Methods development using terrestrial LiDAR for the assessment of forest structures at the tree and stand levels

使用地面激光雷达评估树木和林分水平森林结构的方法开发

• 批准号: RGPIN-2020-05780
• 负责人: Fournier, Richard
• 金额: $ 2.62万
• 依托单位: Université de Sherbrooke
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=755077
• 关键词: Methods; development; using; terrestrial; LiDAR

Terrestrial LiDAR (Light Detection and Ranging) or TLiDAR is a sensor probing its surroundings with laser pulses following a hemispherical scan. The laser returns from the scan produce a cloud composed of several million points, each representing a distance and a reflectance value. The 3D point cloud results from probing the surrounding objects. Exact 3D rendition of objects is efficient for scenes composed of geometric forms, but it is strongly limited in complex environments such as natural forest. This research program aims to develop methods for the use TLiDAR in natural forest environments to estimate a large array of tree and stand structural attributes. The resulting algorithms are planned as a complement of current inventory methods or sometime beyond what in situ conventional measurements can provide. Four categories of scientific questions will be addressed, which are all related to the use of LiDAR sensors in natural forests: (1) develop algorithms overcoming the limitations of TLiDAR data, (2) develop new methods to measure tree/stand structure, (3) expand the methods towards application to different LiDAR sensors, and (4) use the LiDAR metrics for useful relationships in forest ecology (e.g. ecological services, habitat). This Discovery proposal is a follow-up of my previous Discovery grant on a similar topic. It consolidates some advances and it proposes new promising avenues to use TLiDAR in forestry. The four-component approach proposed here addresses what was found as the main potential advances and to deal with current and critical limitations for the use of TLiDAR data in forestry. The first category of contributions deals with data limitations common to all LiDAR sensors, more specifically for signal occlusion and variable sampling density. We propose an approach using voxel to reduce the impact of these two limitations with the use of a mathematical framework to derive surface density. Secondly, in the past years we developed several innovative algorithms to assess tree stems, tree crowns and model tree architecture. We propose (i) developing a new tree isolation algorithm adapted to T-LiDAR data, and (ii) pushing further our use of tree architecture and virtual plots. Thirdly, we plan to adapt several of our TLiDAR algorithms to other LiDAR sensors types, namely to mobile units and to airborne LiDAR; either on an Unmanned Airborne Vehicle or on an airplane. Each sensor having specific configurations, the adaptation of the algorithms will allow taking advantage of different platforms. Fourthly, we wish to expand the use of TLiDAR to broader issues in forest ecology. This will lead to studies dealing with ecological services, forest habitat and biodiversity. Overall, all the planned studies are tailored to an increased capacity to assess forest structure, taking advantage of the vast untapped potential of LiDAR data.

陆生雨（光检测和范围）或三下是一个传感器，在半球形扫描后用激光脉冲探测其周围环境。从扫描中返回激光器会产生一个由数百万点组成的云，每个点代表距离和反射率值。 3D点云是由于探测周围对象而产生的。对物体的精确3D渲染对于由几何形式组成的场景有效，但在复杂的环境（例如天然林）中受到了强烈限制。该研究计划旨在开发在天然森林环境中使用小铁的方法，以估算大量的树木和架构属性。将最终的算法计划作为当前库存方法的补充，或者超出原位常规测量所能提供的。 Four categories of scientific questions will be addressed, which are all related to the use of LiDAR sensors in natural forests: (1) develop algorithms overcoming the limitations of TLiDAR data, (2) develop new methods to measure tree/stand structure, (3) expand the methods towards application to different LiDAR sensors, and (4) use the LiDAR metrics for useful relationships in forest ecology (e.g. ecological services, habitat). 这项发现建议是我以前关于类似主题的发现赠款的后续措施。它巩固了一些进步，并提出了在林业中使用tlidar的新途径。这里提出的四组分方法介绍了发现的主要潜在进步，并应对在林业中使用Tlidar数据的当前和关键局限性。第一类贡献涉及所有激光雷达传感器常见的数据限制，更专门用于信号遮挡和可变采样密度。我们建议使用体素通过使用数学框架来推导表面密度的方法来减少这两个局限性的影响。其次，在过去的几年中，我们开发了几种创新算法来评估树茎，树冠和模型树建筑。我们建议（i）开发一种适合T-LIDAR数据的新树隔离算法，以及（ii）进一步推动我们对树架构和虚拟图的使用。第三，我们计划将几种小铁路算法适应其他LIDAR传感器类型，即移动单元和空气生激光雷达。在无人驾驶的车辆或飞机上。每个具有特定配置的传感器，算法的改编将允许利用不同的平台。第四，我们希望将小铁的使用扩展到森林生态学中更广泛的问题。这将导致研究生态服务，森林栖息地和生物多样性的研究。总体而言，所有计划的研究均量身定制，以提高评估森林结构的能力，利用LiDAR数据的巨大潜力。