Individual Tree Segmentation and Tree Height Estimation Using Leaf-Off and Leaf-On UAV-LiDAR Data in Dense Deciduous Forests

Remote Sensing

Fayun Wu¹  Xiufen Li²  Tian Gao³  Jiaojun Zhu³  Qingda Chen³  Deliang Lu³  Fengyuan Yu³ 

[1]Academy of Inventory and Planning, National Forestry and Grassland Administration, Beijing 100714, China

[2]Agronomy College, Shenyang Agricultural University, Shenyang 110866, China

[3]Qingyuan Forest CERN, National Observation and Research Station, Shenyang 110016, China

关键词: fused dataset;    individual tree segmentation;    temperate deciduous forest;    tree height estimation;    UAV-LiDAR;   

DOI  :  10.3390/rs14122787

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown