62 Hwang and Wiseman: Geospatial Applications for Urban Tree Canopy Assessment Conservancy 2017). There are ongoing efforts to incorporate high-resolution land cover data into i-Tree Landscape (equivalent to our proprietary anal- ysis), which will improve analysis capabilities over what is available currently with the lower resolution NLCD data. At the time of this article, high-resolution land cover maps for the entirety of the Chesapeake Bay Watershed and several major metropolitan areas across the United States had been integrated into i-Tree Landscape. Just like human analysts in the PI method, the IC method is prone to some of the same errors in distin- guishing tree canopy from land covers with similar spectral signatures. Shrubs and tall grasses have been reported to be difficult to distinguish from tree can- opy in leaf-on seasons (Zhou and Troy 2008; Mac- Faden et al. 2012). In our evaluation of the low-resolution IC method of UTC analysis (i-Tree Landscape), we found that groups of shrubs and arti- ficially turfed athletic fields were often classified as trees and nontree vegetation, respectively. For this reason, integration of LiDAR data, which provide height information about land cover, can help to accu- rately distinguish between trees and other nontree vegetation and improve the accuracy of IC (O’Neil- Dunne et al. 2014; Parmehr et al. 2016). While becoming increasingly available, LiDAR data is costly and requires additional geospatial skills and software for processing. The application i-Tree Landscape holds great prom- ise as a widely available UTC assessment tool because most anyone with an internet connection and basic computer skills can obtain UTC and land cover data from it, albeit of an older vintage and with a limited range of analytical tools. The major limitation we find at this time is that the analyst does not have fine-scale control in delimiting the study area. Instead, the ana- lyst has to delineate the study area using one of sev- eral predetermined boundaries—delimiting schemes built into the application. For our study area, we had to settle on a delimitation using US Census Block Groups, three of which overlaid our study area but did not match the boundaries precisely. As a result, we had to take additional steps for a more direct com- parison of UTC estimates in our study area. Because localities and organization often wish to target programs and policies to particular landowners, a proprietary IC analysis of UTC might be preferable. However, i-Tree Landscape offers a low-cost alternative, partic- ularly if the aim is to develop programs and policies ©2020 International Society of Arboriculture targeted at geographic areas larger than the neighbor- hood scale. Limitations of Our Comparisons Although we found no statistically significant differ- ences in the UTC of our study area across the time frames captured in the imagery analysis tools (2008 to 2011), time frame remains a limitation to an unequiv- ocal comparison of the UTC assessment methods. Yet this time frame difference is also instructive from a practical standpoint. That is, any type of comparative analysis of tree and land cover using geospatial meth- ods needs to be cognizant of the date and origin of the imagery. As an example, it is possible that a practi- tioner might wish to compare tree cover across multi- ple districts in a locality at a single point in time. In that case, it would be important to ensure comparabil- ity of the imagery. In our study, the date of the imag- ery could no doubt be a source of error in comparing the UTC assessment methods. Quantitatively, there were no statistical differences in estimates of tree canopy between the time frames. Qualitatively, we can also say that there were no major perturbances of tree cover by development, storms, or pests in the study area for the time frames bracketed by these imagery dates. Without question, there were gains and losses in tree cover from tree planting, canopy growth, and canopy mortality in the study area, but given that this is a relatively stable, middle-aged urban forest, we doubt that the observed differences (and similarities) in UTC and land cover across anal- ysis methods were majorly influenced by canopy cover dynamics. Instead of the differences in image dates, we point to fundamental differences in the geo- spatial methods as the primary source of the varia- tions that we observed: (1) mixed pixels, (2) relief displacements, (3) seasonal imagery differences (i.e., leaf-on vs. leaf-off imagery), and (4) the difficulty in distinguishing small-patch land cover types in close proximity to one another (imagery edge effects). Urban forestry practitioners must be aware of these sources of variation and take appropriate steps to minimize their influence on UTC assessments. Our Recommendations for UTC Assessment Although we observed differences in UTC estimates across the assessment methods in this study, we do not advocate for one method over the other because each has its relative strengths and weaknesses. Instead,
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