RT Journal Article
SR Electronic
T1 Modeling post-logging height growth of black spruce forests by combining airborne LiDAR and historical forestry maps in eastern Canadian boreal forest
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.01.22.427172
DO 10.1101/2021.01.22.427172
A1 Batistin Bour
A1 Victor Danneyrolles
A1 Yan Boucher
A1 Richard A. Fournier
A1 Luc Guindon
YR 2021
UL http://biorxiv.org/content/early/2021/01/22/2021.01.22.427172.abstract
AB Increase in forest disturbance due to land use as well as climate change has led to an expansion of young forests worldwide, which affects global carbon dynamics and forest management. In this study, we present a novel method that combines a single airborne LiDAR acquisition and historical harvesting maps to model height growth of post-logged black spruce-dominated forests in a 1700 km2 eastern Canadian boreal landscape. We developed a random forest model where forest height is a function of stand age, combined with environmental variables. Our results highlight the strong predictive power of this model: least-square regression between predicted and observed height of our validation dataset was very close to the 1:1 relation and strongly supported by validation metrics (R2 = 0.75; relative RMSE = 19%). Moreover, our findings indicated an ecological gradient responsible for differences in height growth at the landscape scale, with better growth rates on mesic slopes compared to badly drained soils on flat lands. With the increased availability of LiDAR data, this method is promising since it can be applied to forests across the globe that are affected by stand-replacing disturbances.Competing Interest StatementThe authors have declared no competing interest.