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13 results for “wildland-urban interface”
Global wildland-urban interface maps in 2000, 2010, and 2020, based on GlobeLand30
<p>This dataset provides global wildland-urban interface (WUI) maps at a spatial resolution of <strong>30 meters </strong>for the years <strong>2000, 2010, and 2020</strong>. The WUI is defined as areas where the 200-meter buffers of urban areas (characterized by artificial surfaces) intersect with the 400-meter buffers of wildland areas, including forests, shrublands, and grasslands. These maps are produced based on land cover classification results from the GlobeLand30 datasets.</p> <p><strong>Projection Information:</strong><br>The projection information aligns with GlobeLand30 standards:</p> <ul> <li><strong>Projection:</strong> UTM (Universal Transverse Mercator) for latitudes from S85 to N85, using a 6-degree zone system without zone numbers.</li> <li><strong>Polar Azimuthal Projection:</strong> Applicable for latitudes from S85 to N90 and N85 to N90, with the projection surface intersecting at the South and North Poles.</li> </ul> <p><strong>Naming Convention:</strong><br>The file naming convention is as follows:</p> <div> <div> <div> <div><strong>WUI_LHH_VV_YYYYlc030.tif</strong></div> <div> </div> </div> </div> </div> <p>Where:</p> <ul> <li><strong>L</strong> = Latitude code (N for the Northern Hemisphere, S for the Southern Hemisphere)</li> <li><strong>HH</strong> = Number of UTM zone</li> <li><strong>VV</strong> = Starting latitude of the tile (each tile crosses 5° latitude)</li> <li><strong>YYYY</strong> = Year mapped</li> <li><strong>lc</strong> = Land cover abbreviation</li> <li><strong>030</strong> = Spatial resolution of 30 meters</li> </ul> <p><strong>Example File Name:</strong><br>For instance, the file named <strong>WUI_n15_45_2020lc030.tif</strong> can be interpreted as follows:</p> <ul> <li><strong>WUI</strong>: Wildland-Urban Interface dataset</li> <li><strong>n</strong>: Northern latitude</li> <li><strong>15</strong>: UTM zone 15</li> <li><strong>45</strong>: Starting latitude of 45 degrees</li> <li><strong>2020</strong>: Product year of 2020</li> <li><strong>lc</strong>: Land cover classification</li> <li><strong>030</strong>: Spatial resolution of 30 meters</li> </ul>
Wildland-Urban Interface maps for the Polish Carpathians for 1860s, 1970s and 2013
<p>The dataset contains three (1860s, 1970s, 2013) detailed Wildland-Urban Interface (WUI) maps of the Polish Carpathians, including information on building density. The maps, available in the form of 10m raster GeoTIFF files, are based on the WUI definition of US Federal Register (USDA and USDI, 2001) as operationalized by (Radeloff et al., 2005), which distinguishes two kinds of WUI: intermix, where housing intermingle with wildland vegetation, and interface, where settlement abuts the wildland areas. Either WUI type requires a housing density higher than 6.17 houses/km2 (1 house/40 acres in the US context). In intermix WUI, there has to be also > 50% wildland vegetation, while the interface WUI, has < 50% wildland vegetation but is within 2.4 km of a wildland vegetation patch larger > 5 km2. Given the ecological context of the Polish Carpathians, we defined wildland vegetation as forests, because forests are the climax vegetation type below the treeline. To assess settlements, we analysed all buildings locations (residential and non-residential), because all buildings reflect human activities. The building density and forest cover share were calculated by using a 500m circular moving window algorithm.</p><p>Acknowledgements<br>The study was supported by the National Science Centre, Poland, contract no. UMO-2019/35/D/HS4/00117 and by the NASA Land Use and Land Cover Change Program.</p>
Worldwide Unified Wildland-Urban Interface (WUWUI) database
<p>This is the Worldwide Unified Wildland-Urban Interface (WUWUI) database developed by the study entitled "Global Expansion of Wildland-Urban Interface (WUI) and WUI fires: Insights from a Multiyear Worldwide Unified Database (WUWUI)" published on Environmental Research Letters (<strong>DOI</strong> 10.1088/1748-9326/ad31da).</p> <p>Please use the latest version.</p>
Map of the global wildland-urban interface
