1. Introduction
The past few years have witnessed the rapid increase of the total wildland–urban interface (WUI) area [
1,
2] and the number of homes located within the WUI in the U.S. [
1,
3]. Additionally, there has been a rise in wildfire suppression and mitigation costs [
3]. The WUI grew from 7.2% of the total land area in 1990 to 9.5% in 2010, adding 189,000 km
2 of land classified as WUI and 12.7 million housing units in the WUI in the U.S. [
1]. According to a recent study, the number of residential homes within the WUI in the U.S. has reached 49 million [
3]. Theobald and Romme [
2] have also projected that the WUI in the U.S. will grow by more than 10% by 2030 as more people move to rural and suburban communities. The WUI is defined as the area where a built environment meets the wildland [
4]. In the Federal Register, Glickman and Babbitt [
4] define the WUI as a populated area in which structures are adjacent to or intermingle with wildland vegetation. There are three main WUI categories: interface, intermix, and occluded WUI [
4]. Interface WUI is where structures and wildland vegetation touch, separated by a clearly defined boundary [
4]. An expanded version of this definition states that interface WUI is where housing units are within 2.4 km of a 5 km
2 or larger patch of vegetation with more than 75% wildland cover [
1,
5]. The structures in an intermix WUI occur within unbroken wildland vegetation but must have a minimum housing density of one house per 40 acres (6.17 houses per km
2) [
4]. This definition has been refined to state that wildland vegetation must cover at least 50% of the area where the structures occur in an intermix WUI area [
1,
5]. Occluded WUI exists where there is an area of wildland fuels surrounded by urban structures (e.g., the green spaces within an urban area) [
4]. Of these three types of WUI, interface and intermix WUI have been widely used in WUI mapping research [
1,
5,
6,
7]. The WUI definition in the Federal Register focuses specifically on housing units as defined in the U.S. Census housing density data when determining structure density [
1,
5]. While there is extensive use of the Federal Register WUI definition in WUI mapping, some researchers use other factors to define the WUI. For example, researchers in Canada expanded the WUI definition to include two other WUI types: WUI-Ind (industrial) and WUI-Inf (infrastructural) [
8]. The inclusion of industrial buildings and other structures when defining the WUI may be necessary due to the possible impacts of wildfire on these assets during and after incidents [
8]. Similarly, the inclusion of infrastructure in the WUI definition may also be important as these structures are related to evacuation and fire protection [
8]. Infrastructure networks (e.g., roads, railroads, and powerlines) could also be sources of wildfire ignition [
8,
9,
10]. Using industrial and infrastructural assets to determine where the WUI is located expands the area significantly, mainly where infrastructure-related structures are present [
8].
Over the past several decades, there has been an increasing trend of significant wildfire occurrence in the western U.S. [
11,
12] as well as an increase in the area burned by wildfire annually [
1,
3,
12]. As climate change has progressed in recent years, there has been a decrease in precipitation during fire seasons [
12] along with an increase in wildland fuel dryness [
13]. As fuel dryness increases, wildfire risk [
14] and the total area burned will likely increase as well [
3,
12]. Wildfire risk can be defined as the combination of three factors: the probability of ignition, the intensity of the fire, and the impacts of the fire on the landscape [
15]. One aspect of wildfire risk is the loss of lives and casualties in wildfires. Between 2014 and 2018, 57 wildfires resulted in casualties, the worst being the Camp Fire in Paradise, California in 2018 with 85 fatalities [
16]. Due to drier fuels [
12,
13], high incidence of anthropogenic wildfire ignition [
17], and the expanding WUI, the wildfire risk in the WUI is likely to increase [
1]. Another aspect of wildfire risk within WUI communities is structure loss. Multiple recent studies examined the factors that determine the likelihood of structure loss within WUI communities [
18,
19,
20]. For example, in a study conducted by Syphard et al. [
21], the main focus is on how the spatial grouping of structures and other factors such as slope, aspect, and elevation relate to structure loss in wildfires. Other research considers different factors such as building materials and construction, risk mitigation practices such as defensible space, and regional variation that may impact structure loss [
20]. As the WUI expands, significantly more structures are at risk of damage or destruction by wildfire [
1,
22]. The increasing risk of structure loss related to wildfire within the WUI tends to drive research as well [
20,
21,
23,
24,
25]. Understanding where the WUI exists is essential when combined with wildfire risk data to formulate decisions related to the management and mitigation of wildfire [
26]. A better understanding of wildfire risk can facilitate decision-making in wildfire policy, fuel management, and community planning in the WUI [
27]. The analysis of wildfire risk is crucial in wildfire management with more frequent, destructive wildfires occurring in the American west [
11,
19]. For example, wildfire risk information can be used to establish defensible space regulations to reduce structure loss in wildfires and distribute wildfire management resources.
