Chad M. Hoffman

The Science of Firescapes: Achieving Fire-Resilient Communities

Abstract Wildland fire management has reached a crossroads. Current perspectives are not capable of answering interdisciplinary adaptation and mitigation challenges posed by increases in wildfire risk to human populations and the need to reintegrate fire as a vital landscape process. Fire science has been, and continues to be, performed in isolated “silos,” including institutions (e.g., agencies versus universities), organizational structures (e.g., federal agency mandates versus local and state procedures for responding to fire), and research foci (e.g., physical science, natural science, and social science). These silos tend to promote research, management, and policy that focus only on targeted aspects of the “wicked” wildfire problem. In this article, we provide guiding principles to bridge diverse fire science efforts to advance an integrated agenda of wildfire research that can help overcome disciplinary silos and provide insight on how to build fire-resilient communities.

Review of fuel treatment effectiveness in forests and rangelands and a case study from the 2007 megafires in central, Idaho, USA

This report provides managers with the current state of knowledge regarding the effectiveness of fuel treatments for mitigating severe wildfire effects. A literature review examines the effectiveness of fuel treatments that had been previously applied and were subsequently burned through by wildfire in forests and rangelands. A case study focuses on WUI fuel treatments that were burned in the 2007 East Zone and Cascade megafires in central Idaho. Both the literature review and case study results support a manager consensus that forest thinning followed by some form of slash removal is most effective for reducing subsequent wildfire severity.

Is burn severity related to fire intensity? Observations from landscape scale remote sensing

Biomass burning by wildland fires has significant ecological, social and economic impacts. Satellite remote sensing provides direct measurements of radiative energy released by the fire (i.e. fire intensity) and surrogate measures of ecological change due to the fire (i.e. fire or burn severity). Despite anecdotal observations causally linking fire intensity with severity, the nature of any relationship has not been examined over extended spatial scales. We compare fire intensities defined by Moderate Resolution Imaging Spectroradiometer Fire Radiative Power (MODIS FRP) products with Landsat-derived spectral burn severity indices for 16 fires across a vegetation structure continuum in the western United States. Per-pixel comparison of MODIS FRP data within individual fires with burn severity indices is not reliable because of known satellite temporal and spatial FRP undersampling. Across the fires, 69% of the variation in relative differenced normalized burn ratio was explained by the 90th percentile of MODIS FRP. Therefore, distributional MODIS FRP measures (median and 90th-percentile FRP) derived from multiple MODIS overpasses of the actively burning fire event may be used to predict potential long-term negative ecological effects for individual fires.

Fuel loadings 5 years after a bark beetle outbreak in south-western USA ponderosa pine forests

Landscape-level bark beetle (Coleoptera: Curculionidae, Scolytinae) outbreaks occurred in Arizona ponderosa pine (Pinus ponderosa Dougl. ex Law.) forests from 2001 to 2003 in response to severe drought and suitable forest conditions. We quantified surface fuel loadings and depths, and calculated canopy fuels based on forest structure attributes in 60 plots established 5 years previously on five national forests. Half of the plots we sampled in 2007 had bark beetle-caused pine mortality and half did not have mortality. Adjusting for differences in pre-outbreak stand density, plots with mortality had higher surface fuel and lower canopy fuel loadings 5 years after the outbreak compared with plots without mortality. Total surface fuels averaged 2.5 times higher and calculated canopy fuels 2 times lower in plots with mortality. Nearly half of the trees killed in the bark beetle outbreak had fallen within 5 years, resulting in loadings of 1000-h woody fuels above recommended ranges for dry coniferous forests in 20% of the mortality plots. We expect 1000-h fuel loadings in other mortality plots to exceed recommended ranges as remaining snags fall to the ground. This study adds to previous work that documents the highly variable and complex effects of bark beetle outbreaks on fuel complexes.

