Assessing alternatives for fuel reduction treatment and Pacific marten conservation in the southern Cascades and northern Sierra Nevada

Forest managers are challenged to restore resilience to forests with an elevated risk of stand-replacing fire by using mechanical thinning and prescribed fire. Implementation of these methods can be constrained by mandates to conserve sensitive wildlife species like the Pacific marten (Martes caurina). Martens avoid simplified forest stands created by these fuel reduction treatments, and populations in the northern Sierra Nevada and southern Cascades are already fragmented. Implementing fuel reduction treatments may therefore threaten forest-dependent species like the Pacific marten by reducing available habitat and habitat connectivity.A crucial question is whether reserving marten habitat from fuel treatment results in an elevated probability of large, intense wildfires compared to permitting treatment in these areas. I used a simulation framework to compare the potential for large fires when fuel treatments were implemented with and without marten habitat reserves, defined as areas where all treatment was prohibited. I also assessed wildfire risk to the dense, high elevation forests typical of marten habitat. I simulated fuel treatments in three watersheds (mean area: ~8,000 ha) and then used randomly-placed ignitions to measure each watershed’s capacity for fire spread. For each watershed, I varied the amount of area treated (10%, 20%, and 30% of the watershed) and the type of marten reserve (none; partial, where some marten habitat was reserved; and complete, where all marten habitat was reserved). I further expanded my simulations to a larger study area (~50,000 ha) to provide a complementary depiction of the relationship between habitat reserves and fuel treatment efficacy at the landscape scale.Prohibiting fuel treatment within marten habitat had no significant effect on the ability of treatments to control the spread of fire. I observed an increase in the capacity for fire spread only in one instance, when the most restrictive reserve strategy reduced the total area eligible for treatment below the target 30%, the most ambitious treatment scenario. These results are optimistic for managers— effective fuels management was possible with simultaneous retention of large blocks of marten habitat. In contrast, wildlife risk to marten habitat increased when fuel reduction treatments were allowed only outside of predicted habitat areas. My simulations provide evidence that allowing some fuel treatment in the margins of predicted habitat could largely eliminate this increased risk, without incursion into core areas where martens are most likely to reside. Silvicultural prescriptions that can retain canopy cover and elements of old forest structure, while also increasing resilience to fire, would be preferable for reducing wildfire risk while mitigating the short-term effects of management action in these areas.Keywords: fuel treatment, wildfire, forest management, pacific marten, marten, lassen, martes caurina, wildland fire, fire managemen

Comparing Remote Sensing and Field-Based Approaches to Estimate Ladder Fuels and Predict Wildfire Burn Severity

While fire is an important ecological process, wildfire size and severity have increased as a result of climate change, historical fire suppression, and lack of adequate fuels management. Ladder fuels, which bridge the gap between the surface and canopy leading to more severe canopy fires, can inform management to reduce wildfire risk. Here, we compared remote sensing and field-based approaches to estimate ladder fuel density. We also determined if densities from different approaches could predict wildfire burn severity (Landsat-based Relativized delta Normalized Burn Ratio; RdNBR). Ladder fuel densities at 1-m strata and 4-m bins (1–4 m and 1–8 m) were collected remotely using a terrestrial laser scanner (TLS), a handheld-mobile laser scanner (HMLS), an unoccupied aerial system (UAS) with a multispectral camera and Structure from Motion (SfM) processing (UAS-SfM), and an airborne laser scanner (ALS) in 35 plots in oak woodlands in Sonoma County, California, United States prior to natural wildfires. Ladder fuels were also measured in the same plots using a photo banner. Linear relationships among ladder fuel densities estimated at broad strata (1–4 m, 1–8 m) were evaluated using Pearson’s correlation (r). From 1 to 4 m, most densities were significantly correlated across approaches. From 1 to 8 m, TLS densities were significantly correlated with HMLS, UAS-SfM and ALS densities and UAS-SfM and HMLS densities were moderately correlated with ALS densities. Including field-measured plot-level canopy base height (CBH) improved most correlations at medium and high CBH, especially those including UAS-SfM data. The most significant generalized linear model to predict RdNBR included interactions between CBH and ladder fuel densities at specific 1-m stratum collected using TLS, ALS, and HMLS approaches (R2 = 0.67, 0.66, and 0.44, respectively). Results imply that remote sensing approaches for ladder fuel density can be used interchangeably in oak woodlands, except UAS-SfM combined with the photo banner. Additionally, TLS, HMLS and ALS approaches can be used with CBH from 1 to 8 m to predict RdNBR. Future work should investigate how ladder fuel densities using our techniques can be validated with destructive sampling and incorporated into predictive models of wildfire severity and fire behavior at varying spatial scales

