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，在半球形扫描后用激光脉冲探测周围环境。激光从扫描返回产生一个由几百万个点组成的云，每个点代表一个距离和一个反射率值。三维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式组成的场景是有效的，但在复杂的环境中，如自然森林，它受到强烈的限制。本研究计划旨在开发在自然森林环境中使用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当前库存方法的补充，或者有时超出原位常规测量所能提供的范围。将解决四类科学问题，这些问题都与在天然林中使用激光雷达传感器有关：(1)开发克服TLiDAR数据限制的算法，(2)开发测量树/林分结构的新方法，(3)扩展应用于不同激光雷达传感器的方法，以及(4)使用激光雷达指标来确定森林生态中的有用关系（例如生态服务，栖息地）。这个探索项目是我之前在类似主题上获得的探索项目资助的后续。它整合了一些进展，并提出了在林业中使用TLiDAR的新的有前途的途径。这里提出的四部分方法解决了发现的主要潜在进展，并处理了在林业中使用TLiDAR数据的当前和关键限制。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来减少这两个限制的影响，并使用数学框架来推导表面密度。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的茎、树冠和模型树的结构。我们建议(i)开发一种适用于T-LiDAR数据的新的树木隔离算法，以及（ii）进一步推动我们对树结构和虚拟地块的使用。第三，我们计划将我们的几种TLiDAR算法应用于其他类型的LiDAR传感器，即移动单元和机载LiDAR；无论是在无人飞行器上还是在飞机上。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TLiDAR的使用扩展到森林生态中更广泛的问题。这将导致有关生态服务、森林生境和生物多样性的研究。总的来说，所有计划的研究都是为了提高评估森林结构的能力，利用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.