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: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=587850
• 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形扫描来探测其周围环境的仪器，每个点都有距离和反射率值。产生的3D云提供了一个明确的，但模糊的，周围对象的再现。即使TLiDAR技术是我们探测环境能力的重要一步，但该工具仍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，数据处理方法受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受控环境（如种植园）中开发算法。有必要进行许多新的开发，以利用TLiDAR在天然森林环境中的能力，特别是支持增强的森林清查。因此，该研究计划旨在开发在天然森林环境中使用TLiDAR所需的方法，以估计大量的属性，这些属性要么目前在森林清查中不可用，要么目前可用但测量不佳。我们还旨在开发程序和算法，以克服TLiDAR在森林应用中的主要局限性。 我提出了一个研究计划，解决了四类与在天然林中使用TLiDAR相关的科学问题：（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指标在森林生态学中的有用关系，以及（4）将地块级数据的空间概括为大面积制图。首先，在林业中使用TLiDAR数据的主要限制是遮挡效应，这导致不透明物体后面的盲区。在森林等复杂环境中，遮挡的影响是显著的，并且具有高度的空间可变性;因此，它会严重影响结果。其他限制包括多扫描地理参考，风的影响，以及非常大的数据集的数据处理。处理TLiDAR限制的一种解决方案是使用点云的体素（3D立方体）表示来基于记录的回波和扫描仪的特性来归一化信息。其次，大多数处理3D点云的算法不是为了估计森林结构属性而设计的。算法需要开发的许多属性：树干直径从地面到活冠，树高，树冠尺寸和空间密度。激光雷达也特别适用于方法开发，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的值。第三，大量的结构指标或（估计）结构属性可以为林分。这些指标/属性是有用的，以提高我们的能力来预测其他林分属性，如木材纤维属性，林分生长潜力，或栖息地的适合性。第四，在样地水平上拍摄的TLiDAR需要用于支持大面积制图。我们提出了几项重点研究：（1）一种新的方法测试涉及树结构模型，（2）结构属性的估计（例如树干直径，树冠尺寸，叶面积和林分开度），（3）将LiDAR指标与预测木材纤维属性联系起来的方法，（4）树种识别或树木/林分活力评估，（5）连接地面和航空数据，以及（6）使用本地（地块级）数据进行空间概括。