Metadata
also available as
Identification_Information:
Citation:
Citation_Information:
Originator: Center for Land use Education And
Research (CLEAR)
Publication_Date: April 2016
Title:
1985lc_06apr2016_v2-03_ctstp83.img
Edition: version 2.03
Geospatial_Data_Presentation_Form: remote-sensing image
Series_Information:
Series_Name: Connecticut's Changing
Landscape Land Cover
Publication_Information:
Publication_Place: Storrs, CT
Publisher: Center for Land use Education And
Research (CLEAR)
Online_Linkage:
Description:
Abstract:
LANDSAT Thematic Mapper (TM) satellite imagery based land cover
classification, circa 1985, for the state of Connecticut including local
watersheds that intersect the state boundary, and towns in south central
Massachusetts that are part of the Quinebaug and
Shetucket Rivers Valley National Heritage Corridor. The classification depicts
12 land cover categories. These are: 1. Developed, 2. Turf & Grass, 3.
Other Grasses 4. Agriculture 5. Deciduous Forest, 6. Coniferous Forest, 7.
Water, 8. Non-forested Wetland, 9. Forested Wetland, 10. Tidal Wetland, 11.
Barren Land, 12. Utility Corridors. Source Landsat TM image data were from
April 26, 1985, May 4, 1988, and August 9, 1985. The classification was
compiled using ERDAS Imagine 2015 by the Center for Land use Education And Research (CLEAR) in the College of Agriculture and
Natural Resources at the University of Connecticut.
Purpose:
To provide a synoptic view of general land cover for the
greater Connecticut area circa 1985. The data is for general information
purposes only and is not suitable for site-specific studies or litigation. The
classification is intended for use in general, area-wide analysis that can
tolerate the errors and inaccuracies within the data.
Supplemental_Information:
NOTE: While derived from CCL v 2.02 land cover, this land
cover classification is considered an update and upgrade to CCL v 2.02 land
cover. This land cover is not compatible or comparable with CCL v 2.02 land
cover products.
Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: 1985
Currentness_Reference: Time period based on
satellite image collection date.
Status:
Progress: Complete
Maintenance_and_Update_Frequency: Unknown
Spatial_Domain:
Bounding_Coordinates:
West_Bounding_Coordinate: -73.755861
East_Bounding_Coordinate: -71.738580
North_Bounding_Coordinate: 42.313836
South_Bounding_Coordinate: 40.937111
Keywords:
Theme:
Theme_Keyword_Thesaurus: FGDC
Theme_Keyword: Land Cover
Theme_Keyword: Land Use
Theme_Keyword: Remote Sensing
Theme_Keyword: Classification
Theme_Keyword: LANDSAT
Theme_Keyword: Thematic Mapper
Theme_Keyword: Satellite Imagery
Place:
Place_Keyword: Connecticut
Place_Keyword:
Quinebaug and Shetucket Rivers Valley
National Heritage Corridor
Access_Constraints: None
Use_Constraints:
These data are the intellectual property of 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. 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.
Point_of_Contact:
Contact_Information:
Contact_Person_Primary:
Contact_Person: Emily Wilson
Contact_Organization:
Center for Land use Education And
Research (CLEAR), The University of Connecticut
Contact_Position: Geospatial Extension
Specialist
Contact_Address:
Address_Type: mailing address
Address: 1066 Saybrook Road, PO Box 70
City:
Haddam
State_or_Province: Connecticut
Postal_Code: 06432-0070
Country: United States
Contact_Voice_Telephone: (860) 345-4511
Contact_Facsimile_Telephone: (860) 345-3357
Contact_Electronic_Mail_Address: emily.wilson@uconn.edu
Hours_of_Service: 9:00 - 5:00 Eastern Standard
Time
Native_Data_Set_Environment:
Cross_Reference:
Citation_Information:
Originator: Center for Land use Education And
Research (CLEAR)
Publication_Date: April 2016
Title:
1985 Land Cover, Greater Connecticut
Edition: version 2.03
Geospatial_Data_Presentation_Form: map
Series_Information:
Series_Name: Connecticut's Changing
Landscape Land Cover
Publication_Information:
Publication_Place: Storrs, CT
Publisher: Center for Land use Education And
Research (CLEAR)
Online_Linkage: <http://clear.uconn.edu>
Data_Quality_Information:
Lineage:
Process_Step:
Process_Description:
CLOUD REMOVAL: The April 26, 1985 Landsat TM image contained
some cloud and cloud shadows that covered some regions in the northwest portion
of Connecticut. To remove these, the affected areas were extracted and
substituted with a May 4, 1988 Landsat TM scene. This scene was chosen because
it was the highest quality (fewest clouds) image close to the 1985 date
available in the CLEAR image data archive. Most of the area covered by clouds
was forested land that did not experience change between 1985 and 1988.
