanches, tsunamis, floods, wildfires, severe storms, and drought, , affect humans worldwide, resulting in deaths, suffering, and economic losses. According to insurance broker Aon, 2010–2019 was the worst decade on record for economic losses due to disasters triggered by natural hazards, amounting to USD 3 trillion, which is USD 1 trillion more than for the period of 2000–2009. In 2019, the economic losses from disasters caused by natural hazards were estimated at over USD 200 billion (UNDRR Annual Report, 2019). In this context, remote sensing shows high potential to provide valuable information, at various spatial and temporal scales, concerning natural processes and their associated risks. The recent advances in remote sensing technologies and analysis, in terms of sensors, platforms, and techniques, are strongly contributing to the development of natural hazards research. With this Special Issue titled “Remote Sensing for Natural Hazards Assessment and Control”, we proposed state-of-the-art research that specifically addresses multiple aspects on the use of remote sensing (RS) for Natural Hazards (NH). The aim was therefore to collect innovative methodologies, expertise, and capabilities to detect, assess, monitor, and model natural hazards. The present Special Issue of Remote Sensing encompasses 18 open access papers presenting scientific studies based on the exploitation of a broad range of RS data and techniques, as well as focusing on a well-assorted sample of NH types.
ISBN:	9783036568331 3036568336 3036568328 9783036568324
DOI:	10.3390/books978-3-0365-6833-1