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The wildland-urban interface (WUI) is the area where urban development occurs in close proximity to wildland vegetation. We generated WUI maps for the conterminous U.S. using building point locations (Carlson et al. 2022), offering higher spatial resolution compared to previously developed WUI maps based on U.S. Census Bureau housing density data (Radeloff et al., 2017). Building point locations were obtained from a Microsoft product released in 2018, which classified building footprints based on high-resolution satellite imagery. Maps were also based on wildland vegetation mapped by the 2016 National Land Cover Dataset (Yang et al., 2018). The mapping algorithm utilized definitions of the WUI from the U.S. Federal...
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Wildland-urban interface (WUI) maps identify areas with wildfire risk, but they are often outdated due to the lack of building data. Convolutional neural networks (CNNs) can extract building locations from remote sensing data, but their accuracy in WUI areas is unknown. Additionally, CNNs are computationally intensive and technically complex making it challenging for end-users, such as those who use or create WUI maps, to apply. We identified buildings pre- and post-wildfire and estimated building destruction for three California wildfires: Camp, Tubbs, and Woolsey. We used a CNN model from Esri to detect buildings from high-resolution imagery. This dataset represents the state-of-the-art of what is readily available...
Categories: Data;
Tags: Building footprint,
California,
Convolutional neural network,
Ecology,
Geography, All tags...
Land Use Change,
Remote Sensing,
USGS Science Data Catalog (SDC),
Wildland fire,
Wildland-urban interface,
biota, Fewer tags
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Wildfires and housing development have increased since the 1990s, presenting unique challenges for wildfire management. However, it is unclear how the relative influences of housing growth and changing wildfire occurrence have altered risk to homes, or the potential for wildfire to threaten homes. We used a random forests model to predict burn probability in relation to weather variables at 1-km resolution and monthly intervals from 1990 through 2019 in the Southern Rocky Mountains ecoregion. We quantified risk by combining the predicted burn probabilities with decadal housing density. We then compared the predicted burn probabilities and risk across the study area with observed values and quantified trends. Finally,...
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Wildfires and housing development have increased since the 1990s, presenting unique challenges for fire management. However, it is unclear how the relative influences of housing growth and changing wildfire occurrence have contributed to risk to homes. We fit a random forest using weather, land cover, topography, and past fire history to predict burn probabilities and uncertainty intervals. Then, we estimated risk at 1-km resolution and monthly intervals from 1990 through 2019 by combining predicted burn probabilities with housing density across the Southern Rocky Mountains. We used 3 scenarios to evaluate how housing growth and changes in burn probability influenced risk individually and combined (observed, 1990...
Categories: Data;
Types: Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: Climatology,
Colorado,
Ecology,
Geography,
Land Use Change, All tags...
New Mexico,
Southern Rocky Mountains,
USGS Science Data Catalog (SDC),
United States,
Utah,
Wyoming,
habitat alteration and disturbance,
habitat fragmentation,
human impacts,
land use and land cover,
wildland fire, Fewer tags
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