In the present study,we examine housing growth in California, Oregon, andWashington in the wildland-urban interface (WUI), the area where homes and other structures abut or intermingle with wildland vegetation. We combine housing density information from the 1990 and 2000 USA censuses with land cover information from the 1992/93 National Land Cover Dataset to demarcate the location and extent of the WUI and its growth, both in terms of area and number of housing units during the 1990s.We overlay the WUI with coarse-scale fire regime condition class information to evaluate implications for wildland fire management. During the 1990s, WUI area in the three-state region increased by 5218 km2 (10.9%) to nearly 53 000 km2 and the number of housing units in the WUI increased over 1 million units (17.6%) and in 2000 encompassed 6.9 million units, 43% of all housing in the region. Over a million new homes were constructed in the WUI, comprising 61% of the new homes constructed in the region. By 2000, there was far more intermixWUI (75% of the WUI area and 64% of the WUI housing units) than interface WUI. Expansion of the WUI accounted for only 13% of WUI housing unit growth and WUI that existed in 1990 encompassed 98% of WUI housing units in 2000. In 2000, there were nearly 1.5 million WUI housing units in areas with 0-35-year fire return intervals and 3.4 million in areas with 35-100+ year fire return intervals. In both these fire regimes, the majority of WUI housing units (66% and 90% respectively) are in areas with a current condition outside the historic range of variability. Housing growth patterns in this three-state region are exacerbating wildland fire problems in the WUI. Any long-term solution to wildland fire issues in the western United States will have to address housing growth patterns. Using a consistent, nationally applicable assessment protocol, the present study reveals the vast extent of WUI in the west coast states and its growth in the 1990s, and provides a foundation for consistent monitoring efforts.
File: Hammer_etal_IJWF_2007.pdf
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The rapid growth of housing in and near the wildland-urban interface (WUI) increases wildfire risk to lives and structures. To reduce fire risk, it is necessary to identify WUI housing areas that are more susceptible to wildfire. This is challenging, because wildfire patterns depend on fire behavior and spread, which in turn depend on ignition locations, weather conditions, the spatial arrangement of fuels, and topography. The goal of our study was to assess wildfire risk to a 60,000 ha WUI area in northwestern Wisconsin while accounting for all of these factors. We conducted 6000 simulations with two dynamic fire models: Fire Area Simulator (FARSITE) and Minimum Travel Time (MTT) in order to map the spatial pattern of burn probabilities. Simulations were run under normal and extreme weather conditions to assess the effect of weather on fire spread, burn probability, and risk to structures. The resulting burn probability maps were intersected with maps of structure locations and land cover types. The simulations revealed clear hotspots of wildfire activity and a large range of wildfire risk to structures in the study area. As expected, the extreme weather conditions yielded higher burn probabilities over the entire landscape, as well as to different land cover classes and individual structures. Moreover, the spatial pattern of risk was significantly different between extreme and normal weather conditions. The results highlight the fact that extreme weather conditions not only produce higher fire risk than normal weather conditions, but also change the fine-scale locations of high risk areas in the landscape, which is of great importance for fire management in WUI areas. In addition, the choice of weather data may limit the potential for comparisons of risk maps for different areas and for extrapolating risk maps to future scenarios where weather conditions are unknown. Our approach to modeling wildfire risk to structures can aid fire risk reduction management activities by identifying areas with elevated wildfire risk and those most vulnerable under extreme weather conditions.
File: BarMassada_2009_FEM.pdf
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Natural resource amenities may be an attractor as people decide where they will live and invest in property. In the American Midwest these amenities range from lakes to forests to pastoral landscapes, depending on the ecological province. We used simple linear regression models to test the hypotheses that physiographic, land cover (composition and spatial pattern), forest characteristics, land use on undeveloped land, public ownership, soil productivity and proximity to urban centers predict changes in population, housing, and seasonal housing densities over a 10-year interval (1980-1990). We then generated multiple-regression models to predict population, total and seasonal housing density change in the most recent decade (1990- 2000) based on ownership and ecological conditions in 1990 and tested them by comparing the predictions to actual change measured by the US Census Bureau. Our results indicate that the independent variables explained between 25 and 40% of the variability in population density change, 42-67% of the variability of total housing density change, and 13-32% of the variability in seasonal housing density change in the 1980s, depending on the province. The strength of the relationships between independent and dependent variables varied by province, and in some cases the sign varied as well. Topographic relief was significantly related to population growth in all provinces, and land cover composition and the presence of water was significantly related to total housing growth in all provinces. There was a surprisingly limited association of any of the independent variables to seasonal housing growth in the northern province, which is commonly perceived to attract seasonal use because of ecological amenities. Proximity to urban centers is related to population and housing density change, but not seasonal housing density change. Our tests indicated that models for population density change showed some utility, but the models for total and seasonal housing density generally performed poorly. Ecologic variables were consistently poor at predicting seasonal housing density change. Our results show that environmental characteristics appear to have some influence on the spatial distribution of population and housing change in the Midwest, although other factors that were not modeled are clearly dominant.
