Browsing by Author "Hawbaker, Todd, committee member"

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Open Access
Cross-scale evaluation of fuel maps in Colorado ponderosa pine-dominated forests
(Colorado State University. Libraries, 2025) Johnston, Katelyn J., author; Hoffman, Chad, advisor; Tinkham, Wade, advisor; Hawbaker, Todd, committee member; Vogeler, Jody, committee member
Fuel maps are used in every aspect of wildfire management, allowing managers to assess fire risk, predict fire behavior and effects, and guide fuel hazard treatment planning. Despite widespread use of national fuel maps like LANDFIRE, FCCS, and FastFuels, quantitative data on their accuracy and biases across ecosystems and scales remain limited. The few studies evaluating LANDFIRE's canopy fuel maps and FCCS have identified a wide range of errors and conflicting bias trends. Additionally, LANDFIRE's 40 standard fire behavior fuel models have yet to be assessed for their ability to represent fuel component loadings, despite growing use in fuel maps like FastFuels for physics-based fire behavior modeling. The accuracy of FastFuels has not been evaluated due to its recent development. The overall objective of this study was to assess the accuracy and bias of three national fuel mapping products – LANDFIRE, FCCS, and FastFuels – at five different scales. To meet this objective, I sampled surface and canopy fuels from seven sites representing the range of ponderosa pine (Pinus ponderosa Dougl. Ex Laws.) fuel complexes across the Colorado Front Range. Plots at each site were arranged in a 5x5 grid of 0.09 ha pixels to allow for accuracy assessment at 0.09, 0.12, 0.81, 1.44, and 2.25 ha scales. My results indicate that all three national fuel mapping products performed poorly across fuel attributes, with systematic biases and mean absolute errors ranging from 36% to 2590%. Errors and biases associated with LANDFIRE canopy metrics suggest that LANDFIRE is likely to overestimate fuel hazards associated with crown fire initiation, but underestimate crown fire spread hazard, while FastFuels underestimates the hazards associated with both crown fire initiation and spread. Similarly, FBFM40 overestimates key surface fuel components, such as fine fuel loading, which would likely lead to overpredicted surface fire behavior. FCCS metrics crucial for smoke and emissions forecasting, particularly 1000-hour fuels, are also overestimated, potentially inflating emissions projections. I found no significant relationship between mean error and map scale from 0.09 ha to 2.25 ha. The variation observed within and between fuel components and layers of LANDFIRE, FCCS, and FastFuels highlights inherent challenges associated with mapping wildland fuels. The high errors and biases observed in my assessment may have broader implications for fire management and planning, warranting further investigation, as these fuels play an important role in dictating fire behavior and effects. Although advancements in remote sensing and modeling offer opportunities to improve these national fuel mapping products, uncertainties in current products should continue to be quantified and considered when implemented in management activities until these improvements are successfully integrated.
Open Access
Mapping values at risk, assessing building loss and evaluating stakeholder expectations of wildfire mitigation in the wildland-urban interface
(Colorado State University. Libraries, 2020) Caggiano, Michael, author; Hoffman, Chad, advisor; Amidon, Tim, committee member; Cheng, Antony S., committee member; Hawbaker, Todd, committee member
The Wildland-Urban Interface (WUI) is an area where residential development extends into undeveloped land. When WUI development occurs in hazard-prone fire-adapted ecosystems, wildfires can have detrimental impacts on human communities by destroying buildings and infrastructure. Wildfires that cause substantial building loss are known as WUI disasters because of their high social and economic costs. WUI disasters tend to occur when wildfires ignite under extreme burning conditions and threaten a large number of homes in hazardous conditions relative to firefighting resources. This combination of factors can lead to significant home loss. WUI disasters annually result in billions of dollars in fire suppression costs and destroy thousands of homes Governments, land managers, and effected stakeholders respond to this threat in numerous ways as they attempt to mitigate the impacts of wildfires and reduce losses in WUI communities. Although wildfire mitigation efforts emphasize the removal of nearby flammable vegetation and the use of nonflammable building materials, one of the critical steps involves developing a map of communities and buildings at risk in the WUI. Despite broad-scale mapping efforts, most WUI maps do not identify building locations at sufficiently fine scales to estimate fire exposure and inform wildfire planning. Defensible space is promoted as the most effective way to reduce home ignition; however, questions remain surrounding its interactions with fire response, and its efficacy under the wide range of potential fire behavior to which homes could be exposed. This dissertation sought to realize three goals: first, it examined the potential of new technologies to map the WUI and the buildings within it at fine scales; second, it evaluated how well existing WUI mapping efforts capture the pattern of building loss observed during WUI disasters; and third, it examined stakeholder perspectives on the efficacy and interactions of defensible space and fire response with regards to protecting homes from WUI disasters. Chapter two evaluates the ability of Object Based Image Analysis to extract WUI building locations from orthoimagery of the wildland-urban interface by testing accuracy and error at multiple scales. I found the approach can extract building locations with high rates of accuracy, and minimal user input. Extracting building locations using this approach can lead to comprehensive datasets of building locations in the WUI, which can be used to create more detailed maps of buildings exposed to wildfires. Such maps have utility for risk mapping, fuel treatment prioritization, and incident management, and can lead to a better understanding regarding the spatial patterns of home loss. Chapter three leverages building location data to quantify the impacts of WUI disasters and evaluate the accuracy of WUI maps. I compare how well existing polygon-based SILVIS WUI maps and point-based WUI maps capture the pattern of building loss and assess building loss in relation to the core components of the WUI definition. Findings can be used to improve existing WUI maps, create point-based WUI maps from building location datasets, identify which homes are most in need of defensible space, and refine risk mapping and identification of wildfire exposure zones. Finally, chapter four assesses stakeholder perspectives regarding the efficacy of defensible space and its interactions with fire response with regards to the stakeholders' ability to protect homes from WUI disasters. This is related to the prior mapping efforts because it speaks to the ways stakeholders co-manage wildfire risk with fire protection authorities, and the actions they take to protect threatened homes mapped using the methods evaluated in chapters one and two. These qualitative methods suggest a wide range in expectations of defensible space efficacy, both in theory and in practice. It is likely that numerous factors reduce the perceived and actual efficacy of defensible space.