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Benchmarking laser scanning and terrestrial photogrammetry to extract forest inventory parameters in a complex temperate forest
International Journal of Applied Earth Observation and Geoinformation ( IF 7.5 ) Pub Date : 2022-09-05 , DOI: 10.1016/j.jag.2022.102999
Daniel Kükenbrink, Mauro Marty, Ruedi Bösch, Christian Ginzler

National forest inventories (NFI) are important for the assessment of the state and development of forests. Traditional NFIs often rely on statistical sampling approaches as well as expert assessment which may suffer from observer bias and may lack robustness for time series analysis. Over the course of the last decade, close-range remote sensing techniques such as terrestrial and mobile laser scanning became ever more established for the assessment of three-dimensional (3D) forest structure. With the ongoing trend to make the systems smaller, easier to use and more efficient, the pathway is being opened for an operational inclusion of such devices within the framework of an NFI to support the traditional field assessment. Close-range remote sensing could potentially speed up field inventory work as well as increase the area in which certain parameters are assessed. Benchmarks are needed to evaluate the performance of different close-range remote sensing devices and approaches, both in terms of efficiency as well as accuracy. In this study we evaluate the performance of two terrestrial (TLS), one handheld mobile (PLS) and two drone based (UAVLS) laser scanning systems to detect trees and extract the diameter at breast height (DBH) in three plots with a steep gradient in tree and understorey vegetation density. As a novelty, we also tested the acquisition of 3D point-clouds using a low-cost action camera (GoPro) in conjunction with the Structure from Motion (SfM) technique and compared its performance with those of the more costly LiDAR devices. Among the many parameters evaluated in traditional NFIs, the focus of the performance evaluation of this study is set on the automatic tree detection and DBH extraction.

The results showed that TLS delivers the highest tree detection rate (TDR) of up to 94.6% under leaf-off and up to 82% under leaf-on conditions and a relative RMSE (rRMSE) for the DBH extraction between 2.5 and 9%, depending on the undergrowth complexity. The tested PLS system (leaf-on) achieved a TDR of up to 80% with an rRMSE between 3.7 and 5.8%. The tested UAVLS systems showed lowest TDR of less than 77% under leaf-off and less than 37% under leaf-on conditions. The novel GoPro approach achieved a TDR of up to 53% under leaf-on conditions. The reduced TDR can be explained by the reduced area coverage due to the chosen circular acquisition path taken with the GoPro approach. The DBH extraction performance on the other hand is comparable to those of the LiDAR devices with an rRMSE between 2 and 9%.

Further benchmarks are needed in order to fully assess the applicability of these systems in the framework of an NFI. Especially the robustness under varying forest conditions (seasonality) and over a broader range of forest types and canopy structure has to be evaluated.



中文翻译:

基准激光扫描和地面摄影测量法提取复杂温带森林中的森林清单参数

国家森林清单 (NFI) 对于评估森林状况和发展非常重要。传统的 NFI 通常依赖于统计抽样方法以及专家评估,这可能会受到观察者偏见的影响,并且可能缺乏时间序列分析的稳健性。在过去十年中,用于评估三维 (3D) 森林结构的近距离遥感技术(如地面和移动激光扫描)变得越来越成熟。随着使系统更小、更易于使用和更高效的持续趋势,正在为在 NFI 框架内包含此类设备以支持传统现场评估的操作开辟道路。近距离遥感可能会加速实地清查工作,并增加评估某些参数的区域。需要基准来评估不同近距离遥感设备和方法的性能,包括效率和准确性。在这项研究中,我们评估了两个地面 (TLS)、一个手持移动 (PLS) 和两个基于无人机 (UAVLS) 的激光扫描系统的性能,以检测树木并在三个具有陡峭梯度的地块中提取胸径 (DBH)树木和林下植被密度。作为一项创新,我们还测试了使用低成本运动相机 (GoPro) 结合运动结构 (SfM) 技术来获取 3D 点云,并将其性能与更昂贵的 LiDAR 设备的性能进行比较。

结果表明,TLS 在离叶条件下提供高达 94.6% 的最高树木检测率 (TDR),在有叶条件下高达 82%,并且 DBH 提取的相对 RMSE (rRMSE) 在 2.5 和 9% 之间,取决于灌木丛的复杂性。经测试的 PLS 系统(单机)实现了高达 80% 的 TDR,rRMSE 介于 3.7 和 5.8% 之间。测试的 UAVLS 系统在离叶条件下显示低于 77% 的最低 TDR,在开叶条件下低于 37%。新颖的 GoPro 方法在叶开条件下实现了高达 53% 的 TDR。减少的 TDR 可以用 GoPro 方法所选择的圆形采集路径减少的区域覆盖来解释。另一方面,DBH 提取性能与 rRMSE 介于 2% 和 9% 之间的 LiDAR 设备相当。

为了充分评估这些系统在 NFI 框架中的适用性,需要进一步的基准测试。尤其是在不同森林条件(季节性)和更广泛的森林类型和树冠结构下的稳健性必须进行评估。

更新日期:2022-09-06
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