C-CAP Great Lakes 1995 Land Cover Metadata

C-CAP Great Lakes 1995-2000 Land Cover

Identification_Information:
Citation:
Citation_Information:
Originator: NOAA Coastal Services Center/Coastal Change Analysis Program(C-CAP)
Publication_Date: 20020712
Title: C-CAP Great Lakes 1995-2000 Land Cover Metadata
Geospatial_Data_Presentation_Form: Map
Publication_Information:
Publication_Place: NOAA CSC, Charleston, SC
Publisher: NOAA Coastal Change Analysis Program (C-CAP)
Online_Linkage: http://www.csc.noaa.gov
Larger_Work_Citation:
Citation_Information:
Originator:
This product is one section of the Great
Lakes study area of which this project
includes the classification of 2000 Landsat 7
data, 1995 Landsat 5 data, and change information.
Publication_Date: 20020712
Title: C-CAP Great Lakes Land Cover Project
Publication_Information:
Publication_Place: NOAA CSC, Charleston, SC
Publisher: NOAA Coastal Change Analysis Program (C-CAP)
Other_Citation_Details:
This change analysis is based on Landsat TM scenes: p25r27
(4/18/2001 and 5/30/96), p25r28 (3/17/2001 and 5/30/96), p26r26
(4/25/2001 and 5/19/95), p26r27 (4/25/2001 and 5/19/95), p26r28
(4/25/2001 and 10/10/95), p27r26 (4/29/2001 and 10/30/94), p27r27
(4/29/2000 and 10/30/94), p27r28 (4/29/2000 and 5/28/96), p28r27
(3/3/2000 and 5/17/95)
The Late-Date Classification is based on these scenes,
p25r27 (4/18/2001), (7/4/2000), (10/11/2001),
p26r26 (4/25/2001),
p26r27 (4/25/2001), (7/3/2000),
p26r28 (4/25/2001), (6/28/2001),
p27r26 (4/29/2001),
p27r27 (4/29/2000),
p27r28 (4/29/2000),
p28r27 (3/3/2000), (7/23/1999), (11/17/2001)
The Early-Date Classification is based on these scenes, p25r27
(5/30/96), p25r28 (5/30/96), p26r26 (5/19/95), p26r27
(5/19/95), p26r28 (10/10/95), p27r26 (10/30/94), p27r27
(10/30/94), p27r28 (5/28/96), p28r27 (5/17/95)

Online_Linkage: http://www.csc.noaa.gov/crs
Description:
Abstract:
This data is a change analysis of c.1995 CCAP land cover and c.2000 CCAP
land cover for the Great Lakes Region of the U.S. This product contains
the land cover information for both dates and change information for the
estimated change areas only.

Purpose:
To improve the understanding of coastal uplands and
wetlands, and their linkages with the distribution,
abundance, and health of living marine resources.
Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 19941030
Ending_Date: 20011117
Currentness_Reference: Date of the Landsat scenes
Status:
Progress: Complete
Maintenance_and_Update_Frequency: 5 years
Spatial_Domain:
Bounding_Coordinates:
West_Bounding_Coordinate: -94.5839
East_Bounding_Coordinate: -88.2553
North_Bounding_Coordinate: 48.7359
South_Bounding_Coordinate: 45.0281
Keywords:
Theme:
Theme_Keyword_Thesaurus: None
Theme_Keyword: Land Cover Analysis
Theme_Keyword: Change Detection Analysis
Place:
Place_Keyword_Thesaurus: None
Place_Keyword: Great Lakes
Place_Keyword: Coastal Zone
Place_Keyword: Minnesota
Access_Constraints: None, except for a possible fee.
Use_Constraints:
Data set is not for use in litigation. While efforts have been
made to ensure that these data are accurate and reliable within
the state of the art, NOAA, cannot assume liability for any
damages, or misrepresentations, caused by any inaccuracies in the
data, or as a result of the data to be used on a particular
system. NOAA makes no warranty, expressed or implied, nor does
the fact of distribution constitute such a warranty.