Wildfire management (e.g., wildfire prevention, suppression, and mitigation) has become more challenging as the WUI expands [
1], anthropogenic wildfires in the U.S. become predominant [
9,
17], and wildfires in the WUI are expected to increase [
24]. As a result, WUI mapping becomes crucial for decision-making in wildfire management. In the early 2000s, WUI research received attention as wildfire and structure loss increased significantly [
6]. However, even with increased attention to the WUI problem, a national WUI map did not exist [
6]. This led to the development of a national WUI dataset based on census block data and the United States Geological Survey (USGS) National Land Cover Database (NLCD) [
6]. Since then, many studies have been conducted to develop or refine different methods to map the WUI within the U.S. [
2,
5,
6,
28,
29] and internationally [
8,
30,
31,
32,
33]. Note that different types of data can be used in different WUI mapping methods. For example, Radeloff et al. [
6] produced their WUI map at a national scale using the structure density in each census block derived from the US Census housing unit counts and vegetative cover data from the USGS NLCD. One limitation of the census-block-based methods is related to the distribution of structures within a census block. For example, many structures could be concentrated in a small area within a large census block so that the structure density meets the criteria for inclusion in the WUI classification. This allows for the entire census block to be classified as WUI even though a large portion of the area does not meet the WUI criteria. This could lead to less precise WUI and possible bias due to the uneven spatial distribution of structures within a census block [
28,
34]. Another limitation is the decreased applicability to local and regional scales when it is crucial to understand where structures are located during and before a wildfire [
28,
34].
Another popular way to map the WUI is to use the fine-grained structure location data instead of the housing unit count data from the U.S. Census [
23,
28,
29]. Using exact structure locations to map the WUI allows for a higher level of precision [
8,
28,
29]. For example, Johnston and Flannigan [
8] utilized physical structure locations from an open structure database named CanVec+ in Canada to map the WUI. Additionally, Bar-Massada et al. [
28] used the structure locations derived from government agency data and digitized from satellite and aerial imagery to map the WUI. Moreover, we can also compile structure location data from other sources such as parcel centroids [
29] or address point data [
35]. Address point data only includes structures with known addresses, excluding some structures from the mapping process [
35]. In the U.S., the Department of Transportation is working with local and state governments to aggregate state, local, and tribal datasets into one cohesive national address point database [
35]. However, a complete national address point dataset is not currently available because some states have address point datasets that exist but are not completely within the public domain [
35]. Thus, it is difficult to use address point data to produce a national WUI map. A relatively recently developed dataset that may be useful as an alternative to address point data is the Microsoft Building Footprint (MBF) dataset [
36]. This polygon dataset includes all the structure footprints derived from a machine learning algorithm in the U.S. [
36]. The MBF dataset presents an opportunity to derive more accurate WUI maps based on structure locations. The MBF dataset has been used in population distribution mapping [
37], wildfire-related structure loss [
23], flood exposure [
38], and WUI mapping [
39,
40,
41]. The release of the MBF dataset makes it possible to produce a structure-based WUI map for the whole U.S. The type of structure location dataset (address point or physical structure location) could also produce variations in the WUI map. Although different types of structure location data exist and can be used for WUI mapping, little research has been done to compare these datasets in WUI mapping. Since address point data and the MBF dataset are two popular datasets used in WUI mapping, we chose to examine the differences of these two types of structure location data in WUI mapping in this study.
This study focused on using two different structure location datasets to improve WUI mapping in Montana. The research objectives of this study were to: (1) derive WUI maps using the MBF and the Montana structure point datasets; (2) compare the following three types of WUI maps in Montana: the WUI maps derived from the Montana structure point dataset (WUI-P), the WUI maps derived from the MBF dataset (WUI-S), and the Radeloff WUI map derived from census data (WUI-Z); (3) analyze the spatial patterns of the derived WUI-P and WUI-S at the county level; and (4) develop a web geographic information system (GIS) application to map the three types of WUI. The novelty of this study is as follows. First, two different structure location datasets were used to map the WUI in Montana. Second, a systematic comparison of the three types of WUI maps in Montana is provided. The remainder of this article is organized as follows.
Section 2 details the study area and the data employed in the study. The proposed methods are included in
Section 3. The results are presented in
Section 4. The discussion and conclusion are in
Section 5 and
Section 6, respectively.
