Impervious Surfaces in Coastal New Hampshire - 2005

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Identification_Information:
Citation:
Citation_Information:
Originator: Complex Systems Research Center, University of New Hampshire
Publication_Date: 20060428
Title: Impervious Surfaces in Coastal New Hampshire - 2005
Edition: 1
Geospatial_Data_Presentation_Form: raster digital data
Publication_Information:
Publication_Place: Durham, New Hampshire
Publisher: Complex Systems Research Center, University of New Hampshire
Online_Linkage:
<URL:http://www.granit.sr.unh.edu/cgi-bin/nhsearch?dset=coastalimperv05>
Larger_Work_Citation:
Citation_Information:
Originator: Complex Systems Research Center, University of New Hampshire
Publication_Date: 19860101
Title: NH GRANIT Database
Publication_Information:
Publication_Place: Durham, New Hampshire
Publisher: Complex Systems Research Center, University of New Hampshire
Online_Linkage: <URL:http://www.granit.sr.unh.edu>
Description:
Abstract:
Impervious surface acreage estimates for 2005 were developed for 48 towns in coastal NH, including the 42 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 5 Thematic Mapper (TM) 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 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.

Purpose:
These data are most appropriately used at a regional scale to generate and evaluate watershed level acreage summaries.
Supplemental_Information:
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 9.x, select "ASCII to Raster" from the "Conversion Tools-To Raster" section of ArcToolbox, select the ascii file, name the output grid, and select "Integer" for Grid Type.

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."

Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: 20051003
Currentness_Reference: Date of TM imagery
Status:
Progress: Complete
Maintenance_and_Update_Frequency: Irregular
Spatial_Domain:
Bounding_Coordinates:
West_Bounding_Coordinate: -71.533243
East_Bounding_Coordinate: -70.632024
North_Bounding_Coordinate: 43.720771
South_Bounding_Coordinate: 42.691233
Keywords:
Theme:
Theme_Keyword_Thesaurus: None
Theme_Keyword: Impervious Surfaces
Theme_Keyword: Land Cover
Theme_Keyword: Land Use
Theme_Keyword: Water Quality
Theme_Keyword: Remote Sensing
Theme_Keyword: Classification
Theme_Keyword: Subpixel
Place:
Place_Keyword_Thesaurus: None
Place_Keyword: United States
Place_Keyword: Northeast
Place_Keyword: New England
Place_Keyword: New Hampshire
Place_Keyword: Coastal
Place_Keyword: NH Estuaries Project
Place_Keyword: NHEP Area
Access_Constraints:
Acknowledgement of GRANIT will be appreciated in products derived from these data.
Use_Constraints:
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.
Point_of_Contact:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: Complex Systems Research Center
Contact_Person: GRANIT Database Manager
Contact_Position: GRANIT Database Manager
Contact_Address:
Address_Type: mailing and physical address
Address: Morse Hall, University of New Hampshire
City: Durham
State_or_Province: NH
Postal_Code: 03824
Country: US
Contact_Voice_Telephone: 603-862-1792
Contact_Facsimile_Telephone: 603-862-0188
Contact_Electronic_Mail_Address: granit@unh.edu
Hours_of_Service: 8:30AM-5PM, EST
Browse_Graphic:
Browse_Graphic_File_Name:
<URL:http://www.granit.sr.unh.edu/cgi-bin/load_file!PATH=/data/database/d-webdata/coastalimperv05/browse.gif>
Browse_Graphic_File_Description: gif image file
Browse_Graphic_File_Type: gif
Native_Data_Set_Environment: ESRI GRID (converted to ASCII grid for ease of transfer)

Data_Quality_Information:
Attribute_Accuracy:
Attribute_Accuracy_Report:
The standard error matrix shows an overall accuracy of 98.3% based on 119 samples. Below is a summary of User's and Producer's Accuracy for the data set:

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.

Logical_Consistency_Report: These data are believed to be logically consistent.
Completeness_Report:
These data are considered complete for the 48 towns in the study area.
Positional_Accuracy:
Horizontal_Positional_Accuracy:
Horizontal_Positional_Accuracy_Report:
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 69 ground control points selected from a Landsat 5 TM reference image.

