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美国基于 1.25 亿个建筑物位置的城乡交错带。

The wildland-urban interface in the United States based on 125 million building locations.

机构信息

SILVIS Lab, Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, Wisconsin, USA.

U.S. Geological Survey, Geosciences and Environmental Change Science Center, Denver, Colorado, USA.

出版信息

Ecol Appl. 2022 Jul;32(5):e2597. doi: 10.1002/eap.2597. Epub 2022 Jun 5.

DOI:10.1002/eap.2597
PMID:35340097
Abstract

The wildland-urban interface (WUI) is the focus of many important land management issues, such as wildfire, habitat fragmentation, invasive species, and human-wildlife conflicts. Wildfire is an especially critical issue, because housing growth in the WUI increases wildfire ignitions and the number of homes at risk. Identifying the WUI is important for assessing and mitigating impacts of development on wildlands and for protecting homes from natural hazards, but data on housing development for large areas are often coarse. We created new WUI maps for the conterminous United States based on 125 million individual building locations, offering higher spatial precision compared to existing maps based on U.S. census housing data. Building point locations were based on a building footprint data set from Microsoft. We classified WUI across the conterminous United States at 30-m resolution using a circular neighborhood mapping algorithm with a variable radius to determine thresholds of housing density and vegetation cover. We used our maps to (1) determine the total area of the WUI and number of buildings included, (2) assess the sensitivity of WUI area included and spatial pattern of WUI maps to choice of neighborhood size, (3) assess regional differences between building-based WUI maps and census-based WUI maps, and (4) determine how building location accuracy affected WUI map accuracy. Our building-based WUI maps identified 5.6%-18.8% of the conterminous United States as being in the WUI, with larger neighborhoods increasing WUI area but excluding isolated building clusters. Building-based maps identified more WUI area relative to census-based maps for all but the smallest neighborhoods, particularly in the north-central states, and large differences were attributable to high numbers of non-housing structures in rural areas. Overall WUI classification accuracy was 98.0%. For wildfire risk mapping and for general purposes, WUI maps based on the 500-m neighborhood represent the original Federal Register definition of the WUI; these maps include clusters of buildings in and adjacent to wildlands and exclude remote, isolated buildings. Our approach for mapping the WUI offers flexibility and high spatial detail and can be widely applied to take advantage of the growing availability of high-resolution building footprint data sets and classification methods.

摘要

荒野-城市交界带(WUI)是许多重要土地管理问题的焦点,例如野火、生境破碎化、入侵物种和人与野生动物冲突。野火是一个特别关键的问题,因为 WUI 中的住房增长增加了野火点火和处于危险中的房屋数量。确定 WUI 对于评估和减轻发展对荒地的影响以及保护房屋免受自然灾害的影响非常重要,但大面积住房开发的数据通常较为粗糙。我们基于 1.25 亿个单独的建筑物位置,为美国大陆创建了新的 WUI 地图,与基于美国人口普查住房数据的现有地图相比,提供了更高的空间精度。建筑物点位置基于 Microsoft 的建筑物足迹数据集。我们使用圆形邻域映射算法和可变半径,在 30 米的分辨率下对美国大陆进行 WUI 分类,以确定住房密度和植被覆盖的阈值。我们使用我们的地图来:(1)确定 WUI 的总面积和包含的建筑物数量,(2)评估邻域大小选择对 WUI 区域包含和 WUI 地图空间模式的敏感性,(3)评估基于建筑物的 WUI 地图和基于人口普查的 WUI 地图之间的区域差异,以及(4)确定建筑物位置精度如何影响 WUI 地图的准确性。我们基于建筑物的 WUI 地图确定美国大陆的 5.6%-18.8%为 WUI,较大的邻域增加了 WUI 区域,但排除了孤立的建筑物集群。除了最小的邻域外,基于建筑物的地图相对于基于人口普查的地图确定了更多的 WUI 区域,尤其是在中北部各州,并且由于农村地区大量的非住房结构,差异很大。总体 WUI 分类准确性为 98.0%。对于野火风险制图和一般用途,基于 500 米邻域的 WUI 地图代表 WUI 的原始联邦登记定义;这些地图包括荒野中和附近的建筑物集群,并排除偏远、孤立的建筑物。我们的 WUI 映射方法具有灵活性和高空间细节,可以广泛应用于利用高分辨率建筑物足迹数据集和分类方法的日益普及。

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