Arboriculture & Urban Forestry 46(1): January 2020 we suggest that practitioners develop a fundamental understanding of not only the available methods and tools but also of how each might address their analyt- ical needs in different ways. For rapid analysis of a large geographic area where the information needed is strictly the percentage of tree canopy cover relative to other land cover types, i-Tree Canopy (or a compa- rable PI tool) is a good choice. This information is useful for preliminary strategic planning or periodic benchmarking of land cover changes. When informa- tion about both the amount and distribution of tree canopy (and its interaction with other geo-oriented data) is also needed, then a wall-to-wall computer- ized classification of imagery would be a better choice. The analytical power of this type of assess- ment is much greater for this purpose, as evidenced by the capabilities of the i-Tree Landscape applica- tion. We recommend a wall-to-wall land cover assess- ment for those who need comprehensive and detailed information about tree canopy, which is important for long-range planning of tree planting and conservation as well as land use planning and local policy creation. i-Tree Canopy and i-Tree Landscape are also excel- lent platforms for enabling citizens to study their urban forests and for educating the public about urban forestry. Ongoing public investment in urban forest assessment tools is needed to empower localities of all sizes and resource capabilities to study their urban forests and make informed decisions about urban for- estry policy, management plans, and practices that conserve and replenish tree canopy. CONCLUSION In this paper, we have provided an overview of the geospatial methods for UTC assessment that are com- monly employed by urban forestry practitioners. Computerized IC of high-resolution aerial or satellite imagery is quickly becoming the gold standard for comprehensively assessing UTC and other land cover types over large geographic areas. While more com- monplace than even five years ago, IC analysis of high-resolution imagery is still often out of reach—in terms of cost or technical complexity—for many small localities and nonprofit organizations seeking to understand their tree canopy and strategize for its conservation. The US Forest Service is addressing these barriers by creating web applications (i-Tree Canopy and i-Tree Landscape) that are free to the public and require limited technical capabilities. Our examination of i-Tree Canopy (a PI tool) on an 63 urbanized college campus revealed that a high level of agreement in UTC and land cover estimates can be achieved in comparison to computerized classifica- tion of high-resolution imagery. Where the land cover map is derived from the low-resolution 2011 NLCD, i-Tree Landscape appears to underestimate UTC, but its capabilities will undoubtedly improve as more high-resolution maps are incorporated into the tool. Information about tree canopy and land cover is fun- damental to urban forestry and has numerous applica- tions for understanding the structure and function of the urban forest and its management needs. A greater understanding of UTC assessment methods and careful choice of assessment tools will advance these efforts. LITERATURE CITED Bhatta, B. 2010. Analysis of Urban Growth and Sprawl from Remote Sensing Data. Springer, Verlag, Berlin, Heidelberg, Germany. 172 pp. Campbell, J.B., and R.H. Wynne. 2011. Introduction to Remote Sensing. Guilford Press, New York, USA. 667 pp. Chesapeake Conservancy. 2017. Land Cover Data Project. Chesa- peake Conservancy, Annapolis, Maryland, USA. Accessed July 2018. Dwyer, J.F., E.G. McPherson, H.W. Schroeder, and R.A. Rown- tree. 1992. Assessing the benefits and costs of the urban forest. Journal of Arboriculture 18(5): 227-234. Eyre, F.H. 1980. Forest Cover Types of the United States and Canada. Society of American Forests, Bethesda, Maryland, USA. 488 pp. Greenfield, E.J., D.J. Nowak, and J.T. Walton. 2009. Assessment of 2001 NLCD percent tree and impervious cover estimates. Photogrammetric Engineering & Remote Sensing 75(11): 1279-1286. Heynen, N., H.A. Perkins, and P. Roy. 2006. The political ecology of uneven urban green space: The impact of political economy on race and ethnicity in producing environmental inequality in Milwaukee. Urban Affairs Review 42(1): 3-25. Hostetler, A.E., J. Roan, D. Martin, V. DeLauer, and J. O’Neil- Dunne. 2013. Characterizing tree canopy loss using multi- source GIS data in Central Massachusetts, USA. Remote Sensing Letters 4(12): 1137-1146. Kaspar, J., D. Kendal, R. Sore, and S.J. Livesley. 2017. Random point sampling to detect gain and loss in tree canopy cover in response to urban densification. Urban Forestry & Urban Greening 24: 26-34. Kimball, L.L., P.E. Wisman, S.D. Day, and J.F. Munsell. 2014. Use of urban tree canopy assessments by localities in the Chesapeake Bay Watershed. Cities and the Environment 7(2): Article 9. King, K.L., and D.H. Locke. 2013. A comparison of three methods for measuring local urban tree canopy cover. Arboriculture & Urban Forestry 39(2): 62-67. Landry, S., and R. Pu. 2010. The impact of land development regulation on residential tree cover: An empirical evaluation ©2020 International Society of Arboriculture
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