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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| Format: | Online |
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| Jezik: | angleščina |
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MDPI - Multidisciplinary Digital Publishing Institute
2023
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| Online dostop: | ONIX_20230307_9783036568324_140 |
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| _version_ | 1869525933160923136 |
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| collection | Directory of Open Access Books |
| description | 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. |
| format | Online |
| id | doab-20.500.12854ir-98130 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-981302024-03-28T03:32:44Z Remote Sensing for Natural Hazards Assessment and Control Mazzanti, Paolo Romeo, Saverio 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 thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::P Mathematics and Science::PH Physics 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. 2023-03-07T16:35:39Z 2023-03-07T16:35:39Z 2023 book ONIX_20230307_9783036568324_140 9783036568324 9783036568331 https://directory.doabooks.org/handle/20.500.12854/98130 eng image/jpeg Attribution 4.0 International https://mdpi.com/books/pdfview/book/6903 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-6833-1 10.3390/books978-3-0365-6833-1 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036568324 9783036568331 406 Basel open access |
| spellingShingle | 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 thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::P Mathematics and Science::PH Physics Remote Sensing for Natural Hazards Assessment and Control |
| title | Remote Sensing for Natural Hazards Assessment and Control |
| title_full | Remote Sensing for Natural Hazards Assessment and Control |
| title_fullStr | Remote Sensing for Natural Hazards Assessment and Control |
| title_full_unstemmed | Remote Sensing for Natural Hazards Assessment and Control |
| title_short | Remote Sensing for Natural Hazards Assessment and Control |
| title_sort | remote sensing for natural hazards assessment and control |
| topic | 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 thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::P Mathematics and Science::PH Physics |
| topic_facet | 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 thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::P Mathematics and Science::PH Physics |
| url | ONIX_20230307_9783036568324_140 |