Process_Date: 2012
Process_Contact:
Contact_Information:
Contact_Person_Primary:
Contact_Person: James Hurd
Contact_Organization:
Center for Land use Education And
Research (CLEAR), The University of Connecticut
Contact_Position: Research Associate
Contact_Address:
Address_Type: mailing address
Address: The University of Connecticut
Address: U-4087, Room 227, 1376 Storrs Road
City:
Storrs
State_or_Province: Connecticut
Postal_Code: 06269-4087
Country: United States
Contact_Voice_Telephone: (860) 468-2840
Contact_Facsimile_Telephone: (860) 486-5408
Contact_Electronic_Mail_Address: james.hurd_jr@uconn.edu
Hours_of_Service: 9:00-5:00 Eastern Standard
Time
Process_Step:
Process_Description:
ROAD NETWORK: Identification of major and local roads was
necessary to adequately quantify land cover change and is critical to
successful application of landscape characterization models such as forest
fragmentation, development (urban) growth, and estimating impervious surfaces.
In order to capture roads, vector (line) road data (circa 1986) from the
Connecticut Department of Environmental Protection were used to extract image
data from the TM scene. All paved roads were selected from the vector layer and
converted to pixels the same size of the source imagery (100 feet). This layer
was then buffered 5 pixels on either side of the road to account for areas of
misalignment between the road layer and Landsat image data. All image pixels
contained within the 5 pixel buffer area were extracted for analysis (Figure
2a). The intent was to focus on the classification of roads by creating an
image that minimized non-road pixels. Sub-pixel classification (SPC) was used
to classify road pixels. The SPC is a supervised classifier that enables the
detection of materials of interest (MOIs) as whole or fractional pixel
composition, with a minimum detectable threshold of 20 percent and in
increments of 10 percent (i.e., 20-30%, 30-40%, …
90-100%). Because of tonal variations in the built landscape, MOIs representing
different brightness classes of road and paved surfaces (i.e. dark, medium, and
bright surfaces) were selected. Any pixel identified by the SPC as a paved
surface, regardless of its percent composition (20% or greater), was considered
a developed pixel. Final results of the SPC did not fully extract the road
network. Those buffered pixels not identified as developed through the SPC
technique were extracted for further evaluation using knowledge-based
classification (KBC) . Bands 4 (near-infrared) and 5
(mid-infrared) were used because, together, they showed the most contrast between
developed pixels and other non-developed pixels. Using KBC, a rule was created
that identified developed pixels as having digital numbers (reflectance values)
between 129 and 143 in band 4 and between 128 and 193 in band 5. In addition, a
pixel had to be contained within the actual rasterized road layer so not to
include other land cover features such a barren areas. The result of this
procedure was the identification of additional developed pixels not identified
using SPC. The SPC and KBC still, unfortunately, did not extract the full
extent of the road network. To correct for this problem, the rasterized road
layer was embedded with the final classification. Onscreen digitizing was
conducted to remove areas of misalignment. While this may appear to be a step
backward, the pixels identified as developed using SPC and KBC were an
invaluable resource in determining the true road alignment that is critical to
an accurate land cover map. The pixels also identified developed features
within the buffered image that were not part of roads. The same techniques were
used on the August 9, 1985 image covering the southeast portion of the analysis
area.