Drought, Tree Mortality, and Wildfire in Forests Adapted to Frequent Fire

Massive tree mortality has occurred rapidly in frequent-fire-adapted forests of the Sierra Nevada, California. This mortality is a product of acute drought compounded by the long-established removal of a key ecosystem process: frequent, lowto moderate-intensity fire. The recent tree mortality has many implications for the future of these forests and the ecological goods and services they provide to society. Future wildfire hazard following this mortality can be generally characterized by decreased crown fire potential and increased surface fire intensity in the short to intermediate term. The scale of present tree mortality is so large that greater potential for “mass fire” exists in the coming decades, driven by the amount and continuity of dry, combustible, large woody material that could produce large, severe fires. For long-term adaptation to climate change, we highlight the importance of moving beyond triage of dead and dying trees to making “green” (live) forests more resilient.

A comparison of level set and marker methods for the simulation of wildland fire front propagation

Simulating an advancing fire front may be achieved within a Lagrangian or Eulerian framework. In the former, independently moving markers are connected to form a fire front, whereas in the latter, values representing the moving front are calculated at points within a fixed grid. Despite a mathematical equivalence between the two methods, it is not clear that both will produce the same results when implemented numerically. Here, we describe simulations of fire spread created using a level set Eulerian approach (as implemented in the wildland–urban interface fire dynamics simulator, WFDS) and a marker method (as implemented in FARSITE). Simulations of surface fire spread, in two different fuels and over domains of increasing topographical complexity, are compared to evaluate the difference in outcomes between the two models. The differences between the results of the two models are minor, especially compared with the uncertainties inherent in the modelling of fire spread.

A methodology to characterize vertical accuracies in lidar-derived products at landscape scales

A u g u s t 2 0 1 3 709 (Hudak et al., 2009). These high precision DEMs are already being implemented in numerous applications, including habitat assessment (Martinuzzi et al., 2009), forest succession and volume (Goodwin et al., 2006; Falkowski et al., 2009), snow depth (Hopkinson et al., 2004; Deems et al., 2006), hydrologic modeling (Bowen and Waltermire, 2002; Murphy et al., 2008), carbon sequestration (Asner, 2009), glacial monitoring (Abermann et al., 2010), and fl oodplain assessments (Marks and Bates, 2000). The versatility of lidar is still being explored, and its potential not only reaches across the research and resource management spectrum but into municipal and logistical applications of both the public and private sectors. Although lidar has demonstrated ability to produce high quality DEMs and is useful for assessing terrain, vegetation, and ecosystem characteristics over large areas, there are still errors that must be quantifi ed. This is critical when using the data to develop subsequent products and metrics (Smith et al., 2009; Tinkham et al., 2011), because these errors can have signifi cant impacts on the quality and accuracy of each derived product. For example, errors in forest inventory metrics such as tree height which is used to estimate timber volume can lead to large fi nancial implications (Gatziolis et al., 2011), mischaracterization of fi ne-scale morphology of hydrological features can produce incorrect fl ow predictions, and assessments of objects with low vertical heights (e.g., shrubs ~0.30 m) can be missed in some classifi cations potentially lowering forage availability estimates (Glenn et al., 2011). The impacts of lidar acquisition parameters and environmental conditions on vertical and horizontal accuracy of lidar-derived DEMs have been well studied (Su and Bork, 2006; Bater and Coops, 2009; TriglavČ ekada et al., 2009). Prior studies have investigated the infl uence of vegetation and topographic features within semi-arid shrub-steppe (Tinkham et al., 2011), piedmont (Hodgson and Bresnahan, 2004), and forested (Hyyppä et al., 2005; Tinkham et al., 2012) ecosystems on errors within lidar-derived DEMs at point and plot scales. However, these studies are predominately limited in their spatial inference to where coincident lidar and fi eld surveying data are present. Given DEMs and the products that are subsequently derived from them are generally applied to landscape scale problems, a spatially explicit method for predicting error is needed (Glennie, 2007). Bater and Coops (2009) applied a classifi cation tree approach to a lidar data set and spatially predicted the DEM vertical error based on Abstract Light detection and ranging (lidar) is the premier technology for high-resolution elevation measurements in complex landscapes. Lidar error assessments allow for objective interpretation of Digital Elevation Models (DEMs) and products reliant on these layers. The purpose of this study is to spatially estimate the vertical error of a lidar-derived DEM across seven cover types through modeling of fi eld survey data. We use thirty-four variables and ground-based fi eld survey data in a Random Forest regression to predict elevation error. Four variables captured the variability within the lidar errors, with three variables relevant to the distribution of returns within the vegetation and one relating to the terrain form. Good agreement was observed when comparing the survey against the model predictions (μ = −0.02 m, s = 0.13 m, and RMSE = 0.14 m). With most lidar products reliant upon accurate production of DEMs, providing spatially explicit assessments of uncertainty at the landscape level will increase user confi dence in lidar products.