Native_Data_Set_Environment: ERDAS Imagine 8.4 on Dell Pentium 3 Windows 2000

Data_Quality_Information:
Attribute_Accuracy:
Attribute_Accuracy_Report:

A team of field investigators participated in field collection of
verification points in October 2001. Data validation teams consisted of
personnel from the NOAA Coastal Services Center. Each team was equipped
with a portable color laptop computer linked to a Global Positioning
System (GPS). The field station runs software that supports the classified
data as a raster background with the road network as a vector overlay with
a simultaneous display of live GPS coordinates. Accuracy assessment points
were generated with ERDAS Imagine software using a stratified random
sample in 3x3 pixel homogeneous windows. This data collected will be used
to produce accuracy assessments for the Great Lakes C-CAP data. This
data is provisional, it is not ready for dissemination and has not been
verified at this time. This field work will allow for the verification of
the late-date classification only.

The early-date classification is derived from the re-classification of
the of the change areas of the late-date (c.2000)classification using the
early-date (c.1995) imagery. Therefore its accuracy is inherently tied to
the accuracy of the late-date classification. The late-date classification
was based primarily on the fieldwork collected. Accuracy tests of later
versions of this provisional early-date classification will involve logic
change analysis.

Field work was collected in order to classify the late-date classification
to 85% accuracy, and to verify its accuracy.
A team of field investigators participated in field collection of
verification points in October 2001. Data validation teams consisted of
personnel from the NOAA Coastal Services Center. Each team was equipped
with a portable color laptop computer linked to a Global Positioning
System (GPS). The field station runs software that supports the classified
data as a raster background with the road network as a vector overlay with
a simultaneous display of live GPS coordinates. Accuracy assessment points
were generated with ERDAS Imagine software using a stratified random
sample in 3x3 pixel homogeneous windows. This data collected will be used
to produce accuracy assessments for the Great Lakes C-CAP data. This
data is provisional, it is not ready for dissemination and has not been
verified at this time.

Pre-processing steps:
Each Landsat TM scene was geo-referenced by USGS EROS DATA CENTER. Then
EarthSat staff verified the scenes for spatial accuracy to within 1 pixel
consistently. The data was geo-referenced to Albers Conical Equal Area,
with a spheroid of GRS 1980, and Datum of WGS84. The data units is in
meters.

Ancillary Datasets:
Non-TM image datasets used are NWI, TIGER2000, and field-collected points.
Both datasets were rasterized to 30 meter pixels and used to mask the
classified layer. This stratified thematic layer was
then labeled for appropriate categories. The NWI was used to classify the
wetland categories and the TIGER2000 was used for the human developed
categories. The TIGER2000 data was also rasterized to 90 meter pixels in
order to include some of the low intensity developed with occurred more
than one pixel from the roads. These ancillary data were used in a visual
capacity as well as a rule-based model approach.

Field-Collected Data:
Field-collected points were obtained in October 2001 and May/June 2002.
NOAA staff joined the EarthSat team in the field to ensure proper field
techniques were used, and to collect their own points for accuracy
assessment of the final classification. These collected points were used
to develop the late-date (2000 era) classification from which this file
was derived.

In traditional methods of field verification, a paper map was used, and a
random point was plotted, navigated to, and observed. Under this
procedure, 30 sites per day per team were considered a strong success.
Another option is a "windshield survey" approach. This is a biased system
in which observers simply note what they can observe from transportation
routes. This method provides for more observations and allows the user to
judge the "fitness for use" of the data through continuous, intensive, and
focused comparison of the data to the landscape.

The EarthSat method combines stratified random samples buffered by TIGER
road ancillary data and windshield surveys approaches. This hybrid method
is used to determine the accuracy trends in the data. Using available
Global Positioning System (GPS)/Database GUI/laptop computers, C-CAP field
teams can reach 300-500 or more sites per day. The technology includes:
Laptop computers
Real-time GPS Receiver interface software with database applications
Computer based real-time fieldwork database entry and manipulation
Georeferenced digital satellite imagery and classified land cover analysis
imagery
GIS ancillary data, such as roads, other land cover analyses, and digital
elevation models

