Estimation of individual stem volume and diameter from segmented UAV laser scanning datasets in Pinus taeda L. plantations
Estimation of individual stem volume and diameter from segmented UAV laser scanning datasets in Pinus taeda L. plantations
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DOI：
10.1080/01431161.2022.2161853
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发表时间：
2023-01
影响因子：
3.4
通讯作者：
M. Sumnall;T. Albaugh;David R. Carter;R. Cook;W. Hession;O. Campoe;R. Rubilar;R. Wynne;V. Thomas-V.
中科院分区：
文献类型：
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作者：
M. Sumnall;T. Albaugh;David R. Carter;R. Cook;W. Hession;O. Campoe;R. Rubilar;R. Wynne;V. Thomas-V.
摘要：单棵树周围的竞争性邻域对其胸径 (DBH) 和单棵树干体积 (SV) 具有显着影响。传统的实地活动中很少记录距离相关的竞争指数指标，因为它们很难收集并且空间有限。遥感数据可以克服这些限制，同时提供大面积森林属性的估计。我们使用无人飞行器激光扫描数据来描绘单个树冠（ITC），并计算树冠大小和距离相关的竞争指数来估计 DBH 和 SV。我们对比了两种方法：(i) 随机森林 (RF) 和 (ii) 向后逐步线性多元回归 (LMR)。我们利用了火炬松人工林中的现有实验，包括多种种植密度、基因型和造林水平。虽然树木种植密度确实影响了 ITC 的正确划分，但 61% 至 99%（平均 86%）与种植位置正确相关。最准确的 RF 和 LMR 模型都包含与 ITC 规模和竞争性邻里相关的指标。 RF 和 LMR 的 DBH 估计值相似：RMSE 分别为 3.05 和 3.13 cm（R2 0.64 和 0.62）。 RF 的 SV 估计值略好于 LMR：RMSE 分别为 0.06 和 0.07 m3（R2 0.77 和 0.70）。我们的结果证明，在分析激光扫描数据时，ITC 规模和竞争指数指标可以提高 DBH 和 SV 估计的准确性。提供准确且近乎完整的森林清查的能力对于森林管理规划具有巨大的潜力。
ABSTRACT The competitive neighbourhood surrounding an individual tree can have a significant influence on its diameter at breast height (DBH) and individual tree stem volume (SV). Distance dependent competition index metrics are rarely recorded in traditional field campaigns because they are laborious to collect and are spatially limited. Remote sensing data could overcome these limitations while providing estimation of forest attributes over a large area. We used unoccupied aerial vehicle laser scanning data to delineate individual tree crowns (ITCs) and calculated crown size and distance-dependent competition indices to estimate DBH and SV. We contrasted two methods: (i) Random Forest (RF) and (ii) backwards-stepwise, linear multiple regression (LMR). We utilized an existing experiment in Pinus taeda L. plantations including multiple planting densities, genotypes and silvicultural levels. While the tree planting density did affect the correct delineation of ITCs, between 61% and 99% (mean 86%) were correctly linked to the planting location. The most accurate RF and LMR models all included metrics related to ITC size and competitive neighbourhood. The DBH estimates from RF and LMR were similar: RMSE 3.05 and 3.13 cm (R2 0.64 and 0.62), respectively. Estimates of SV from RF were slightly better than for LMR: RMSE 0.06 and 0.07 m3 (R2 0.77 and 0.70), respectively. Our results provide evidence that ITC size and competition index metrics may improve DBH and SV estimation accuracy when analysing laser-scanning data. The ability to provide accurate, and near-complete, forest inventories holds a great deal of potential for forest management planning.