388 Fig. 3 Tree canopy cover by mapping zone based on aerial photo-interpretation Environmental Management (2010) 46:378–390 only used to estimate the overall proportion of cover in each mapping zone, not the accuracy of individual pixels. For tree canopy cover, the greatest difference between photo-interpretation and NLCD values tended to be in the zones with the greatest tree cover (Figs. 1 and 3). The underestimation of tree cover was greatest in developed land (13.7%), followed by forest (11.7%), other (8.0%) and agriculture and grass lands (6.7%). One potential reason for the underestimation in tree canopy cover may be related to the tree cover structure and distribution. As tree canopy cover tends to become less contiguous (more individual tree crowns) due to development patterns, the NLCD may be underestimating these smaller clumps of canopy cover. This pattern may partly explain why developed lands have greater overall underestimation of tree canopy cover than forest lands. The potential for greater underestimation would tend to increase with greater tree cover and this underestimation may be exacerbated as the canopy cover becomes more fragmented or unevenly distributed. As the overall difference between photo-interpretation and NLCD impervious cover is relatively small (1.4%) and impervious cover has likely increased between the NLCD and Google Earth images, the NLCD impervious cover estimates at the zone scale are reasonable. However, the underestimation of impervious cover tends to be greatest in the eastern United States (Fig. 2), which is the most urbanized portion of the country (Nowak and others 2005) and likely has had some of the greatest urban development in the 2000s (Nowak and Walton 2005). These results are 123 comparable to impervious surface area assessment of the mid-Atlantic region that showed that NLCD underesti- mated impervious cover by approximately 5%, with the underestimation occurring regardless of development intensity (Jones and Jarnagin 2009). The underestimation for the mid-Atlantic region (zone 60) in this study was 3.3%. Geographic differences in accuracy have also been shown for NLCD land-cover classes (e.g., shrubland, grassland, deciduous forests) (Wickham and others 2010). Other factors that may affect the impervious and canopy underestimation are varying geographies (e.g., topography, vegetation types), different processing or training methods, and varied interpretations and applications of the NLCD protocols used among the 12 different teams within their assigned mapping zones. One example of an interpretation that may result in the varied underestimation of surface cover is the selection of training sites and data used in developing the sub-pixel classiﬁcation and algorithm used to process the Landsat imagery. The image processing and classiﬁcation derived from rural training sites may vary considerably from training data obtained from urban areas, or from more homogeneous land cover to more heteroge- neous land cover sites (Greenﬁeld and others 2009; Walton and others 2008). Underestimation of tree and impervious cover may also be partly due to masking during map development, including the varied selection of ancillary data used for masking (Homer and others 2007; Homer and others 2004; Huang and others 2001; MRLC 2009; Yang and others 2003). While masking was generally used to decrease overestimation resulting from the NLCD