Many studies have examined how fuels, topography, climate, and fire weather influence fire severity. Less is known about how different forest management practices influence fire severity in multi‐owner landscapes, despite costly and controversial suppression of wildfires that do not acknowledge ownership boundaries. In 2013, the Douglas Complex burned over 19,000 ha of Oregon & California Railroad (O&C) lands in Southwestern Oregon,USA. O&C lands are composed of a checkerboard of private industrial and federal forestland (Bureau of Land Management,BLM) with contrasting management objectives, providing a unique experimental landscape to understand how different management practices influence wildfire severity. Leveraging Landsat based estimates of fire severity (Relative differenced Normalized Burn Ratio, RdNBR) and geospatial data on fire progression, weather, topography, pre‐fire forest conditions, and land ownership, we asked (1) what is the relative importance of different variables driving fire severity, and (2) is intensive plantation forestry associated with higher fire severity? Using Random Forest ensemble machine learning, we found daily fire weather was the most important predictor of fire severity, followed by stand age and ownership, followed by topographic features. Estimates of pre‐fire forest biomass were not an important predictor of fire severity. Adjusting for all other predictor variables in a general least squares model incorporating spatial autocorrelation, mean predicted RdNBRwas higher on private industrial forests (RdNBR521.85 ± 18.67 [mean ±SE]) vs.BLMforests (398.87 ± 18.23) with a much greater proportion of older forests. Our findings suggest intensive plantation forestry characterized by young forests and spatially homogenized fuels, rather than pre‐fire biomass, were significant drivers of wildfire severity. This has implications for perceptions of wildfire risk, shared fire management responsibilities, and developing fire resilience for multiple objectives in multi‐owner landscapes.

Forest inventory and monitoring programs are needed to provide timely, spatially complete (i.e. mapped), and verifiable information to support forest management, policy formulation, and reporting obligations. Satellite images, in particular data from the Landsat Thematic Mapper and Enhanced Thematic Mapper (TM/ETM+) sensors, are often integrated with field plots from forest inventory programs, leveraging the complete spatial coverage of imagery with detailed ecological information from a sample of plots to spatially model forest conditions and resources. However, in remote and unmanaged areas such as Canada's northern forests, financial and logistic constraints can severely limit the availability of inventory plot data. Additionally, Landsat spectral information has known limitations for characterizing vertical vegetation structure and biomass; while clouds, snow, and short growing seasons can limit development of large area image mosaics that are spectrally and phenologically consistent across space and time. In this study we predict and map forest structure and aboveground biomass over 37millionha of forestland in Saskatchewan, Canada. We utilize lidar plots—observations of forest structure collected from airborne discrete-return lidar transects acquired in 2010—as a surrogate for traditional field and photo plots. Mapped explanatory data included Tasseled Cap indices and multi-temporal change metrics derived from Landsat TM/ETM+ pixel-based image composites. Maps of forest structure and total aboveground biomass were created using a Random Forest (RF) implementation of Nearest Neighbor (NN) imputation. The imputation model had moderate to high plot-level accuracy across all forest attributes (R2 values of 0.42–0.69), as well as reasonable attribute predictions and error estimates (for example, canopy cover above 2m on validation plots averaged 35.77%, with an RMSE of 13.45%, while unsystematic and systematic agreement coefficients (ACuns and ACsys) had values of 0.63 and 0.97 respectively). Additionally, forest attributes displayed consistent trends in relation to the time since and magnitude of wildfires, indicating model predictions captured the dominant ecological patterns and processes in these forests. Acknowledging methodological and conceptual challenges based upon the use of lidar plots from transects, this study demonstrates that using lidar plots and pixel compositing in imputation mapping can provide forest inventory and monitoring information for regions lacking ongoing or up-to-date field data collection programs

Thinning and prescribed fire are widely used to restore fire-suppressed forests, yet there are few studies of their effectiveness in Sierran mixed-conifer forest. We compared stand conditions in replicated plots before and after a combination of thinning and burning treatments against a reconstruction of the same forest in 1865. The historical forest had 67 stems/ha (trees ≥5 cm DBH), equal percentages of shade-tolerant and -intolerant tree species, stems randomly distributed at the stand scale, and a flat diameter distribution across size classes. The pretreatment forest averaged 469 stems/ha, which comprised 84% shade-tolerant and 14% shade-intolerant species, were highly clustered, and had a reverse-J-shaped diameter distribution. Thinning treatments failed to approximate historical composition, spatial pattern, or diameter distribution. Treatments left too many small trees, removed too many intermediate-sized trees (50–75 cm DBH), and retained a reverse-J-shaped diameter distribution. Current old growth comprises fewer large trees than historical conditions, suggesting that treatments should retain more intermediate-sized trees to provide for future large-tree recruitment. Understory thinning with prescribed fire significantly reduced stem density and produced a spatial pattern closest to historical conditions. Mixed-conifer restoration needs thinning prescriptions that vary by species and flexible rather than rigid upper diameter limits to retain some trees in all size classes.