Impervious surface acreage estimates for 2000 were developed for 48 towns in
coastal NH, including the 43 towns within the designated Zones A and B of the
NH Estuaries Project Area. Data were developed using a combination of subpixel
and traditional image classification techniques applied to Landsat 7 Enhanced
Thematic Mapper (ETM+) imagery. Additionally, road centerline data from the
NH Department of Transportation were "burned in" to the data to capture narrow,
linear features where pavement exists.
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
Two related data sets, Impervious Surfaces in Coastal New Hampshire - 1990
and Impervious Surfaces in Coastal New Hampshire - 2005, are also available.
Derived from similar data and using similar techniques, they provide
prior and subsequent estimates of impervious surface coverage.
These data are most appropriately used at a regional scale to generate and
evaluate watershed level acreage summaries.
Data distribution tile: NHEP Area ascii grid
Users of ESRI software will need the Spatial Analyst extension or GRID.
To import the ascii grid in ArcView 3.x, first enable the Spatial Analyst
extension. Select "Import Data Source" from the FILE menu, and select
"Ascii Raster" from the dialogue window that appears. To import the
ascii grid in ArcGIS 8.x, select "ASCII to Grid" from the "Import to Raster"
data conversion section of ArcToolbox, select the ascii file, name the output
grid, and select "Integer" for Grid Type.
Development of the 2000 Impervious Surface data was funded in part by a grant from
the Office of State Planning, 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. 2001. Impervious Surfaces in Coastal
New Hampshire - 2000. University of New Hampshire, Durham, NH."
Currentness_Reference: Date of TM imagery
Theme_Keyword: Impervious Surfaces
Theme_Keyword: Land Cover
Theme_Keyword: Land Use
Theme_Keyword: Water Quality
Theme_Keyword: Remote Sensing
Place_Keyword: United States
Place_Keyword: New England
Place_Keyword: New Hampshire
Place_Keyword: NH Estuaries Project
Place_Keyword: NHEP Area
Acknowledgement of GRANIT would be appreciated in products
derived from these data.
Users must assume responsibility to determine the appropriate use
of these data. Because of the nature of the source imagery (30m pixels),
it is not recommended that the data be used at scales greater than 1:60,000.
Consult the Data Quality Section for background on the development of
the data set, and the Attribute Accuracy Report for a more detailed
description of the accuracy of these data.
Contact_Organization: Complex Systems Research Center
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
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.
Logical_Consistency_Report: These data are believed to be logically consistent.
These data are considered complete for the 48 towns in the study area.
Georeferencing RMS less than 0.5 pixels.
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 approximately 25-30 ground control points selected from a
Landsat 5 TM reference image.
Vertical_Positional_Accuracy_Report: Vertical positional accuracy was not assessed.
Ancillary data comprised numerous holdings from the GRANIT archive (the NH
statewide GIS), including watershed boundaries, panchromatic Digital Orthophotoquads
(DOQs), Digital Raster Graphics (DRGs), NH Department of Transportation road
centerlines, Digital Elevation Models (DEMs), SPOT panchromatic (10 meter
resolution) images, and US Fish and Wildlife Service National Wetlands Inventory
Source_Currentness_Reference: ground condition
Source_Contribution: Basis of image processing for the classification
The impervious surface mapping began by conducting a generalized, traditional supervised
classification on the 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.
A body of 75 training sites, representing various types of impervious surfaces, was
utilized in the traditional classification. These data were available as a result
of numerous land cover classifications conducted within the project area over the
past several years. Coupled with local knowledge, the training data were used to
perform maximum likelihood classifications on the satellite imagery, yielding a
data set of developed/undeveloped features for each year. The developed/urban
class included areas characterized by a high percentage (typically 50% or greater)
of constructed materials (asphalt, concrete, buildings, etc.). The identification
of specific areas as urban was based strictly on features visible in the imagery,
and thus only the areas within large subdivisions that were actually constructed
were classified as urban.
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). The 2000 TM data set was initially used to
generate 15-20 potential signatures, which were evaluated by running an MOI classification
and displaying the results on the underlying imagery. The results were evaluated both by
visual inspection of 1998 USGS Digital Orthophotoquads (DOQs), and by reference to personal
knowledge of the area. However, it is important to recognize that the evaluation of
each classification compared the presence/absence of impervious surface MOI and not the
actual percentage mapped per image pixel, as we had no data to effect the latter type of
Signatures were marked as "good", having "potential", or "unusable". Good signatures were
those that provided tight classifications and would require little if any on-screen editing.
Signatures having "potential" were those that mapped much of an area correctly, but would
need some data clean up. Potential signatures were also those that could be altered using
classification tolerances, (a standard feature of the subpixel classification routine),
such that more or fewer image pixels would be included in the classification set. Signatures
were considered "unusable" when too many pixels were included in the classification and an
unreasonable amount of on-screen editing would be required to produce an acceptable data set.
As a result of these signature derivations and classification tests, 12 signatures were
accepted to generate the final impervious surface data set. These signatures provided a
reasonable classification that could be edited to derive a provisional impervious surface
Unlike traditional supervised classifications, the subpixel approach typically produces
classifications based on a single signature. Accordingly, 12 data sets were produced and
subsequently 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.
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 August, 2002
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 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.) However, the pavement characteristic
was only available for the public road data set. Thus, an editing task was required to
identify the surface type (paved/unpaved) of private roads, and the paved private roads
were assigned a default pavement width of 20 ft. The pavement width characteristic was
then used to "burn" the paved roads (public and private) into the classified data set.
Process_Date: 2002 - various
Raster_Object_Type: Grid Cell
Grid_Coordinate_System_Name: State Plane Coordinate System 1983
SPCS_Zone_Identifier: New Hampshire
Planar_Coordinate_Encoding_Method: row and column
Planar_Distance_Units: survey feet
Horizontal_Datum_Name: North American Datum of 1983
Ellipsoid_Name: Geodetic Reference System 80
Value codes are as follows:
0 - 0% of this grid cell is impervious
1 - 0-19% of this grid cell is impervious
2 - 20-29% of this grid cell is impervious
3 - 30-39% of this grid cell is impervious
4 - 40-49% of this grid cell is impervious
5 - 50-59% of this grid cell is impervious
6 - 60-69% of this grid cell is impervious
7 - 70-79% of this grid cell is impervious
8 - 80-89% of this grid cell is impervious
9 - 90-99% of this grid cell is impervious
10- 100% of this grid cell is impervious
The classification describes each pixel as having a percentage of the
Material of Interest or MOI (impervious surface material) ranging from 20
to 100, reported in increments of 10%. Additional processing using road
centerline data resulted in the inclusion of the 0-19% range.
Contact_Organization: Complex Systems Research Center
Contact_Person: GRANIT Database Manager
Contact_Position: GRANIT Database Manager
Address_Type: mailing and physical address
Address: Morse Hall, University of New Hampshire
Hours_of_Service: 8:30AM-5PM. EST
Digital data in NH GRANIT represent the efforts of the
contributing agencies to record information from the cited
source materials. Complex Systems Research Center, under
contract to the NH Office of State Planning, and in
consultation with cooperating agencies, maintains a
continuing program to identify and correct errors in these
data. OSP, CSRC, and the cooperating agencies make no claim
as to the validity or reliability or to any implied uses of