4.7 Article

Individual Tree Canopy Parameters Estimation Using UAV-Based Photogrammetric and LiDAR Point Clouds in an Urban Park

期刊

REMOTE SENSING
卷 13, 期 11, 页码 -

出版社

MDPI
DOI: 10.3390/rs13112062

关键词

UAV; photogrammetry; LiDAR sensor; dense point cloud; urban tree

资金

  1. Australian Research Council [DP 150103135, BNUT/395022/1400]

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The study compares point clouds produced by UAV-photogrammetry and -LiDAR in an urban park, along with estimated tree canopy parameters. Results show a high correlation between UAV-photogrammetry and -LiDAR point clouds, with R-2 values exceeding 99.54%, and the estimated tree canopy parameters showing correlations above 95%.
Estimation of urban tree canopy parameters plays a crucial role in urban forest management. Unmanned aerial vehicles (UAV) have been widely used for many applications particularly forestry mapping. UAV-derived images, captured by an onboard camera, provide a means to produce 3D point clouds using photogrammetric mapping. Similarly, small UAV mounted light detection and ranging (LiDAR) sensors can also provide very dense 3D point clouds. While point clouds derived from both photogrammetric and LiDAR sensors can allow the accurate estimation of critical tree canopy parameters, so far a comparison of both techniques is missing. Point clouds derived from these sources vary according to differences in data collection and processing, a detailed comparison of point clouds in terms of accuracy and completeness, in relation to tree canopy parameters using point clouds is necessary. In this research, point clouds produced by UAV-photogrammetry and -LiDAR over an urban park along with the estimated tree canopy parameters are compared, and results are presented. The results show that UAV-photogrammetry and -LiDAR point clouds are highly correlated with R-2 of 99.54% and the estimated tree canopy parameters are correlated with R-2 of higher than 95%.

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