Arboriculture & Urban Forestry 46(1): January 2020 using high-resolution images that make it easier to discriminate trees from other land cover types (Rich- ardson and Moskal 2014). These same sources of error, discussed in greater detail later, can also be troublesome for the IC method. There are two primary differences between the IC method and the PI method of UTC assessment. First, IC is a census of every pixel in the imagery rather than a sample of representative areas within the imag- ery. As such, a “wall-to-wall” land cover classifica- tion map is obtained for the study area. While there may be errors in classification of the pixels (described later), every pixel is classified, and there is no statisti- cal sampling error as with the PI method. The second difference is that the IC method relies on spectral analysis by computer algorithms rather than on the visual acuity of a human analyst to classify the land cover in the imagery. Once an algorithm is properly “trained” to distinguish land cover types, it can be deployed to autonomously analyze and classify the land cover of every pixel in the imagery (Myeong et al. 2001), making wall-to-wall classification feasible for very large land areas. It is important to note here that i-Tree Landscape, although considered an IC method in the scope of this paper, does not perform this computerized classification of land cover in real time. Rather, it simply displays the land cover maps that have been previously created and then provides an interface to query this land cover for a user-defined study area. Although it is more technically complex to pro- gram and train algorithms, the resulting wall-to-wall IC is much richer in information content because it not only calculates the amount of UTC but also the distribution of UTC across the study area. When combined with other types of geospatial data (e.g., population demographics), an IC assessment also affords opportunities for complex geospatial analysis of the urban ecosystem. Some of these capabilities are built into i-Tree Landscape and provide a power- ful decision-support tool for practitioners. The IC method of UTC assessment is not without limitations. Consideration must be given to the spec- ification of the imagery used in the analysis—such as its geometric, spatial, and temporal resolutions— because this can significantly affect the IC quality (Campbell and Wynne 2011). Spatial resolution is particularly important. Previous studies found that the 2001 NLCD (30-m spatial resolution) underestimated 53 tree canopy in urban areas by up to 28.4% locally (Greenfield et al. 2009; Nowak and Greenfield 2010). The 2001 NLCD had limited detection capabilities for urban tree canopy because a single pixel, covering an area of 900 m2 , may contain a combination of land cover types (called a mixed pixel), preventing accu- rate detection and differentiation of tree canopy from other cover types (Landry and Pu 2010). Although the 2011 NLCD used in i-Tree Land- scape has improved capabilities for UTC assessment, the spatial resolution is still coarse (30 m), and mixed pixels remain a challenge to accuracy. Most propri- etary IC assessments of UTC performed by contrac- tors or scientists use high-resolution (1 m or less) imagery and advanced geospatial techniques (e.g., object-based image analysis) that improve accuracy of UTC assessments (MacFaden et al. 2012; McGee et al. 2012; O’Neil-Dunne et al. 2014). In addition, high-resolution imagery is increasingly combined with LiDAR (Light Detection and Ranging) data, which provides height information for ground-based objects and can help to discriminate between trees, shrubs, and tall grass (MacFaden et al. 2012; Ucar et al. 2016). These advanced techniques require special- ized knowledge of remote sensing to process and analyze the data for generating statistics about UTC coverage (Walton et al. 2008) and are all important considerations for practitioners who may wish to obtain a proprietary IC assessment of UTC from a contractor. While i-Tree Landscape takes away some of these concerns for the practitioner, it is still import- ant to recognize that i-Tree Landscape incorporates mostly low-resolution imagery, and the land cover classification maps it displays are likely to underesti- mate UTC. High-resolution land cover maps are being added incrementally to the application and will improve its capabilities over time. In the following sections of this paper, we present a case study in which we examine these geospatial methods for assessing UTC on an urbanized college campus in the eastern United States. For the case study, we first conducted a series of replicated UTC assessments using the PI method found in i-Tree Can- opy. There we analyzed the effect of the point sample size on the accuracy and estimation error for UTC and other land cover types. We then compared these find- ings with two UTC assessments that were performed using the IC method: a proprietary analysis using high- spatial-resolution imagery and a low-spatial-resolution ©2020 International Society of Arboriculture
January 2020
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