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

Shangshu Cai, , Shuangna Jin, Jie Shao, Linyuan Li, Sisi Yu, Guangjian Yan
International Journal of Digital Earth pp 1-16; doi:10.1080/17538947.2021.1921862

Abstract: 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 (R2 = 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.
Keywords: Canopy cover / light detecting and ranging / unmanned aerial vehicle / within-crown gaps / pit-free CHM

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