Improving the estimation of canopy cover from UAV-LiDAR data using a pit-free CHM-based method
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 po...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2021-10-01
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| Series: | International Journal of Digital Earth |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/17538947.2021.1921862 |
| _version_ | 1797678506647224320 |
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| author | Shangshu Cai Wuming Zhang Shuangna Jin Jie Shao Linyuan Li Sisi Yu Guangjian Yan |
| author_facet | Shangshu Cai Wuming Zhang Shuangna Jin Jie Shao Linyuan Li Sisi Yu Guangjian Yan |
| author_sort | Shangshu Cai |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T23:00:46Z |
| format | Article |
| id | doaj.art-ac1f78ea28844f90a1b3e4b71097f918 |
| institution | Directory Open Access Journal |
| issn | 1753-8947 1753-8955 |
| language | English |
| last_indexed | 2024-03-11T23:00:46Z |
| publishDate | 2021-10-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj.art-ac1f78ea28844f90a1b3e4b71097f9182023-09-21T14:57:10ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552021-10-0114101477149210.1080/17538947.2021.19218621921862Improving the estimation of canopy cover from UAV-LiDAR data using a pit-free CHM-based methodShangshu Cai0Wuming Zhang1Shuangna Jin2Jie Shao3Linyuan Li4Sisi Yu5Guangjian Yan6Sun Yat-Sen UniversitySun Yat-Sen UniversityJointly Sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of SciencesSun Yat-Sen UniversityBeijing Forestry UniversityAerospace Information Research Institute, Chinese Academy of SciencesJointly Sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of SciencesAccurate 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.http://dx.doi.org/10.1080/17538947.2021.1921862canopy coverlight detecting and rangingunmanned aerial vehiclewithin-crown gapspit-free chm |
| spellingShingle | Shangshu Cai Wuming Zhang Shuangna Jin Jie Shao Linyuan Li Sisi Yu Guangjian Yan Improving the estimation of canopy cover from UAV-LiDAR data using a pit-free CHM-based method International Journal of Digital Earth canopy cover light detecting and ranging unmanned aerial vehicle within-crown gaps pit-free chm |
| title | Improving the estimation of canopy cover from UAV-LiDAR data using a pit-free CHM-based method |
| title_full | Improving the estimation of canopy cover from UAV-LiDAR data using a pit-free CHM-based method |
| title_fullStr | Improving the estimation of canopy cover from UAV-LiDAR data using a pit-free CHM-based method |
| title_full_unstemmed | Improving the estimation of canopy cover from UAV-LiDAR data using a pit-free CHM-based method |
| title_short | Improving the estimation of canopy cover from UAV-LiDAR data using a pit-free CHM-based method |
| title_sort | improving the estimation of canopy cover from uav lidar data using a pit free chm based method |
| topic | canopy cover light detecting and ranging unmanned aerial vehicle within-crown gaps pit-free chm |
| url | http://dx.doi.org/10.1080/17538947.2021.1921862 |
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