Abstract
Tree height is an important vegetative structural parameter, and its accurate estimation is of significant ecological and commercial value. We collected UAV images of six tree species distributed throughout a subtropical campus during three periods from March to late May, during which some deciduous trees shed all of their leaves and then regrew, while other evergreen trees kept some of their leaves. The UAV imagery was processed by computer vision and photogrammetric software to generate a three-dimensional dense point cloud. Individual tree height information extracted from the dense photogrammetric point cloud was validated against the manually measured reference data. We found that the number of leaves in the canopy affected tree height estimation, especially for deciduous trees. During leaf-off conditions or the early season, when leaves were absent or sparse, it was difficult to reconstruct the 3D canopy structure fully from the UAV images, thus resulting in the underestimation of tree height; the accuracy improved considerably when there were more leaves. For Terminalia mantaly and Ficus virens, the root mean square errors (RMSEs) of tree height estimation reduced from 2.894 and 1.433 m (leaf-off) to 0.729 and 0.597 m (leaf-on), respectively. We provide direct evidence that leaf-on conditions have a positive effect on tree height measurements derived from UAV photogrammetric point clouds. This finding has important implications for forest monitoring, management, and change detection analysis.
Highlights
Tree height is an important vegetative structural parameter and one of the key attributes of forest inventories, and its accurate estimation is critical for many ecological and commercial applications.Previously, digital aerial photographs and LiDAR point clouds [1,2,3,4] were the major data sources that were utilized to find this information; recent advancements in unmanned aerial vehicles (UAVs) and sensor technology have provided another remote sensing toolset for obtaining tree height information [5,6,7,8].Both LiDAR and image sensors can be attached to UAVs to collect required data
In this study of tree height estimation from UAV photogrammetric point cloud, we demonstrated the clear and distinct differences in the accuracies of height estimation among tree species and during phenological times for the same species
The main objective of this study was to understand some vegetative factors that can affect the accuracies of tree height estimations derived from UAV-acquired images
Summary
Digital aerial photographs (including satellite and aerial images) and LiDAR (light detection and ranging) point clouds [1,2,3,4] were the major data sources that were utilized to find this information; recent advancements in unmanned aerial vehicles (UAVs) and sensor technology have provided another remote sensing toolset for obtaining tree height information [5,6,7,8]. Both LiDAR and image sensors can be attached to UAVs to collect required data. Compared to the UAV LiDAR system [9], which is capable of penetrating forest canopy layers and reaching the ground, the UAV camera has the advantage of being more affordable, portable, and easier to deploy.
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