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-2014-04508
• 负责人: Fournier, Richard
• 金额: $ 2.19万
• 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: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=633968
• 关键词: Methods; development; using; terrestrial; LiDAR

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.

陆生雨（光检测和范围）或小三下是一种仪器，其周围环境的半球扫描由数百万点组成，每个点都有距离和反射率值。最终的3D云提供了周围对象的显式（尽管模糊，但模糊不清）。即使Tlidar技术是我们探究环境的能力迈出的重要一步，该工具的局限性需要克服。这些局限性随环境的复杂性而增长。机载激元被广泛使用，数据处理的方法受益于全球大量出版物和专业知识。相反，关于小铁的研究和专业知识的主要优势。大多数在森林环境中使用Tlidar使用的研究从孤立的树木或种植园等受控环境中开发了算法。有许多新的发展需要利用天然森林环境中的小铁的能力，尤其是为了支持增强的森林库存。该研究计划旨在开发在天然森林环境中使用小铁的方法来估计大量属性所需的方法，这些属性目前尚未在森林库存中可用，或者目前可用但效果不佳。我们还旨在开发程序和算法，以克服森林应用中小铁的主要局限性。我提出了一项研究计划，该计划解决了与在天然林中使用tlidar有关的四个类别的科学问题：（1）克服tlidar数据的局限性，（2）开发新方法来测量树/架子的结构，（3）使用LIDAR指标用于森林生态中的有用关系，以及（4）（4）（4）（4）绘图绘图的空间属性数据绘制了大区域的空间态度。首先，在林业中使用小铁数据的主要局限性是遮挡效应，这导致不透明物体后面的盲区。像森林这样的复杂环境中闭塞的作用是显着的，并且在空间上是高度可变的。因此，它可以严重偏向结果。其他局限性包括Multiscans的地球群，风的效果以及非常大的数据集的数据处理。处理小标志限制的一种解决方案是使用点云的体素（3D立方体）表示，以根据记录的回报和扫描仪的特征来归一化信息。其次，大多数处理3D点云的算法并非旨在估计森林结构属性。需要针对许多属性开发算法：从地面到活的冠，树高和树冠尺寸和空间密度的躯干直径。特利达（Tlidar）也特别适应方法开发，以估计林分的总叶面积，否则很难从直接/破坏性或间接方法中提取这一值。第三，可以提供大量的结构指标或（估计的）结构属性。这些指标/属性可用于提高我们预测其他林分属性（如木纤维属性，生长潜力或栖息地的适用性）的能力。再过，需要将小铁路带到地块水平上，以支持绘制大面积。我们提出了一些重点研究：（1）一种涉及树木建筑模型的方法测试的新方法，（2）估计结构属性（例如，直径，躯干直径，冠状尺寸，叶子面积和敞开的开放度），（3）方法，（3）链接到lidar属性的方法，并预测木纤维属性，（4）物种识别（4）物种的链接（4）物种的链接（4），（4）物种的链接（4），（4）物种的链接（4），（4）物种的链接（4）。空气传播的数据和（6）使用局部（情节级）数据进行空间泛化。