Abstract:

Canopy height is a key driver of forest biodiversity and carbon cycling. Accurate estimates of canopy height are needed for assessing mechanisms relating to ecological patterns and processes of tree height limitations. At global scales forest canopy height patterns are largely controlled by climate, while local variation at fine scales is due to differences in disturbance history and local patterns in environmental conditions. The relative effect of local environmental drivers on canopy height is poorly understood partly due to gaps in data on canopy height and methods for examining limiting factors. Here, we used airborne laser scanning (ALS) data on vegetation structure of boreal forests to examine the effects of environmental factors on potential maximum forest canopy height.

Relationships between maximum canopy height from ALS measures and environmental variables were examined to assess factors limiting tree height. Specifically, we used quantile regression at the 0.90 quantile to relate maximum canopy height with environmental characteristics of climate (i.e. mean annual temperature [MAT] and mean annual precipitation), terrain (i.e. slope) and depth-to-water (DTW) across a 33000 km2 multiple use boreal forest landscape in northeast Alberta, Canada.

Maximum canopy height was positively associated with MAT, terrain slope and terrain-derived DTW, collectively explaining 33.2% of the variation in heights. The strongest explanatory variable was DTW explaining 26% of canopy height variation with peatland forests having naturally shorter maximum canopy heights, but also more sites currently at their maximum potential height. In contrast, the most productive forests (i.e. mesic to xeric upland forests) had the fewest sites at their potential maximum height, illustrating the effects of long-term forest management, wildfires and general anthropogenic footprints on reducing the extent and abundance of older, taller forest habitat in Alberta’s boreal forest.

Key words: forest succession, vegetation structure, quantile regression, remote sensing, airborne laser scanning (ALS)