Paper Info
Title
Improving The Estimation Of Canopy Cover From Uav-Lidar Data Using A Pit-Free Chm-Based Method
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 (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.
Year
DOI
Venue
2021
10.1080/17538947.2021.1921862
INTERNATIONAL JOURNAL OF DIGITAL EARTH
Keywords
DocType
Volume
Canopy cover, light detecting and ranging, unmanned aerial vehicle, within-crown gaps, pit-free CHM
Journal
14
Issue
ISSN
Citations 
10
1753-8947
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Shangshu Cai111.37
Wuming Zhang200.34
Shuangna Jin300.34
Jie Shao401.69
Linyuan Li500.34
Sisi Yu600.68
Yan, G.7910.04