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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Periodic wildfire maintains the integrity and species composition of many ecosystems, including the mediterranean-climate shrublands of California. However, human activities alter natural fire regimes, which can lead to cascading ecological effects. Increased human ignitions at the wildland-urban interface (WUI) have recently gained attention, but fire activity and risk are typically estimated using only biophysical variables. Our goal was to determine how humans influence fire in California and to examine whether this influence was linear, by relating contemporary (2000) and historic (1960-2000) fire data to both human and biophysical variables. Data for the human variables included fine-resolution maps of the WUI produced using housing density and land cover data. Interface WUI, where development abuts wildland vegetation, was differentiated from intermix WUI, where development intermingles with wildland vegetation. Additional explanatory variables included distance to WUI, population density, road density, vegetation type, and ecoregion. All data were summarized at the county level and analyzed using bivariate and multiple regression methods. We found highly significant relationships between humans and fire on the contemporary landscape, and our models explained fire frequency (R2 = 0.72) better than area burned (R2 = 0.50). Population density, intermix WUI, and distance to WUI explained the most variability in fire frequency, suggesting that the spatial pattern of development may be an important variable to consider when estimating fire risk. We found nonlinear effects such that fire frequency and area burned were highest at intermediate levels of human activity, but declined beyond certain thresholds. Human activities also explained change in fire frequency and area burned (1960- 2000), but our models had greater explanatory power during the years 1960-1980, when there was more dramatic change in fire frequency. Understanding wildfire as a function of the spatial arrangement of ignitions and fuels on the landscape, in addition to nonlinear relationships, will be important to fire managers and conservation planners because fire risk may be related to specific levels of housing density that can be accounted for in land use planning. With more fires occurring in close proximity to human infrastructure, there may also be devastating ecological impacts if development continues to grow farther into wildland vegetation.
File: Syphard etal EA 2007.pdf
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File: Radeloff_etal_CJFR1999_0.pdf
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MODIS active fire data offer new information about global fire patterns. However, uncertainties in detection rates can render satellite-derived fire statistics difficult to interpret.We evaluated theMODIS 1 kmdaily active fire product to quantify detection rates for both Terra andAquaMODIS sensors, examined how cloud cover and fire size affected detection rates, and estimated how detection rates varied across the United States. MODIS active fire detections were compared to 361 reference fires (?18 ha) that had been delineated using pre- and post-fire Landsat imagery. Reference fires were considered detected if at least one MODIS active fire pixel occurred within 1 km of the edge of the fire. When active fire data from both Aqua and Terra were combined, 82% of all reference fireswere found, but detection rateswere less forAqua and Terra individually (73% and 66% respectively). Fires not detected generally had more cloudy days, but not when the Aqua data were considered exclusively. MODIS detection rates decreased with fire size, and the size at which 50% of all fires were detected was 105 ha when combining Aqua and Terra (195 ha for Aqua and 334 ha for Terra alone). Across the United States, detection rates were greatest in theWest, lower in the Great Plains, and lowest in the East. The MODIS active fire product captures large fires in the U.S. well, but may under-represent fires in areas with frequent cloud cover or rapidly burning, small, and low-intensity fires.We recommend that users of the MODIS active fire data perform individual validations to ensure that all relevant fires are included.
File: Hawbaker_RSE_08.pdf
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In this article, we provide an overview of the demographic trends that have impacted and will continue to impact the ''wicked'' wildfire management problem in the United States, with particular attention to the emergence of the wildland-urban interface (WUI). Although population growth has had an impact on the emergence of the WUI, the deconcentration of population and housing, amenity-driven population growth in select nonmetropolitan counties, and interregional population shifts to the West and Southeast have had and will continue to have much greater impacts. In the coming decades, we can expect the retirement of the baby boom generation to exacerbate these trends.
File: Hammer_2009_SocNatRes.pdf
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Periodic wildfire is an important natural process in Mediterranean-climate ecosystems, but increasing fire recurrence threatens the fragile ecology of these regions. Because most fires are human-caused, we investigated how human population patterns affect fire frequency. Prior research in California suggests the relationship between population density and fire frequency is not linear. There are few human ignitions in areas with low population density, so fire frequency is low. As population density increases, human ignitions and fire frequency also increase, but beyond a density threshold, the relationship becomes negative as fuels become sparser and fire suppression resources are concentrated. We tested whether this hypothesis also applies to the other Mediterranean-climate ecosystems of the world. We used global satellite databases of population, fire activity, and land cover to evaluate the spatial relationship between humans and fire in the world's five Mediterranean-climate ecosystems. Both the mean and median population densities were consistently and substantially higher in areas with than without fire, but fire again peaked at intermediate population densities, which suggests that the spatial relationship is complex and nonlinear. Some land-cover types burned more frequently than expected, but no systematic differences were observed across the five regions. The consistent association between higher population densities and fire suggests that regardless of differences between land-cover types, natural fire regimes, or overall population, the presence of people in Mediterranean-climate regions strongly affects the frequency of fires; thus, population growth in areas now sparsely settled presents a conservation concern. Considering the sensitivity of plant species to repeated burning and the global conservation significance of Mediterranean-climate ecosystems, conservation planning needs to consider the human influence on fire frequency. Fine-scale spatial analysis of relationships between people and fire may help identify areas where increases in fire frequency will threaten ecologically valuable areas.
