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VOXEL-BASED APPROACH FOR ESTIMATING URBAN TREE VOLUME FROM TERRESTRIAL LASER SCANNING DATA

机译：基于体素的估算从地面激光扫描数据估算城市树卷的方法

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The importance of single trees and the determination of related parameters has been recognized in recent years, e.g. for forest inventories or management. For urban areas an increasing interest in the data acquisition of trees can be observed concerning aspects like urban climate, CO_(2) balance, and environmental protection. Urban trees differ significantly from natural systems with regard to the site conditions (e.g. technogenic soils, contaminants, lower groundwater level, regular disturbance), climate (increased temperature, reduced humidity) and species composition and arrangement (habitus and health status) and therefore allometric relations cannot be transferred from natural sites to urban areas. To overcome this problem an extended approach was developed for a fast and non-destructive extraction of branch volume, DBH (diameter at breast height) and height of single trees from point clouds of terrestrial laser scanning (TLS). For data acquisition, the trees were scanned with highest scan resolution from several (up to five) positions located around the tree. The resulting point clouds (20 to 60 million points) are analysed with an algorithm based on voxel (volume elements) structure, leading to an appropriate data reduction. In a first step, two kinds of noise reduction are carried out: the elimination of isolated voxels as well as voxels with marginal point density. To obtain correct volume estimates, the voxels inside the stem and branches (interior voxels) where voxels contain no laser points must be regarded. For this filling process, an easy and robust approach was developed based on a layer-wise (horizontal layers of the voxel structure) intersection of four orthogonal viewing directions. However, this procedure also generates several erroneous "phantom" voxels, which have to be eliminated. For this purpose the previous approach was extended by a special region growing algorithm. In a final step the volume is determined layer-wise based on the extracted branch areas A_(i) of this horizontal cross-section multiplied by the thickness of the voxel layer. A significant improvement of this method could be obtained by a reasonable determination of the threshold for excluding sparsely filled voxels for noise reduction which can be defined based on the function change of filled voxels. Field measurements were used to validate this method. For a quality assessment nine deciduous trees were selected for control and were scanned before felling and weighing. The results are in good accordance to the control trees within a range of only -5.1% to +14.3%. The determined DBH values show only minor deviations, while the heights of trees are systematically underestimated, mainly due to field measurements. Possible error sources including gaps in surface voxels, influence of thin twigs and others are discussed in detail and several improvements of this approach are suggested. The advantages of the algorithm are the robustness and simple structure as well as the quality of the results obtained. The drawbacks are the high effort both in data acquisition and analysis, even if a remarkable data reduction can be obtained by the voxel structure.
机译：近年来，单棵树和相关参数的确定的重要性已经认识到，例如，已经认识到。对于森林库存或管理。对于城市地区，有关城市气候，CO_（2）余额和环境保护等方面，可以观察到树木采集的日益增长的兴趣。城市树木与现场条件（例如介绍土壤，污染物，较低的地下水位，常规干扰），气候（增加温度，降低湿度）和物种组成和排列（习惯和健康状况）以及各种各样的关系不能从天然网站转移到城市地区。为了克服这一问题，开发了一种扩展的方法，用于快速和无损提取分支体积，DBH（乳房高度）和从地面激光扫描点（TLS）点云的单树高度。对于数据采集，从位于树周围的几个（最多五个）位置，使用最高扫描分辨率扫描树木。通过基于体素（体积元素）结构的算法分析所得到的点云（20至60百万点），导致适当的数据减少。在第一步中，进行两种降噪：消除隔离的体素以及具有边缘点密度的体素。为了获得正确的体积估计，茎和分支内的体素（内部体素）必须被视为不含激光点的阀杆和分支（内部体素）。对于该填充过程，基于四个正交观察方向的层面（体素结构的水平层）开发了一种简单且鲁棒的方法。然而，该过程还产生了几个错误的“幻影”体素，必须消除。为此目的，通过特殊区域生长算法扩展了先前的方法。在最终步骤中，基于该水平横截面的提取的分支区域A_（i）乘以体素层的厚度，该体积是基于体积横截面的厚度。通过合理测定阈值可以通过用于噪声降低的稀疏填充体素来获得的可显着改善，可以基于填充体素的函数变化来定义。现场测量用于验证此方法。对于质量评估，选择九棵落叶树进行对照，并在砍伐和称重之前扫描。结果符合对照树的范围内仅为-5.1％至+ 14.3％。所确定的DBH值仅显示轻微的偏差，而树的高度是系统地低估的，主要是由于现场测量值。可以详细讨论包括表面体素中的间隙，薄枝和其他物体的影响的可能误差来源，并提出了几种改进这种方法的改进。算法的优点是鲁棒性和简单的结构以及所获得的结果的质量。缺点是数据采集和分析中的高努力，即使可以通过体素结构获得显着的数据还原。

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