Forest structural diversity metrics describing diversity in tree size and crown shape within forest stands can be used as indicators of biodiversity. These diversity metrics can be generated using airborne laser scanning (LiDAR) data to provide a rapid and cost effective alternative to ground-based inspection. Measures of tree height derived from LiDAR can be significantly affected by the canopy conditions at the time of data collection, in particular whether the canopy is under leaf-on or leaf-off conditions, but there have been no studies of the effects on structural diversity metrics. The aim of this research is to assess whether leaf-on/leaf-off changes in canopy conditions during LiDAR data collection affect the accuracy of calculated forest structural diversity metrics. We undertook a quantitative analysis of LiDAR ground detection and return height, and return height diversity from two airborne laser scanning surveys collected under leaf-on and leaf-off conditions to assess initial dataset differences. LiDAR data were then regressed against field-derived tree size diversity measurements using diversity metrics from each LiDAR dataset in isolation and, where appropriate, a mixture of the two. Models utilising leaf-off LiDAR diversity variables described DBH diversity, crown length diversity and crown width diversity more successfully than leaf-on (leaf-on models resulted in R² values of 0.66, 0.38 and 0.16, respectively, and leaf-off models 0.67, 0.37 and 0.23, respectively). When LiDAR datasets were combined into one model to describe tree height diversity and DBH diversity the models described 75% and 69% of the variance (R² of 0.75 for tree height diversity and 0.69 for DBH diversity). The results suggest that tree height diversity models derived from airborne LiDAR, collected (and where appropriate combined) under any seasonal conditions, can be used to differentiate between simple single and diverse multiple storey forest structure with confidence.

描述森林林分内树木大小和树冠形状多样性的森林结构多样性指标可以用作生物多样性指标。可以使用机载激光扫描（LiDAR）数据生成这些多样性指标，以提供快速，经济高效的替代地面检查的方法。数据收集时，冠层条件会极大地影响源自LiDAR的树高测量，特别是冠层是处于叶上还是叶下条件下，但尚未研究对结构多样性的影响指标。这项研究的目的是评估LiDAR数据收集过程中冠层状况的叶上/叶下变化是否影响所计算的森林结构多样性指标的准确性。我们对LiDAR地面检测和返回高度进行了定量分析，并通过两次在叶上和叶下条件下收集的机载激光扫描调查对返回高度多样性进行了评估，以评估初始数据集的差异。然后，使用隔离的每个LiDAR数据集中的多样性指标（适当时使用两者的混合物），将LiDAR数据与场派树大小多样性测量值进行回归。使用叶状LiDAR分集变量的模型比叶上分枝更成功地描述了DBH分集，冠长分集和冠宽分集（叶分集导致 然后，使用隔离的每个LiDAR数据集中的多样性指标（适当时使用两者的混合指标），将LiDAR数据与场派树大小多样性测量值进行回归。使用叶状LiDAR分集变量的模型比叶上分枝更成功地描述了DBH分集，冠长分集和冠宽分集（叶分集导致 然后，使用隔离的每个LiDAR数据集中的多样性指标（适当时使用两者的混合物），将LiDAR数据与场派树大小多样性测量值进行回归。使用叶状LiDAR分集变量的模型比叶上分枝更成功地描述了DBH分集，冠长分集和冠宽分集（叶分集导致[R的0.66，0.38和0.16，²值，和叶断模型0.67，分别0.37和0.23，）。当将LiDAR数据集组合成一个模型来描述树的高度多样性和DBH多样性时，这些模型描述了75％和69％的方差（对于树的高度多样性，R²为0.75，对于DBH多样性为R9）。结果表明，在任何季节条件下收集（适当时结合使用）的机载LiDAR得出的树高多样性模型可用于区分简单的单层和多层森林结构。