File: Syphard_2009_ConsBio.pdf
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Identifying areas of the wildland-urban interface (WUI) that are prone to severe wildfire is an important step in prioritizing fire prevention and preparedness projects. Our objective is to determine at a regional scale the relative risk of severe wildfire in WUI areas and the numbers of people and houses in high-risk areas. For a study area in northern lower Michigan, we first develop a spatial database of WUI areas (both intermix and interface) using housing data from the 2000 US Census and 1994 vegetation data from the Gap Analysis Project of the Michigan Department of Natural Resources. Then, we develop a spatial database of historic (pre-1900) fire regimes and current (1994) fuels to identify areas with high risk of standreplacing fires. High-risk areas historically supported jack pine (P. banksiana Lamb.) and mixed pine forests with stand-replacing fire rotations less than 100 years and currently support upland conifer and hardwood forests. Analysis of the databases shows that 26% of the study area is WUI. About 25% of the WUI has relatively high fire risk. Over 88% of the WUI with high fire risk has low housing density (<1 house per 2 ha) and is classified as intermix where fuels and structures intermingle. The predominance of high-risk intermix areas with low-density housing has implications for planning effective fuel treatments and evacuation plans.
File: Haight_etal_JOF_2004.pdf
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Fire simulation studies that use models such as FARSITE often assume that ignition locations are distributed randomly, because spatially explicit information about actual ignition locations are dif ? cult to obtain. However, many studies show that the spatial distribution of ignition locations, whether human- caused or natural, is non-random. Thus, predictions from ? re simulations based on random ignitions may be unrealistic. However, the extent to which the assumption of ignition location affects the predictions of ? re simulation models has never been systematically explored. Our goal was to assess the difference in ? re simulations that are based on random versus non-random ignition location patterns. We conducted four sets of 6000 FARSITE simulations for the Santa Monica Mountains in California to quantify the in ? uence of random and non-random ignition locations and normal and extreme weather conditions on ? re size distributions and spatial patterns of burn probability. Under extreme weather conditions, ? res were signi ? cantly larger for non-random ignitions compared to random ignitions (mean area of 344.5 ha and 230.1 ha, respectively), but burn probability maps were highly correlated (r = 0.83). Under normal weather, random ignitions produced signi ? cantly larger ? res than non-random ignitions (17.5 ha and 13.3 ha, respectively), and the spatial correlations between burn probability maps were not high (r = 0.54), though the difference in the average burn probability was small. The results of the study suggest that the location of ignitions used in ? re simulation models may substantially in ? uence the spatial predictions of ? re spread patterns. However, the spatial bias introduced by using a random ignition location model may be minimized if the ? re simulations are conducted under extreme weather conditions when ? re spread is greatest.
File: BarMassada2011EMS.pdf
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Wildland fire is a major concern in the wildland-urban interface (WUI), where human structures intermingle with wildland vegetation. Reducing wildfire risk in the WUI is more complicated than in wildland areas, owing to interactions between spatial patterns of housing and wildland fuels. Fuel treatments are commonly applied in wildlands surrounding WUI communities. Protecting the immediate surroundings of structures and building with fire-resistant materials might be more effective, but limited resources and uncooperative homeowners often make these impractical. Our question was how to allocate fuel treatments in the WUI under these constraints. We developed an approach to allocate fuel breaks around individual or groups of structures to minimise total treatment area. Treatment units were ranked according to their housing density and fire risk. We tested this method in a Wisconsin landscape containing 3768 structures, and found that our treatment approach required considerably less area than alternatives (588 v. 1050 ha required to protect every structure independently). Our method may serve as a baseline for planning fuel treatments in WUI areas where it is impractical to protect every single house, or when fire-proofing is unfeasible. This approach is especially suitable in regions where spotting is a minor cause of home ignitions.
File: BarMassada2011IJWF.pdf
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Fire is an important natural disturbance process in arid grasslands but current fire regimes are largely the result of both human and natural processes and their interactions. The collapse of the Soviet Union in 1991 spurred substantial socioeconomic changes and was ultimately followed by a rapid increase in burned area in southern Russia. What is unclear is whether this increase in burned area was caused by decreasing livestock numbers, vegetation changes, climate change, or interactions of these factors. Our research goal was to identify the driving forces behind the increase in burned area in the arid grasslands of southern Russia. Our study area encompassed 19,000 km2 in the Republic of Kalmykia in southern Russia. We analyzed annual burned area from 1986 to 2006 as a function of livestock population, NDVI, precipitation, temperature, and broad-scale oscillation indices using best subset regressions and structural equation modeling. Our results supported the hypothesis that vegetation recovered within 5-6 years after the livestock declined in the beginning of the 1990s, to a point at which large fires could be sustained. Climate was an important explanatory factor for burning, but mainly after 1996 when lower livestock numbers allowed fuels to accumulate. Ultimately, our results highlight the complexity of coupled human-natural systems, and provide an example of how abrupt socioeconomic change may affect fire regimes.
File: dubininetal.pdf
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