2002 Impervious Surfaces, Connecticut and New York Portions of the Long Island Sound Watershed

Metadata also available as

Frequently-anticipated questions:


What does this data set describe?

Title:

2002 Impervious Surfaces, Connecticut and New York Portions of the Long Island Sound Watershed

Abstract:

Landsat Thematic Mapper satellite imagery based impervious surface estimation, circa 2002, for the Connecticut and New York portions of the Long Island Sound watershed. The impervious surface estimation depicts 10 levels of impervious surfaces. These are 0. 0 to 9 percent impervious, 1. 10 to 19 percent impervious, 2. 20 - 29 percent impervious, 3. 30 - 39 percent impervious, 4. 40 - 49 percent impervious, 5. 50 - 59 percent impervious, 6. 60 - 69 percent impervious, 7. 70 - 79 percent impervious, 8. 80 - 89 percent impervious, 9. 90 - 100 percnt impervious. Source Landsat TM image data were from September 8, 2002. The impervious surface estimation was compiled using the Sub-Pixel Classifier module in ERDAS Imagine 8.7 by the Center for Land use Education And Research (CLEAR) in the College of Agriculture and Natural Resources at the University of Connecticut.

  1. How should this data set be cited?

Center for Land use Education And Research (CLEAR), and The University of Connecticut, March 2006, 2002 Impervious Surfaces, Connecticut and New York portions of the Long Island Sound watershed: Center for Land use Education And Research (CLEAR), Storrs, CT.

Funded by the US Environmental Protection Agency Long Island Sound Office

Online Links:

  1. What geographic area does the data set cover?

West_Bounding_Coordinate: -74.048228

East_Bounding_Coordinate: -71.757744

North_Bounding_Coordinate: 42.116499

South_Bounding_Coordinate: 40.664597

  1. What does it look like?
  2. Does the data set describe conditions during a particular time period?

Calendar_Date: September 8, 2002: majority area coverage

Currentness_Reference: Time period based on satellite image collection date.

  1. What is the general form of this data set?

Geospatial_Data_Presentation_Form: map

  1. How does the data set represent geographic features?

a.    How are geographic features stored in the data set?

This is a Raster data set. It contains the following raster data types:

      • Dimensions 6219 x 5267 x 1, type Pixel

b.    What coordinate system is used to represent geographic features?

The map projection used is Lambert Conformal Conic.

Projection parameters:

Standard_Parallel: 41.200000

Standard_Parallel: 41.866667

Longitude_of_Central_Meridian: -72.750000

Latitude_of_Projection_Origin: 40.833333

False_Easting: 999999.999996

False_Northing: 499999.999998

Planar coordinates are encoded using row and column
Abscissae (x-coordinates) are specified to the nearest 100.000000
Ordinates (y-coordinates) are specified to the nearest 100.000000
Planar coordinates are specified in survey feet

The horizontal datum used is North American Datum of 1983.
The ellipsoid used is Geodetic Reference System 80.
The semi-major axis of the ellipsoid used is 6378137.000000.
The flattening of the ellipsoid used is 1/298.257222.

  1. How does the data set describe geographic features?

0. 0% - 9%

Map background and areas with an estimated impervious surface of 0 - 9 percent. (Source: CLEAR)

ObjectID

Internal feature number. (Source: ESRI)

Sequential unique whole numbers that are automatically generated.

Value

Red

Green

Blue

Count

Class_names

Opacity

1. 10% - 19%

Areas with an estimated impervious surface of 10 - 19 percent. (Source: CLEAR)

2. 20% - 29%

Areas with an estimated impervious surface of 20 - 29 percent. (Source: CLEAR)

3. 30% - 39%

Areas with an estimated impervious surface of 30 - 39 percent. (Source: CLEAR)

4. 40% - 49%

Areas with an estimated impervious surface of 40 - 49 percent. (Source: CLEAR)

5. 50% - 59%

Areas with an estimated impervious surface of 50 - 59 percent. (Source: CLEAR)

6. 60% - 69%

Areas with an estimated impervious surface of 60 - 69 percent. (Source: CLEAR)

7. 70% - 79%

Areas with an estimated impervious surface of 70 - 79 percent. (Source: CLEAR)

8. 80% - 89%

Areas with an estimated impervious surface of 80 - 89 percent. (Source: CLEAR)

9. 90% - 100%

Areas with an estimated impervious surface of 90 - 100 percent. (Source: CLEAR)


Who produced the data set?

  1. Who are the originators of the data set? (may include formal authors, digital compilers, and editors)
    • Center for Land use Education And Research (CLEAR)
    • The University of Connecticut
  2. Who also contributed to the data set?
  3. To whom should users address questions about the data?

Center for Land use Education And Research (CLEAR), The University of Connecticut
c/o James Hurd
Research Associate

(860) 486-2840 (voice)
(860) 486-5408 (FAX)
james.hurd_jr@uconn.edu


Why was the data set created?

To provide a synoptic view and specific location of impervious surfaces for the greater Connecticut and Long Island Sound region, circa 1990. The data is for general information purposes only and is not suitable for site-specific studies or litigation. The impervious surface estimation is intended for use in general, area-wide analysis that can tolerate the errors and inaccuracies within the data.