5. Discussion
The first goal of this study was to use two different structure location datasets to generate WUI maps with multiple buffer distances in Montana. The generated WUI maps show how the buffer distance affects the total area of interface and intermix WUI. In the case of the total area, the patterns of WUI-P and WUI-S related to buffer distance presented in this study are similar to those shown in a previous study done by Bar-Massada et al. [
28] in some ways but differ in relation to at which buffer distance the highest area of WUI occurs. We found that the intermix WUI has a greater total area as compared to interface WUI in our study, which aligns with the findings in the previous study [
28]. Another similarity between the two studies is that the interface WUI area in WUI-P peaks at the same buffer distance of 200 m. However, the intermix WUI in our study peaks at 200 m, while the intermix WUI in all study areas in the previous study conducted by Bar-Massada et al. [
28] peaks at larger buffer distances. This difference could be due to the larger area of our study site. As for the behavior of WUI-S in this study, the peak area for both intermix and interface WUI occurs at the 500 m buffer and then decreases. Similar to the previous study conducted by Bar-Massada et al. [
28], the smallest area occurs at the 100 m buffer distance. The trend that appears when examining the number of structures within the WUI as the buffer distance changes is distinct from the trend in WUI area. The number of structures that fall within WUI-P and WUI-Z is the greatest at the smallest buffer and decreases as the buffer size increases. This trend is consistent with the results found in a previous study conducted by Bar-Massada et al. [
28] and a more recent study done by Carlson et al. [
39]. We employed the buffer distances used by Bar-Massada et al. in a previous study [
28] to compare the two structure location datasets in WUI mapping. Although buffer distance will affect the derived WUI, little research has been conducted to examine the ideal buffer distance for different types of applications in WUI management. Future research needs to be conducted to further identify the ideal buffer distance for different WUI applications. For example, we can use historical house loss data and the WUI generated with different buffer distances to determine the ideal buffer distance for generating WUI maps that can be used for relevant applications related to house loss.
The results of the map comparison analysis in this study are similar to the findings in the previous study done by Bar-Massada et al. [
28] with regard to WUI-P. The percentage agreement between WUI-P and WUI-Z for Montana is similar to the percentage agreement within the Grand County, Colorado in the previous study conducted by Bar-Massada et al. [
28], which could be related to the similarities in topography, as both study areas contain mountainous and flat terrains. The percentage agreement between WUI-S and WUI-Z is lower than that in the previous study [
28]. The lower level of agreement between WUI-S and WUI-Z could be due to the larger number of structures included in the MBF dataset as compared to the Montana address/structure framework dataset. The increased number of structures would likely have the greatest impact on the rural areas where outbuildings are included in the MBF dataset but are not in the address point dataset. It could be possible to refine the MBF data to reduce the number of structures and include only the structures that could be residential. One potential way to accomplish this could be to classify each structure in the MBF dataset by performing a spatial join using the OpenStreetMap (OSM) land use polygon data to determine which structures could be classified as residential. Then we can eliminate the non-residential structures and those structures that are identified as residential but are too small (e.g., sheds or other outbuildings) or too large (e.g., commercial structures or schools) [
37]. The abovementioned procedure could increase the agreement between the WUI-S and WUI-Z as the WUI-Z dataset structure density is based on housing units and does not consider non-residential structures. As the Montana address framework dataset does not include a standardized classification system for all addresses, we can use the OSM land use dataset to determine if an address point is in a residential polygon and remove all non-residential address points. This can increase the agreement between WUI-P and WUI-Z. Note that OSM data can be inconsistent in terms of data quality because OSM is a crowdsourcing project [
53]. Thus, more research on the data quality of OSM data should be conducted if we use OSM data to improve WUI mapping. Additionally, the population estimation procedure in this study evenly distributes the population over all structure points within a block group. Thus, trimming each structure point dataset can also improve the accuracy of the WUI population estimates. It should be noted that the necessity of the data trimming process depends on the intent and purpose of the WUI to be generated.
The spatial analysis shows distinct patterns between and at smaller buffer distances, and the patterns differ less at larger buffer distances. The spatial patterns for the two variables at each buffer distance do not differ significantly between WUI-P and WUI-S. However, the difference between and within the WUI is apparent. For , the LL clusters are in the eastern portion of Montana, while HH clusters are concentrated in the western part of the state. These patterns are possibly linked to the population distribution within the state. These patterns remain mostly constant as the buffer distance increases. In contrast, the cluster patterns shown for are sensitive to the increase in buffer distance. At smaller buffer distances the HH clusters are predominantly in the east, likely due to the inclusion of individual structures at those buffer distances. As the buffer distance increases, fewer HH clusters are identified in the east with more appearing in the western portion of the state. The greater shift of the clusters could be related to a higher sensitivity of due to the change in the number of structures required to meet the structure density threshold as buffer distance increases. More research related to the spatial patterns of WUI could help explain the sensitivity of the cluster patterns.
Lastly, the WUI maps that have been compared in this study may beg the question of which dataset or buffer distance best represents the location of the WUI. This is a challenging question as the selection of method or dataset depends on the purpose of the WUI maps and the availability of relevant data in a study area [
7,
40]. For example, the homeowners in Montana may find the WUI-S generated using the MBF with a 100 m buffer distance to be most useful as the defensible space distance recommended by Montana DNRC [
54] is less than 100 m and a single structure will meet the density threshold for WUI [
39]. The WUI-S (100 m buffer distance) will allow homeowners to easily identify any structure on their property that may be at risk to wildfire damage. The best buffer distance for community planners and wildfire managers is 500 m as the number of structures required to meet the structure density threshold is closest to the structure density in the WUI definition widely used for wildfire management or community planning purposes [
39].