Bark beetles and wildfires: How does forest recovery change with repeated disturbances in mixed conifer forests?

Increased wildfire activity and recent bark beetle outbreaks in the western United States have increased the potential for interactions between disturbance types to influence forest characteristics. However, the effects of interactions between bark beetle outbreaks and subsequent wildfires on forest succession remain poorly understood. We collected data in dry mixed conifer forests across Idaho and western Montana to test whether vegetation responses differ between sites experiencing single and repeated disturbances. We compared tree seedling density and age, surface fuel loading, and stand structure characteristics in stands that experienced either high severity wildfire, large-scale tree mortality from bark beetles, or stands that experienced high bark beetle mortality followed by severe wildfire within 3–8 years of attack. Tree seedling density was 300–400% higher in gray bark beetle-affected stands than burned sites, but there was no evidence that a beetle and wildfire interaction affected seedling de...

Spatial variability of surface fuels in treated and untreated ponderosa pine forests of the southern Rocky Mountains

There is growing consensus that spatial variability in fuel loading at scales down to 0.5 m may govern fire behaviour and effects. However, there remains a lack of understanding of how fuels vary through space in wildland settings. This study quantifies surface fuel loading and its spatial variability in ponderosa pine sites before and after fuels treatment in the southern Rocky Mountains, USA. We found that spatial semivariance for 1- and 100-h fuels, litter and duff following thin-and-burn treatments differed from untreated sites, and was lower than thin-only sites for all fuel components except 1000-h fuels. Fuel component semivariance increased with mean fuel component loading. The scale of spatial autocorrelation for all fuel components and sites ranged from <1 to 48 m, with the shortest distances occurring for the finest fuel components (i.e. duff, litter). Component mean fuel particle diameter strongly predicted (R2 = 0.88) the distance needed to achieve sample independence. Additional work should test if these scaling relationships hold true across forested ecosystems, and could reveal fundamental processes controlling surface fuel variability. Incorporating knowledge of spatial variability into fuel sampling protocols will enhance assessment of wildlife habitat, and fire behaviour and effects modelling, over singular stand-level means.

High resolution mapping of development in the wildland-urban interface using object based image extraction

The wildland-urban interface (WUI), the area where human development encroaches on undeveloped land, is expanding throughout the western United States resulting in increased wildfire risk to homes and communities. Although census based mapping efforts have provided insights into the pattern of development and expansion of the WUI at regional and national scales, these approaches do not provide sufficient detail for fine-scale fire and emergency management planning, which requires maps of individual building locations. Although fine-scale maps of the WUI have been developed, they are often limited in their spatial extent, have unknown accuracies and biases, and are costly to update over time. In this paper we assess a semi-automated Object Based Image Analysis (OBIA) approach that utilizes 4-band multispectral National Aerial Image Program (NAIP) imagery for the detection of individual buildings within the WUI. We evaluate this approach by comparing the accuracy and overall quality of extracted buildings to a building footprint control dataset. In addition, we assessed the effects of buffer distance, topographic conditions, and building characteristics on the accuracy and quality of building extraction. The overall accuracy and quality of our approach was positively related to buffer distance, with accuracies ranging from 50 to 95% for buffer distances from 0 to 100 m. Our results also indicate that building detection was sensitive to building size, with smaller outbuildings (footprints less than 75 m2) having detection rates below 80% and larger residential buildings having detection rates above 90%. These findings demonstrate that this approach can successfully identify buildings in the WUI in diverse landscapes while achieving high accuracies at buffer distances appropriate for most fire management applications while overcoming cost and time constraints associated with traditional approaches. This study is unique in that it evaluates the ability of an OBIA approach to extract highly detailed data on building locations in a WUI setting.