A primary goal of urban forestry planning and management is reaching at least minimum levels of tree cover density (Nowak et al., 2010). Accurate measures of tree cover density are needed so planners know where to plant trees and how many trees to plant. Tree cover density is most often measured using remotely-sensed imagery (e.g., rectified satellite imagery). Google Earth produces high-quality remotely-sensed images of most places on earth that can be used to objectively calculate tree cover density at little or no cost to the user. This combination of no cost and easy access has made the use of remotely-sensed images for measuring tree cover density and for procuring information about a site ubiquitous in design and planning circles (Janssen & Rosu, 2012; Sheppard & Cizek, 2009). Given the advantages provided by remotely-sensed images, it is likely that many designers and planners feel that they can assess a site and make rational decisions about it by relying on Google Earth imagery rather than on visiting a site and using other eyelevel methods (Power, Neville, Devereux, Haynes, & Barnes, 2013). In recent years, tree cover density measurements derived from remotely-sensed images have been widely adopted to evaluate and compare tree cover density at the scale of states (Nowak & Greenfield, 2012a), cities (McPherson, Simpson, Xiao, & Wu, 2011; Nowak & Greenfield, 2012b), and communities (Kardan et al., 2015; Klemm, Heusinkveld, Lenzholzer, & van Hove, 2015). Eye-level tree cover density measurements are used much less often. Eye-level measures include calculating tree coverdensity fromeye-levelphotography (Jiang, Chang, & Sullivan, 2014) and asking landscape experts or ordinary people to subjectively rate tree cover density from eye-level photographs of actual landscape scenes (Jiang, Larsen, Deal, & Sullivan, 2015; Nordh, Hartig, Hagerhall, & Fry, 2009). It is plausible, however, that eye-level photography, especially panoramic photography that has a similar visual scope to human vision, may better represent people’s perceptions of landscapes than remotely-sensed imagery, and may be a better tool than remotely-sensed imagery in efforts to understand the impact of urban forestry on human health and well-being. Many studies have reported strong, positive associations between the density of vegetation in urban landscapes and the health and wellbeing of individuals (Li & Sullivan, 2016; Parsons, Tassinary, Ulrich, Hebl, & Grossman-Alexander, 1998; Ulrich, 1984; van den Berg, Koole, & van der Wulp, 2003). Previous studies suggest, moreover, that eye-level photographs are more accurate at measuring tree cover density and procuring information about a site than remotely sensed imagery (Kweon, Ellis, Lee, & Rogers, 2006; Leslie, Sugiyama, Ierodiaconou, & Kremer, 2010). To our best knowledge, no empirical studies have examined the extent of agreement among remotely-sensed and eye-level methods of measuring tree cover density at the site scale. In addition, it is unclear whether remotely-sensed or eye-level methods would be better in certain circumstances (such as when tree cover density is low or high). To what extent can designers and planners eschew traditional forms of procuring information about a site (e.g., by measuring tree cover density from eye-level photographs or walking the site) and rely instead on information conveyed by remotely-sensed images? Should municipal officials establish standards of tree cover density in cities by relying on information conveyed solely by remotely-sensed images?