In preparation for field accuracy data collection, TIGER roads were
acquired, registered to the digital imagery, and mosaicked. A 300 meter
buffer of the land cover imagery is generated based upon the TIGER roads.
Random samples are collected from the buffered land cover analysis image
in 3 x 3 pixel windows stratified by land cover classification category.
The random samples are then used to create a database with a graphical
user interface (GUI). Field teams navigate by GPS interface with
georeferenced TIGER roads, land cover images, and Thematic Mapper (TM)
images to field sites. Observations are recorded on the laptop for later
manipulation. The items that are typically noted in the field include:
Canopy cover
Vegetation types by species (where applicable)
Land Cover characterization
Soils (if relevant)
Special conditions and remarks
Photography/video ID number
Date/time
X,Y location (Z if relevant)
The field trips lasted 6 full fieldwork days (first light to dark) and was
geared to developing training sites for continued processing and a
database of point observations. Each team was expected to collect at
least 300 points of observations per day stratified across all relevant
classes for the geographic area of interest (AOI). At least twenty of
these sites per day were selected for more detailed inventory and digital
photography collection. Ten of these areas should be composed of standard
land cover classes expected to be dominant in the geographic AOI. The
corresponding 10 areas should represent odd or difficult conditions for
image processing. While in the field the analyst is expected to choose
point observations representing good, bad, and difficult areas in volume
stratified across the classification scheme. This approach gives the
analyst a view of the fitness for use of the data. At the end of the
trip, each analyst should have a detailed and documented data set to
continue image processing.

The first fieldwork was performed by four teams. The Great Lakes region
was broken into four roughly even fieldwork zones broken up by state,
corresponding to the four teams. The teams consisted of at least one
EarthSat employee and at least one NOAA employee. Most teams had three
workers. Team 1 covered the zone containing New York State. Team 2 covered
the area Pennsylvania, Ohio, and Indiana. Team 3 covered the area of
Illinois and Southern Wisconsin. Team 4 covered the area of Minnesota and
Northern Wisconsin. In all, EarthSat sent six people into the field and
NOAA sent five.

The second fieldwork involved the sending of five EarthSat workers into
the field with field collection equipment for the specific task of
understanding the cultivated and grassland features and their relation to
each other. The trips took place over about one week during which
thousands of points were collected. One worker drove to the Team 1 region
of New York state and collected points and photos for four days. The other
four workers landed in Chicago with two workers heading north to Wisconsin
and back, and the other two driving to Pennsylvania and back to Chicago.
This fieldwork undoubtedly increased the accuracy of these two categories
and therefore the entire land cover product. The field data were processed
and compiled into a set of ArcView files, which were delivered to NOAA CSC
on June 12, 2002.

Post-Processing Steps:
After each scene was classified, a mosaicking algorithm was applied to all
the scenes in a team area to join the data into four team areas. Also the
four team areas were mosaicked into one study region. This mosaic
algorithm first creates a hierarchy of classes so that when the overlay
takes place, the preferred classes dominate. The hierarchy of classes is
as follows in descending order of dominance: class 1, 20, 19, 16, 17, 6,
8, 7, 9, 5, 4, 13, 14, 15, 10, 11, 12, 18, 22, 21, 3, 2. This hierarchy
was applied to the overlap between the scenes being mosaicked. The
hierarchy was applied automatically in most cases but in some areas the
discrepancy in the overlap was highlighted and changed. Thus the overlap
areas in the mosaicking process were assessed.

The early-date classifications were additionally mosaicked to overlay the
final late-date classifications. This was done for each team area. Then
this file was used as a comparison to the late-date final classification
for each team. The comparison was made by performing a bivariate analysis.
This contains data for both dates of imagery and all change information.

Known Problems:
There are some unclassified pixels in this bivariate analysis because they
are propagated from the same unclassified pixels present in the early-date
classification. These unclassified pixels where covered by cloud or data
drops. More data has been ordered to replace these pixels. These
corrections will be made in the final product.

Spatial Filters:
A spatial majority filter was applied to the cultivated category (class 4)
and in some instances to class 5. The filter was applied with a .85 or .75
majority threshold within a 3x3 matrix. This reduced speckle within
agricultural fields. Also a spatial auto-correlation algorithm was applied
to the land cover. This algorithm uses low pass filters and recodes stray
pixels to most likely classes based on surrounding pixels.

Logical_Consistency_Report:
Tests for logical consistency indicate that all row and column
positions in the selected latitude/longitude window contain data.
Conversion and integration with vector files indicates that all
positions are consistent with earth coordinates covering the same
area. Attribute files appear to be logically consistent.

Completeness_Report:
The classification scheme comprehensively includes all anticipated land
covers, and all pixels have been classified. The NOAA Coastal Change
Analysis Program (C-CAP): Guidance for Regional Implementation, NOAA
National Marine Fisheries Service Report 123, discusses the interagency
effort to develop the land cover classification scheme and defines all
categories.
Positional_Accuracy:
Horizontal_Positional_Accuracy:
Horizontal_Positional_Accuracy_Report:
Landsat scenes were georeferenced by Eros Data Center.
Spatial accuracy accessed by Earth Satellite Corporation
is found to be to 1 pixel accuracy or less.
Vertical_Positional_Accuracy:
Vertical_Positional_Accuracy_Report:
There was no terrain correction in the georeferencing
procedure.

