Urban forest monitoring based on multiple features at the single tree scale by UAV

doi:10.1016/j.ufug.2020.126958

	Urban forest monitoring based on multiple features at the single tree scale by UAV
	Wang, Xiaofeng 2,3; Wang, Yi 2; Zhou, Chaowei 2; Yin, Lichang 1; Feng, Xiaoming 4
刊名	URBAN FORESTRY & URBAN GREENING
	2021-03-01
卷号	58 页码:10
关键词	Aerial photogrammetry Multiple features Random forest classification Single tree segmentation Tree height UAV
ISSN号	1618-8667
DOI	10.1016/j.ufug.2020.126958
通讯作者	Wang, Xiaofeng(wangxf@chd.edu.cn)
英文摘要	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.
资助项目	Second Tibetan Plateau Scientific Expedition and Research Program[2019QZKK0405] ; National Key Research and Development Project of China[2018YFC0507300] ; Chinese Academy of Sciences[XDA2002040201]
WOS研究方向	Plant Sciences ; Environmental Sciences & Ecology ; Forestry ; Urban Studies
语种	英语
出版者	ELSEVIER GMBH
WOS记录号	WOS:000620651800004
资助机构	Second Tibetan Plateau Scientific Expedition and Research Program ; National Key Research and Development Project of China ; Chinese Academy of Sciences
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/160461]
专题	中国科学院地理科学与资源研究所
通讯作者	Wang, Xiaofeng
作者单位	1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China 2.Changan Univ, Sch Land Engn, Xian 710064, Peoples R China 3.Changan Univ, Key Lab Shaanxi Land Consolidat Project, Xian 710064, Peoples R China 4.Chinese Acad Sci, Res Ctr Ecoenvironm Sci, State Key Lab Urban & Reg Ecol, Beijing 100085, Peoples R China
推荐引用方式 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,2021,58:10.
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,58,10.
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):10.