Estimates of forest stocking density per hectare (N_Ha) are important in characterising ecological conditions and assessing changes in forest dynamics after disturbances due to pyrogenic, anthropogenic and biotic factors. We use Unmanned Aircraft Systems (UAS) LiDAR with mean point density of 1485 points m⁻² across 39 flight sites to develop a bottom-up approach for individual tree and crown delineation (ITCD). The ITCD algorithm was evaluated across mixed species eucalypt forests (MSEF) using 2790 field measured stem locations across a broad range of dominant eucalypt species with randomly leaning trunks and highly irregular intertwined canopy structure. Two top performing ITCD algorithms in benchmarking studies resulted in poor performance when optimised to our plot data (mean F_score: 0.61 and 0.62), which emphasises the challenge posed for ITCD in the structurally complex conditions of MSEF. To address this, our novel bottom-up ITCD algorithm uses kernel densities to stratify the vegetation profile and differentiate understorey from the rest of the vegetation. For vegetation above understorey, the ITCD algorithm adopted a novel watershed clustering procedure on point density measures within horizontal slices. A Principal Component Analysis (PCA) procedure was then applied to merge the slice-specific clusters into trunks, branches, and canopy clumps, before a voxel connectivity procedure clustered these biomass segments into overstorey trees. The segmentation process only requires two parameters to be calibrated to site-specific conditions across 39 MSEF sites using a Shuffled Complex Evolution (SCE) optimiser. Across the 39 field sites, the ITCD algorithm had mean F_score of 0.91, True Positive (TP) trees represented 85% of measured trees and predicted plot-level stocking (N_P) averaged 94% of actual stocking (N_Ob). As a representation of plot-level basal area (BA), TP trees represented 87% of BA, omitted trees represented slightly smaller trees and made up 8% of BA, and a further 5% of BA had commission error. Spatial maps of N_Ha using 0.5 m grid-cells showed that omitted trees were more prevalent in high density forest stands, and that 63% of grid-cells had a perfect estimate of N_Ha, whereas a further 31% of the grid-cells overestimate or underestimate one tree within the search window. The parsimonious modelling framework allows for the two calibrated site-specific parameters to be predicted (R²: 0.87 and 0.66) using structural characteristics of vegetation clusters within sites. Using predictions of these two site-specific parameters across all sites results in mean F_Score of 0.86 and mean TP of 0.77, under circumstances where no ground observations were required for calibration. This approach generalises the algorithm across new UAS LiDAR data without undertaking time-consuming ground measurements within tall eucalypt forests with complex vegetation structure.

每公顷森林蓄积密度（N _Ha）的估算对于表征生态条件和评估由热原，人为和生物因素引起的扰动后森林动态的变化非常重要。我们使用平均点密度为1485点m ^-2的无人飞机系统（UAS）LiDAR在39个飞行地点进行飞行，以针对树和树冠轮廓（ITCD）制定自下而上的方法。使用2790个实地测得的，广泛分布的主要桉树物种，具有随机倾斜的树干和高度不规则交织的树冠结构的树干位置，对整个混合物种桉树林（MSEF）进行了ITCD算法评估。在基准测试研究中，两种性能最佳的ITCD算法在对我们的样地数据进行优化后（平均F_得分）导致了较差的性能：0.61和0.62），强调了在MSEF结构复杂的条件下ITCD所面临的挑战。为了解决这个问题，我们新颖的自下而上的ITCD算法使用核密度对植被剖面进行分层，并区分下层与其他植被。对于下层以上的植被，ITCD算法对水平切片内的点密度度量采用了一种新颖的分水岭聚类程序。然后，在体素连接程序将这些生物量片段聚集成高大的树木之前，应用主成分分析（PCA）程序将特定于切片的聚类合并到树干，分支和冠层丛中。使用Shuffled Complex Evolution（SCE）优化器，分割过程仅需要将两个参数校准到39个MSEF站点的站点特定条件。˚F_得分为0.91，真阳性（TP）树中表示测定树木和预测的积级长袜（85％Ñ _P）平均实际放养（94％Ñ _Ob的）。作为地块级基础面积（BA）的表示形式，TP树占BA的87％，省略树占较小的树，占BA的8％，另外BA的5％有佣金错误。使用0.5 m网格单元的N _Ha空间图表明，在高密度林分中，遗漏的树木更为普遍，并且63％的网格单元对N _Ha的估计较为理想。，而另外31％的网格单元则高估或低估了搜索窗口中的一棵树。简约建模框架允许使用站点内植被簇的结构特征来预测两个校准站点特定参数（R ²：0.87和0.66）。在不需要地面观测以进行校准的情况下，使用所有站点的这两个站点特定参数的预测结果得出的平均F_得分为0.86，平均TP为0.77。这种方法可在新的UAS LiDAR数据上推广算法，而无需在具有复杂植被结构的高桉树林中进行耗时的地面测量。