File: Gustafson_etal_LE_2005.pdf
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Housing growth is a primary form of landscape change that is occurring throughout the world. Because of the ecological impacts of housing growth, understanding the patterns of growth over time is imperative in order to better inform land use planning, natural resource management, and conservation. Our primary goal was to quantify hotspots of housing growth in the North Central United States over a 60-year time frame (1940-2000) using a spatial statistical approach. Specifically, our objectives were to: (1) determine where housing growth hotspots exist; (2) determine if hotspots are changing in space and over time; and, (3) investigate if hotspots differ based upon the type of measurement and scale of analysis. Our approach was based on a spatial statistical framework (Getis-Ord G* statistic) that compared local housing growth patterns with regional growth rates. Over the 60-year period the number and mean area of hotspots, measured both as absolute and percent growth, remained largely constant. However, total area of all hotspots increased significantly over time as measured by absolute growth. Spatially, the hotspots shifted over time and exhibited different patterns based upon the measurement. Absolute growth hotspots exhibited patterns of expanding sets of rings around urban centers, whereas percent growth hotspots exhibited both expanding rings and shifting locations throughout rural locations. When increasing the neighborhood size used to discern hotspots from 5 to 50 km, the number of hotspots decreased while their size increased. Regardless of neighborhood size, ~95 and ~88% of the landscape, as measured by absolute and percent growth, respectively, never contained a hotspot. Overall our results indicate that housing growth is occurring at distinct locations on the landscape, which change in space and time, and are influenced by the scale of analysis and type of measure. In general these results provide useful information for the natural resource, planning, and policy communities.
File: Lepczyk et al 2007 Landscape Ecology.pdf
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Housing growth and its environmental effects pose major conservation challenges. We sought to (1) quantify spatial and temporal patterns of housing growth across the U.S. Midwest from 1940-2000, (2) identify ecoregions strongly affected by housing growth, (3) assess the extent to which forests occur near housing, and (4) relate housing to forest fragmentation. We used data from the 2000 U.S. Census to derive fine-scale backcasts of decadal housing density. Housing data were integrated with a 30-m resolution U.S. Geological Survey land cover classification. The number of housing units in the Midwest grew by 146% between 1940 and 2000. Spatially, housing growth was particularly strong at the fringe of metropolitan areas (suburban sprawl) and in nonmetropolitan areas (rural sprawl) that are rich in natural amenities such as lakes and forests. The medium-density housing (4-32 housing units/km2) category increased most in area. Temporally, suburban housing growth was especially high in the post-World War II decades. Rural sprawl was highest in the 1970s and 1990s. The majority of midwestern forests either contained or were near housing. Only 14.8% of the region's forests were in partial block groups with no housing. Housing density was negatively correlated with the amount of interior forest. The widespread and pervasive nature of sprawl shown by our data is cause for conservation concern. Suburban sprawl has major environmental impacts on comparatively small areas because of the high number of housing units involved. In contrast, rural sprawl affects larger areas but with less intensity because associated housing densities are lower. The environmental effects per house, however, are likely higher in the case of rural sprawl because it occurs in less-altered areas. Conservation efforts will need to address both types of sprawl to be successful.
File: Radeloff_etal_ConsBio2005.pdf
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Roads are conspicuous components of landscapes and play a substantial role in defining landscape pattern. Previous studies have demonstrated the link between roads and their effects on ecological processes and landscape patterns. Less understood is the placement of roads, and hence the patterns imposed by roads on the landscape in relation to factors describing land use, land cover, and environmental heterogeneity. Our hypothesis was that variation in road density and landscape patterns created by roads can be explained in relation to variables describing land use, land cover, and environmental factors. We examined both road density and landscape patterns created by roads in relation to suitability of soil substrate as road subgrade, land cover, lake area and perimeter, land ownership, and housing density across 19 predominantly forested counties in northern Wisconsin, USA. Generalized least squares regression models showed that housing density and soils with excellent suitability for road subgrade were positively related to road density while wetland area was negatively related. These relationships were consistent across models for different road types. Landscape indices showed greater fragmentation by roads in areas with higher housing density, and agriculture, grassland, and coniferous forest area, but less fragmentation with higher deciduous forest, mixed forest, wetland, and lake area. These relationships provide insight into the complex relationships among social, institutional, and environmental factors that influence where roads occur on the landscape. Our results are important for understanding the impacts of roads on ecosystems and planning for their protection in the face of continued development.