Classification of Plot-Level Fire-Caused Tree Mortality in a Redwood Forest Using Digital Orthophotography and Lidar

Swanton Pacific Ranch is an approximately 1,300 ha working ranch and forest in northern Santa Cruz County, California, managed by California Polytechnic State University, San Luis Obispo (Cal Poly). On August 12, 2009, the Lockheed Fire burned 300 ha of forestland, 51% of the forested area on the property, with variable fire intensity and mortality. This study used existing inventory data from 47 permanent 0.08 ha (1/5 ac) plots to compare the accuracy of classifying mortality resulting from the fire using digital multispectral imagery and LiDAR. The percent mortality of trees at least 25.4 cm (10”) DBH was aggregated to three classes (0-25, 25-50, and 50-100%). Three separate Classification Analysis and Regression Tree (CART) models were created to classify plot mortality. The first used the best imagery predictor variable of those considered, the Normalized Difference Vegetation Index (NDVI) calculated from 2010 National Agricultural Imagery Program (NAIP) aerial imagery, with shadowed pixel values adjusted, and non-canopy pixels removed. The second used the same NDVI in combination with selected variables from post-fire LiDAR data collected in 2010. The third used the same NDVI in combination with selected variables from differenced LiDAR data calculated using post-fire LiDAR and pre-fire LiDAR collected in 2008. The imagery alone was 74% accurate; the imagery and post-fire LiDAR model was 85% accurate, while the imagery and differenced LiDAR model was 83% accurate. These findings indicate that remote sensing data can accurately estimate post-fire mortality, and that the addition of LiDAR data to imagery may yield only modest improvement

Big Fires, Big Trees, and Big Plots: Enhancing our Ecological Understanding of Fire with Unprecedented Field Data

Wildfire is an inexorable process in western landscapes, posing a major challenge to land managers: how can we use fire to restore healthy forests without jeopardizing human communities? The purpose of this dissertation is to produce research that will help guide management and support effective wildland fire use in fire-prone forests. I utilized a longitudinal dataset from a single, large forest plot that burned under serendipitous circumstances during the 2013 Rim Fire. My research revealed that post-fire mortality models under-predict mortality of large trees, and may need to be re-calibrated to perform well under future climates. I used satellite-derived data to estimate fire severity, and found that while severity maps may be accurate at broad scales, they failed to capture fine-scale patterns in fire effects. I examined the spatial elements of fire-related mortality, and demonstrate that beetles, pathogens, and inter-tree competition mediated fire effects and provoked complex, spatially structured mortality for years following fire. Finally, I disentangled the interactive effects of fire, beetles, and drought to provide a more mechanistic understanding of compound disturbance dynamics. This represents the first collection of fire ecology research to emerge from a single, exhaustively sampled, longitudinal monitoring plot. This dissertation not only enhances our ecological understanding of fire, it demonstrates the profound potential for large-scale observational research to contribute novel perspectives to the field of fire science

Using Pre-Fire High Point Cloud Density LiDAR Data to Predict Fire Severity in Central Portugal

