Individual tree detection and counting based on high-resolution imagery and the canopy height model data
Individual Tree Detection-and-Counting (ITDC) is among the important tasks in town areas, and numerous methods are proposed in this direction. Despite their many advantages, still, the proposed methods are inadequate to provide robust results because they mostly rely on the direct field investigatio...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-11-01
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| Series: | Geo-spatial Information Science |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/10095020.2023.2299146 |
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| author | Ye Zhang Moyang Wang Joseph Mango Liang Xin Chen Meng Xiang Li |
| author_facet | Ye Zhang Moyang Wang Joseph Mango Liang Xin Chen Meng Xiang Li |
| author_sort | Ye Zhang |
| collection | DOAJ |
| description | Individual Tree Detection-and-Counting (ITDC) is among the important tasks in town areas, and numerous methods are proposed in this direction. Despite their many advantages, still, the proposed methods are inadequate to provide robust results because they mostly rely on the direct field investigations. This paper presents a novel approach involving high-resolution imagery and the Canopy-Height-Model (CHM) data to solve the ITDC problem. The new approach is studied in six urban scenes: farmland, woodland, park, industrial land, road and residential areas. First, it identifies tree canopy regions using a deep learning network from high-resolution imagery. It then deploys the CHM-data to detect treetops of the canopy regions using a local maximum algorithm and individual tree canopies using the region growing. Finally, it calculates and describes the number of individual trees and tree canopies. The proposed approach is experimented with the data from Shanghai, China. Our results show that the individual tree detection method had an average overall accuracy of 0.953, with a precision of 0.987 for woodland scene. Meanwhile, the R2 value for canopy segmentation in different urban scenes is greater than 0.780 and 0.779 for canopy area and diameter size, respectively. These results confirm that the proposed method is robust enough for urban tree planning and management. |
| format | Article |
| id | doaj-art-cd3cd0209f9547dda8a79f0c76b24e03 |
| institution | Kabale University |
| issn | 1009-5020 1993-5153 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geo-spatial Information Science |
| spelling | doaj-art-cd3cd0209f9547dda8a79f0c76b24e032024-12-11T11:57:33ZengTaylor & Francis GroupGeo-spatial Information Science1009-50201993-51532024-11-012762162217810.1080/10095020.2023.2299146Individual tree detection and counting based on high-resolution imagery and the canopy height model dataYe Zhang0Moyang Wang1Joseph Mango2Liang Xin3Chen Meng4Xiang Li5Key Laboratory of Geographic Information Science (Ministry of Education) and School of Geographic Sciences, East China Normal University, Shanghai, ChinaKey Laboratory of Geographic Information Science (Ministry of Education) and School of Geographic Sciences, East China Normal University, Shanghai, ChinaDepartment of Transportation and Geotechnical Engineering, University of Dar es Salaam, Dar es salaam, TanzaniaCollege of Surveying and Geographic Informatics, Tongji University, Shanghai, ChinaSchool of Ecology and Environmental Sciences, East China Normal University, Shanghai, ChinaKey Laboratory of Geographic Information Science (Ministry of Education) and School of Geographic Sciences, East China Normal University, Shanghai, ChinaIndividual Tree Detection-and-Counting (ITDC) is among the important tasks in town areas, and numerous methods are proposed in this direction. Despite their many advantages, still, the proposed methods are inadequate to provide robust results because they mostly rely on the direct field investigations. This paper presents a novel approach involving high-resolution imagery and the Canopy-Height-Model (CHM) data to solve the ITDC problem. The new approach is studied in six urban scenes: farmland, woodland, park, industrial land, road and residential areas. First, it identifies tree canopy regions using a deep learning network from high-resolution imagery. It then deploys the CHM-data to detect treetops of the canopy regions using a local maximum algorithm and individual tree canopies using the region growing. Finally, it calculates and describes the number of individual trees and tree canopies. The proposed approach is experimented with the data from Shanghai, China. Our results show that the individual tree detection method had an average overall accuracy of 0.953, with a precision of 0.987 for woodland scene. Meanwhile, the R2 value for canopy segmentation in different urban scenes is greater than 0.780 and 0.779 for canopy area and diameter size, respectively. These results confirm that the proposed method is robust enough for urban tree planning and management.https://www.tandfonline.com/doi/10.1080/10095020.2023.2299146Individual tree detection-and-counting (ITDC)deep learninghigh-resolution imageryCanopy Height Model data (CHM) |
| spellingShingle | Ye Zhang Moyang Wang Joseph Mango Liang Xin Chen Meng Xiang Li Individual tree detection and counting based on high-resolution imagery and the canopy height model data Geo-spatial Information Science Individual tree detection-and-counting (ITDC) deep learning high-resolution imagery Canopy Height Model data (CHM) |
| title | Individual tree detection and counting based on high-resolution imagery and the canopy height model data |
| title_full | Individual tree detection and counting based on high-resolution imagery and the canopy height model data |
| title_fullStr | Individual tree detection and counting based on high-resolution imagery and the canopy height model data |
| title_full_unstemmed | Individual tree detection and counting based on high-resolution imagery and the canopy height model data |
| title_short | Individual tree detection and counting based on high-resolution imagery and the canopy height model data |
| title_sort | individual tree detection and counting based on high resolution imagery and the canopy height model data |
| topic | Individual tree detection-and-counting (ITDC) deep learning high-resolution imagery Canopy Height Model data (CHM) |
| url | https://www.tandfonline.com/doi/10.1080/10095020.2023.2299146 |
| work_keys_str_mv | AT yezhang individualtreedetectionandcountingbasedonhighresolutionimageryandthecanopyheightmodeldata AT moyangwang individualtreedetectionandcountingbasedonhighresolutionimageryandthecanopyheightmodeldata AT josephmango individualtreedetectionandcountingbasedonhighresolutionimageryandthecanopyheightmodeldata AT liangxin individualtreedetectionandcountingbasedonhighresolutionimageryandthecanopyheightmodeldata AT chenmeng individualtreedetectionandcountingbasedonhighresolutionimageryandthecanopyheightmodeldata AT xiangli individualtreedetectionandcountingbasedonhighresolutionimageryandthecanopyheightmodeldata |