Assessing variability in post‐fire forest structure along gradients of productivity in the Canadian boreal using multi‐source remote sensing

Abstract

AbstractAimForest regeneration following fire is an important component of the global carbon cycle, but it is difficult to monitor over large and remote forested regions, such as Canada's north. In this study, we aim to (1) characterize how forest regeneration following fire varies across the Canadian boreal and (2) determine if this variability is captured by satellite‐derived estimates of productivity.LocationCanadian boreal.MethodsWe relate structural measurements from light detection and ranging (lidar) data to gross primary productivity (GPP) estimates from the MODerate Resolution Imaging Spectroradiometer (MODIS) along a 25‐year chronosequence of forest regeneration following fire. Over 400 patches that burned from 1985–2009 were analysed, with fire information obtained from a national Landsat‐derived record of forest change.ResultsIn the first 15 years since fire (YSF), estimates of percent canopy cover (> 2 m) were typically low regardless of GPP (mean = 11.0–16.0%, SD = 7.8–8.9%) and correlations to GPP were relatively weak (r = 0.18–0.48). Canopy cover was more variable between stands by 16–25 YSF (mean = 16.2–21.7%, SD = 16.0–17.1%), and correlations to GPP were stronger (r = 0.63–0.71, P < 0.01). Conversely, variability in stand height (75th height percentile) remained low at 16–25 YSF (mean = 4.9–5.0 m, SD = 0.9–1.1 m) and weakly related to GPP (r = 0.16–0.21).Main conclusionsSatellite‐derived estimates of productivity capture differences in canopy structure across the boreal, but only after 15 YSF. While canopy cover varied strongly along gradients of productivity from 16–25 YSF, differences in vertical growth were less pronounced due to slow boreal growth rates. Our results provide important insights into how satellite‐derived estimates of productivity are realized structurally, as understanding regional variation in forest regeneration is critical to quantifying carbon dynamics in forests. Combining lidar‐derived estimates of structure with Landsat‐derived disturbance history is a valuable approach for characterizing variability in post‐fire structure over large forested areas.