Airborne laser scanning (ALS) is one of the most innovative remote sensing tools with a recognized important utility for characterizing forest stands. Currently, the most common ALS-based method applied in the estimation of forest stand characteristics is the area-based approach (ABA). The aim of this study was to analyse how three ABA methods affect growing stock volume (GSV) estimates at the sample plot and forest stand levels. We examined (1) an ABA with point cloud metrics, (2) an ABA with canopy height model (CHM) metrics and (3) an ABA with aggregated individual tree CHM-based metrics. What is more, three different modelling techniques: multiple linear regression, boosted regression trees and random forest, were applied to all ABA methods, which yielded a total of nine combinations to report. An important element of this work is also the empirical verification of the methods for estimating the GSV error for individual forest stand. All nine combinations of the ABA methods and different modelling techniques yielded very similar predictions of GSV for both sample plots and forest stands. The root mean squared error (RMSE) of estimated GSV ranged from 75 to 85 m3 ha−1 (RMSE% = 20.5–23.4 per cent) and from 57 to 64 m3 ha−1 (RMSE% = 16.4–18.3 per cent) for plots and stands, respectively. As a result of the research, it can be concluded that GSV modelling with the use of different ALS processing approaches and statistical methods leads to very similar results. Therefore, the choice of a GSV prediction method may be more determined by the availability of data and competences than by the requirement to use a particular method.

中文翻译：

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使用各种基于 ALS 区域的方法模拟林分立木蓄积量

机载激光扫描 (ALS) 是最具创新性的遥感工具之一，在表征森林林分方面具有公认的重要用途。目前，最常用的基于 ALS 的林分特征估计方法是基于面积的方法 (ABA)。本研究的目的是分析三种 ABA 方法如何影响样地和林分水平的立木蓄积量 (GSV) 估计值。我们检查了 (1) 具有点云指标的 ABA，(2) 具有冠层高度模型 (CHM) 指标的 ABA，以及 (3) 具有聚合的基于 CHM 的单个树指标的 ABA。更重要的是，三种不同的建模技术：多元线性回归、增强回归树和随机森林，被应用于所有 ABA 方法，总共产生了九种组合来报告。这项工作的一个重要组成部分也是对估计单个林分的 GSV 误差的方法的经验验证。ABA 方法和不同建模技术的所有九种组合对样本地块和林分都产生了非常相似的 GSV 预测。估计 GSV 的均方根误差 (RMSE) 范围为 75 至 85 m3 ha-1 (RMSE% = 20.5-23.4%) 和 57 至 64 m3 ha-1 (RMSE% = 16.4-18.3%)地块和立场，分别。作为研究的结果，可以得出结论，使用不同的 ALS 处理方法和统计方法进行 GSV 建模会导致非常相似的结果。因此，GSV 预测方法的选择可能更多地取决于数据的可用性和能力，而不是使用特定方法的要求。