Tree information in urban areas plays a significant role in many fields of study, such as ecology and environmental management. Airborne LiDAR scanning (ALS) excels at the fast and efficient acquisition of spatial information in urban-scale areas. Tree extraction from ALS data is an essential part of tree structural studies. Current raster-based methods that use canopy height models (CHMs) suffer from the loss of 3D structure information, whereas the existing point-based methods are non-robust in complex environments. Aiming at making full use of the canopy’s 3D structure information that is provided by point cloud data, and ensuring the method’s suitability in complex scenes, this paper proposes a new point-based method for tree extraction that is based on 3D morphological features. Considering the elevation deviations of the ALS data, we propose a neighborhood search method to filter out the ground and flat-roof points. A coarse extraction method, combining planar projection with a point density-filtering algorithm is applied to filter out distracting objects, such as utility poles and cars. After that, a Euclidean cluster extraction (ECE) algorithm is used as an optimization strategy for coarse extraction. In order to verify the robustness and accuracy of the method, airborne LiDAR data from Zhangye, Gansu, China and unmanned aircraft vehicle (UAV) LiDAR data from Xinyang, Henan, China were tested in this study. The experimental results demonstrated that our method was suitable for extracting trees in complex urban scenes with either high or low point densities. The extraction accuracy obtained for the airborne LiDAR data and UAV LiDAR data were 99.4% and 99.2%, respectively. In addition, a further study found that the aberrant vertical structure of the artificially pruned canopy was the main cause of the error. Our method achieved desirable results in different scenes, with only one adjustable parameter, making it an easy-to-use method for urban area studies.

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<p>The multidimensional arrangement of upper canopy features is a physical driver of energy and water balance under various canopies, and standard modeling approaches integrate leaf area index (LAI) and canopy closure (CC) to describe canopies. However, it is unclear how the canopy affects the component and interception of rainfall within the forest system. We generated multi-layered forest point clouds from trunk to canopy using fusion of drone and terrestrial LiDAR data then classified wood and foliage elements using a clustering algorithm to build a high precision physical model for describing throughfall, stemflow and interception. The experiment was conducted in the thinning plantation forest located in Tochigi prefecture, Japan. Rainfall observation for the three components is important for model development. Throughfall was computed from 20 rain gauges distributed on a grid under the forest canopy, 3 stemflow collectors was set up around the tree trunks connected to a bucket with water level sensor. We developed a capacity model to describe canopy saturation with foliage points, a voxel-based method was used to create 3D representations of forest canopies, and an analysis of these point-derived canopy structures and volume were performed to assess the canopy's capacity to contain rainfall. For stemflow modeling, we use a runoff model to simulate the additional rainfall accumulates to the tree trunk through branches when the tree canopy is saturated. Preliminary simulation results show that: (1) fusion and registration of drone and terrestrial LiDAR data can greatly improve the point cloud accuracy and enrich the information contents such as coordinate geo-reference and filling of missing structures; (2) a strong correlation between the rainfall observed canopy interception results and the estimated canopy volume, and the volume-based interception prediction model has a high accuracy, with an R<sup>2</sup> from 0.84 to 0.91 compared to past observations. (3) stemflow is related to the projected volume of the canopy and the proportion of wooden structure point clouds, and as the runoff path increases, there is a greater probability that oversaturated precipitation will concentrate on the trunk rather than drip off. High accuracy physical model of tree canopy can well describe the interactions between the rainfall to canopy and illustrate the mechanism.</p> <p> </p>

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