[EN], The wall-to-wall prediction of fuel structural characteristics conducive to high fire severity is essential to provide integrated insights for implementing pre-fire management strategies designed to mitigate the most harmful ecological effects of fire in fire-prone plant communities. Here, we evaluate the potential of high point cloud density LiDAR data from the Portuguese áGiLTerFoRus project to characterize pre-fire surface and canopy fuel structure and predict wildfire severity. The study area corresponds to a pilot LiDAR flight area of around 21,000 ha in central Portugal intersected by a mixed-severity wildfire that occurred one month after the LiDAR survey. Fire severity was assessed through the differenced Normalized Burn Ratio (dNBR) index computed from pre- and post-fire Sentinel-2A Level 2A scenes. In addition to continuous data, fire severity was also categorized (low or high) using appropriate dNBR thresholds for the plant communities in the study area. We computed several metrics related to the pre-fire distribution of surface and canopy fuels strata with a point cloud mean density of 10.9 m−2. The Random Forest (RF) algorithm was used to evaluate the capacity of the set of pre-fire LiDAR metrics to predict continuous and categorized fire severity. The accuracy of RF regression and classification model for continuous and categorized fire severity data, respectively, was remarkably high (pseudo-R2 = 0.57 and overall accuracy = 81%) considering that we only focused on variables related to fuel structure and loading. The pre-fire fuel metrics with the highest contribution to RF models were proxies for horizontal fuel continuity (fractional cover metric) and the distribution of fuel loads and canopy openness up to a 10 m height (density metrics), indicating increased fire severity with higher surface fuel load and higher horizontal and vertical fuel continuity. Results evidence that the technical specifications of LiDAR acquisitions framed within the áGiLTerFoRus project enable accurate fire severity predictions through point cloud data with high density.SIPortuguese Foundation for Science and Technolog

Doctor of Philosophy

dissertationWith increasing wildfire activity throughout the western United States comes an increased need for wildland firefighters to protect civilians, structures, and public resources. In order to mitigate threats to their safety, firefighters employ the use of safety zones (SZ: areas where firefighters are free from harm) and escape routes (ER: pathways for accessing SZ). Currently, SZ and ER are designated by firefighters based on ground-level information, the interpretation of which can be error-prone. This research aims to provide robust methods to assist in the ER and SZ evaluation processes, using remote sensing and geospatial modeling. In particular, I investigate the degree to which lidar can be used to characterize the landscape conditions that directly affect SZ and ER quality. I present a new metric and lidar-based algorithm for evaluating SZ based on zone geometry, surrounding vegetation height, and number of firefighters present. The resulting map contains a depiction of potential SZ throughout Tahoe National Forest, each of which has a value that indicates its wind- and slope-dependent suitability. I then inquire into the effects of three landscape conditions on travel rates for the purpose of developing a geospatial ER optimization model. I compare experimentally-derived travel rates to lidar-derived estimates of slope, vegetation density, and ground surface roughness, finding that vegetation density had the strongest negative effect. Relative travel impedances are then mapped throughout Levan Wildlife Management Area and combined with a route-finding algorithm, enabling the identification of maximally-efficient escape routes between any two known locations. Lastly, I explore a number of variables that can affect the accurate characterization of understory vegetation density, finding lidar pulse density, overstory vegetation density, and canopy height all had significant effects. In addition, I compare two widely-used metrics for understory density estimation, overall relative point density and normalized relative point density, finding that the latter possessed far superior predictive power. This research provides novel insight into the potential use of lidar in wildland firefighter safety planning. There are a number of constraints to widespread implementation, some of which are temporary, such as the current lack of nationwide lidar data, and some of which require continued study, such as refining our ability to characterize understory vegetation conditions. However, this research is an important step forward in a direction that has potential to greatly improve the safety of those who put themselves at risk to ensure the safety of life and property

Horizontal and vertical forest complexity interact to influence potential fire behavior

