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.

摘要

木材体积方程的参数化传统上是通过破坏性采样进行的，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设置，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，我们提出了一种用于从陆地激光扫描 (TLS) 数据中对商业体积方程进行参数化的非破坏性和全自动方法，旨在适用于任何物种和林分类型。它基于对沿茎的直径和每棵树的高度的估计，包括用于自动识别和校正异常值的强大系统。该实现考虑了几种类型的体积方程，使用直径和高度估计选择和参数化最合适的方程。该方法在一个Pinus pinaster地块有 428 棵树，陡峭的斜坡，低矮的树枝和茂密的林下。结果表明，97% 的树木被自动检测到，高度和直径估计的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。自动体积估计中的RMSE为 0.0233 m ³，使用操作员审查的直径为0.0149 m ³。