Vertical_Positional_Accuracy:
Vertical_Positional_Accuracy_Report: Vertical positional accuracy was not assessed.
Lineage:
Source_Information:
Source_Citation:
Citation_Information:
Originator: USGS and NASA
Publication_Date: 20051003
Title: Landsat 5 Thematic Mapper
Edition: One
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:
Publication_Place: Sioux Falls, SD
Publisher: EROS Data Center, USGS
Other_Citation_Details:
Landsat path/row reference: path 12, row 30

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.

Type_of_Source_Media: CD-ROM
Source_Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: 20051003
Source_Currentness_Reference: ground condition
Source_Citation_Abbreviation: TM
Source_Contribution: Basis of image processing for the classification
Process_Step:
Process_Description:
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). 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.

Source_Used_Citation_Abbreviation: TM
Process_Date: 2006 - various

Spatial_Data_Organization_Information:
Direct_Spatial_Reference_Method: Raster
Raster_Object_Information:
Raster_Object_Type: Grid Cell
Row_Count: 3332
Column_Count: 1972
Vertical_Count: 1

Spatial_Reference_Information:
Horizontal_Coordinate_System_Definition:
Planar:
Grid_Coordinate_System:
Grid_Coordinate_System_Name: State Plane Coordinate System 1983
State_Plane_Coordinate_System:
SPCS_Zone_Identifier: New Hampshire
Transverse_Mercator:
Scale_Factor_at_Central_Meridian: 0.999967
Longitude_of_Central_Meridian: -71.666667
Latitude_of_Projection_Origin: 42.500000
False_Easting: 984250.000000
False_Northing: 0.000000
Planar_Coordinate_Information:
Planar_Coordinate_Encoding_Method: row and column
Coordinate_Representation:
Abscissa_Resolution: 93.500000
Ordinate_Resolution: 93.500000
Planar_Distance_Units: survey feet
Geodetic_Model:
Horizontal_Datum_Name: North American Datum of 1983
Ellipsoid_Name: Geodetic Reference System 80
Semi-major_Axis: 6378137.000000
Denominator_of_Flattening_Ratio: 298.257222

Entity_and_Attribute_Information:
Overview_Description:
Entity_and_Attribute_Overview:
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
Entity_and_Attribute_Detail_Citation:
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.

Distribution_Information:
Distributor:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: Complex Systems Research Center
Contact_Person: GRANIT Database Manager
Contact_Position: GRANIT Database Manager
Contact_Address:
Address_Type: mailing and physical address
Address: Morse Hall, University of New Hampshire
City: Durham
State_or_Province: NH
Postal_Code: 03824
Country: US
Contact_Voice_Telephone: 603-862-1792
Contact_Facsimile_Telephone: 603-862-0188
Contact_Electronic_Mail_Address: granit@unh.edu
Hours_of_Service: 8:30AM-5PM. EST
Distribution_Liability:
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 Energy and 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 these data.
Standard_Order_Process:
Digital_Form:
Digital_Transfer_Information:
Format_Name: ASCII Grid
Format_Version_Number: One
Transfer_Size: 13.1 MB
Digital_Transfer_Option:
Online_Option:
Computer_Contact_Information:
Network_Address:
Network_Resource_Name: <URL:http://www.granit.sr.unh.edu>
Fees:
No charge when downloaded from the Internet. Cost of reproduction for provision on CD/ROM of other media.
Ordering_Instructions:
Email GRANIT (granit@unh.edu) or order from web site (www.granit.sr.unh.edu).
Turnaround: Two weeks.
Technical_Prerequisites: Non

Metadata_Reference_Information:
Metadata_Date: 20060428
Metadata_Contact:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: Complex Systems Research Center
Contact_Person: GRANIT Database Manager
Contact_Position: GRANIT Database Manager
Contact_Address:
Address_Type: mailing and physical address
Address: Morse Hall, University of New Hampshire
City: Durham
State_or_Province: NH
Postal_Code: 03824
Country: US
Contact_Voice_Telephone: 603-862-1792
Contact_Facsimile_Telephone: 603-862-0188
Contact_Electronic_Mail_Address: granit@unh.edu
Hours_of_Service: 8:30AM-5PM, EST
Metadata_Standard_Name: FGDC Content Standards for Digital Geospatial Metadata
Metadata_Standard_Version: FGDC-STD-001-1998

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