2022 Spring.Includes bibliographical references.Wildland fire behavior is a dynamic process controlled by complex interactions among fuels, weather, and topography. There is significant need to better understand the role of fuels and, particularly, complex arrangements of fuels, on potential fire behavior and effects as a there is a growing emphasis on forest treatments that emulate the heterogenous structures of historical forest ecosystems. Ideally such treatments are intended to reduce fire hazard while concurrently improving resilience to a wide range of disturbance agents and restoring the natural ecosystem dynamics that maintained these forest structures. One way to evaluate how the complex forest structures created by these treatments will influence fire behavior are modeling approaches that account for dynamic interactions between fire, fuels, and wind. These physical fire models build from computational fluid dynamics methods to include processes of heat transfer, vegetative fuel dehydration and pyrolysis, and gas phase ignition and combustion. In this work, several aspects of horizontal and vertical forest structure were evaluated to understand how spatial complexity influences fire behavior, with a particular emphasis on the transition of a surface fire into tree crowns. I used a combination of spatially explicit field data and a physics-based wildfire model, the Wildland-Urban Interface Fire Dynamics Simulator (WFDS), to deepen our fundamental understanding of fire behavior, inform the design of forest treatments that aim to achieve a variety of ecological and social objectives, and develop hypotheses related to the pattern-process feedbacks that contributed to the maintenance of resilient forests across millennia. Chapter 2 presents a simulation study focused on the relationship between horizontal forest structure and surface to crown heat transfer and crown fire initiation. The results indicated that relative to larger 7- and 19-tree groups, isolated individual trees and 3-tree groups had greater convective cooling and reduced canopy heat flux. Because isolated individuals and 3-tree groups were exposed to less thermal energy, they required a greater surface fireline intensity to initiate torching and had less crown consumption than trees within larger groups. Similarly, I found that increased crown separation distance between trees reduced the net heat flux leading to reduced ignition potential. These findings identify the potential physical mechanisms responsible for supporting the complex forest structures typical of high-frequency fire regimes and may be useful for managers designing fuel hazard reduction and ecological restoration treatments. Chapter 3 extends chapter 2 by investigating how different levels and types of vertical heterogeneity influence crown fire transition and canopy consumption within tree groups. These results show the importance of fuel stratum gap (or canopy base height) on vertical fire propagation, however vertical fire propagation was mediated by the level of horizontal connectivity in the upper crown layers. This suggests that the fuel stratum gap cannot fully characterize the torching hazard. The results also indicate that as the surface fire line intensity increases, the influence of horizontal connectivity on canopy consumption is amplified. At the scale of individual tree groups, the perceived hazard of small, understory trees and vertical fuel continuity may be offset by lower horizontal continuity (or canopy bulk density) within the midstory and overstory crown layers. Chapter 4 compares outcomes from four real-world forest treatments that cover a range of potential treatment approaches to evaluate their impacts of forest spatial pattern and potential fire behavior. My results indicate that restoration treatments created greater vertical and horizontal structural complexity than the fuel hazard reduction treatments but resulted in similar reductions to potential fire severity. However, the restoration treatments did increase the surface fire rate of spread which suggests some potential fire behavior tradeoffs among treatment approaches. Overall, these results suggest the utility of restoration treatments in achieving a wide range of management objectives, including fire hazard reduction, and that they can be used in concert with traditional fuel hazard reduction treatments to reduce landscape scale fire risk. Together this work shows that tree spatial pattern can significantly influence crown fire initiation and canopy consumption through alterations to net heat transfer and feedbacks among closely spaced trees. At the scale of the tree group these results suggest that larger tree groups may sustain higher levels of canopy consumption and mortality as they are easier to ignite and, in cases with small separation between crowns, can sustain horizontal spread resulting in density-depended crown damage. These findings carry over to vertically complex groups where the spatial relationship between small, understory trees and larger, overstory trees has a large impact on the ability of fire to be carried vertically. Further, in these vertically complex groups reducing the density (and/or increasing the horizontal separation) of the overstory trees, resulted in lower rates of crown fuel consumption, therefore, mitigating some of the "laddering" effect caused by the presence of small understory trees. These complex interactions between vertical and horizontal aspects of stand structure were born out in my evaluation of the measured forest treatments, where similar crown fire behavior reductions were observed across various stand structures. Overall, this work shows that forest managers can apply treatments to achieve a wide range of ecological benefits while simultaneously increasing fire resistance and resilience

Using Remote Sensing to Characterize Disturbance during a Severe Drought in the Sierra Nevada

