Advances in Remote Sensing of Postfire Environmental Damage and Recovery Dynamics
Understanding forest fire regimes involves characterizing spatial distribution, recurrence, intensity, seasonality, size, and severity. In recent years, knowledge of damage levels can be directly related to the environmental impact of fire and, at the same time, it is a valuable estimator of fire in...
-д хадгалсан:
| Формат: | Online |
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| Хэл сонгох: | англи |
| Хэвлэсэн: |
MDPI - Multidisciplinary Digital Publishing Institute
2022
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| Нөхцлүүд: | |
| Онлайн хандалт: | ONIX_20221117_9783036556680_98 |
| Шошгууд: |
Шошго байхгүй, Энэхүү баримтыг шошголох эхний хүн болох!
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| _version_ | 1869522134640885760 |
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| collection | Directory of Open Access Books |
| description | Understanding forest fire regimes involves characterizing spatial distribution, recurrence, intensity, seasonality, size, and severity. In recent years, knowledge of damage levels can be directly related to the environmental impact of fire and, at the same time, it is a valuable estimator of fire intensity, when the data about it are not available. Remote sensing may be seen as a tool to accurately assess burn severity and to predict the potential effects of forest fires on ecosystems, thus making the prediction of the regeneration of the plant community and the effects on ecosystems easier. This information is basic to facilitate decision-making in the post-fire management of fire-prone ecosystems. Nowadays, there has been intense research activity in relation to burned areas, burn severity, and vegetation regeneration because fires in many areas of the planet are becoming more severe and extensive, and their correct evaluation and follow-up is posing great challenges to current scientists. The current advances in remote sensing and related sciences will allow us to evaluate the damage with greater precision and to know with greater reliability the dynamics of recovery. This reprint contains studies on new remote sensing technologies, new sensors, data collections, and processing methodologies that can be successfully applied in burn severity mapping, vegetation recovery monitoring, and post-fire management of fire-prone ecosystems affected by large fires. We hope this book can help readers become more familiar with this knowledge and foster an increased interest in this field. |
| format | Online |
| id | doab-20.500.12854ir-93841 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-938412024-04-11T15:11:17Z Advances in Remote Sensing of Postfire Environmental Damage and Recovery Dynamics Fernández-Manso, Alfonso Quintano, Carmen arctic tundra fire vegetation recovery C- and L-band SAR SAR backscatter wildfire Araucaria araucana Landsat 8 OLI normalized burn ratio normalized difference vegetation index char soil index mid-infrared burned index classification thresholds transfer learning model SSTCA burn severity forest fire SVR Landsat Mediterranean energy balance evapotranspiration land surface temperature land surface albedo dNBR post-fire recovery time series LandTrendr K-means driving factors pine forests alpine treeline ecotone repeat photography monoplotting lidar fire composite burn index Tree canopy cover RTM Sentinel-2A burned areas detection shade fraction image linear spectral mixing model VIIRS PROBA-V Landsat-8 OLI time-series Google Earth Engine NBR random forest fire history support vector machine fuzzy logic wildland fire extent wildland fire severity small unmanned aircraft systems landsat mask region-based convolutional neural network small unmanned aircraft system canopy cover tree mortality ecological disturbance ecosystem functioning EFAs fire severity satellite image time-series wildfires prescribed burns SAR fire impact radar burn ratio post-fire restoration change detection UAS structure-from-motion California forest structure fire management airborne laser scanner ALS thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TQ Environmental science, engineering and technology Understanding forest fire regimes involves characterizing spatial distribution, recurrence, intensity, seasonality, size, and severity. In recent years, knowledge of damage levels can be directly related to the environmental impact of fire and, at the same time, it is a valuable estimator of fire intensity, when the data about it are not available. Remote sensing may be seen as a tool to accurately assess burn severity and to predict the potential effects of forest fires on ecosystems, thus making the prediction of the regeneration of the plant community and the effects on ecosystems easier. This information is basic to facilitate decision-making in the post-fire management of fire-prone ecosystems. Nowadays, there has been intense research activity in relation to burned areas, burn severity, and vegetation regeneration because fires in many areas of the planet are becoming more severe and extensive, and their correct evaluation and follow-up is posing great challenges to current scientists. The current advances in remote sensing and related sciences will allow us to evaluate the damage with greater precision and to know with greater reliability the dynamics of recovery. This reprint contains studies on new remote sensing technologies, new sensors, data collections, and processing methodologies that can be successfully applied in burn severity mapping, vegetation recovery monitoring, and post-fire management of fire-prone ecosystems affected by large fires. We hope this book can help readers become more familiar with this knowledge and foster an increased interest in this field. 2022-11-17T16:27:20Z 2022-11-17T16:27:20Z 2022 book ONIX_20221117_9783036556680_98 9783036556680 9783036556673 https://directory.doabooks.org/handle/20.500.12854/93841 eng image/jpeg Attribution 4.0 International https://mdpi.com/books/pdfview/book/6270 https://mdpi.com/books/pdfview/book/6270 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-5668-0 10.3390/books978-3-0365-5668-0 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036556680 9783036556673 306 Basel open access |
