Partitioning biomass in Canadian boreal forests with fine-scale structure characterization from terrestrial laser scanners

利用地面激光扫描仪的精细结构表征来划分加拿大北方森林的生物量

• 批准号: 566759-2021
• 负责人: Béland, Martin
• 金额: $ 1.82万
• 依托单位: Université Laval
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=721308
• 关键词: Partitioning; biomass; Canadian; boreal; forests

Precise tree-level estimates of biomass partitioning are fundamental to compiling forest resource inventories, carbon accounting or wood products. Without certainty in biomass inventories, it becomes difficult to make capital investments in bioeconomy projects and limits forest sector potential. As of today, several provinces and territories rely on traditional forest inventory for spatializing biomass estimates. This project aims at producing high value information from terrestrial lidar (TLS) data of two types: 1) wood and leaf biomass portioning, and 2) forest fire combustible estimation. It is thus guided by two research questions: 1) Can TLS be used to replace current destructive measurements to provide species-specific models of biomass partitioning (stem, branch, and leaf) while retaining an accuracy requirement of 15%? And 2) Can broader and better fine-scale structure characterization support the next generation fire behavior products to better characterize and map fuel types, which in turn would allow better predictions of fire behavior and risk at national scale? The principal objective is to implement a suite of validated algorithms into an operational, open-access, software pipeline with systematic output controls (human-assisted retroaction) to facilitate the processing of TLS data for characterizing forest structure and estimate tree biomass partitioning. The pipeline will implement four main algorithms to process TLS point clouds of forest plots: (1) segmentation of individual tree crown, (2) classification of wood vs. leaf material, (3) characterization of the stem form and branching structure, and (4) estimation of the leaf area index (LAI) of trees. The collaborative, inter-disciplinary research project presented here will address knowledge gaps and generate significant outcomes for the Canadian forest sector, including: a) improved biomass supply access, security and competitiveness, b) an improved anticipation and response to forest wildfire risks, and c) training the future workforce, with an emphasis on diversity.

对树木生物量分配的精确估计是编制森林资源清单、碳核算或木材产品的基础。如果生物量清单没有确定性，就很难对生物经济项目进行资本投资，并限制了森林部门的潜力。截至目前，一些省份和地区依靠传统的森林清查来估算生物量的空间。本项目旨在从地面激光雷达（TLS）数据中获得高价值的信息：1)木材和叶片生物量分配，2)森林可燃物估算。因此，有两个研究问题指导：1)TLS是否可以用来取代目前的破坏性测量，以提供物种特定的生物量分配模型（茎、枝和叶），同时保持15%的精度要求？2)更广泛和更好的精细尺度结构表征是否能支持下一代火灾行为产品更好地表征和绘制燃料类型，从而更好地预测全国范围内的火灾行为和风险？主要目标是将一套经过验证的算法实现到一个可操作的、开放访问的、具有系统输出控制（人工辅助回溯）的软件管道中，以促进TLS数据的处理，以表征森林结构和估计树木生物量分配。该管道将实现四种主要算法来处理森林样地的TLS点云：(1)单个树冠的分割，(2)木材与叶片材料的分类，(3)茎形和分支结构的表征，以及(4)树木叶面积指数（LAI）的估计。本次提出的跨学科合作研究项目将为加拿大林业部门填补知识空白，并产生重大成果，包括：a)改善生物质供应渠道、安全性和竞争力，b)提高对森林野火风险的预测和应对能力，以及c)培训未来的劳动力，重点是多样性。