Remote Sensing for Natural Hazards Assessment and Control

• Other Authors: Mazzanti, Paolo (Editor), Romeo, Saverio (Editor)
• Format: Electronic Book Chapter
• Language: English
• Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2023
• Subjects: Research & information: general; Physics; wildfires; hillslope erosion; satellite imagery; rainfall erosivity; RUSLE; rockfall source areas; identification; relief; slope angle; rock mass strength; rockfall susceptibility; land subsidence; Geographic Information System (GIS); InSAR; machine learning algorithm; meta-heuristics; Iran; k-Nearest Neighbor; Random Forest; fires; Landsat 8; Sentinel 2; Terra; ASTER; MODIS; burned; mapping; hazard chain; turbidity; suspended sediment detection; extreme climate events; tailing dam risk management; spatiotemporal pattern mining; El Niño; remote sensing; geographic information system; flash floods; visual analysis; SAR offset tracking; glacier surface velocity; glacier instability; glacier hazards; ice avalanches; ENSO; glacier mass balance; glacier surface energy; earthquake; coseismic effects; field line resonance; acoustic gravity waves; lithosphere-magnetosphere coupling; burnt area monitoring; Australia; Sydney; wildfire; earth observation; mid-resolution sensors; time series analysis; burn severity; climate zones; deep learning; PRISMA; burned area; Sentinel-2; morphological operator; convolutional neural network; casualty prediction; importance assessment; spatial division; support vector regression; digital image correlation; phase correlation; optical flow; time series image stack; landslides; ground motion identification; displacement mapping; UAS; risk assessment; random forest; DInSAR; Yan'an city; settlement prediction; reclaimed land; exponential model; Asaoka method; wide-area deformation; deformation detection; time-series InSAR; stacking; Turpan-Hami basin; heavy rainfall; shallow landslides; TRIGRS model; spatial distribution; susceptibility assessment; Longchuan County; Guangdong Province; MT-InSAR; ground deformation monitoring; Sentinel-1A/B; image partition; block adjustment; Gaofen-2; Interferometric synthetic aperture radar (InSAR); freeze-thaw processes; permafrost; Qilian Mountains; natural hazards; hazard; vulnerability
• Online Access: DOAB: download the publication; DOAB: description of the publication

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.