| Urban forest monitoring based on multiple features at the single tree scale by UAV | |
| Wang, Xiaofeng; Wang, Yi; Zhou, Chaowei; Yin, Lichang; Feng, Xiaoming | |
| 2021-03 | |
| Source Publication | URBAN FORESTRY & URBAN GREENING
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| ISSN | 1618-8667 |
| Volume | 58Pages:- |
| Abstract | Fine monitoring of tree species is essential to supporting the urban forest management. Data acquired from unmanned aerial vehicles (UAVs) not only have very high spatiotemporal resolution, but also contain the vertical structure of trees which is important in the fine recognition of vegetation types. However, the research of combining multi-dimensional features in classification is still very limited. In our study, we extracted the spectral information, vegetation morphological parameters, texture information, and vegetation indexes based on UAV ultrahigh resolution images to build an object-oriented-based random forest (RF) classifier at the single tree scale. Establishing 6 classification scenarios that combines multiple data sources, multi-dimensional features, and multiple classification algorithms, our results show that: (1) UAV images can effectively detect surface fragments. The accuracy of RF classification based on UAV multiple features was high at 91.3 %, which was 20.5 % higher than the results by using high-resolution Baidu maps; (2) for mapping the tree species of urban forest, tree morphological characteristics, texture information, and vegetation indexes improved the classification accuracy by 2.9 %, 1.9 %, and 7.1 %, respectively, resulting in meaningful improvement of classification effects; and (3) the accuracy of RF classification based on UAV data was much higher than the maximum likelihood classification (MLC) results. Compared with the latter, the former can effectively avoid salt and pepper noise. The workflow of information extraction and urban forest classification based on UAV images in this paper yields high performance, which has important significance as a reference for future relevant research. |
| Department | 城市与区域生态国家重点实验室 |
| Keyword | Aerial photogrammetry Multiple features Random forest classification Single tree segmentation Tree height UAV |
| WOS Research Area | Plant Sciences ; Environmental Studies ; Forestry ; Urban Studies |
| Document Type | 期刊论文 |
| Identifier | https://ir.rcees.ac.cn/handle/311016/45600 |
| Collection | 城市与区域生态国家重点实验室 |
| Affiliation | 1.Changan Univ, Sch Land Engn, Xian 710064, Peoples R China 2.Changan Univ, Key Lab Shaanxi Land Consolidat Project, Xian 710064, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China 4.Chinese Acad Sci, Res Ctr Ecoenvironm Sci, State Key Lab Urban & Reg Ecol, Beijing 100085, Peoples R China |
| Recommended Citation GB/T 7714 | Wang, Xiaofeng,Wang, Yi,Zhou, Chaowei,et al. Urban forest monitoring based on multiple features at the single tree scale by UAV[J]. URBAN FORESTRY & URBAN GREENING,2021,58:-. |
| APA | Wang, Xiaofeng,Wang, Yi,Zhou, Chaowei,Yin, Lichang,&Feng, Xiaoming.(2021).Urban forest monitoring based on multiple features at the single tree scale by UAV.URBAN FORESTRY & URBAN GREENING,58,-. |
| MLA | Wang, Xiaofeng,et al."Urban forest monitoring based on multiple features at the single tree scale by UAV".URBAN FORESTRY & URBAN GREENING 58(2021):-. |
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| File Name/Size | DocType | Version | Access | License | ||
| Urban forest monitor（8168KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Download | |
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