Impervious surface acreage is mapped in percent ranges by individual grid cell. Users may generate area estimates by factoring the cell size (8742.25 sq. ft. or .2 acres) by the low, midpoint, or high end of the assigned range.
Six related data sets, Impervious Surfaces in Coastal New Hampshire (2005, 2000, and 1990) and Impervious Surfaces in Southern York County, Maine (2005, 2000, and 1990) are also available. Derived from similar data and using similar techniques, they provide prior estimates of impervious surface coverage.
Development of the 2010 Impervious Surface data was funded by a grant from the Piscataqua Region Estuaries Project, as authorized by the U.S. Environmental Protection Agency's National Estuary Program.
Please cite as "New Hampshire GRANIT. 2011. Impervious Surfaces in Coastal New Hampshire and Southern York County, Maine - 2010. University of New Hampshire, Durham, NH."
DECRIPTION PRODUCER'S ACCURACY USER'S ACCURACY Not impervious 93.3% 98.0% Impervious 97.9% 93.0%
The above error matrix reports the approximate accuracy of the results. It presents classified data results (e.g. derived from image processing) relative to reference data (e.g. data acquired via field visits or from some other source of known reliability). However, it is important to note that this standard methodology does not fully characterize the reliability of the results because the impervious surface pixels were mapped on a percentage basis. The accuracy assessment only evaluates the presence/absence of imperviousness at a given site, not the specific percentage impervious.
Further, two constraints were applied during selection of the assessment sites. First, a road proximity constraint was applied (within 5 pixels or approximately 500 feet of a road centerline) to facilitate the completion of the assessment. Second, each impervious surface feature was "shrunk" by 1 pixel width prior to the selection process to exclude confusion among edge pixels.
By constraining the accuracy assessment selection technique, the site selections were probably biased in favor of those areas that are most easily mapped (e.g. large parking lots, buildings, and residential subdivisions rather than single houses and isolated features). Nevertheless, the assessment provides a general estimate of the data reliability.
Ancillary data comprised numerous holdings from the GRANIT and Maine Office of GIS archives, including watershed boundaries, Department of Transportation road centerlines (ME - September, 2009; NH - May, 2011), National Agricultural Imagery Program color imagery (2009 for both ME and NH), and 1-ft. resolution orthoimagery (NH - April, 2010).
The impervious surface mapping began by conducting a traditional unsupervised classification on the georeferenced TM data set to generate an initial delineation of the developed/undeveloped land features. Past mapping efforts indicated that the subpixel technique may omit certain types of impervious features, due in part to the variety of specific surface types that constitute impervious surfaces. The generalized mapping was conducted to anticipate some of these "gaps". It also provided a reference data set to supplement the visual interpretation of the subsequent subpixel classifications. The unsupervised classification produced 50 clusters which were coded into one of two categories - impervious and non-impervious. The impervious category included areas characterized by a high percentage (typically 50% or greater) of constructed materials (asphalt, concrete, buildings, etc.) This dataset was then recoded into final, two class image comprising these categories.
Some obvious misclassifications were identified in the preliminary results. Tidal flats and wetlands, shallow water and scrub-shrub wetlands most often contributed to the problematic situations. These "problem pixels" were addressed using either an iterative process, whereby training data were added/deleted and the classification re-run, or by using on-screen editing to delete misclassified pixels in the final data set. After satisfactory results were obtained, the data were available for subsequent use.
The ERDAS Imagine Subpixel analysis tool was then applied to derive additional estimates of "proportion of imperviousness" for each urban cell in the study area. This methodology (more fully described at www.discover-aai.com and www.erdas.com) is capable of detecting materials of interest (MOI) - in this case, impervious surfaces - that occur within each pixel. The classification describes each pixel as having a percentage of the MOI ranging from 20 to 100, reported in increments of 10%. Additional processing using road centerline data, described further below, resulted in the inclusion of the lower, 0-19% range. Note that the spatial extent of the impervious surface (the MOI) within each pixel is not identified. Rather, the entire pixel is reported as having a certain percentage of the MOI. By factoring the area of each pixel by the percent of that pixel containing the MOI, acreage summaries may be generated.
The subpixel processing approach followed generally accepted techniques (Flanagan, 2000; Flanagan and Civco, 2001; ERDAS, 2000). Sixty-seven signatures representing various impervious surface types were evaluted for use in the classification, with 27 used to produce the final, pre-edited classification.
The post processing phase of the project was designed to enhance the classification phase by addressing two specific issues - the correction of any remaining, obvious errors in the classification results, and the incorporation (or "burning in") of road centerline data to optimize the mapping of pavement as an impervious surface feature. Two ancillary data sets were obtained for this phase:
- National Agricultural Imagery Program (NAIP) 2009 imagery, 1-meter resolution, for ME and NH; and - Maine/New Hampshire Departments of Transportation road centerline data - both public and private roads.
The provisional impervious surface classification included some recurring errors - typically misclassified pixels occurring in open water, wetland and forests. The image analyst could often quickly identify these errors using pattern recognition, past experience and in some cases, DOQ/NAIP reference images. Errors were removed from the classification by defining polygons around the misclassifications and recoding, as appropriate.
Finally, the methodology included the incorporation of public and private road data in the final product, where the imperviousness of each pixel was assigned based on the road pavement width. (Because of their relatively narrow, linear shape, road features are occasionally omitted in the classification phase.) Some public roads and most/all private road data did not include the pavement type/width. A field data collection task was required to identify the surface type (paved/unpaved) of these roads. The subset of paved roads were assigned a default pavement width of 20 ft. The pavement width characteristic was then used to "burn" all paved roads (public and private) into the classified data set.