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Modeling post-logging height growth of black spruce forests by combining airborne LiDAR and historical forestry maps in eastern Canadian boreal forest

Batistin Bour, Victor Danneyrolles, Yan Boucher, Richard A. Fournier, Luc Guindon
doi: https://doi.org/10.1101/2021.01.22.427172
Batistin Bour
1Département de Géomatique appliquée, Centre d’Application et de Recherche en Télédétection (CARTEL), Université de Sherbrooke, 2500 Boulevard de l’Université, Sherbrooke, QC JIK 2R1, Canada
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Victor Danneyrolles
1Département de Géomatique appliquée, Centre d’Application et de Recherche en Télédétection (CARTEL), Université de Sherbrooke, 2500 Boulevard de l’Université, Sherbrooke, QC JIK 2R1, Canada
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  • For correspondence: victor.danneyrolles@usherbrooke.ca
Yan Boucher
2Direction de la recherche forestière, Ministère des Forêt, de la Faune et des Parcs du Québec, 2700 rue Einstein, Québec, QC G1P 3W8, Canada
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Richard A. Fournier
1Département de Géomatique appliquée, Centre d’Application et de Recherche en Télédétection (CARTEL), Université de Sherbrooke, 2500 Boulevard de l’Université, Sherbrooke, QC JIK 2R1, Canada
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Luc Guindon
3Natural Resources Canada, Canadian Forest Service, Laurentian Forestry Centre, 1055 du P.E.P.S., St. Sainte-Foy, P.O. Box 10380, Québec, QC G1V 4C7, Canada
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Abstract

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 Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted January 22, 2021.
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Modeling post-logging height growth of black spruce forests by combining airborne LiDAR and historical forestry maps in eastern Canadian boreal forest
Batistin Bour, Victor Danneyrolles, Yan Boucher, Richard A. Fournier, Luc Guindon
bioRxiv 2021.01.22.427172; doi: https://doi.org/10.1101/2021.01.22.427172
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Modeling post-logging height growth of black spruce forests by combining airborne LiDAR and historical forestry maps in eastern Canadian boreal forest
Batistin Bour, Victor Danneyrolles, Yan Boucher, Richard A. Fournier, Luc Guindon
bioRxiv 2021.01.22.427172; doi: https://doi.org/10.1101/2021.01.22.427172

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