File: hawbaker_etal_LE_2005.pdf
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Roads are important components of landscapes; they fragment habitat, facilitate invasive species spread, alter hydrology, and influence patterns of land use. Previous research on the ecological impacts of roads may have underestimated their effect because currently available sources of road data do not include the full road network. We compared differences in road density and landscape pattern among U.S. Census Bureau TIGER line files, U.S. Geological Survey 1:100,000-scale digital line graphs, and U.S. Geological Survey 1:24,000-scale digital raster graphics in northern Wisconsin to road data derived from 1:40,000-scale digital orthophotos. Road density measured from digital orthophotos (2.82 km/km2) was significantly greater than that of digital raster graphics (1.62 km/km2) and more than double that of digital line graphs (1.21 km/km2) and TIGER (1.27 km/km2) data. The increased road densities in raster graphics and orthophoto data were mainly due to the addition of minor roads. When all roads were used to define patch boundaries, landscape metrics produced with orthophoto data showed significantly greater levels of fragmentation than those based on line or raster graphics. For example, maximum patch size was 1074 ha and total edge was 109 km for line graphs, compared with 686 ha and 211 km for orthophoto data. Roads are missing in commonly used data, primarily because mapping standards systematically exclude minor roads. These standards are not ecologically based and may result in false assumptions about the ecological effects of roads. We recommend that future studies take special consideration of the completeness of road data and consider whether all ecologically relevant roads are included.
File: Hawbaker_and_radeloff_consbio_2004.pdf
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Natural resource managers throughout the United States frequently cite the increasing proximity of forestland to human development as a growing concern (Wear et al. 1996, Riemann and Tillman 1999). The expansion of urban areas, suburban development, and the influx of residential and recreational development into previously forested areas may reduce the amount of forest interior habitat, exacerbate the invasion of exotic species (Theobald et al. 1997), limit the range of forest management practices that can be used (Cubbage et al. 1995, Wear et al. 1999), and alter the structure of native vegetation (Riemann and Tillman 1999). Nonmetropolitan areas throughout the U.S. Midwest are undergoing significant increases in housing growth rates. Such rural sprawl is especially prominent in areas with attractive recreational and aesthetic amenities (Radeloff et al. 2001, Hammer et al. 2003). In the Upper Great Lakes, many summer-oriented recreational counties have 30-50% of all housing units rated as seasonal-use dwellings (Beale and Johnson 1998). While each single new house causes negligible impact, the accumulation of these individual changes over time and within a landscape or region may constitute a major impact (Theobald et al. 1997). Housing change may affect timber harvest when forest area declines due to deforestation and when management practices on the remaining forests are altered in response to a changing social context (Hull and Stewart 2002). Prior research has indicated that timber-harvesting rates may be closely related to human population density (Barlow et al. 1998, Dennis 1989, 1990, Wear et al. 1999). However, by using population density as a predictive variable these studies do not consider the possible effects of seasonal homes, which increase housing density without corollary increases in population density. Solid assessments of these effects are thus critical to predicting future timber production and sustainable harvest levels. Here, we examine the relationship of housing density to basal area, removals, and mortality of forests in Michigan, Minnesota, and Wisconsin.
File: Sabor_etal_2003_0.pdf
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Landscape ecology continues to mature as its theoretical grounding is strengthened, its precepts and principles become increasingly accepted in other disciplines, and its broad multidisciplinary perspective becomes adopted as a framework for a growing body of empirical work. The same may be said about a social landscape analysis that draws upon its theoretical foundations in applied demography, human ecology, and rural community studies. In this article, we highlight the theoretical parallels between concepts, principles, and theories in landscape ecology and those in demography. The objective is to expand the scope of landscape ecology by including a more coherent characterization of people, social organizational structure and social relations on the land. We believe an enhanced landscape framework that fully embraces social and demographic processes is essential for obtaining a truly comprehensive understanding of landscape patterns and processes.
File: Field_etal_SNR2003.pdf
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Humans influence the frequency and spatial pattern of fire and contribute to altered fire regimes, but fuel loading is often the only factor considered when planning management activities to reduce fire hazard. Understanding both the human and biophysical landscape characteristics that explain how fire patterns vary should help to identify where fire is most likely to threaten values at risk.We used human and biophysical explanatory variables to model and map the spatial patterns of both fire ignitions and fire frequency in the Santa Monica Mountains, a human-dominated southern California landscape. Most fires in the study area are caused by humans, and our results showed that fire ignition patterns were strongly influenced by human variables. In particular, ignitions were most likely to occur close to roads, trails, and housing development but were also related to vegetation type. In contrast, biophysical variables related to climate and terrain (January temperature, transformed aspect, elevation, and slope) explained most of the variation in fire frequency. Although most ignitions occur close to human infrastructure, fires were more likely to spread when located farther from urban development. How far fires spread was ultimately related to biophysical variables, and the largest fires in southern California occurred as a function of wind speed, topography, and vegetation type. Overlaying predictive maps of fire ignitions and fire frequency may be useful for identifying high-risk areas that can be targeted for fire management actions.
File: Syphard_etAl_IJWF_2008.pdf
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