About

Van R. Kane is an Associate Research Professor at the School of Environmental and Forest Sciences at the University of Washington. His research focuses on ecosystem science, forest structure, climate change and adaptation, fire science, landscape ecology, remote sensing, and geoinformatics. In his Forest Resilience Laboratory, Dr. Kane and his team are forest and fire landscape ecologists who utilize high-fidelity remote sensing data, especially airborne lidar, to analyze forest and landscape dynamics from stands to large-scale landscapes. His work studies the causes and effects of stressors such as climate change, management legacies, and fire on forest resilience. Dr. Kane's research includes examining the impacts of fire and drought, habitat, biomass, forest assessments, and restoration planning aimed at improving forest resilience. He collaborates with forest managers to apply his findings to real-world forest management and conservation efforts. His projects include remote sensing-based forest monitoring, evaluating fire effects, and supporting forest management decisions across various regions, including California, Alaska, and Oregon.

Research topics

• Environmental science
• Geography
• Ecology
• Remote sensing
• Physical geography

Selected publications

• Big trees burning: Divergent wildfire effects on large trees in open‐ vs. closed‐canopy forests

  Ecosphere · 2025-09-01 · 3 citations

  articleOpen access

  Abstract Wildfire activity has accelerated with climate change, sparking concerns about uncharacteristic impacts on mature and old‐growth forests containing large trees. Recent assessments have documented fire‐induced losses of large‐tree habitats in the US Pacific Northwest, but key uncertainties remain regarding contemporary versus historical fire effects in different forest composition types, specific impacts on large trees within closed versus open canopies, and the role of fuel reduction treatments. Focusing on the 2021 Schneider Springs Fire, which encompassed 43,000 ha in the eastern Cascade Range of Washington and burned during a period of severe drought, this study addresses three interrelated questions: (1) Are burn severity distributions consistent with historical fire regimes in dry, moist, and cold forest types? (2) How does burn severity vary among forest structure classes, particularly large trees with open versus closed canopies? (3) How do fuel reduction treatments influence forest structure and burn severity inside and outside of treated areas? Within each forest type, burn severity proportions were similar to historical estimates, with lower overall severity in dry forests than in moist and cold forests. However, across all forest types combined, high‐severity fire affected 30% (4500 ha) of large‐tree locations with tree diameters >50 cm. In each forest type, burn severity was lower in locations with large‐open structure (<50% canopy cover) than in locations with large‐closed structure (>50% canopy cover). Burn severity also was lower inside than outside treated sites in all structure classes, and untreated large‐closed forests tended to burn at lower severity closer to treatments. These results highlight the susceptibility of dense, late‐successional forests to contemporary fires, even in events with widespread potentially beneficial effects consistent with historical fire regimes. These results also illustrate the effectiveness of treatments that shift large‐closed to large‐open structures and suggest that treatments may help mitigate fire effects in adjacent large‐closed forests. Long‐term monitoring and adaptive management will be essential for conserving critical wildlife habitats and fostering ecosystem resilience to climate change, wildfires, and other disturbances.

  PublisherOA PDFDOI

• Brief Communication: RxGaming- A Flexible Open-Source Tool Using Aerial Lidar to Incorporate Tree Spatial Patterns in Mechanical Treatments

  Journal of Forestry · 2025-10-21

  articleOpen accessSenior author

  Dry forest ecosystems in the western United States face the pressure of uncharacteristically severe wildfires and widespread drought-induced mortality as a result of fire exclusion, past management practices, and climate change. Implementing forest treatments that incorporate individual tree, clump, and opening (ICO) patterns can help to increase forest resilience to these disturbances. We explore tradeoffs in meeting treatment goals while incorporating ICO concepts in a case study using RxGaming, a publicly available, open-source software tool. The RxGaming tool provides a framework for decision making via 1) visualization and assessment of current stand structure and 2) treatment simulations using an algorithm that incorporates ICO-based thinning methods and reflects user-defined objectives.

