Abstract
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.
Highlights
The term “urban tree” refers to a woody perennial plant growing in cities and the surrounding areas [1]
Large balconies and facades of tall buildings are the main components of mis-extractions points)
This paper proposed a new point-based method for tree extraction, using Airborne LiDAR scanning (ALS) point cloud data in urban areas, by redefining the flat areas and using the 3D morphological features of trees
Summary
The term “urban tree” refers to a woody perennial plant growing in cities and the surrounding areas [1]. Urban trees play a crucial role in enhancing environmental quality and are recognized as fundamental to city livability, resilience, and sustainability [2,3]. Trees improve air quality by absorbing gaseous pollutants through leaf stomata and dissolving water-soluble pollutants onto moist leaf surfaces. Tree canopies weaken the urban heat-island effect by reducing air temperature through shading and evapotranspiration. As well as these benefits, trees reduce urban flood risk because stormwater runoff is mitigated by rainwater interception and storage in urban tree canopies [1,4,5]. Urban trees have important ecological functions in providing habitats for urban wildlife, abating noise, decreasing wind speed, increasing surface runoff and conditioning the urban microclimate [6,7], maintaining urban ecological balance, and protecting
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