Arboriculture & Urban Forestry 46(1): January 2020 of Blacksburg (Figure 1B). Using ArcMap® Version 10 (Esri, Redlands, CA), we clipped our study area out of the land cover map and calculated the area comprising each of the same four land cover classes used in our i-Tree Canopy analysis. Our second IC assessment of UTC entailed land cover classification from low-resolution imagery in i-Tree Landscape. Knowing that the land cover data derived from 2011 NLCD is much coarser than the data in the proprietary high-resolution analysis, we were interested to see how they compared for our campus study area. An analysis limitation of i-Tree Landscape is that the user cannot delineate a precise study area on the application’s web map. Instead, the user must choose a boundary from a library of several predetermined boundary types. The boundary that most closely overlaid our campus study area was the selection of three contiguous US Census Block Groups, which is the smallest scale boundary-delimiting scheme built into the application. This resulted in an analysis area of 4.52 km2 that encompassed some nonuniversity residential and commercial land on the east side of campus and was larger than the primary study area of 3.58 km2 (Figure 1C). Therefore, for a more direct comparison of the UTC estimate between the analysis methods, we took additional steps out- side of i-Tree Landscape to clip out our study area from the 2011 NLCD US Forest Service tree canopy data (US Geological Survey 2017). This is a raster dataset embedded in i-Tree Landscape where each pixel in the map represents the percent of tree cover for a 30-m spatial-resolution pixel. For example, a pixel with a value of 30 means that tree canopy cov- ers 30% of the 900 m2 area (30 m × 30 m). Photo Interpretation (PI) Method For our PI method, we used the web-based applica- tion i-Tree Canopy (Figure 1A). In using this applica- tion, the analyst is led through a series of steps to (1) define the assessment area on the aerial photo, (2) define the land cover classes to be assessed, and (3) randomize and visually interpret sampling points on the aerial photo. As the analyst classifies the land cover of each randomized point on Google Maps™ , the application keeps a running total of sample size, percent of land area in each cover class, and the stan- dard error associated with each cover class estimate. The assessment is concluded when the analyst com- pletes a predetermined point sample size. 55 Our aim was to examine the effect of increasingly larger point sample sizes on the resultant land cover estimates and associated standard errors; therefore, we analyzed eight different point sample sizes. Based on recommendations provided on the i-Tree Canopy website, we started with sample sizes of 500 and 1,000 points (US Forest Service 2011). These equated to point sampling intensities in our study area (3.58 km2 ) of about 139 and 278 points per km2 , respec- tively. We then chose six additional point sample sizes (sampling intensities) that bracketed the aver- age sampling intensity (4.1 points per km2 in a UTC study of 20 U.S. cities (Nowak and Green- field 2012). These point sample sizes (# per km2 ) reported ) were 10 (2.8), 12 (3.3), 25 (6.9), 50 (13.9), 100 (27.8), and 250 (69.4). For each of the eight sample sizes, we conducted ten independently replicated runs of i-Tree Canopy for our study area. All replicated runs were conducted by a primary analyst who had extensive experience working with aerial photos and a high level of familiarity with the campus landscape features. We predefined four land cover classes for PI of our study area: water (WA), impervious surface (IS), nontree vegetation (NTV), and tree canopy (TC). We also included a category for uninterpretable points, to which we assigned sample points that fell onto shadow areas or at the edge between two land cover types. Two additional analysts independently reviewed the uninterpretable points and assigned them into one of the four land cover classes. When these two analysts did not agree, the primary analyst made a final decision for classification. The three geospatial methods for this study used imagery that was collected in two different time frames: the high-resolution imagery analysis (propri- etary IC method) used 2008 NAIP imagery, the low-resolution imagery analysis (i-Tree Landscape) used 2011 NLCD canopy cover maps, and the PI method (i-Tree Canopy) used 2011 to 2012 Google Maps™ imagery. Although we were limited to the imagery that was available through each analysis method, the only way we could address the differing dates was to conduct additional PI-based assessments using ArcGIS® (five replications with 500 sample points each on the 2008 NAIP image) to examine dif- ferences in tree cover in the study area between 2008 and 2011. ©2020 International Society of Arboriculture
January 2020
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