Classification of Street Tree Species Using UAV Tilt Photogrammetry
As an important component of the urban ecosystem, street trees have made an outstanding contribution to alleviating urban environmental pollution. Accurately extracting tree characteristics and species information can facilitate the monitoring and management of street trees, as well as aiding landsc...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-01-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/13/2/216 |
| _version_ | 1827602545006084096 |
|---|---|
| author | Yutang Wang Jia Wang Shuping Chang Lu Sun Likun An Yuhan Chen Jiangqi Xu |
| author_facet | Yutang Wang Jia Wang Shuping Chang Lu Sun Likun An Yuhan Chen Jiangqi Xu |
| author_sort | Yutang Wang |
| collection | DOAJ |
| description | As an important component of the urban ecosystem, street trees have made an outstanding contribution to alleviating urban environmental pollution. Accurately extracting tree characteristics and species information can facilitate the monitoring and management of street trees, as well as aiding landscaping and studies of urban ecology. In this study, we selected the suburban areas of Beijing and Zhangjiakou and investigated six representative street tree species using unmanned aerial vehicle (UAV) tilt photogrammetry. We extracted five tree attributes and four combined attribute parameters and used four types of commonly-used machine learning classification algorithms as classifiers for tree species classification. The results show that random forest (RF), support vector machine (SVM), and back propagation (BP) neural network provide better classification results when using combined parameters for tree species classification, compared with those using individual tree attributes alone; however, the K-nearest neighbor (KNN) algorithm produced the opposite results. The best combination for classification is the BP neural network using combined attributes, with a classification precision of 89.1% and F-measure of 0.872, and we conclude that this approach best meets the requirements of street tree surveys. The results also demonstrate that optical UAV tilt photogrammetry combined with a machine learning classification algorithm is a low-cost, high-efficiency, and high-precision method for tree species classification. |
| first_indexed | 2024-03-09T05:20:58Z |
| format | Article |
| id | doaj.art-383667bd817641fd8dcb7cda882c0b0f |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T05:20:58Z |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-383667bd817641fd8dcb7cda882c0b0f2023-12-03T12:40:28ZengMDPI AGRemote Sensing2072-42922021-01-0113221610.3390/rs13020216Classification of Street Tree Species Using UAV Tilt PhotogrammetryYutang Wang0Jia Wang1Shuping Chang2Lu Sun3Likun An4Yuhan Chen5Jiangqi Xu6The College of Forestry, Beijing Forestry University, Beijing 100083, ChinaThe College of Forestry, Beijing Forestry University, Beijing 100083, ChinaThe College of Forestry, Beijing Forestry University, Beijing 100083, ChinaThe College of Forestry, Beijing Forestry University, Beijing 100083, ChinaThe College of Forestry, Beijing Forestry University, Beijing 100083, ChinaThe College of Forestry, Beijing Forestry University, Beijing 100083, ChinaThe College of Forestry, Beijing Forestry University, Beijing 100083, ChinaAs an important component of the urban ecosystem, street trees have made an outstanding contribution to alleviating urban environmental pollution. Accurately extracting tree characteristics and species information can facilitate the monitoring and management of street trees, as well as aiding landscaping and studies of urban ecology. In this study, we selected the suburban areas of Beijing and Zhangjiakou and investigated six representative street tree species using unmanned aerial vehicle (UAV) tilt photogrammetry. We extracted five tree attributes and four combined attribute parameters and used four types of commonly-used machine learning classification algorithms as classifiers for tree species classification. The results show that random forest (RF), support vector machine (SVM), and back propagation (BP) neural network provide better classification results when using combined parameters for tree species classification, compared with those using individual tree attributes alone; however, the K-nearest neighbor (KNN) algorithm produced the opposite results. The best combination for classification is the BP neural network using combined attributes, with a classification precision of 89.1% and F-measure of 0.872, and we conclude that this approach best meets the requirements of street tree surveys. The results also demonstrate that optical UAV tilt photogrammetry combined with a machine learning classification algorithm is a low-cost, high-efficiency, and high-precision method for tree species classification.https://www.mdpi.com/2072-4292/13/2/216tree species classificationstreet treesUAVmachine learningtilt photogrammetry |
| spellingShingle | Yutang Wang Jia Wang Shuping Chang Lu Sun Likun An Yuhan Chen Jiangqi Xu Classification of Street Tree Species Using UAV Tilt Photogrammetry Remote Sensing tree species classification street trees UAV machine learning tilt photogrammetry |
| title | Classification of Street Tree Species Using UAV Tilt Photogrammetry |
| title_full | Classification of Street Tree Species Using UAV Tilt Photogrammetry |
| title_fullStr | Classification of Street Tree Species Using UAV Tilt Photogrammetry |
| title_full_unstemmed | Classification of Street Tree Species Using UAV Tilt Photogrammetry |
| title_short | Classification of Street Tree Species Using UAV Tilt Photogrammetry |
| title_sort | classification of street tree species using uav tilt photogrammetry |
| topic | tree species classification street trees UAV machine learning tilt photogrammetry |
| url | https://www.mdpi.com/2072-4292/13/2/216 |
| work_keys_str_mv | AT yutangwang classificationofstreettreespeciesusinguavtiltphotogrammetry AT jiawang classificationofstreettreespeciesusinguavtiltphotogrammetry AT shupingchang classificationofstreettreespeciesusinguavtiltphotogrammetry AT lusun classificationofstreettreespeciesusinguavtiltphotogrammetry AT likunan classificationofstreettreespeciesusinguavtiltphotogrammetry AT yuhanchen classificationofstreettreespeciesusinguavtiltphotogrammetry AT jiangqixu classificationofstreettreespeciesusinguavtiltphotogrammetry |