Between 2012 and 2016, California experienced an extreme period of drought and high temperatures. During this period there were two particularly notable disturbances in the southern Sierra Nevada. In 2013, the first major disturbance occurred in the form of the Rim Fire. At 1,041 km2, the Rim Fire is the largest fire ever recorded in the Sierra Nevada. Throughout the drought, but particularly in the latter years, there was also epidemic levels of tree mortality, particularly mortality tied to native bark beetles (Dendroctonus spp.) killing pines (Pinus spp.).Using the southern Sierra Nevada as a case study, I investigated the novel insights a next-generation imaging spectroscopy satellite would be able to give in understanding fire and tree mortality globally. Specifically, I showed the potential for imaging spectroscopy based spectral mixture analysis (SMA) as an assessment of fire severity. SMA cover fractions allow for a remotely sensed fire severity metric that would be more readily compared at the global level than those currently in use, such as difference normalized burn ratio (dNBR). I also demonstrated that using the random forest machine learning algorithm, a simulated spaceborne imaging spectrometer would be able to more accurately identify the location of red stage tree mortality compared to existing multispectral satellites such as Landsat. In addition, remote sensing was used to gain an understanding of the impact and drivers of tree mortality. For a 2,240 ha watershed, a model of tree crown locations, with species and height identified, was created based on a combination of high spatial resolution airborne imaging spectroscopy and lidar. Then, high-resolution multispectral imagery was interpreted to determine a tree’s 2016 status. In the area investigated, the net effect of the drought was to reduce the number of live conifer stems taller than 15 m at the crown level by 75%, primarily due to the death of ponderosa pine. Finally, the factors that distinguished conifers that were alive in 2016 from conifers that died between 2015 and 2016, both within the 2,240 ha watershed and across the southern Sierra Nevada were examined. Trees that survived were typically associated with being located in stands with tree species and height class heterogeneity. Stands in wetter, cooler parts of the Sierra Nevada during the drought were also more likely to survive

Coupling remote sensing with wildfire spread modeling in Mediterranean areas

Wildfires are a threat to the ecosystems and in the future this threat could become stronger due to climate change. Spatially explicit fire spread models are effective tools to study fire behavior and wildfire risk. However, to run fire spread simulations, one of the most important inputs is represented by fuel models and this information is not always available. In the last decades, remote sensing technologies have offered valuable information for the classification and characterization of fuels. For this reason, in this work we created accurate maps of main fuel types for Mediterranean areas combining multispectral and LiDAR data. This information improves the current available information, which derives from the Land Use Map of Sardinia. We also enhanced the characterization of canopy fuel models using LiDAR data producing canopy layers ready to be used for wildfire spread modeling. Finally, we compared the variation in simulated wildfire spread and behavior determined by the use of fine-scale maps v. lower resolution maps. In these simulations, we assessed also the effect of using LiDAR-derived canopy layers as well. The results showed more accurate outputs when using our custom fuel and canopy layers produced in this work. In conclusion, this work suggests that the use of LiDAR and satellite imagery data can contribute to improve estimates of modeled wildfire behavior

Fire Environment Analysis at Army Garrison Camp Williams in Relation to Fire Behavior Potential for Gauging Fuel Modification Needs

Large fires (400 ha +) occur about every seven to ten years in the vegetation types located at US Army Garrison Camp Williams (AGCW) practice range located near South Jordan, Utah. In 2010 and 2012, wildfires burned beyond the Camp’s boundaries into the wildland-urban interface. The political and public reaction to these fire escapes was intense. Researchers at Utah State University were asked to organize a system of fuel treatments that could be developed to prevent future escapes. The first step of evaluation was to spatially predict fuel model types derived from a random forests classification approach. Fuel types were mapped according to fire behavior fuel models with an overall validation of 72.3% at 0.5 m resolution. Next, using a combination of empirical and semi-empirical based methods, potential fire behavior was analyzed for the dominant vegetation types at AGCW on a climatological basis. Results suggest the need for removal of woody vegetation within 20 m of firebreaks and a minimum firebreak width of 8 m in grassland fuels. In Utah juniper (Juniperus osteosperma (Torr.) Little), results suggest canopy coverage of 25% or less while in Gambel oak (Quercus gambelii Nutt.) stands along the northern boundary of the installation, a fuelbreak width of 60 m for secondary breaks and 90 m for primary breaks is recommended