Process_Date: 2002
Process_Step:
Process_Description:
CLASSIFICATION: Pixels identified as developed in the previous
step were eliminated from the 1985 Landsat TM image. An area of approximately
18 x 10 miles along the central coast of Connecticut was subset from the
overall analysis area for deriving classification signature statistics. This
area was selected because it contained a significant amount of all categories
identified in the classification scheme. ISODATA classification was performed
generating 100 signature clusters. These clusters were identified as belonging
to one of the land cover categories. Maximum likelihood classification was
applied to the entire analysis area for each class using selected signatures
with a distance image as output. In the distance image, pixel values represent
the spectral distance from the class signature where the lower the value, the
more similar a pixel is to that specific class signature. For most classes,
visual examination of the distance image with the TM image resulted in the
identification of a threshold used with the Knowledge Engineer to derive a
complete land cover image. This method was adequate for all classes but two:
tidal wetlands and bare farm fields. In April, both of these land cover
features have little green, growing vegetation and, in a satellite image, look
very similar to other land cover features lacking vegetation such as quarries,
beaches, and concrete. A previous land cover project for Connecticut based on
satellite imagery from both spring and summer was used to identify tidal
wetlands and agricultural fields. The methodology was successful at identifying
tidal wetlands and agricultural fields because, unlike quarries, beaches, and
concrete, both tidal wetlands and agricultural fields usually have vegetation
in the summertime. Those pixels that remained unclassified were extracted from
the TM image and ISODATA classification was performed. The clusters were
identified as belonging to one of the land cover categories. All classification
layers were then merged to create a single classified image with all pixels
belonging to a single category.
Process_Date: 2002
Process_Step:
Process_Description:
CLASSIFICATION CLEAN-UP: Several steps were taken to
"clean-up" the classification: A digital elevation model (USGS
National Elevation Dataset) was used to identify areas classified as wetlands
in areas with steep northwest facing slopes. Using the Knowledge Engineer, any
pixel identified as non-forested or forested wetlands that fell on a slope of
12 degrees or more was reassigned to deciduous forest. A 12 degree slope
threshold was selected following visual examination of the image, DEM data,
existing wetlands, and corresponding slopes calculated from the DEM. Several
majority filters were used to eliminate isolated pixels resulting in a more
uniform classification. Utility right-of-ways were added because they can be
considered significant fragmenting features to the forest landscape. They were
digitized onscreen from the forest classes only. Additional forested wetlands
were derived from pixels classified as forest but identified as wetland on the
rasterized versions of GIS hydrography data available from CT DEP, Mass GIS and
Westchester County GIS. Lastly, extensive on-screen digitizing was used to
remove any remaining apparent errors.
Process_Date: 2002
Process_Step:
Process_Description:
DATA REVISION: Errors in how the analysis area was clipped
were discovered in the 1985 land cover image version 1.01. The clip layer
consists of the Connecticut state boundary, Massachusett
towns that are part of the Quinebaug and Shetucket
Rivers Valley National Heritage Corridor, and interecting
local watershed boundaries. The problem existed for those areas outside the
Connecticut state boundary and Massachusett towns due
to the local watershed layer containing an X and Y shift. The result is that
some areas were erroneously excluded from analysis. Landsat data was re-clipped
using a corrected local watershed layer, classified, and fused with the
original (version 1.01) land cover image.
Process_Date: 2002
Process_Step:
Process_Description:
NEW FOR VERISON 2.02 ----- INCLUSION OF AGRICULTURAL FIELDS:
Five existing land cover maps (1992 MRLC, 2000 NLCD, GAP, LERIS 1990, and LERIS
1995) for Connecticut were compared to identify agricultural areas. Where three
or more of these land cover maps agreed, and the CCL 1985 land cover map
identified Other Grasses, then the pixels were relabeled as Agricultural
Fields. These agricultural areas were compared to NAIP high resolution color
imagery collected in 2006 and to the original 1985 Landsat image to determine
if the area was agriculture. Any errors were identified and labeled
appropriately.
Process_Date: 2007-2008
Process_Step:
Process_Description:
NEW FOR VERISON 2.02 ----- CONVERSION OF DEVELOPMENT TO VEGETATION:
Due to the process of classifying multiple years of Landsat image data that
make up the Connecticut's Changing Landscape dataset, and following the
assumption that the devloped category will not
convert to other land cover categories over time, it was found that the
developed category was being over estimated, particularly in the highly
urbanized areas. To reduce the amount of development classified, pixels
identified as development in the most recent Landsat data (from 2006) were
extracted and reclassified as either development, turf & grass, or
deciduous forest. These were then embedded in each of the five dates of land
cover (1985, 1990, 1995, 2002, 2006). The result was a
better representation of the development and vegetative patterns in the land
cover maps, particularly in the highly urbanized areas.
