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Measuring post-fire effects at landscape scales is critical to an ecological understanding of wildfire effects. Predominantly this is accomplished with either multi-spectral remote sensing data or through ground-based field sampling plots. While these methods are important, field data is usually limited to opportunistic post-fire observations, and spectral data often lacks validation with specific variables of change. Additional uncertainty remains regarding how best to account for environmental variables influencing fire effects (e.g., weather) for which observational data cannot easily be acquired, and whether pre-fire agents of change such as bark beetle and timber harvest impact model accuracy. This study quantifies wildfire effects by correlating changes in forest structure derived from multi-temporal Light Detection and Ranging (LiDAR) acquisitions to multi-temporal spectral changes captured by the Landsat Thematic Mapper and Operational Land Imager for the 2012 Pole Creek Fire in central Oregon. Spatial regression modeling was assessed as a methodology to account for spatial autocorrelation, and model consistency was

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Accurate estimates of growth and structural changes are key for forest management tasks such as determination of optimal rotation times, optimal rotation times, site indices and for identifying areas experiencing difficulties to regenerate. Estimation of structural changes, especially for biomass, is also key to quantify greenhouse gas (GHG) emissions/sequestration. We compared two different modeling strategies to estimate changes in V, BA and B, at three different spatial aggregation levels using auxiliary information from two light detection and ranging (LiDAR) flights. The study area is Blacks Mountains Experimental Forest, a ponderosa pine dominated forest in Northern California for which two LiDAR acquisitions separated by six years were available. Analyzed strategies consisted of (1) directly modeling the observed changes as a function of the LiDAR auxiliary information ( δ -modeling method) and (2) modeling V, BA and B at two different points in time, including a term to account for the temporal correlation, and then computing the changes as the difference between the predicted values of V, BA and B for time two and time one. We analyzed predictions and measures of uncertainty at three different level of aggregation (i.e., pixels, stands or compartments and the entire study area). Results showed that changes were very weakly correlated with the LiDAR auxiliary information. Both modeling alternatives provided similar results with a better performance of the δ -modeling for the entire study area; however, this method also showed some inconsistencies and seemed to be very prone to extrapolation problems. The y -modeling method, which seems to be less prone to extrapolation problems, allows obtaining more outputs that are flexible and can outperform the δ -modeling method at the stand level. The weak correlation between changes in structural attributes and LiDAR auxiliary information indicates that pixel-level maps have very large uncertainties and estimation of change clearly requires some degree of spatial aggregation; additionally, in similar environments, it might be necessary to increase the time lapse between LiDAR acquisitions to obtain reliable estimates of change.

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Recent wildfires across western North America have burned with uncharacteristically high severity, representing a substantial departure from natural fire regimes. In mixed‐conifer and pine–oak ecosystems of the southern Cascade Range, widespread shifts in stand structure and composition have led to a diversity of post‐wildfire vegetation responses. When recent wildfire “footprints” reburn in subsequent fires, their recovery pathways are complex. In order to understand the effects of overlapping mixed‐severity fires, we quantified changes in overstory and midstory structure and species composition for time periods prior to and following two large overlapping wildfires in the southern Cascades: the 2000 Storrie and 2012 Chips Fires. Plots were stratified into 16 severity combinations (unburned, low, moderate, and high in the Storrie Fire combined with the same four categories in the Chips Fire: e.g., moderate Storrie/high Chips) across the 9000‐ha overlapping burned area. Following the two fires, tree quadratic mean diameter and stand density declined for most species, but changes were species‐specific. Compared with preburn values, importance values for fire‐sensitive white fir (Abies concolor) were reduced by 66%, while resprouting fire‐resilient California black oak (Quercus kelloggii) importance values increased by 37% in severity combinations that included at least one high‐severity fire. Greatest shifts were documented in sites that burned twice at high severity, where resulting vegetation was dominated by oak sprout clumps and resprouting and fire‐stimulated montane chaparral species, while unburned and low‐severity strata retained a substantial component of Douglas‐fir (Pseudotsuga menziesii) and white fir. Results suggest that repeated moderate‐ and high‐severity fires can result in ecosystem state shifting toward fire‐resilient oak‐shrub communities in this fire‐prone landscape. Managers seeking greater landscape resilience can implement treatments such as thinning and prescribed burning, while taking advantage of fire‐created patches such as these in areas where the likelihood of a hotter and drier future makes the reestablishment of continuous forest cover unrealistic.