  PublisherOA PDFDOI

• Active-fire landscapes demonstrate structural resistance to subsequent fire and drought

  Forest Ecology and Management · 2025-11-21

  articleSenior author
  PublisherDOI

• Assessing fuel treatments and burn severity using global and local analyses

  Fire Ecology · 2025-07-29 · 3 citations

  articleOpen accessSenior author

  Abstract Background Wildfires in western U.S. dry forest ecosystems have increased in size and severity during recent decades due primarily to more than a century of fire suppression, exclusion of Indigenous fire, and a rapidly warming climate. Fuel treatments have been employed to restore historical forest conditions and mitigate burn severity. However, their influence on burn severity in the context of other environmental variables and firefighting operations has not been extensively explored. The 2021 Bootleg Fire in south-central Oregon provided an opportunity to evaluate the effectiveness of mechanical thinning (Tx), broadcast burning (Rx), and both treatments combined (TxRx) near the Sycan Marsh Preserve, where pre-fire LiDAR data were also available. Results We assessed burn severity 1 year after the Bootleg Fire accounting for the local variability of top environmental drivers, fuel treatments, and firefighting operations. We modeled the influence of burn severity drivers using Random Forest and examined mean predictor effects (global scale) and their spatially explicit variability across observations (local scale) using SHapley Additive exPlanations (SHAP) analysis. Within units treated with broadcast burning, the percentage of area burned at low severity was over 80%. In contrast, units treated with thinning-only and untreated forests were dominated by area burned at moderate (45%) and high (42%) severity, respectively. All treatment types facilitated firefighting operations. Broadcast burning units, in which suppression activities occurred during the Bootleg Fire, showed a marginal decrease in predicted burn severity. Under consistent severe weather conditions, our results underscored the central role of fuel characteristics, including fuel treatments, and their local variability in influencing burn severity. The most important determinant of burn severity was Rx, followed by top drivers representing fuel structure and accumulation. Conclusions Our study highlights that fuel characteristics and broadcast burning disproportionally impacted burn severity, with Rx being the most effective and economical treatment. By creating a reproducible framework to explain burn severity, at both global and local scales, we gained nuanced insights about the drivers of burn severity that could inform and enhance fire and fuel management practices across multi-ownership landscapes.

  PublisherOA PDFDOI

• When do contemporary wildfires restore forest structures in the Sierra Nevada?

  Fire Ecology · 2024-09-30 · 11 citations

  articleOpen accessSenior author

  Abstract Background Following a century of fire suppression in western North America, managers use forest restoration treatments to reduce fuel loads and reintroduce key processes like fire. However, annual area burned by wildfire frequently outpaces the application of restoration treatments. As this trend continues under climate change, it is essential that we understand the effects of contemporary wildfires on forest ecosystems and the extent to which post-fire structures are meeting common forest restoration objectives. In this study, we used airborne lidar to evaluate fire effects across yellow pine and mixed conifer (YPMC) forests of California’s Sierra Nevada. We quantified the degree to which forest structures in first-entry burned areas (previously unburned since ~ 1900s) and unburned controls aligned with restoration targets derived from contemporary reference sites. We also identified environmental conditions that contributed to more restorative fire effects. Results Relative to unburned controls, structural patterns in first-entry burned areas aligned more closely with reference sites. Yet, across all burn severities, first-entry wildfires were only moderately successful at meeting targets for canopy cover (48% total area) and ladder fuels (54% total area), and achieving these targets while also producing tree clump and opening patterns aligning with reference sites was less common (16% total area). Moderate-severity patches had the highest proportion of restorative fire effects (55–64% total area), while low- and high-severity patches were either too dense or too open, respectively. Our models (and publicly-available mapped predictions) indicated a higher probability of restorative effects within 1 km of previous fires, within the mid-upper climate range of the YPMC zone, and under moderate fire intensities (~ 1–2 m flame lengths). Conclusions First-entry wildfires can sometimes restore structural conditions by reducing canopy cover and ladder fuels and increasing structural heterogeneity, especially within moderate-severity patches. However, these initial fires represent just one step toward restoring dry forest ecosystems. Post-fire landscapes will require additional low- to moderate-intensity fires and/or strategic management interventions to fully restore structural conditions. In yet unburned forests, managers could prioritize mechanical treatments at lower elevations, early-season burning at mid to high elevations, and resource objective wildfires in landscapes with mosaics of past wildfires.

