380 photo-interpretation. Each random point was interpreted as to its cover type to statistically estimate the percentage tree canopy and impervious cover within each mapping zone. Despite its widespread and growing use, past editions of Google Earth and its content have been known to have issues regarding unknown dates of imagery (dates of imagery currently are provided) and erroneous content (Goodchild 2007; Potere 2008; Sheppard and Cizek 2009). Potere (2008) speciﬁcally found that the horizontal posi- tional accuracy of Google Earth imagery for several developed countries, including the United States, had a root mean squares error of 22.6 m and had a mean error of 19 m. However, the positional accuracy will have a neg- ligible effect on results in this study as the cover estimates are based on random samples within large geographic areas (mapping zones). Sample points that are off from a given coordinate will still produce a valid random sample of points within the mapping zone area for the cover analysis. Inaccurate horizontal positions would only affect the sample for points near the boundary of the map zones as some points may actually represent areas outside of the mapping zone. Given the large zone area relative to the mapping zone boundary, the potential number or effect of points interpreted outside the mapping zone is negligible. There are other aerial sources of data to compare NLCD-derived values (e.g., digital orthoimagery quarter quadrangles), however, Google Earth imagery provides one of the best means to assess overall tree canopy and impervious cover as it offers nearly complete coverage of the conterminous United States with interpretable images. Trained photo-interpreters with experience interpreting leaf-off and leaf-on imagery classiﬁed each point as trees (yes/no), impervious surface (yes/no), or as a non-inter- pretable image. As reﬂected in the 2001 NLCD, tree can- opy and impervious cover designations are not mutually exclusive (e.g., tree cover over sidewalk or road), and the photo interpreters were instructed to determine if the tree canopy covered an impervious surface, in which case it was classiﬁed as both tree and impervious. Most points (99.6%) fell on images that were readily interpretable (high-reso- lution imagery). Points falling on imagery with medium to coarse resolution (e.g., 30-m resolution) or with atmo- spheric obstructions (clouds) were considered non-inter- pretable and not included in the ﬁnal analysis. Overall, 63 of the 65 mapping zones had at least 99% interpretable points. The lowest percent of interpretable points was 93% in zone 9. Four photo-interpreters were used, with each mapping zone being assessed by one photo-interpreter. Photo-inter- pretation results were veriﬁed by having 100 points within each zone reinterpreted by another photo-interpreter. Some disagreements with the audit values were due to changes in Google imagery between the original interpretation and the 123 Environmental Management (2010) 46:378–390 audit. Zones with less than 90% agreement were reinter- preted and rechecked until at least 95% agreement was attained. Overall, the audit control checks resulted in a 95% average agreement between the original interpretation and the audit values. To help understand how differences within zones might differ by land-cover classes, interpreted points in each zone were stratiﬁed into 4 groups based on general NLCD land- cover classes (general LC class): (1) Trees/shrubs (NLCD classes: deciduous forest, evergreen forest, mixed forest, scrub/shrub, and woody wetland); (2) Agriculture/grass- land (classes: grassland/herbaceous, pasture/hay, and cul- tivated crops); (3) Developed (classes: developed, open space, low intensity, medium intensity, and high intensity); and (4) Other (classes: barren land and emergent herba- ceous wetland) (MRLC 2010). General NLCD classes with small areas within a zone would have a relatively small sample size. For general classes with a sample size of less than 20 interpretable points, additional random points were interpreted to ensure a minimum sample size of 20. Within each general LC class in each zone, the per- centage of tree canopy or impervious cover (p) was cal- culated as the number of sample points (x) hitting the cover attribute divided by the total number of interpretable sample points (n) within the general LC class (p = x/n). The standqarﬃdﬃﬃﬃﬃﬃeﬃﬃrﬃﬃrﬃoﬃr of the estimate (SE) was calculated as SE ¼ p ð1n pÞ (Lindgren and McElrath 1969). This method has been used to assess canopy cover in many cities (e.g., Nowak and others 1996). Total cover and SE for each mapping zone was calculated by weighting the general LC class cover estimates by NLCD general LC class area in each zone. A 95 and 99% conﬁdence interval of the photo-interpreted cover values was used to test for differences between the photo-interpreted and NLCD pre- dicted cover values for each zone. That is, the NLCD estimate was determined to be signiﬁcantly different from a photo-interpreted value if the NLCD value was outside the 95% conﬁdence interval bounds of the interpreted value. In some cases, the number of points falling on tree canopy or impervious cover within an NLCD general LC class would be zero, and thus the standard error and con- ﬁdence interval estimate of the percentage cover would also be zero. In this case, any non-zero NLCD cover esti- mate would be considered signiﬁcantly different from the interpreted values, no matter how small the difference. To avoid these minor differences being considered signiﬁ- cantly different from cover values of zero, the standard error of the zero cover estimates was calculated using a sample size of one for the cover estimate (i.e., x = 1 instead of x = 0). These adjustments for the test of sig- niﬁcance are noted in the appropriate tables (Tables 1, 2). Spearman correlations were also used to determine if