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

地面激光雷达（光探测和测距）或TLiDAR是一种传感器，它在半球扫描后用激光脉冲探测周围环境。从扫描返回的激光产生由数百万个点组成的云，每个点代表距离和反射率值。3D点云是探测周围物体的结果。物体的精确三维再现对于由几何形状组成的场景是有效的，但它在复杂的环境如自然森林中受到很大的限制。该研究计划旨在开发在天然森林环境中使用TLiDAR来估计大量树木和林分结构属性的方法。由此产生的算法计划作为补充，目前的库存方法或超出原位常规测量可以提供的某个时候。将解决四类科学问题，这些问题都与LiDAR传感器在天然林中的使用有关：（1）开发克服TLiDAR数据局限性的算法，（2）开发测量树木/林分结构的新方法，（3）将方法扩展到不同的LiDAR传感器，以及（4）使用LiDAR度量在森林生态学中的有用关系（例如生态服务、栖息地）。 这个发现提案是我以前关于类似主题的发现补助金的后续行动。它巩固了一些进展，并提出了在林业中使用TLiDAR的新的有前途的途径。这里提出的四个组成部分的方法解决了被认为是主要的潜在进展，并处理当前和关键的限制，在林业中使用激光雷达数据。第一类贡献涉及所有LiDAR传感器共同的数据限制，更具体地说是信号遮挡和可变采样密度。我们提出了一种使用体素的方法，以减少这两个限制的影响，使用一个数学框架来获得表面密度。其次，在过去的几年里，我们开发了一些创新的算法来评估树干，树冠和模型树架构。我们建议（i）开发一种新的树木隔离算法，适用于T-LiDAR数据，（ii）进一步推动我们使用的树架构和虚拟地块。第三，我们计划将我们的几种TLiDAR算法应用于其他LiDAR传感器类型，即移动的单元和机载LiDAR;无论是在无人机上还是在飞机上。每个传感器都有特定的配置，算法的适应将允许利用不同的平台。第四，我们希望将激光雷达的使用范围扩大到森林生态中更广泛的问题。这将导致对生态服务、森林生境和生物多样性的研究。总体而言，所有计划中的研究都是针对提高评估森林结构的能力而进行的，利用了激光雷达数据的巨大未开发潜力。

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.