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

陆地LiDAR(光探测和测距)或TLiDAR是一种通过由数百万个点组成的半球扫描来探测其周围环境的仪器，每个点都有一个距离和一个反射率值。生成的3D云提供了周围对象的清晰但模糊的再现。即使TLiDAR技术是我们探测环境能力的重要一步，该工具也有需要克服的局限性。这些限制随着环境的复杂性而增加。 机载激光雷达被广泛使用，数据处理方法得益于世界各地的大量出版物和专业知识。相反，关于TLiDAR的研究和专业知识要少得多。大多数在森林环境中使用TLiDAR的研究都是从孤立的树木或在种植园等受控环境中开发算法。需要有许多新的发展，以利用TLiDAR在自然森林环境中的能力，特别是支持加强森林清查。因此，这项研究计划旨在开发在天然森林环境中使用TLiDAR来估计大量属性所需的方法，这些属性要么是目前森林调查中没有的，要么是目前可用的但衡量不力的。我们还致力于开发程序和算法，以克服TLiDAR在森林应用中的主要限制。 我提出了一个研究计划，解决与在天然林中使用TLiDAR有关的四类科学问题：(1)克服TLiDAR数据的局限性，(2)开发新的方法来测量树木/林分结构，(3)使用LiDAR度量在森林生态中的有用关系，以及(4)将地块级别的数据空间概括为大范围制图。首先，TLiDAR数据在林业中应用的主要限制是遮挡效应，这会导致不透明目标后面的盲区。在森林这样的复杂环境中，遮挡的影响是显著的，并且在空间上是高度可变的；因此，它可能会严重地偏离结果。其他限制包括多扫描地理配准、风的影响以及对超大型数据集的数据处理。处理TLiDAR限制的一种解决方案是使用点云的体素(3D立方体)表示，以基于记录的返回和扫描仪的特征来归一化信息。其次，大多数处理三维点云的算法并不是为了估计森林结构属性而设计的。需要为许多属性开发算法：从地面到活树冠的树干直径、树高、树冠尺寸和空间密度。TLiDAR还特别适合于方法开发，以估计林分的总叶面积，否则很难从直接/破坏或间接方法中提取这个值。第三，可以为展台提供大量的结构指标或(估计的)结构属性。这些指标/属性有助于提高我们预测其他林分属性的能力，如木材纤维属性、林分生长潜力或生境适宜性。第四，需要使用地块级别的TLiDAR来支持大范围的制图。我们提出了几个重点研究：(1)涉及树木结构模型的方法测试的新方法；(2)从TLiDAR点云估计结构属性(如树干直径、树冠尺寸、叶面积和林分开度)；(3)连接LiDAR度量以预测木材纤维属性的方法；(4)树木/林分活力的物种识别或评估；(5)连接地面和航空数据；以及(6)使用当地(地块级别)数据进行空间综合。

Terrestrial LiDAR (Light Detection and Ranging) or TLiDAR is an instrument probing its surroundings with a hemispherical scan composed of several million points, each of which has a distance and a reflectance value. The resulting 3D cloud provides an explicit, although fuzzy, rendition of the surrounding objects. Even if TLiDAR technology is an important step forward in our ability to probe our environments, this tool has limitations that need to be overcome. These limitations grow with the complexity of the environment. Airborne LiDAR is widely used and the methods for data processing benefit from a large pool of publications and expertise worldwide. Conversely, studies and expertise on TLiDAR are much less predominant. Most studies using TLiDAR in forest environments developed algorithms from isolated trees or within a controlled environment like a plantation. There is a need for many new developments to take advantage of the capabilities of the TLiDAR in natural forest environments, particularly to support enhanced forest inventory. Thus this research program aims to develop the methods required to use TLiDAR in natural forest environments to estimate a large array of attributes, which either are not currently available in the forest inventory or are currently available but poorly measured. We also aim to develop procedures and algorithms to overcome the main limitations of TLiDAR in forest applications. I propose a research program that tackles the four categories of scientific questions related to the use of TLiDAR in natural forests: (1) overcoming the limitations of TLiDAR data, (2) developing new methods to measure tree/stand structure, (3) using LiDAR metrics for useful relationships in forest ecology, and (4) the spatial generalization of plot-level data to large area mapping. Firstly, the main limitation for the use of TLiDAR data in forestry is the occlusion effect, which results in blind zones behind opaque objects. The effect of occlusion in a complex environment like a forest is significant and highly spatially variable; consequently, it can seriously bias the results. Other limitations include multiscans georeference, the effect of wind, and data processing of very large datasets. One solution to handle TLiDAR limitations is the use of a voxel (3D cube) representation of the point cloud to normalize the information based on the recorded returns and the scanner’s characteristics. Secondly, most algorithms dealing with 3D point clouds are not designed to estimate forest structural attributes. Algorithms need to be developed for many attributes: trunk diameter from the ground up to the live crown, tree height, and tree crown dimensions and spatial density. TLiDAR is also particularly well adapted for method development to estimate the total leaf area of a stand, a value otherwise difficult to extract from either direct/destructive or indirect methods. Thirdly, a large number of structural metrics or (estimated) structural attributes can be made available for the stand. These metrics/attributes are useful to improve our ability to predict other stand attributes like the wood fiber attributes, stand growth potential, or habitat suitability. Forthly, TLiDAR taken at the plot level need to be used in support of mapping large area. We propose several focused studies on: (1) a new approach for method testing involving tree architectural models, (2) estimation of structural attributes (e.g. trunk diameter, crown dimensions, leaf area & stand openness) from the TLiDAR point cloud, (3) methods linking LiDAR metrics to predict wood fiber attributes, (4) species identification or assessment of tree/stand vigor, (5) linking terrestrial and airborne data, and (6) use of local (plot-level) data for spatial generalization.