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Impervious Surfaces in Coastal New Hampshire - 1990

Impervious Surfaces in Coastal New Hampshire - 1990

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Metadata:


Identification_Information:
Citation:
Citation_Information:
Originator: Complex Systems Research Center, University of New Hampshire
Publication_Date: 20021231
Title: Impervious Surfaces in Coastal New Hampshire - 1990
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=coastalimperv90/nh>
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 1990 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 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 - 2005, are also available. Derived from similar data and using similar techniques, they provide subsequent 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 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 1990 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 - 1990. University of New Hampshire, Durham, NH."

Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: 19900908
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 would 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/coastalimperv90/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 98.6% based on 139 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 98.9% 98.9% Impervious 97.9% 97.9%

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 approximately 25-30 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: 19900908
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, 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 (NWI) maps.

Type_of_Source_Media: CD-ROM
Source_Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: 19900908
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 generalized, traditional supervised classifications 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). A set of 15-20 potential signatures, previously generated from a Landsat 7 Enhanced Thematic Mapper (ETM+) image acquired September 27, 2000, was evaluated by running an MOI classification and displaying the results on the underlying imagery. These signatures were augmented by a set of 10 additional signatures derived from the 1990 image data. 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 comparison.

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, 20 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 data set.

Unlike traditional supervised classifications, the subpixel approach typically produces classifications based on a single signature. Accordingly, 20 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, no historic road database was available. Thus, an editing task was required to subset the public and private roads that existed in 1990 (using 1992 DOQ images, 1992 SPOT imagery, and 1990 TM imagery). In addition, the private road data did not include pavement type/width. A second editing task was required to identify the surface type (paved/unpaved) of private roads. The paved roads were then subset and 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.

Source_Used_Citation_Abbreviation: TM
Process_Date: 2002 - 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 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 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: 20020131
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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