| spellingShingle | arctic tundra fire vegetation recovery C- and L-band SAR SAR backscatter wildfire Araucaria araucana Landsat 8 OLI normalized burn ratio normalized difference vegetation index char soil index mid-infrared burned index classification thresholds transfer learning model SSTCA burn severity forest fire SVR Landsat Mediterranean energy balance evapotranspiration land surface temperature land surface albedo dNBR post-fire recovery time series LandTrendr K-means driving factors pine forests alpine treeline ecotone repeat photography monoplotting lidar fire composite burn index Tree canopy cover RTM Sentinel-2A burned areas detection shade fraction image linear spectral mixing model VIIRS PROBA-V Landsat-8 OLI time-series Google Earth Engine NBR random forest fire history support vector machine fuzzy logic wildland fire extent wildland fire severity small unmanned aircraft systems landsat mask region-based convolutional neural network small unmanned aircraft system canopy cover tree mortality ecological disturbance ecosystem functioning EFAs fire severity satellite image time-series wildfires prescribed burns SAR fire impact radar burn ratio post-fire restoration change detection UAS structure-from-motion California forest structure fire management airborne laser scanner ALS thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TQ Environmental science, engineering and technology Advances in Remote Sensing of Postfire Environmental Damage and Recovery Dynamics |
| title | Advances in Remote Sensing of Postfire Environmental Damage and Recovery Dynamics |
| title_full | Advances in Remote Sensing of Postfire Environmental Damage and Recovery Dynamics |
| title_fullStr | Advances in Remote Sensing of Postfire Environmental Damage and Recovery Dynamics |
| title_full_unstemmed | Advances in Remote Sensing of Postfire Environmental Damage and Recovery Dynamics |
| title_short | Advances in Remote Sensing of Postfire Environmental Damage and Recovery Dynamics |
| title_sort | advances in remote sensing of postfire environmental damage and recovery dynamics |
| topic | arctic tundra fire vegetation recovery C- and L-band SAR SAR backscatter wildfire Araucaria araucana Landsat 8 OLI normalized burn ratio normalized difference vegetation index char soil index mid-infrared burned index classification thresholds transfer learning model SSTCA burn severity forest fire SVR Landsat Mediterranean energy balance evapotranspiration land surface temperature land surface albedo dNBR post-fire recovery time series LandTrendr K-means driving factors pine forests alpine treeline ecotone repeat photography monoplotting lidar fire composite burn index Tree canopy cover RTM Sentinel-2A burned areas detection shade fraction image linear spectral mixing model VIIRS PROBA-V Landsat-8 OLI time-series Google Earth Engine NBR random forest fire history support vector machine fuzzy logic wildland fire extent wildland fire severity small unmanned aircraft systems landsat mask region-based convolutional neural network small unmanned aircraft system canopy cover tree mortality ecological disturbance ecosystem functioning EFAs fire severity satellite image time-series wildfires prescribed burns SAR fire impact radar burn ratio post-fire restoration change detection UAS structure-from-motion California forest structure fire management airborne laser scanner ALS thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TQ Environmental science, engineering and technology |
| topic_facet | arctic tundra fire vegetation recovery C- and L-band SAR SAR backscatter wildfire Araucaria araucana Landsat 8 OLI normalized burn ratio normalized difference vegetation index char soil index mid-infrared burned index classification thresholds transfer learning model SSTCA burn severity forest fire SVR Landsat Mediterranean energy balance evapotranspiration land surface temperature land surface albedo dNBR post-fire recovery time series LandTrendr K-means driving factors pine forests alpine treeline ecotone repeat photography monoplotting lidar fire composite burn index Tree canopy cover RTM Sentinel-2A burned areas detection shade fraction image linear spectral mixing model VIIRS PROBA-V Landsat-8 OLI time-series Google Earth Engine NBR random forest fire history support vector machine fuzzy logic wildland fire extent wildland fire severity small unmanned aircraft systems landsat mask region-based convolutional neural network small unmanned aircraft system canopy cover tree mortality ecological disturbance ecosystem functioning EFAs fire severity satellite image time-series wildfires prescribed burns SAR fire impact radar burn ratio post-fire restoration change detection UAS structure-from-motion California forest structure fire management airborne laser scanner ALS thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TQ Environmental science, engineering and technology |
| url | ONIX_20221117_9783036556680_98 |