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.
Two related data sets, Impervious Surfaces in Coastal New Hampshire - 2000 and Impervious Surfaces in Coastal New Hampshire - 1990, are also available. Derived from similar data and using similar techniques, they provide prior estimates of impervious surface coverage.
Development of the 2005 Impervious Surface data was funded by a grant from the New Hampshire Estuaries Project, as authorized by the U.S. Environmental Protection Agency pursuant to Section 320 of the Clean Water Act.
Please cite as "New Hampshire GRANIT. 2006. Impervious Surfaces in Coastal New Hampshire - 2005. University of New Hampshire, Durham, NH."
DECRIPTION PRODUCER'S ACCURACY USER'S ACCURACY Not impervious 96.7% 96.7% Impervious 98.3% 09.3%
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 467 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.
A geometric model was generated from the source imagery, which was then used to reference the data to New Hampshire Stateplane coordinates, NAD83. The model was derived using 69 ground control points selected from a Landsat 5 TM reference image.
Ancillary data comprised numerous holdings from the GRANIT archive (the NH statewide GIS), including watershed boundaries, Digital Raster Graphics (DRGs), NH Department of Transportation road centerlines (November, 2005), Digital Elevation Models (DEMs), National Agricultural Imagery Program color imagery (2003), and US Fish and Wildlife Service National Wetlands Inventory (NWI) maps.
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). A unique aspect of the subpixel software is that signatures are transferable from one image to another. In this case, four signatures (out of 20 evaluated) derived from the ETM+ image used to produce the 2000 impervious data set were used to process the 2005 TM image. Thirteen new signatures (out of 50 evaluated) were generated directly from the 2005 image.
Unlike traditional supervised classifications, the subpixel approach typically produces classifications based on a single signature. Accordingly, the 17 data sets were merged into one. This was achieved by "layer stacking" the images and then using Imagine statistical functions to select the maximum layer value (e.g. maximum percentage of imperviousness) at each pixel. These results were then merged with the results of the initial unsupervised classification. Where there was overlap, the subpixel impervious pixels (with the percent imperviousness) took precedence over the pixels mapped as impervious from the unsupervised processing. Pixels mapped as impervious from the unsupervised classification but not captured by the subpixel processing were coded as 100% impervious.
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:
- US Fish & Wildlife Service National Wetlands Inventory (NWI) data, based on aerial photography acquired in the mid-1980's, as archived in the GRANIT database; and - New Hampshire Department of Transportation (NHDOT) road centerline data - both public and private roads, as of November, 2005
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. Because many of the misclassified pixels occurred in wetlands, NWI data were converted to a grid format and used as a mask to rapidly isolate and review potential problem areas. However, pixels concurrent with the NWI grid were not simply converted to non-impervious status, because of numerous cases where wetlands had been filled since the NWI photo date and were properly coded as impervious.
Finally, the methodology included the incorporation of NHDOT 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.