Lineage:
Source_Information:
Source_Citation:
Citation_Information:
Originator: Earth Satellite Corporation
Publication_Date: Unknown
Title: Coastal Change Analysis Program (C-CAP) Bivariate Change Analysis of Coastal WI and MN(Team 4 region)
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:
Publication_Place: Sioux Falls, SD
Publisher: EROS Data Center
Online_Linkage: http://edc.usgs.gov/eros-home.html
Type_of_Source_Media: cdrom
Source_Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 19941030
Ending_Date: 20011127
Source_Currentness_Reference: publication date
Source_Citation_Abbreviation: EROS
Source_Contribution: EROS

Process_Step:
Process_Description:
This classification is to fulfill the third delivery of the NOAA
C-CAP. The dataset was created by Earth Satellite Corporation.
This product is change analysis layer called a Bivariate layer. It is a
graphical representation of a change matrix. The study
area is the US Great Lakes Coastal Region. This product is one
of a series of 4 mosaics covering a portion of that region.

The process history of this file is as follows: First, Landsat 7
data covering the study area was ortho-rectified at EROS Data
Center. Next the datasets were assessed for spatial (horizontal)
accuracy by Earth Satellite Corporation. Scenes with an offset
greater than 1 pixel were not accepted. The data was then subset
by the boundaries of the study area. Cloud cover was removed and
in some cases was replaced by same-era data using a statistical
prediction technique with the software Cubist. The individual
scenes were then classified. Unsupervised classification was used
to create a signature file: 233 classes, 20 iterations, 0.999
convergence threshold, 3X3 skip factor. The signature file was
then run through a Supervised Classification process: Maximum
Likelihood, no non-parametric rule. The resulting clusters were
labeled using the Earth Satellite Corporation developed
addition to Imagine 8.5 called GeoTools. In GeoTools the "Summary"
function was performed using the field-collected data and
classified scene overlap to label the clusters of the image.
Subsequent small corrections were made to the data by renaming a
few clusters and, in some cases, AOIs were used.
Then the classified data were stratified by NWI data to classify the
wetland classes. In some scenes where the NWI overlayed a pixel, the
pixel was changed to the we version of its own class (i.e. deciduous
changed to palustrine forest). In other scenes the entire NWI area plus
other wet areas missed by NWI were classified separately. The human
developed categories were refined by using TIGER2000 files (rasterized)
to stratify a clustered file of 233 unlabeled classes. Then clusters
were labeled for High Intensity Developed or Low Intensity Developed
within the 30 meter rasterized TIGER2000 stratification. This process
was also done using a 90 meter version of the rasterized TIGER2000 data
but only where AOI's were used stratify out larger cities. Then a water
model was used to refine the water category. The water model uses image
algebra to determine water boundaries and gives several options for
accepting water pixels. Also a model was developed to refine the
cultivated category by distinguishing cultivated pixels from grassland
pixels better. This model utilized different dates of same-year imagery
to use greenness cycle as a criteria for classification. The model
calculated an NDVI for each date which were then subtracted from each
other and threshold of change applied. Sometimes the threshold was
manually determined, and sometimes it was automated using 1 standard
deviation on either side of 0. Cluster-busting and screen digitizing
were used extensively in the production of these scenes. Also comments
from the consultant and NOAA CSC staff on the rough classification (the
previous version) were used to find and correct problem areas.

After the late-date (c.2000) imagery was classified satisfactorily, the
change detection portion of the project began. EarthSat has developed a
change detection technique called Cross Correlation Analysis (CCA). CCA
is able to overcome many limitations of conventional change detection
methods. It performs well regardless of seasonal differences because it
uses former class boundaries summarized with new class signatures to
determine the relationship between pixel values and a feature class.
There is no reliance on direct pixel value comparison between the
different scenes. This approach isolates change, or reduces change
detection artifacts better than conventional change detection
procedures. Spatial, spectral, radiometric, and temporal resolutions are
compensated for by this procedure.

The procedure uses the late-date multi-spectral image with an early-date
thematic clustered file and the early-date multi-spectral image with a
late-date thematic clustered file in a three-step process. First, a
statistically clustered early date image (1000 clusters)is superimposed
upon the recent multi-spectral image. Class boundaries from the
clustered images separate image pixels into distinct class zones. All
pixels falling within a particular class zone are collected to determine
the "expected" class average spectral response and standard deviation.

