Assessing genetic gains in coastal Douglas-fir using advanced remote sensing

了解木材供应来源对加拿大的可持续森林管理至关重要。这一点的关键 ** 是选择最佳的种子再生，其中包括选择最佳的基因型，以确保 ** 最大的目标增长，产量属性和气候适应。不列颠哥伦比亚省在许多关键森林木材物种（如花旗松）的育种试验 ** 方面处于领先地位，但对田间优良树木的评估 ** 虽然是所有树木改良计划的一个组成部分，但却是一项非常广泛、昂贵和耗时的活动。机载光探测和测距（LiDAR）是一种最近的遥感技术，已被证明在林业应用中极具价值。通过利用来自飞机和无人机的超密集机载激光雷达数据，我们将提取精英树木，并获得关于树高、树冠大小和形状、分枝和树叶面积的极其详细的信息。我们将利用这些属性来评估这些遗传差异如何影响树木的结构和生长。这项研究将在不列颠哥伦比亚省温哥华岛上或附近的一些树木育种地点进行，与来自不列颠哥伦比亚省森林、土地和自然资源运营部树木改良分支的合作者一起进行，并将通过一系列遗传试验来检查树木形态的差异。项目交付成果包括从超密度LiDAR数据中提取详细的个体树冠属性的新方法，树木和树冠尺寸图，以及基于LiDAR分析的树冠特征差异分析，以及对树木生长和产量的后续影响。我们还将编制一份最佳做法指南，说明如何使用超密集激光雷达数据评估树木繁殖调查，供森林、土地和自然资源业务部清查分支用于今后的监测。

Understanding sources of wood supply is critical for sustainable forest management in Canada. Critical to this**is the selection of the optimum seed for regeneration which involves selecting the best genotypes to ensure**maximum target growth, yield attributes and climate adaptation. British Columbia is a leader in breeding trials**for a number of key forest timber species, such as Douglas-fir, yet assessment of the elite trees in the field,**while an integral component of all tree improvement programs, represents a very extensive, expensive, and**time consuming activity. Airborne Light Detection and Ranging (LiDAR) is a recent remote sensing**technology that has proven extremely valuable in forestry applications. By utilizing super-dense airborne**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.****