Street tree health from space? An evaluation using WorldView-3 data and the Washington D.C. Street Tree Spatial Database

《Street tree health from space? An evaluation using WorldView-3 data and the Washington D.C. Street Tree Spatial Database》

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作者: Fang Fang;Brenden McNeil;Timothy Warner;Gregory Dahle;Earl Eutsler

来源: URBAN FORESTRY & URBAN GREENING,Vol.49,Issue1,Article 126634

语言: 英文

关键字: Urban trees;Remote sensing;Urban street tree health;High resolution images;WorldView-3

作者单位: Department of Geology and Geography, West Virginia University, Morgantown, WV, 26505, USA;Department of Urban and Regional Planning, University of Illinois at Urbana-Champaign, Champaign, IL, 61820, USA;Division of Forestry & Natural Resources, West Virginia University, Morgantown, WV, 26505, USA;Urban Forestry Division, District Department of Transportation, Washington, DC, 20003, USA;Department of Geology and Geography, West Virginia University, Morgantown, WV, 26505, USA;Department of Urban and Regional Planning, University of Illinois at Urbana-Champaign, Champaign, IL, 61820, USA;Division of Forestry & Natural Resources, West Virginia University, Morgantown, WV, 26505, USA;Urban Forestry Division, District Department of Transportation, Washington, DC, 20003, USA

摘要: Spatially accurate and timely information on tree health is an essential component of maintaining sustainability in a species-rich urban setting. Our objective was to evaluate the potential of WorldView-3 (WV-3) satellite images from June 11th, July 30th and August 30th, 2017 to detect field-measured health condition classes of 2308 trees within the District of Columbia Department of Transportation’s Street Tree Spatial Database. For each street tree in each image, we measured six vegetation indices (VIs), and find that NDVI1 (defined as the normalized ratio of the red and the first of the near infrared bands) on the July image shows the strongest statistical difference among the VI values of trees classified in the field during 2017 as in good, fair, or poor health condition. This result confirms that high spatial resolution remote sensing images provided the opportunity for a timely tree health assessment at individual tree crown level. Notably, the variability in VI attributable to health condition classes is smaller than the large declines in VIs between the June and August image dates. This greendown phenological pattern occurs similarly for trees in all health condition classes, and is thus an essential consideration when comparing VIs from different years or months for tree health assessment. The tree health condition discrimination was overwhelmed by this greendown phenology and the spectral variability in these species-rich urban settings. Based on these findings, we propose useful strategies of using these high-resolution WV-3 data for street tree health management in other cities.