2018
DOI: 10.3390/rs10101645
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Potential of Sentinel-2A Data to Model Surface and Canopy Fuel Characteristics in Relation to Crown Fire Hazard

Abstract: Background: Crown fires are often intense and fast spreading and hence can have serious impacts on soil, vegetation, and wildlife habitats. Fire managers try to prevent the initiation and spread of crown fires in forested landscapes through fuel management. The minimum fuel conditions necessary to initiate and propagate crown fires are known to be strongly influenced by four stand structural variables: surface fuel load (SFL), fuel strata gap (FSG), canopy base height (CBH), and canopy bulk density (CBD). Howe… Show more

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Cited by 34 publications
(18 citation statements)
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“…Besides the identification of burned areas, dense multitemporal series of optical satellite observations can be used to extract much more wildfire-related information that can rarely be measured after a wildfire takes place, information regarding, for example, the active fires and the pre-fire land cover type affected by the wildfires. Such thematic data play a major role in supporting knowledge about wildfires since they can be exploited to derive information related to fire fuel load [66] and therefore used to estimate other components of the fire regime and the amount of smoke emitted, and consequently the wildfire impact on air quality. Finally, the comparison of pre-fire and post-fire conditions can be used to monitor ecosystem responses through the restoration of vegetation and to account for environmental losses, such as habitat loss or changes in ecosystem services.…”
Section: Operational Servicesmentioning
confidence: 99%
“…Besides the identification of burned areas, dense multitemporal series of optical satellite observations can be used to extract much more wildfire-related information that can rarely be measured after a wildfire takes place, information regarding, for example, the active fires and the pre-fire land cover type affected by the wildfires. Such thematic data play a major role in supporting knowledge about wildfires since they can be exploited to derive information related to fire fuel load [66] and therefore used to estimate other components of the fire regime and the amount of smoke emitted, and consequently the wildfire impact on air quality. Finally, the comparison of pre-fire and post-fire conditions can be used to monitor ecosystem responses through the restoration of vegetation and to account for environmental losses, such as habitat loss or changes in ecosystem services.…”
Section: Operational Servicesmentioning
confidence: 99%
“…Remote sensing can assist fuel mapping through the characterization of horizontal and vertical forest structure components [20] providing the necessary temporal, spectral and spatial coverage, while being less time-consuming than field surveys or aerial photo interpretation [12]. Several studies have attempted fuel type or properties mapping using passive multispectral remote sensing images [19,[21][22][23] and similar levels of accuracy were found between the use of medium-resolution sensors and very high-resolution ones according to Arroyo et al [18]. Although little comparison has been carried out between the use of multispectral and hyperespectral data, Lasaponara et al [24] determined an increase of around 20% of the overall accuracy when classifying Prometheus fuel types with MIVIS respect to Landsat TM.…”
Section: Introductionmentioning
confidence: 99%
“…To the best of our knowledge, this is the first attempt to classify fuel types at regional scale using low density ALS data, multispectral Sentinel 2 data, and field work that compares the performance of classification using both datasets together or separately. Sentinel 2 data have been used to characterize canopy fuel characteristics [23], fuel types [49] or mapping wildfire ignition [42], while the integration with ALS for fuel mapping at regional scale requires further analysis. Furthermore, the research analyzes whether differences in the presence of fuel types exists between areas previously burned in 1994 or unburned ones.…”
Section: Introductionmentioning
confidence: 99%
“…Different authors [8,9] have preferred to use only active sensors, which provide values related to vegetation metrics, while others [10][11][12][13][14] have privileged the combination of multispectral imagery with LiDAR information to improve classification accuracies in forest environments. In all cases, the use of remote sensing substantially reduces the cost of the estimation process [15].…”
Section: Introductionmentioning
confidence: 99%