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Performance of terrestrial laser scanning to characterize managed Scots pine (Pinus sylvestris L.) stands is dependent on forest structural variation
ISPRS Journal of Photogrammetry and Remote Sensing ( IF 12.7 ) Pub Date : 2020-09-01 , DOI: 10.1016/j.isprsjprs.2020.08.017
Tuomas Yrttimaa , Ninni Saarinen , Ville Kankare , Jari Hynynen , Saija Huuskonen , Markus Holopainen , Juha Hyyppä , Mikko Vastaranta

There is a limited understanding of how forest structure affects the performance of methods based on terrestrial laser scanning (TLS) in characterizing trees and forest environments. We aim to improve this understanding by studying how different forest management activities that shape tree size distributions affect the TLS-based forest characterization accuracy in managed Scots pine (Pinus sylvestris L.) stands. For that purpose, we investigated 27 sample plots consisting of three different thinning types, two thinning intensities as well as control plots without any treatments. Multi-scan TLS point clouds were collected from the sample plots, and a point cloud processing algorithm was used to segment individual trees and classify the segmented point clouds into stem and crown points. The classified point clouds were further used to estimate tree and forest structural attributes. With the TLS-based forest characterization, almost 100% completeness in tree detection, 0.7 cm (3.4%) root-mean-square-error (RMSE) in diameter-at-breast-height measurements, 0.9–1.4 m (4.5–7.3%) RMSE in tree height measurements, and <6% relative RMSE in the estimates of forest structural attributes (i.e. mean basal area, number of trees per hectare, mean volume, basal area-weighted mean diameter and height) were obtained depending on the applied thinning type. Thinnings decreased variation in horizontal and vertical forest structure, which especially favoured the TLS-based tree detection and tree height measurements, enabling reliable estimates for forest structural attributes. A considerably lower performance was recorded for the control plots. Thinning intensity was noticed to affect more on the accuracy of TLS-based forest characterization than thinning type. The number of trees per hectare and the proportion of suppressed trees were recognized as the main factors affecting the accuracy of TLS-based forest characterization. The more variation there was in the tree size distribution, the more challenging it was for the TLS-based method to capture all the trees and derive the tree and forest structural attributes. In general, consistent accuracy and reliability in the estimates of tree and forest attributes can be expected when using TLS for characterizing managed boreal forests.



中文翻译:

陆地激光扫描以表征受管理的樟子松(Pinus sylvestris L.)林分的性能取决于森林的结构变化

在表征树木和森林环境时,对森林结构如何影响基于陆地激光扫描(TLS)的方法的性能知之甚少。我们旨在通过研究影响树形大小分布的不同森林管理活动如何影响管理型苏格兰松(樟子松)中基于TLS的森林表征精度,来提高这种理解。L.)站立。为此,我们调查了27种样地,这些样地由三种不同的稀疏类型,两种稀疏强度以及未经任何处理的对照样地组成。从样地中收集了多扫描TLS点云,并使用点云处理算法对单个树进行分割,并将分割后的点云分类为茎点和冠点。分类的点云被进一步用于估计树木和森林的结构属性。借助基于TLS的森林特征,几乎100%的树木检测完好性,乳房高度直径测量值的均方根误差(RMSE)为0.7厘米(3.4%),0.9-1.4 m(4.5-7.3)树木高度测量值中的RMSE,而森林结构属性(即平均基础面积,每公顷树木的数量,平均体积,根据所应用的稀疏类型,获得基面积加权平均直径和高度)。间伐减少了水平和垂直森林结构的变化,这尤其有利于基于TLS的树木检测和树木高度测量,从而可以对森林结构属性进行可靠的估计。对于对照图,记录的性能明显较低。人们注意到,间伐强度比间伐类型对基于TLS的森林表征准确性的影响更大。每公顷的树木数量和被抑制树木的比例被认为是影响基于TLS的森林特征描述准确性的主要因素。树木大小分布的差异更大,基于TLS的方法捕获所有树木并导出树木和森林结构属性的挑战更大。通常,当使用TLS来描述受管理的北方森林时,可以期望在树木和森林属性的估计中具有一致的准确性和可靠性。

更新日期:2020-09-01
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