  PublisherOA PDFDOI

• Reconciling Species Conservation and Ecosystem Resilience: Northern Spotted Owl Habitat Sustainability in a Fire-Dependent Forest Landscape

  SSRN Electronic Journal · 2024-01-01 · 2 citations

  preprintOpen access
  PublisherDOI

• Learning from wildfires: A scalable framework to evaluate treatment effects on burn severity

  Ecosphere · 2024-12-01 · 12 citations

  articleOpen access

  Abstract Interruption of frequent burning in dry forests across western North America and the continued impacts of anthropogenic climate change have resulted in increases in fire size and severity compared to historical fire regimes. Recent legislation, funding, and planning have emphasized increased implementation of mechanical thinning and prescribed burning treatments to decrease the risk of undesirable ecological and social outcomes due to fire. As wildfires and treatments continue to interact, managers require consistent approaches to evaluate treatment effectiveness at moderating burn severity. In this study, we present a repeatable, remote sensing–based, analytical framework for conducting fire‐scale assessments of treatment effectiveness that informs local management while also supporting cross‐fire comparisons. We demonstrate this framework on the 2021 Bootleg Fire in Oregon and the 2021 Schneider Springs Fire in Washington. Our framework used (1) machine learning to identify key bioclimatic, topographic, and fire weather drivers of burn severity in each fire, (2) standardized workflows to statistically sample untreated control units, and (3) spatial regression modeling to evaluate the effects of treatment type and time since treatment on burn severity. The application of our framework showed that, in both fires, recent prescribed burning treatments were the most effective at reducing burn severity relative to untreated controls. In contrast, thinning‐only treatments only produced low/moderate‐severity effects under the more moderate fire weather conditions in the Schneider Springs Fire. Our framework offers a robust approach for evaluating treatment effects on burn severity at the scale of individual fires, which can be scaled up to assess treatment effectiveness across multiple fires. As climate change brings increased uncertainty to dry forest ecosystems of western North America, our framework can support more strategic management actions to reduce wildfire risk and foster resilience.

  PublisherDOI

• Improved aboveground biomass estimation and regional assessment with aerial lidar in California’s subalpine forests

  Carbon Balance and Management · 2024-12-20 · 7 citations

  articleOpen access

  BACKGROUND: Understanding the impacts of climate change on forest aboveground biomass is a high priority for land managers. High elevation subalpine forests provide many important ecosystem services, including carbon sequestration, and are vulnerable to climate change, which has altered forest structure and disturbance regimes. Although large, regional studies have advanced aboveground biomass mapping with satellite data, typically using a general approach broadly calibrated or trained with available field data, it is unclear how well these models work in less prevalent and highly heterogeneous forest types such as the subalpine. Monitoring biomass using methods that model uncertainty at multiple scales is critical to ensure that local relationships between biomass and input variables are retained. Forest structure metrics from lidar are particularly valuable alongside field data for mapping aboveground biomass, due to their high correlation with biomass. RESULTS: We estimated aboveground woody biomass of live and dead trees and uncertainty at 30 m resolution in subalpine forests of the Sierra Nevada, California, from aerial lidar data in combination with a collection of field inventory data, using a Bayesian geostatistical model. The ten-fold cross-validation resulted in excellent model calibration of our subalpine-specific model (94.7% of measured plot biomass within the predicted 95% credible interval). When evaluated against two commonly referenced regional estimates based on Landsat optical imagery, root mean square error, relative standard error, and bias of our estimations were substantially lower, demonstrating the benefits of local modeling for subalpine forests. We mapped AGB over four management units in the Sierra Nevada and found variable biomass density ranging from 92.4 to 199.2 Mg/ha across these management units, highlighting the importance of high quality, local field and remote sensing data. CONCLUSIONS: By applying a relatively new Bayesian geostatistical modeling method to a novel forest type, our study produced the most accurate and precise aboveground biomass estimates to date for Sierra Nevada subalpine forests at 30 m pixel and management unit scales. Our estimates of total aboveground biomass within the management units had low uncertainty and can be used effectively in carbon accounting and carbon trading markets.

  PublisherOA PDFDOI

• Reconciling species conservation and ecosystem resilience: Northern spotted owl habitat sustainability in a fire-dependent forest landscape

  Forest Ecology and Management · 2024-06-28 · 10 citations

  articleOpen access

  Maintaining dense forest habitats for the threatened northern spotted owl (NSO) has proven challenging in seasonally dry, fire-dependent landscapes where low-density conditions were historically dominant and are generally more climate- and disturbance-resilient. To better inform the dual, sometimes-conflicting objectives of species conservation and forest resilience, we developed an approach to evaluate NSO habitat sustainability by: (1) quantifying the structure of high suitability habitat (HSH) associated with NSO using two remotely sensed platforms, (2) estimating current and historical HSH abundance, and (3) identifying HSH locations more likely to persist given current and future forest-zone climate projections and increasing risk of severe wildfire. Tall, closed-canopy conditions effectively comprised the key structural features of HSH, providing a means to map habitat through time. Both historical amounts and contemporary spatial patterns of HSH and other forest and non-forest conditions around occupied NSO sites indicated that HSH and forest resilience goals can be congruent at multiple scales. Independent lines of evidence suggest HSH historically composed ∼18–24% of the dry and moist mixed-conifer landscape – considerably lower levels than current management goals in many areas. Projected shifts in climate and severe-fire likelihood suggest substantial spatial and temporal shifts where HSH will be sustainable into the future – mainly in currently moist as well as some cold forest types. These findings can inform the potential convergence and trade-offs of species conservation and disturbance resilience goals across local and regional landscapes, based on the inherent capacity of the landscape to support both goals under projected shifts in climate and wildfire.

