Recent advances in LiDAR sensors and robotic technologies have raised the question of whether handheld mobile laser scanning (HMLS) systems can allow for the performing of forest inventories (FIs) without the use of conventional ground measurement (CGM) techniques. However, the reliability of such an approach for forest planning applications, particularly in non-uniform forests under mountainous conditions, remains underexplored. This study aims to address these issues by assessing the accuracy of HMLS-derived data based on the calculation of basic forest attributes such as the number of trees, dominant height and basal area. To this end, near-natural forests of a national park (NE Türkiye) were surveyed using the HMLS and CGM techniques for a management plan renewal project. Taking CGM results as reference, we compared each forest attribute pair based on two datasets collected from 39 sample plots at the forest (landscape) scale. Diameter distributions and the influence of stand characteristics on HMLS data accuracy were also analyzed at the plot scale. The statistical results showed no significant difference between the two datasets for any investigated forest attributes (P > 0.05). The most and the least accurately calculated attributes were quadratic mean diameter (root mean square error (RMSE) = 1.3 cm, 4.5 per cent) and stand volume (RMSE = 93.7 m3 ha−1, 16.4 per cent), respectively. The stand volume bias was minimal at the forest scale (15.65 m3 ha−1, 3.11 per cent), but the relative bias increased to 72.1 per cent in a mixed forest plot with many small and multiple-stemmed trees. On the other hand, a strong negative relationship was detected between stand maturation and estimation errors. The accuracy of HMLS data considerably improved with increased mean diameter, basal area and stand volume values. Eventually, we conclude that many forest attributes can be quantified using HMLS at an accuracy level required by forest planning and management-related decision making. However, there is still a need for CGM in FIs to capture qualitative attributes, such as species mix and stem quality.

中文翻译：

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评估移动激光扫描对近天然林林分级森林清查的潜力

LiDAR 传感器和机器人技术的最新进展提出了一个问题，即手持式移动激光扫描 (HMLS) 系统是否可以在不使用传统地面测量 (CGM) 技术的情况下执行森林清查 (FI)。然而，这种方法在森林规划应用中的可靠性，特别是在山区条件下的非均匀森林中，仍未得到充分探索。本研究旨在通过基于基本森林属性（例如树木数量、优势高度和断面面积）的计算来评估 HMLS 衍生数据的准确性来解决这些问题。为此，使用 HMLS 和 CGM 技术对国家公园 (NE Türkiye) 的近天然森林进行了调查，用于管理计划更新项目。以CGM结果为参考，我们根据从森林（景观）尺度的 39 个样本地块收集的两个数据集比较了每个森林属性对。还在样地尺度上分析了直径分布和林分特征对 HMLS 数据准确性的影响。统计结果显示，对于任何调查的森林属性，两个数据集之间没有显着差异（P > 0.05）。计算最准确和最不准确的属性分别是二次平均直径（均方根误差 (RMSE) = 1.3 厘米，4.5%）和林分体积（RMSE = 93.7 m3 ha-1，16.4%）。森林规模的林分体积偏差最小（15.65 m3 ha-1，3.11%），但在有许多小树和多茎树木的混交林地块中，相对偏差增加到 72.1%。另一方面，在林分成熟度和估计误差之间检测到强烈的负相关关系。HMLS 数据的准确性随着平均直径、断面面积和林分体积值的增加而显着提高。最终，我们得出结论，许多森林属性可以使用 HMLS 在森林规划和管理相关决策制定所需的准确度水平上进行量化。然而，FI 中的 CGM 仍然需要捕获定性属性，例如物种组合和茎质量。