气候变化领域集成服务门户(CCPortal): Discriminating tree species at different taxonomic levels using multi-temporal WorldView-3 imagery in Washington D.C., USA

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DOI	10.1016/j.rse.2020.111811
	Discriminating tree species at different taxonomic levels using multi-temporal WorldView-3 imagery in Washington D.C., USA
	Fang F.; McNeil B.E.; Warner T.A.; Maxwell A.E.; Dahle G.A.; Eutsler E.; Li J.
发表日期	2020
ISSN	00344257
卷号	246
英文摘要	With the promise of transformative changes for the management of rural and urban forests, the discrimination of tree species from satellite imagery has been a long-standing goal of remote sensing. For the species-rich urban setting of Washington, D.C. USA, we evaluate current prospects toward this goal by combining a Random Forest (RF) object-based tree species classification method with two large datasets 1) A suite of 12 very high resolution (VHR) WorldView-3 images (WV-3), whose image acquisition date cover each pheno-phase of the growing season from April to November; and 2) the 16,496 street trees from Washington D.C. Department of Transportation's (DDOT) field inventory. We classify the 19 most abundant tree species with an overall accuracy of 61.3% and classify the ten most abundant genera with an overall accuracy of 73.7%. We observe that (1) there are larger declines in accuracy when attempting to classify species in the same genus, and (2) the most valuable phenological period is fall senescence for classification at different taxonomic levels. Especially if satellite data can be matched to the key pheno-phases, our study highlights that current VHR satellite sensors now have the radiometric, spectral, and spatial resolution to potentially help manage species-rich urban forests. © 2020 Elsevier Inc.
英文关键词	Phenology; Street trees; Tree species; Urban; Washington D.C.; WorldView3
语种	英语
scopus关键词	Classification (of information); Decision trees; Forestry; Large dataset; Remote sensing; Satellite imagery; Department of Transportation; Field inventories; Overall accuracies; Rural and urban forests; Satellite data; Satellite sensors; Spatial resolution; Very high resolution; Urban transportation; algorithm; growing season; phenology; remote sensing; satellite imagery; spatial resolution; spatiotemporal analysis; taxonomy; tree; urban forestry; WorldView; Washington
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179281
作者单位	Department of Urban and Regional Planning, University of Illinois at Urbana-Champaign, Champaign, IL 61820, United States; Department of Geology and Geography, West Virginia University, Morgantown, WV 26505, United States; Davis College, Division of Forestry & Natural Resources, West Virginia University, Morgantown, WV 26505, United States; Urban Forestry Division, District Department of Transportation, Washington, DC 20003, United States; State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, 830011, China
推荐引用方式 GB/T 7714	Fang F.,McNeil B.E.,Warner T.A.,et al. Discriminating tree species at different taxonomic levels using multi-temporal WorldView-3 imagery in Washington D.C., USA[J],2020,246.
APA	Fang F..,McNeil B.E..,Warner T.A..,Maxwell A.E..,Dahle G.A..,...&Li J..(2020).Discriminating tree species at different taxonomic levels using multi-temporal WorldView-3 imagery in Washington D.C., USA.Remote Sensing of Environment,246.
MLA	Fang F.,et al."Discriminating tree species at different taxonomic levels using multi-temporal WorldView-3 imagery in Washington D.C., USA".Remote Sensing of Environment 246(2020).