Subventions et des contributions :

Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2020-2021).

Understanding sources of wood supply is critical for sustainable forest management in Canada. Critical to thisx000D
is the selection of the optimum seed for regeneration which involves selecting the best genotypes to ensurex000D
maximum target growth, yield attributes and climate adaptation. British Columbia is a leader in breeding trialsx000D
for a number of key forest timber species, such as Douglas-fir, yet assessment of the elite trees in the field,x000D
while an integral component of all tree improvement programs, represents a very extensive, expensive, andx000D
time consuming activity. Airborne Light Detection and Ranging (LiDAR) is a recent remote sensingx000D
technology that has proven extremely valuable in forestry applications. By utilizing super-dense airbornex000D
LiDAR data from both aircraft and drones, we will extract elite trees and derive extremely detailed information on tree height, crown size and shape, branching and foliage area. We will utilize these attributes to then assess how these genetic differences impact tree architecture and growth. This research will be undertaken at a number of tree breeding sites on, or near, Vancouver Island, British Columbia with collaborators from the Tree Improvement Branch of the British Columbia Ministry of Forests, Lands and Natural Resource Operations to and will examine differences in tree morphology with a range of genetic trials. Project deliverables include new methods to extract detailed individual crown attributes from super-dense LiDAR data, tree and crown size maps, and analysis of differences in crown characteristics based on LiDAR analysis across the elite trees and the subsequent implication for tree growth and yield. We will also produce a best practice guide describing the use of super-dense LIDAR data for assessment of tree breeding surveys for use by the Inventory Branch of the Ministry of Forests, Lands and Natural Resource Operations for future monitoring.x000D
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