  PublisherDOI

• Synthesizing field plot and airborne remote sensing data to enhance national forest inventory mapping in the boreal forest of Interior Alaska

  Science of Remote Sensing · 2024-12-30 · 2 citations

  articleOpen accessSenior author

  The boreal biome, the largest terrestrial biome on Earth, is increasingly vulnerable to climate change due to warming twice as rapidly as the global average. Climate change has increased the temperature, frequency, severity, and amount of area burned, which is leading to changes in the spatial extent of forest type and species range. These rapid ecological shifts necessitate fine-scale monitoring of forest type to detect potential type conversions and guide management interventions. In this study, we present a framework for forest type classification combining field plots and high-resolution remote sensing data using machine learning models in the boreal forest of Interior Alaska. For this purpose, we conducted forest type classification at three different levels, including 1. forest and nonforest, 2. hardwood, softwood, and nonforest, and 3. three dominant forest types, including paper birch, black spruce, white spruce, and nonforest. To achieve this goal, we compared the performance of two advanced modeling approaches, the convolutional neural network (CNN) and the XGBoost model. Our datasets included field and high-resolution topographic metrics including elevation, slope, aspect, and solar radiation and canopy height derived from lidar (1 m) and 44 vegetation indices derived from high-resolution (1 m) visible to near infrared (VNIR) hyperspectral data collected by NASA Goddard's Lidar, Hyperspectral and Thermal Imager (G-LiHT) sensor. The remote sensing data were collected under variable sky conditions (clear to overcast) throughout a 1-month growing-season period, and field data collected by United States Department of Agriculture (USDA) Forest Service, Forest Inventory and Analysis program (FIA). In this framework, we also studied the importance of topographic and remote sensing variables for the classification of forest types. We found the CNN model outperformed the XGBoost model in terms of overall accuracy and a macro average F1 score for all three different forest type classifications. The CNN model achieved an overall accuracy of 93.1% for forest or nonforest, 82.6% for hardwood, softwood, and nonforest, and 74.7% for three dominant forest types including paper birch, black spruce, and white spruce along with nonforest. Among the various topographic factors, we found that elevation was the most important factor for discriminating all forest types. In addition, we found that canopy height and vegetation indices including Photochemical Reflectance Index (PRI) (R531 & R570), Pigment Specific Normalized Difference (PSND) (R635 & R800), and Gitelson and Merzlyak (GM1) (R550 & R750) were important for differentiating between hardwood and softwood while Anthocyanin Reflectance Index (ARI1) (R550 & R700) was important for differentiating between forest and nonforest. The high-resolution forest type information can improve our ecological understanding of boreal forest dynamics, estimate above ground biomass, and carbon, and support the national forest inventory and forest managers. • Enhance national forest inventory analysis with remote sensing in Interior Alaska. • High-resolution lidar and hyperspectral data was collected over larger area of IA. • Fine grained classification of boreal forest type. • Convolution neural network outperformed XGBoost classifier. • Canopy height and elevation were most important for forest type classification.

  PublisherDOI

Frequent coauthors

• James A. Lutz

  Utah State University

  42 shared

• Robert J. McGaughey

  37 shared

• Jerry F. Franklin

  University of Washington

  31 shared

• Derek J. Churchill

  University of Washington

  26 shared

• Malcolm P. North

  United States Department of Agriculture

  23 shared

• Nicholas A. Povak

  Pacific Southwest Research Station

  23 shared

• Jonathan T. Kane

  University of Washington

  23 shared

• Paul F. Hessburg

  Pacific Northwest Research Station

  21 shared

Labs

• Van R. Kane's Forest Resilience LaboratoryPI

Education

• PhD Forest Ecology, College of the Environment

  University of Washington

  2010

• BS General Arts and Sciences

  Pennsylvania State University

  1977