The Wildland-Urban Interface (WUI), where vegetation and built-up structures intermingle, encompasses a variety of territorial elements that interact spatially, being variable both in space and time. Mapping the WUI at finer scales is paramount to assess wildfire exposure and define tailored mitigation strategies. Our aim was to develop a semi-automated method to map the WUI at municipal level, leveraging recent advances in data and technology. We tested the procedure in four municipalities of mainland Portugal with different fire history, biophysical conditions, and sociodemographic contexts. We considered WUI as either intermix or interface. Our approach integrates both building location data and high-resolution vegetation maps, to calculate the density of buildings and forest cover proportion within different circular moving window sizes. Within each radius, we evaluated the total area and spatial distribution of the WUI types, as well as the number of buildings within WUI and within the fire perimeters recorded between the years 2000 and 2022 and analysed the differences between municipalities. We then compared the mapped WUI with previous WUI mappings for mainland Portugal, to identify common spots and potential spatial divergences. We found that the area mapped as WUI within all four municipalities ranged from about 400 km to 1135 km depending on the radius size. A distinct distribution for each type of WUI was observed as the radius size increased: the intermix WUI showed a tendency to increase, and the interface WUI increased only between the radius of 100 and 200 m, decreasing gradually in subsequent radii. Between 39.4% and 45.5% of the nearly 200,000 buildings in the study areas were within WUI, depending on radius size and a total of 5436 buildings were within the historic fire perimeter. Although the comparison with other maps showed fair agreement, due to differences in data and methodology, common areas mapped as WUI were found, which suggests that these areas should receive greater attention from decision-makers regarding fire management strategies, since their classification as WUI remains consistent across different methodologies.
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http://dx.doi.org/10.1016/j.jenvman.2024.122098 | DOI Listing |
Sci Data
December 2024
Key Lab of Biological Resources and Biosecurity of Xizang Autonomous Region, Institute of Plateau Biology of Xizang Autonomous Region, Lhasa, 850001, China.
NPJ Digit Med
December 2024
Digital Health Care Integration, Stanford Health Care, Palo Alto, CA, USA.
In this study, we investigate the performance of computer vision AI algorithms in predicting patient disposition from the emergency department (ED) using short video clips. Clinicians often use "eye-balling" or clinical gestalt to aid in triage, based on brief observations. We hypothesize that AI can similarly use patient appearance for disposition prediction.
View Article and Find Full Text PDFJ Hered
December 2024
School of Natural Resources and the Environment, University of Arizona, Tucson, AZ 85721, USA.
Urbanization impacts the structure and viability of wildlife populations. Some habitat generalists, such as bobcats (Lynx rufus), maintain populations at the intersection of wild and urban habitats (wildland urban interface, WUI), but impacts of urbanization on bobcat social structure are not well understood. Although commonly thought to establish exclusive home ranges among females, instances of mother-daughter home range sharing have been documented.
View Article and Find Full Text PDFZootaxa
August 2024
Henan International Joint Laboratory of Taxonomy and Systematic Evolution of Insecta; Xinxiang; Henan 453003; China.
A new species of Etrocorema, E. wui sp. nov.
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