Process_Date: 2008
Process_Step:
NEW FOR VERISON 2.03 ----- HEADSUP DIGITIZING was performed throughout the analysis area to correct apparent classification errors. Additionally, areas previously classified as Deciduous Forest or Coniferous Forest but appeared in the current Landsat imagery to be forest clear-cut areas based on spectral reflectance was reclassified as Other Grasses.
Process_Date: 2016
Process_Step:
Process_Description: Metadata imported.
Source_Used_Citation_Abbreviation:
Process_Date: 20160406
Process_Time: 11303700
Spatial_Data_Organization_Information:
Direct_Spatial_Reference_Method: Raster
Raster_Object_Information:
Raster_Object_Type: Pixel
Row_Count: 5001
Column_Count: 5457
Vertical_Count: 1
Spatial_Reference_Information:
Horizontal_Coordinate_System_Definition:
Planar:
Map_Projection:
Map_Projection_Name: Lambert Conformal Conic
Lambert_Conformal_Conic:
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_Coordinate_Information:
Planar_Coordinate_Encoding_Method: row and column
Coordinate_Representation:
Abscissa_Resolution: 100.000000
Ordinate_Resolution: 100.000000
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:
Detailed_Description:
Entity_Type:
Entity_Type_Label: 1985lc_06apr2016_v2-03_ctstp83.img.vat
Attribute:
Attribute_Label: ObjectID
Attribute_Definition: Internal feature number.
Attribute_Definition_Source: ESRI
Attribute_Domain_Values:
Unrepresentable_Domain:
Sequential unique whole numbers that are automatically
generated.
Attribute:
Attribute_Label: Value
Attribute_Definition: Class numerical identifier
(see class name description)
Attribute:
Attribute_Label: Red
Attribute_Definition: Red class color value
(0-255)
Attribute:
Attribute_Label: Green
Attribute_Definition: Green class color value
(0-255)
Attribute:
Attribute_Label: Blue
Attribute_Definition: Blue class color value
(0-255)
Attribute:
Attribute_Label: Count
Attribute_Definition: Number of pixels identified
as a class feature
Attribute:
Attribute_Label: Class_names
Attribute_Definition: Class name
Attribute_Domain_Values:
Enumerated_Domain:
Enumerated_Domain_Value: 1. Developed
Enumerated_Domain_Value_Definition:
High-density built-up areas typically associated with
commercial, industrial and residential activities and transportation routes.
These areas can be expected to contain a significant amount of impervious
surfaces, roofs, roads, and other concrete and asphalt surfaces.
Enumerated_Domain:
Enumerated_Domain_Value: 2. Turf & Grass
Enumerated_Domain_Value_Definition:
A compound category of undifferentiated maintained grasses
associated mostly with developed areas. This class contains cultivated lawns
typical of residential neighborhoods, parks, cemeteries, golf courses, turf
farms, and other maintained grassy areas. Also may include some agricultural
fields and/or other grasses due to similar spectral reflectance properties.
Enumerated_Domain:
Enumerated_Domain_Value: 3. Other Grasses
Enumerated_Domain_Value_Definition:
Includes non-maintained grassy areas commonly found along
transportation routes and other developed areas, and within and surrounding
airport properties. Also includes forested clear-cut areas, solar panel farms,
and some abandoned agricultural areas that appear to be undergoing conversion
to woody scrub and shrub cover.
Enumerated_Domain:
Enumerated_Domain_Value: 4. Agricultural Fields
Enumerated_Domain_Value_Definition:
Includes areas that are under agricultural uses such as crop
production and/or active pasture. Also likely to include some abandoned
agricultural areas that have not undergone conversion to woody vegetation.
Enumerated_Domain:
Enumerated_Domain_Value: 5. Deciduous Forest
Enumerated_Domain_Value_Definition:
Includes southern New England mixed hardwood forests. Also
includes scrub areas characterized by patches of dense woody vegetation. May
also include isolated low density residential areas.
Enumerated_Domain:
Enumerated_Domain_Value: 6. Coniferous Forest
Enumerated_Domain_Value_Definition:
Includes southern New England mixed softwood forests. May
also include isolated low density residential areas.
Enumerated_Domain:
Enumerated_Domain_Value: 7. Water
Enumerated_Domain_Value_Definition:
Open water bodies and watercourses with relatively deep
water.
Enumerated_Domain:
Enumerated_Domain_Value: 8. Non-forested Wetland
Enumerated_Domain_Value_Definition:
Includes areas that predominately are wet throughout most of
the year and that have a detectable vegetative cover (therefore not open
water). Also includes some small water courses due to spectral characteristics
of mixed pixels that include both water and vegetation.
