Improving the estimation of canopy cover from UAV-LiDAR data using a pit-free CHM-based method

• Published in: International journal of digital earth Vol. 14; no. 10; pp. 1477 - 1492
• Main Authors: Cai, Shangshu, Zhang, Wuming, Jin, Shuangna, Shao, Jie, Li, Linyuan, Yu, Sisi, Yan, Guangjian
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 03.10.2021; Taylor & Francis Ltd; Taylor & Francis Group
• Subjects: Accuracy; Canopies; Canopy; Canopy cover; Environment models; Environmental modeling; Forest management; Lidar; light detecting and ranging; pit-free CHM; Plant cover; unmanned aerial vehicle; Unmanned aerial vehicles; within-crown gaps
• ISSN: 1753-8947; 1753-8955
• DOI: 10.1080/17538947.2021.1921862

Accurate and rapid estimation of canopy cover (CC) is crucial for many ecological and environmental models and for forest management. Unmanned aerial vehicle-light detecting and ranging (UAV-LiDAR) systems represent a promising tool for CC estimation due to their high mobility, low cost, and high point density. However, the CC values from UAV-LiDAR point clouds may be underestimated due to the presence of large quantities of within-crown gaps. To alleviate the negative effects of within-crown gaps, we proposed a pit-free CHM-based method for estimating CC, in which a cloth simulation method was used to fill the within-crown gaps. To evaluate the effect of CC values and within-crown gap proportions on the proposed method, the performance of the proposed method was tested on 18 samples with different CC values (40−70%) and 6 samples with different within-crown gap proportions (10−60%). The results showed that the CC accuracy of the proposed method was higher than that of the method without filling within-crown gaps (R 2  = 0.99 vs 0.98; RMSE = 1.49% vs 2.2%). The proposed method was insensitive to within-crown gap proportions, although the CC accuracy decreased slightly with the increase in within-crown gap proportions.