Pre-fire measures of boreal forest structure and composition inform interpretation of post-fire spectral recovery rates

Abstract

Wildfire is a key driver of forest dynamics in boreal forests; however, the annual area burned in boreal forests is highly variable, with increasing wildfire activity documented over the past half century. Post-fire recovery has important implications for carbon balance, and for the wide range of ecosystem goods and services provisioned by the boreal forest. Monitoring post-fire recovery is challenging given the vast and often inaccessible areas impacted, coupled with the marked variability in post-fire conditions and recovery processes. Field assessments of recovery are constrained in space and time, whereas remotely sensed data are spatially explicit, cover large areas, and can retrospectively provide assessments of pre- and post-fire conditions and establish spectral recovery baselines. However, there is a need to link spectral measures of recovery to manifestations of post-fire forest recovery on the ground. Understanding how different forest characteristics influence spectral recovery is key to the successful application of spectral recovery metrics for forest management applications in different forest environments. Herein, using a synthesis of plot data for the North American boreal forest, we assess the influence of pre- and post-fire field-measured characteristics on spectral recovery rates, with a focus on stem density and composition in Canadian boreal forests. Plots that experienced rapid spectral recovery (as measured using Landsat-derived NBR time series data) were associated with a transition from conifer to broadleaf or were broadleaf pre-fire. Plots that experienced a decrease in stem density took significantly longer to spectrally recover than plots that experienced no change in stem density. Plots that had not yet spectrally recovered by the end of the time series were associated with higher elevations, drier sites, greater pre-fire basal area, and had the greatest change magnitude. Our results emphasize that recovery is a process that is highly variable and that knowledge of pre-fire condition is important for characterizing and interpreting measures of post-fire spectral recovery for forest management applications. Remotely sensed time series data are uniquely able to provide information on both pre- and post-fire condition. Our analysis of pre- and post-fire field measures provided novel insights regarding the influence of forest composition and changes in stem density on measures of post-fire spectral recovery in boreal forests.