Enumerated_Domain:
Enumerated_Domain_Value: 9. Forested Wetland
Enumerated_Domain_Value_Definition:
Includes areas depicted as wetland, but with forested cover.
Also includes some small water courses due to spectral characteristics of mixed
pixels that include both water and vegetation.
Enumerated_Domain:
Enumerated_Domain_Value: 10. Tidal Wetland
Enumerated_Domain_Value_Definition:
Emergent wetlands, wet throughout most of the year, with
distinctive marsh vegetation and located in areas influenced by tidal change.
Enumerated_Domain:
Enumerated_Domain_Value: 11. Barren Land
Enumerated_Domain_Value_Definition:
Mostly non-agricultural areas free from vegetation, such as
sand, sand and gravel operations, bare exposed rock, mines, and quarries. Also
includes some urban areas where the composition of construction materials
spectrally resembles more natural materials. Also includes some bare soil
agricultural fields.
Enumerated_Domain:
Enumerated_Domain_Value: 12. Utility Corridors
Enumerated_Domain_Value_Definition:
Includes utility corridors. This category was manually
digitized on-screen from corridors visible in the Landsat satellite imagery.
The class was digitized from the deciduous and coniferous categories only.
Attribute:
Attribute_Label: Opacity
Attribute_Definition: Transparency level of class
feature (0-1)
Attribute:
Attribute_Label: Acres
Attribute_Definition: Area of land cover feature.
Attribute:
Attribute_Label: Sq
mi
Attribute_Definition: Area of land cover feature.
Overview_Description:
Entity_and_Attribute_Overview:
The 12 land cover categories represent general land cover
features as they existed during the time of the Landsat Thematic Mapper image
collection. These categories were selected based on the constraints of the
source imagery, but also on the goals of the project. By maintaining general
land cover categories, it can be expected that thematic accuracy will be
higher.
Distribution_Information:
Distributor:
Contact_Information:
Contact_Person_Primary:
Contact_Person: Emily Wilson
Contact_Organization:
Center for Land use Education And
Research (CLEAR), The University of Connecticut
Contact_Position: Geospatial Extension
Specialist
Contact_Address:
Address_Type: mailing address
Address: 1066 Saybrook Road, PO Box 70
City:
Haddam
State_or_Province: Connecticut
Postal_Code: 06438-0070
Country: United States
Contact_Voice_Telephone: (860) 345-4511
Contact_Facsimile_Telephone: (860) 345-3357
Contact_Electronic_Mail_Address: emily.wilson@uconn.edu
Hours_of_Service: 9:00-5:00 eastern
Standard Time
Resource_Description: Downloadable Data
Standard_Order_Process:
Digital_Form:
Digital_Transfer_Information:
Format_Name: ERDAS IMAGINE
Format_Version_Number: ERDAS IMAGINE 2015
Format_Information_Content:
Data can be downloaded for the entire analysis area, within
Connecticut boundary, or by Connecticut Planning Region
File_Decompression_Technique: ZIP file
Transfer_Size: 0.000
Fees:
Free
Ordering_Instructions:
Go to <http://clear.uconn.edu
>
Metadata_Reference_Information:
Metadata_Date: 20090203
Metadata_Contact:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization:
Center for Land use Education And
Research (CLEAR), The University of Connecticut
Contact_Person: James Hurd
Contact_Position: Research Associate
Contact_Address:
Address_Type: mailing address
Address: The University of Connecticut
Address: U-4087, Room 227, 1376 Storrs Road
City:
Storrs
State_or_Province: Connecticut
Postal_Code: 06269-4087
Country: United States
Contact_Voice_Telephone: (860) 486-2840
Contact_Facsimile_Telephone: (860) 486-5408
Contact_Electronic_Mail_Address: james.hurd_jr@uconn.edu
Hours_of_Service: 9:00 - 5:00 Eastern Standard
Time
Metadata_Standard_Name: FGDC Content Standards for
Digital Geospatial Metadata
Metadata_Standard_Version: FGDC-STD-001-1998
Metadata_Time_Convention: local time
Metadata_Extensions:
Online_Linkage: <http://www.esri.com/metadata/esriprof80.html>
Profile_Name: ESRI Metadata Profile