How was the data set created?

  1. From what previous works were the data drawn?
  2. How were the data generated, processed, and modified?

This impervious surface data was derived through sub-pixel classification of Landsat Thematic Mapper satellite imagery using the Sub-pixel Classifier (SPC), an add-on module to Leica Geosystem’s ERDAS Imagine image processing software. The general SPC process requires four steps. These are image preprocessing, image environmental correction, signature derivation, and MOI classification. The first two steps are autonomous – preprocessing resulting in a hidden companion file to the original image being classified, and the environmental correction resulting in a CORENV companion file that contains information pertaining to atmospheric and environmental correction factors. Signature derivation is conducted manually by selecting pixels that represent a minimum of 90 percent imperviousness. Because of the diverse reflectance characteristics of impervious surfaces, signatures were individually created for bright, medium, dark, and very dark sub-classes of impervious surfaces. These four brightness levels represent various spectral characteristics of the landscape including concrete and asphalt from newly paved conditions to older, more aged conditions, and various reflective characteristics of different roofing and construction materials. It should be noted that these distinctions among impervious surface classes are spectrally-based and do not imply the function of the impervious surface (i.e. roof, road, parking lot, etc…). These impervious sub-classes were grouped into a single signature file known as a ‘family’ using the optional Signature Combiner function in the SPC. The classification process utilizes the initial preprocessed image, corresponding environmental correction file, and the ‘family’ signature file.

Following the initial sub-pixel classification, a series of post-processing steps were performed to generate the final impervious surface data layer. The overall goal of the project was to create a consistent set of impervious surface layers for four (1985, 1990, 1995, 2002) dates over a 17 year period. Three primary concerns existed. These were 1) eliminate the chance of a given pixel to fluctuate between dates such as losing imperviousness between 1985 and 1990, gaining imperviousness between 1990 and 1995, then losing imperviousness again between 1995 and 2002, 2) producing unlikely increases or decreases in imperviousness over time, and 3) filling in pixels that were not detected by the sub-pixel classifier as being impervious in one or more dates but likely to be impervious in reality. The post-processing steps combine the results of each impervious surface process conducted on each of the four dates using the procedure described above to generate impervious surface data layers that are consistent and eliminate existing variability among the dates. In addition, post-processing also included the masking of non-developed features from the impervious surface data layers to eliminate potential errors, add a 10 percent impervious category (the Sub-pixel Classifier can only detect features down to 20 percent) and to make the impervious surface data layers consistent with land cover data derived from the same Landsat satellite imagery. The result is a final impervious layer that more realistically reflects the true impervious conditions in the landscape.

  1. What similar or related data should the user be aware of?

Center for Land use Education And Research (CLEAR), and The University of Connecticut, October, 2005, March, 2006, 2002 Impervious Surfaces, Connecticut and New York Portions of the Long Island Sound Watershed

Online Links:


How reliable are the data; what problems remain in the data set?


How can someone get a copy of the data set?

Are there legal restrictions on access or use of the data?

Access_Constraints: None

Use_Constraints: None

  1. Who distributes the data set? (Distributor 1 of 1)

Center for Land use Education And Research (CLEAR), The University of Connecticut
c/o Sandy Prisloe
Geospatial Extension Specialist
UConn Cooperative Extension System
Haddam, Connecticut 06438-0070
United States

(860) 345-4511 (voice)
(860) 345-3357 (FAX)
sandy.prisloe@uconn.edu

Hours_of_Service: 9:00 - 5:00 Eastern Standard Time

  1. What's the catalog number I need to order this data set?

Downloadable Data

  1. What legal disclaimers am I supposed to read?

These data are the intellectual property of the US Environmental Protection Agency Long Island Sound Office and the Center for Land use Education And Research (CLEAR) at the University of Connecticut. They may be used for educational and non-commercial purposes provided proper attribution is given. CLEAR permits but does not support secondary distribution of this data. CLEAR is committed to offering users accurate, useful, and current information about the state and region. Although every effort has been made to ensure the accuracy of information, errors and conditions originating from the source data and processing may be reflected in the data supplied. The user must be aware of data conditions and bear responsibility for the appropriate use of the information with respect to possible errors, original map scale, collection methodology, currency of data, and other conditions specific to this data.

  1. How can I download or order the data?
    • Availability in digital form:

Data format:

Size: 0.000

    • Cost to order the data:

Who wrote the metadata?

Dates:

Last modified: 11-Oct-2005

Metadata author:

Center for Land use Education And Research (CLEAR), The University of Connecticut
c/o James D. Hurd, Daniel L. Civco
Research Associate, Associate Professor
The University of Connecticut
Storrs, Connecticut 06269-4087
United States

(860) 486-2840 (voice)
(860) 486-5408 (FAX)
James.Hurd_Jr@UConn.edu

Hours_of_Service: 9:00 - 5:00 Eastern Standard Time

Metadata standard:

FGDC Content Standards for Digital Geospatial Metadata (FGDC-STD-001-1998)

Metadata extensions used:

         <http://www.esri.com/metadata/esriprof80.html>


Generated by mp version 2.8.6 on Tue Oct 11 14:53:18 2005