Precise identification of individual tree species in urban areas with high canopy density by multi-sensor UAV data in two seasons
Unmanned aerial vehicle (UAV) platforms with multi-sensors can provide data with high resolution for tree species classification. However, few studies have explored the fusion of multi-sensor UAV data in two seasons for urban tree species classification with high canopy density. Therefore, in this p...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-08-01
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| Series: | International Journal of Digital Earth |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2025.2496804 |
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| author | Qixia Man Pinliang Dong Baolei Zhang Haijian Liu Xinming Yang Jingru Wu Chunhui Liu Changyin Han Cong Zhou Zhuang Tan |
| author_facet | Qixia Man Pinliang Dong Baolei Zhang Haijian Liu Xinming Yang Jingru Wu Chunhui Liu Changyin Han Cong Zhou Zhuang Tan |
| author_sort | Qixia Man |
| collection | DOAJ |
| description | Unmanned aerial vehicle (UAV) platforms with multi-sensors can provide data with high resolution for tree species classification. However, few studies have explored the fusion of multi-sensor UAV data in two seasons for urban tree species classification with high canopy density. Therefore, in this paper, UAV hyperspectral, LiDAR data, and visible images in two seasons were used to explore their performance in urban tree species classification with the following steps: (1) extraction of spectral, structural and texture features; (2) classification of 14 urban tree species using the random forest classifier based on different feature combinations; (3) classification of individual tree species in high canopy density urban areas using the proposed hybrid pixel- and object-based classification method; (4) investigation of the seasonal effects on urban tree species classification with multi-source UAV data; and (5) accuracy assessment. By comparative experiments, the results demonstrate that the fusion of spectral and three-dimensional spatial information, the proposed hybrid method, and the integration of two-season datasets significantly improve the overall accuracy of urban tree species classification. The advantages and imitations of multi-sensor UAV data fusion are also discussed. This study is expected to provide a new method for precise census and refined management of urban forest resources. |
| format | Article |
| id | doaj-art-574709b592ee4fd6bb5cb70c93f699d4 |
| institution | Kabale University |
| issn | 1753-8947 1753-8955 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj-art-574709b592ee4fd6bb5cb70c93f699d42025-08-25T11:24:33ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552025-08-0118110.1080/17538947.2025.2496804Precise identification of individual tree species in urban areas with high canopy density by multi-sensor UAV data in two seasonsQixia Man0Pinliang Dong1Baolei Zhang2Haijian Liu3Xinming Yang4Jingru Wu5Chunhui Liu6Changyin Han7Cong Zhou8Zhuang Tan9College of Geography and Environment, Shandong Normal University, Jinan, People's Republic of ChinaDepartment of Geography and the Environment, University of North Texas, Denton, TX, USACollege of Geography and Environment, Shandong Normal University, Jinan, People's Republic of ChinaRemote Sensing and Earth Science Research Institute, Hangzhou Normal University, Hangzhou, People's Republic of ChinaJinan Environmental Research Institute, Jinan, People's Republic of ChinaCollege of Geography and Environment, Shandong Normal University, Jinan, People's Republic of ChinaCollege of Geography and Environment, Shandong Normal University, Jinan, People's Republic of ChinaCollege of Geography and Environment, Shandong Normal University, Jinan, People's Republic of ChinaCollege of Geography and Environment, Shandong Normal University, Jinan, People's Republic of ChinaCollege of Geography and Environment, Shandong Normal University, Jinan, People's Republic of ChinaUnmanned aerial vehicle (UAV) platforms with multi-sensors can provide data with high resolution for tree species classification. However, few studies have explored the fusion of multi-sensor UAV data in two seasons for urban tree species classification with high canopy density. Therefore, in this paper, UAV hyperspectral, LiDAR data, and visible images in two seasons were used to explore their performance in urban tree species classification with the following steps: (1) extraction of spectral, structural and texture features; (2) classification of 14 urban tree species using the random forest classifier based on different feature combinations; (3) classification of individual tree species in high canopy density urban areas using the proposed hybrid pixel- and object-based classification method; (4) investigation of the seasonal effects on urban tree species classification with multi-source UAV data; and (5) accuracy assessment. By comparative experiments, the results demonstrate that the fusion of spectral and three-dimensional spatial information, the proposed hybrid method, and the integration of two-season datasets significantly improve the overall accuracy of urban tree species classification. The advantages and imitations of multi-sensor UAV data fusion are also discussed. This study is expected to provide a new method for precise census and refined management of urban forest resources.https://www.tandfonline.com/doi/10.1080/17538947.2025.2496804UAVLiDAR datahyperspectral datarandom forest classifiertree species classification |
| spellingShingle | Qixia Man Pinliang Dong Baolei Zhang Haijian Liu Xinming Yang Jingru Wu Chunhui Liu Changyin Han Cong Zhou Zhuang Tan Precise identification of individual tree species in urban areas with high canopy density by multi-sensor UAV data in two seasons International Journal of Digital Earth UAV LiDAR data hyperspectral data random forest classifier tree species classification |
| title | Precise identification of individual tree species in urban areas with high canopy density by multi-sensor UAV data in two seasons |
| title_full | Precise identification of individual tree species in urban areas with high canopy density by multi-sensor UAV data in two seasons |
| title_fullStr | Precise identification of individual tree species in urban areas with high canopy density by multi-sensor UAV data in two seasons |
| title_full_unstemmed | Precise identification of individual tree species in urban areas with high canopy density by multi-sensor UAV data in two seasons |
| title_short | Precise identification of individual tree species in urban areas with high canopy density by multi-sensor UAV data in two seasons |
| title_sort | precise identification of individual tree species in urban areas with high canopy density by multi sensor uav data in two seasons |
| topic | UAV LiDAR data hyperspectral data random forest classifier tree species classification |
| url | https://www.tandfonline.com/doi/10.1080/17538947.2025.2496804 |
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