<p>The wildland-urban interface (WUI) is where buildings and wildland vegetation meet or intermingle. It is where human-environmental conflicts and risks are concentrated, including the loss of houses and lives to wildfire, habitat loss and fragmentation, and the spread of zoonotic diseases. However, a global analysis of the WUI has been lacking.</p> <p>This dataset features a global, 10 m resolution map of the wildland-urban interface that was developed in a recent study by the authors of this dataset (see corresponding publication).</p> <p><strong>Temporal extent</strong></p> <p>The data contains data representative for ca. 2020.</p> <p><strong>Data format and units</strong></p> <p>The data are organized in tiles of 100 km x 100 km and follow the EQUI7 tiling grid and projection system. The images are compressed GeoTiff files (*.tif). There is a mosaic in GDAL Virtual format (*.vrt), which can readily be opened in most Geographic Information Systems. Please consider the generation of image pyramids before using *.vrt files.</p> <p>The raster dataset contains Wildland-urban interface (WUI) data (one layer), 10 m spatial resolution, 8 discrete classes:</p> <p>1 - Forest/Shrubland/Wetland-dominated Intermix WU</p> <p>2 - Forest/Shrubland/Wetland-dominated Interface WUI</p> <p>3 - Grassland-dominated Intermix WUI</p> <p>4 - Grassland -dominated Interface WUI</p> <p>5 - Non-WUI: Forest/Shrub/Wetland-dominated</p> <p>6 - Non-WUI: Grassland-dominated</p> <p>7 - Non-WUI: Urban</p> <p>8 - Non-WUI: Other</p> <p>In addition, the data contain tabular data on WUI area, population and biomass in the WUI, as well as wildfire area and people affected by wildfire in the WUI per world region, country, subnational administrative unit and biome.</p> <p>The data also contain the key algorithm for WUI mapping (also accessible here: https://github.com/franzschug/global_wildland_urban_interface).</p> <p><strong>Further information</strong></p> <p>For further information, please see the publication or contact Franz Schug (fschug@wisc.edu). Visit the website of SILVIS lab, University of Wisconsin-Madison (http://silvis.forest.wisc.edu/globalwui) to learn more about the Wildland-Urban Interface.</p> <p>The data can be interactively visualizes in a web viewer <a href="https://geoserver.silvis.forest.wisc.edu/geodata/fast/globalwui/">here.</a></p> <p><strong>Corresponding publication</strong></p> <p>Schug, Franz<sup>*</sup>; Bar-Massada, Avi; Carlson, Amanda R.; Cox, Heather; Hawbaker, Todd J.; Helmers, David; Hostert, Patrick; Kaim, Dominik; Kasraee, Neda K.; Martinuzzi, Sebastián; Mockrin, Miranda H.; Pfoch, Kira A.; Radeloff, Volker C. The global wildland-urban interface, DOI: 10.1038/s41586-023-06320-0</p> <p><strong>Funding</strong></p> <p>This research was funded by the NASA Land Cover and Land Use Change Program under agreement 80NSSC21K0310.</p>
Damage cadasters for wildland-urban interface fires in Chile, 2019-2022
<p><span>Wildland-urban interface (WUI) regions are particularly vulnerable to wildfires due to their proximity to both nature and urban developments, posing significant risks to lives and property. To enhance our understanding of the risk profiles in WUI areas, we analysed seven fire case studies in central Chile. </span></p> <p><span>In the paper <em>"</em><span><em>Modelling the vulnerability of urban settings to WUI fires in Chile",</em> </span>We developed a mixed-methods approach for conducting local-scale analyses which involved field surveys, remote-sensing through satellite and drone imagery, and GIS-based analysis of the collected data. The methodology led to the generation of a georeferenced dataset of damaged and undamaged dwellings, including 16 variables representing their physical characteristics, spatial arrangement, and the availability of fire suppression resources. A binary classification model was then used to assess the relative importance of these attributes as indicators of vulnerability. The analysis revealed that spatial arrangement factors have a greater impact on damage prediction than the structural conditions and fire preparedness of individual units. Specifically, factors such as dwelling proximity to neighbours, distance to vegetation, proximity to the border of dwelling groups, and distance from the origin of the fire substantially contribute to the prediction of fire damage. Other structural attributes associated with less affluent homes may also increase the likelihood of damage, although further data is required for confirmation. This study provides insights for the design, planning, and governance of WUI areas in Chile, aiding the development of risk mitigation strategies for both built structures and the broader territorial area.</span></p>
Data from: Evidence of extensive home range sharing among mother-daughter bobcat pairs in the wildland-urban interface of the Tucson Mountains
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Data from: Hunger mediates apex predator's risk avoidance response in wildland-urban interface
