Non-destructive tree volume estimation through quantitative structure modelling: Comparing UAV laser scanning with terrestrial LIDAR
Non-destructive tree volume estimation through quantitative structure modelling: Comparing UAV laser scanning with terrestrial LIDAR
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DOI：
10.1016/j.rse.2019.111355
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发表时间：
2019-11-01
影响因子：
13.5
通讯作者：
Kooistra, Lammert
中科院分区：
文献类型：
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作者：
Brede, Benjamin;Calders, Kim;Kooistra, Lammert
地上生物质 (AGB) 产品校准和验证需要百米尺度的地面参考图，以匹配星载任务的分辨率。传统的森林清查方法使用异速生长方程进行单树 AGB 估算，存在偏差且准确性较低，尤其是在处理大树时。地面激光扫描 (TLS) 和显式树木建模显示出直接估计树木体积的巨大潜力，但代价是需要大量的实地工作。本研究旨在评估新型无人机激光扫描 (UAV-LS) 是否可以克服这一限制，同时提供可比较的结果。为此，在荷兰温带森林中测试了 UAV-LS 与 TLS 进行显式树木建模的性能比较。总共对 5 个林分的 200 棵胸径 (DBH) 范围为 6 至 91 厘米的树木（包括针叶树和落叶树种）进行了扫描、分割，随后使用 TreeQSM 进行了建模。 TreeQSM 是一种从激光扫描仪点云构建显式树模型的方法。与TLS衍生模型的直接比较表明，UAV-LS可靠地模拟了成熟山毛榉和橡树林中直径> 30 cm的树干和树枝的体积，一致性相关系数（CCC）为0.85，RMSE为1.12 m（3）。包含较小的分支体积会导致相当大的高估，并且 CCC 对应值下降 0.51，RMSE 增加至 6.59 m(3)。在挪威云杉林中，密集的林分阻碍了树干的感知，并进一步将 CCC 降低至 0.36。此外，幼小的小树也会造成问题，因为无法正确描绘树干周长，并将 CCC 降低至 0.01。这种对支架的依赖性表明冠层结构对 UAV-LS 体积建模能力有很大影响。改进的飞行路径、重复的捕获飞行或替代建模策略可以提高 UAV-LS 在这些条件下的建模性能。这项研究有助于使用 UAV-LS 在与卫星 AGB 产品校准和验证相关的尺度上进行快速树木体积和 AGB 估计。
Above-Ground Biomass (AGB) product calibration and validation require ground reference plots at hectometric scales to match space-borne missions' resolution. Traditional forest inventory methods that use allometric equations for single tree AGB estimation suffer from biases and low accuracy, especially when dealing with large trees. Terrestrial Laser Scanning (TLS) and explicit tree modelling show high potential for direct estimates of tree volume, but at the cost of time demanding fieldwork. This study aimed to assess if novel Unmanned Aerial Vehicle Laser Scanning (UAV-LS) could overcome this limitation, while delivering comparable results. For this purpose, the performance of UAV-LS in comparison with TLS for explicit tree modelling was tested in a Dutch temperate forest. In total, 200 trees with Diameter at Breast Height (DBH) ranging from 6 to 91 cm from 5 stands, including coniferous and deciduous species, have been scanned, segmented and subsequently modelled with TreeQSM. TreeQSM is a method that builds explicit tree models from laser scanner point clouds. Direct comparison with TLS derived models showed that UAV-LS reliably modelled the volume of trunks and branches with diameter > 30 cm in the mature beech and oak stand with Concordance Correlation Coefficient (CCC) of 0.85 and RMSE of1.12 m(3). Including smaller branch volume led to a considerable overestimation and decrease in correspondence to CCC of 0.51 and increase in RMSE to 6.59 m(3). Denser stands prevented sensing of trunks and further decreased CCC to 0.36 in the Norway spruce stand. Also small, young trees posed problems by preventing a proper depiction of the trunk circumference and decreased CCC to 0.01. This dependence on stand indicated a strong impact of canopy structure on the UAV-LS volume modelling capacity. Improved flight paths, repeated acquisition flights or alternative modelling strategies could improve UAV-LS modelling performance under these conditions. This study contributes to the use of UAV-LS for fast tree volume and AGB estimation on scales relevant for satellite AGB product calibration and validation.