In the second step, a Z-score is calculated for each pixel in the recent
multi-spectral image. The Z-score is a measure of the distance exhibited
by an individual member of a population from the central tendency of the
population. One Z-score file is calculated for each date of imagery.

EarthSat has created an eml to make more efficient the use of CCA by a
team of interpreters. The inputs to this process are created by
clustering the leaf-off late-date image to 1000 clusters. If the leaf-
off scene is too cloudy or otherwise unsuitable, then another date is
used. Then the early-date image is clustered to 1000 clusters as well.
The other inputs are the early-date and late-date raw images. The
outputs are Z-statistic files, or measures of probability of change.
These Z-score files are thresholded to create a binary change layer. The
two binary files are overlayed to produce one file representing change
between the dates.

The early-date classification began by a process developed by
EarthSat called Inverse CCA. Inverse CCA is the process of summarizing
classified late- date imagery with the early-date clustered scene in
order to semi-automate cluster labeling. In EarthSat's production
environment it is used regularly as an initial interpretation to update
imagery that is later altered to be more correct by human interpreters.
It is only run on areas of change, which was determined in the last
step, using CCA.

The Inverse CCA, or automated updating of the change area,
reduces the human subjectivity in labeling the change areas. It labels
by using the late-date interpretation as training to label the earlier
date. Theoretically it labels the updates similarly to the original
interpreter would have labeled the features. In practice, however, the
classes must be checked for discrepancies because there are some
situations whereby the classes could be incorrectly labeled. Since the
process relies on sampling and summarizing the data in each class
separately, the more pixels to sample in a class, the more accurate the
automated interpretation is. Some classes in the classification scheme,
such as wetlands, have a narrow definition and generally do not include
many pixels in a scene. Therefore Inverse CCA tends to under-estimate
the wetland category in the update of some scenes. Classes typically
including large numbers of pixels, i.e. the deciduous class, tend to be
accurately estimated, and sometimes over-estimated. In summary, not
enough pixels in a class may lead to errors of exclusion, while too many
pixels in a class may lead to errors of inclusion in the updated
classification. This is why a human interpreter must check the results.

Inverse CCA uses three inputs: the early-date statistically
clustered image, the late-date classified image, and the combined Z-
scores into one binary change/no change file. It first
summarizes the clustered image with the late-date classification. The
summary information is used to determine what percentage of each cluster
falls within each class of the original classification. The process then
determines what area of the late date clustered image to consider for
updating based on the combined Z-score layer. The result is the change
area clusters recoded to represent the class which it intersects the
majority of the time. This procedure is a little different from the
typical update procedure because it is the early-date that is being
"updated" with information from the late-date.

The result of the Inverse CCA based on the late-date classification was
submitted as the rough classification for the early-date. This
provisional product is based on the re-working of the rough product by
masking and clustering. Also manual screen digitizing was involved.

Individual scenes were then mosaicked into one of 4 study areas making
up the Great Lakes region. The mosaic process includes re-ordering the
classes in order of "importance". The scenes were then mosaicked such
that pixels of certain classes took precedence over pixels of other
classes. This was done for the late-date classification, and early-date
classification. The binary change layer was also mosaicked.

The Bivariate Analysis was created from these layers. Two
classifications now exist, a 2000 era land cover and a 1995 land cover
for just the change pixels. The bivariate analysis information will be
placed directly into the spreadsheet error matrix created by NOAA. The
Bivariate Analysis will be useful to NOAA scientists as it is basically
a visual version of the error matrix. Using the Bivariate Analysis it is
possible to view the old scene, new scene, and areas of change, just by
copying and pasting the color column. Each pixel is represented by
attributes describing what class it was and is.

The bivariate analysis shows all of the necessary information for the
analysis of change between two images. It is the third step in the CCA
updating process after the CCA and Inverse CCA. The CCA determines areas
of change. The Inverse CCA interprets that change. The bivariate
analysis updates the image and aids in the analysis of the change. It
produces the square of the number of classes in the updated
classification, i.e. a 21 class image will produce a 441 class bivariate
image. The classes include each class change to each other class change
quantified by number of pixels. Also, it allows the analyst to toggle
between old and new date classifications to visually see the
differences. One of the outputs of this procedure is the fully "updated"
early-date classified image.