1. Conflicts between large mammalian predators and humans present a challenge to conservation efforts, as these events drive human attitudes and policies concerning predator species. Unfortunately, generalities portrayed in many empirical carnivore landscape selection studies do not provide an explanation for a predator's occasional use of residential development preceding a carnivore-human conflict event. In some cases, predators may perceive residential development as a risk-reward tradeoff. 2. We examine whether state dependent mortality-risk sensitive foraging can explain an apex carnivore's (Puma concolor) occasional utilization of residential areas. We assess whether puma balance the risk and rewards in a system characterized by a gradient of housing densities ranging from wildland to suburban. Puma GPS location data, characterized as hunting and feeding locations, were used to assess landscape variables governing hunting success and hunting site selection. Hunting site selection behavior was then analyzed conditional on indicators of hunger state. 3. Residential development had a high energetic reward for puma, based on increases in prey availability and hunting success rates associated with increased housing density. Despite a higher energetic reward, hunting site selection analysis indicated that pumas generally avoided residential development, a landscape type attributed with higher puma mortality risk. However, when a puma experienced periods of extended hunger, risk avoidance behavior toward housing waned. 4. This study demonstrates that an apex carnivore faces a tradeoff between acquiring energetic rewards and avoiding risks associated with human housing. Periods of hunger can help explain an apex predator's occasional use of developed landscapes and thus the rare conflicts in the wildland-urban interface. Apex carnivore movement behaviors in relation to human conflicts are best understood as a three-player community level interaction incorporating wild prey distribution.
Wildland-urban interface fire dynamics simulator input files for pyric tree spatial patterning interactions in historical and contemporary mixed conifer forests, California, USA
<p><span>Tree spatial patterns in dry coniferous forests of the western US, and analogous ecosystems globally, were historically aggregated, comprising a mixture of single trees and groups of trees. Modern forests, in contrast, are generally more homogeneous and overstocked than their historical counterparts. As these modern forests lack regular fire, pattern formation and maintenance is generally attributed to fire. Accordingly, fires in modern forests may not yield historically analogous patterns. However, direct observations on how selective tree mortality among pre-existing forest structure shapes tree spatial patterns is limited. In this study, we (1) simulated fires in historical and contemporary counterpart plots in a Sierra Nevadan mixed-conifer forest, (2) estimated tree mortality, and (3) examined tree spatial patterns of live trees before and after fire, and of fire-killed trees. Tree mortality in the historical period was clustered and density-dependent, because trees were aggregated and segregated by tree size before fire. Thus, fires maintained an aggregated distribution of tree groups. Tree mortality in the contemporary period was widespread, except for dispersed large trees, because most trees were a part of large, interconnected tree groups. Thus, post-fire tree patterns were more uniform and devoid of moderately sized tree groups. Post-fire tree patterns in the historical period, unlike the contemporary period, were within the historical range of variability identified for the western US. This divergence suggests that decades of forest dynamics without significant disturbances has altered the historical means of pyric pattern formation. Our results suggest that ecological silvicultural treatments, such as forest restoration thinnings, which emulate qualities of historical forests may facilitate the reintroduction of fire as a means to reinforce forest structural heterogeneity.</span></p>
Characterizing the Dynamics of Wildland-Urban Interface and the Potential Impacts on Fire Activities in Alaska from 2000 to 2010
<p>The material contains the shapefiles of wildland-urban interface in Alaska in 2000 and 2010, which are derived from census data and National Land Cover Database.</p>
Data from: Hunger mediates apex predator's risk avoidance response in wildland-urban interface
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Wildland-urban interface fire dynamics simulator input files for pyric tree spatial patterning interactions in historical and contemporary mixed conifer forests, California, USA
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Wildland-urban interface in California using remote sensing data
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Characterizing the Dynamics of Wildland-Urban Interface and the Potential Impacts on Fire Activities in Alaska from 2000 to 2010
<p>The material contains the shapefiles of wildland-urban interface in Alaska in 2000 and 2010, which are derived from census data and National Land Cover Database.</p>
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