An algorithm for the automatic parametrization of wood volume equations from Terrestrial Laser Scanning point clouds: application in Pinus pinaster
An algorithm for the automatic parametrization of wood volume equations from Terrestrial Laser Scanning point clouds: application in Pinus pinaster
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DOI：
10.1080/15481603.2021.1972712
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发表时间：
2021-09
影响因子：
6.7
通讯作者：
C. Prendes;Carlos Cabo;C. Ordóñez;J. Majada;E. Canga
中科院分区：
文献类型：
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作者：
C. Prendes;Carlos Cabo;C. Ordóñez;J. Majada;E. Canga
木材体积方程的参数化传统上是通过破坏性抽样进行的，这是高度资源密集型的。这些方程必须为每个物种和一组条件专门建立，这意味着在许多情况下，它们是不可行的或不存在的。在这里，我们提出了一个无损的和全自动化的方法参数化的商品量方程从陆地激光扫描（TLS）数据，其目的是适用于任何物种和林分类型。它是基于估计直径沿着干和高度的每棵树，包括一个强大的系统，自动识别和纠正异常值。该实现考虑了几种类型的体积方程，使用直径和高度估计来选择和参数化最合适的方程。该方法进行了测试，在松树pinaster阴谋与428棵树，陡坡，低枝和茂密的林下植物。结果表明，97%的树木被自动检测，树高和直径估计的RMSE分别为1.52 m和1.14 cm。自动选择体积比方程作为测试数据集的最佳选项。自动体积估计的RMSE为0.0233 m3，使用操作员审查的直径为0.0149 m3。
ABSTRACT The parametrization of wood volume equations has traditionally been carried out with destructive samplings, which are highly resource-intensive. These equations must be specifically set up for each species and set of conditions, meaning that, in many cases, they are unfeasible or non-existent. Here, we present a nondestructive and fully automated methodology for the parametrization of merchantable volume equations from terrestrial laser scanning (TLS) data, which aims at being applicable to any species and stand typology. It is based on the estimation of diameters along the stem and the height of each tree, including a robust system for the automatic identification and correction of anomalous values. The implementation considers several types of volume equations, the most suitable equation being selected and parameterized using the diameter and height estimations. The methodology was tested in a Pinus pinaster plot with 428 trees, steep slopes, low branches and dense understory. The results showed that 97% of trees were automatically detected, and RMSE of the height and diameter estimations was 1.52 m and 1.14 cm, respectively. A volume ratio equation was automatically selected as the best option for the test dataset. RMSE in automatic volume estimations was 0.0233 m3, and 0.0149 m3 using diameters reviewed by an operator.