Individual Tree Segmentation and Tree Height Estimation Using Leaf-Off and Leaf-On UAV-LiDAR Data in Dense Deciduous Forests
Individual Tree Segmentation and Tree Height Estimation Using Leaf-Off and Leaf-On UAV-LiDAR Data in Dense Deciduous Forests
复制标题
在茂密的落叶林中使用落叶和落叶无人机激光雷达数据进行单树分割和树高估计
DOI：
10.3390/rs14122787
复制
发表时间：
2022-06-01
期刊：
影响因子：
5
通讯作者：
Yu, Fengyuan
中科院分区：
文献类型：
--
作者：
Chen, Qingda;Gao, Tian;Yu, Fengyuan
精确的单木分割（ITS）对于森林管理和森林生态系统研究至关重要。无人机激光雷达（UAV - LiDAR）在林分和景观尺度的单木分割及树高估计方面具有优势。然而，树冠紧密交错的茂密落叶林对单木分割的效果构成了挑战。期望通过获取穿过树冠的有效激光雷达点以及采用合适的算法来解决这一问题。在本研究中，引入了一种融合了落叶期和长叶期点云的新型无人机激光雷达数据集（FULD），通过在中国东北茂密落叶林中对比不同类型的分割算法（即分水岭分割、点云分割和分层堆叠分割），来评估其在单木分割和树高估计方面的协同效益。在四个典型林分中进行了实地验证，包括阔叶混交林（MBF）、蒙古栎林（MOF）、针阔混交林（MBCF）和落叶松人工林（LPF）。结果表明，FULD与分层堆叠分割（LSS）算法相结合，在所有森林类型中均产生了最高的精度（F分数：0.70至0.85）。FULD在树高估计方面也表现更佳，个体水平的均方根误差（RMSE）为1.54米。与仅使用长叶期数据集相比，FULD估计树高的均方根误差降低了0.22至0.27米，且正确分割的树木数量增加了12.3%，这主要得益于几乎所有胸径水平检测率的提高以及低胸径水平检测精度的提升。这些改进归因于FULD从树干到树梢的丰富点云，以及LSS算法在分割时纳入了每一层的点云。这些研究结果为未来有更多多时相激光雷达数据可用时指导FULD的应用提供了有益的见解。
Accurate individual tree segmentation (ITS) is fundamental to forest management and to the studies of forest ecosystem. Unmanned Aerial Vehicle Light Detection and Ranging (UAV-LiDAR) shows advantages for ITS and tree height estimation at stand and landscape scale. However, dense deciduous forests with tightly interlocked tree crowns challenge the performance for ITS. Available LiDAR points through tree crown and appropriate algorithm are expected to attack the problem. In this study, a new UAV-LiDAR dataset that fused leaf-off and leaf-on point cloud (FULD) was introduced to assess the synergetic benefits for ITS and tree height estimation by comparing different types of segmentation algorithms (i.e., watershed segmentation, point cloud segmentation and layer stacking segmentation) in the dense deciduous forests of Northeast China. Field validation was conducted in the four typical stands, including mixed broadleaved forest (MBF), Mongolian oak forest (MOF), mixed broadleaf-conifer forest (MBCF) and larch plantation forest (LPF). The results showed that the combination of FULD and the layer stacking segmentation (LSS) algorithm produced the highest accuracies across all forest types (F-score: 0.70 to 0.85). The FULD also showed a better performance on tree height estimation, with a root mean square error (RMSE) of 1.54 m at individual level. Compared with using the leaf-on dataset solely, the RMSE of tree height estimation was reduced by 0.22 to 0.27 m, and 12.3% more trees were correctly segmented by the FULD, which are mainly contributed by improved detection rate at nearly all DBH levels and by improved detection accuracy at low DBH levels. The improvements are attributed to abundant points from the bole to the treetop of FULD, as well as each layer point being included for segmentation by LSS algorithm. These findings provide useful insights to guide the application of FULD when more multi-temporal LiDAR data are available in future.