The bivariate analysis eml relies on two inputs: the updated
classification of change areas from Inverse CCA, the late date
classification. The first step in
the procedure updates the classification by substituting the old
classification for the areas not interpreted by Inverse CCA. In other
words the holes of no change are filled in with the late-date data. The
data area mask is necessary to deal with no data areas.
Then the following formula is applied to
the updated file:
BA = [(X-1)*Z]+Y
where BA = Bivariate Analysis, X = updated classification, Y = old
classification, and Z = the highest number of classes in the
classification scheme.
One output is the bivariate file itself while the other is the "updated"
or finalized early-date classification mosaic

All processing was performed using Imagine 8.4 and 8.5 and Earth
Satellite Corporation developed additions, models, and eml scripts.
Field data collection was performed in the period of October 15 - 20,
2001. Four teams of 2-3 people collected and labeled GPS points over the
study area. Teams consisted of at least 1 Earth Satellite employee and
at least 1 NOAA employee. Digital photos were also taken during field
work.

Attributes for this product are as follows:
0 Background
1 Unclassified (Cloud, Shadow, etc
2 High Intensity Developed
3 Low Intensity Developed
4 Cultivated Land
5 Grassland
6 Deciduous Forest
7 Evergreen Forest
8 Mixed Forest
9 Scrub/Shrub
10 Palustrine Forested Wetland
11 Palustrine Scrub/Shrub Wetland
12 Palustrine Emergent Wetland
13 Estuarine Forested Wetland
14 Estuarine Scrub/Shrub Wetland
15 Estuarine Emergent Wetland
16 Unconsolidated Shore
17 Bare Land
18 Water
19 Tundra
20 Snow/Ice
21 Palustrine Aquatic Bed

Process_Date: 20020712
Process_Contact:
Contact_Information:
Contact_Person_Primary:
Contact_Organization: NOAA Coastal Services Center Coastal Change Analysis Program (C-CAP)
Contact_Person: CRS Program Manager
Contact_Position: CRS Program Manager
Contact_Address:
Address_Type: mailing and physical address
Address: 2234 S. Hobson Ave.
City: Charleston
State_or_Province: SC
Postal_Code: 29405
Country: USA
Contact_Voice_Telephone: 843-740-1210
Contact_Facsimile_Telephone: 843-740-1224
Contact_Electronic_Mail_Address: clearinghouse@csc.noaa.gov
Hours_of_Service: 8:00 am to 5:00 p.m. EST.

Process_Step:
Process_Description: Classification
Process_Date: Unknown
Process_Contact:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: NOAA Coastal Services Center Coastal Change Analysis Program (C-CAP)
Contact_Position: CRS Program Manager
Contact_Address:
Address_Type: mailing and physical address
Address: 2234 S. Hobson Ave.
City: Charleston
State_or_Province: SC
Postal_Code: 29405
Country: USA
Contact_Voice_Telephone: 843-740-1210
Contact_Facsimile_Telephone: 843-740-1224
Contact_Electronic_Mail_Address: csc@csc.noaa.gov
Hours_of_Service: Monday to Friday, 8 a.m. to 5 p.m., EST

Spatial_Data_Organization_Information:
Direct_Spatial_Reference_Method: Raster

Spatial_Reference_Information:
Horizontal_Coordinate_System_Definition:
Planar:
Map_Projection:
Map_Projection_Name: Universal Transverse Mercator
Universal Transverse Mercator:
Zone: 15 North
False_Easting: 0.00000
False_Northing: 0.00000
Planar_Coordinate_Information:
Planar_Coordinate_Encoding_Method: Row and column
Coordinate_Representation:
Abscissa_Resolution: 30 meter
Ordinate_Resolution: 30 meter
Planar_Distance_Units: Meters
Geodetic_Model:
Horizontal_Datum_Name: North American Datum 1983
Ellipsoid_Name: GRS80

Entity_and_Attribute_Information:
Detailed_Description:
Entity_Type:
Entity_Type_Label: US Great Lakes Coastal Zone
Entity_Type_Definition:
US Great Lakes coastal zone as delineated by
NOAA using scene boundaries, hydrological units,
and county boundaries
Entity_Type_Definition_Source: unknown
Attribute:
Attribute_Label: Landcover Classification
Attribute_Definition:
Landcover Classification as determined by
NOAA Coastal Change Analysis Program (C- CAP): Guidance for Regional
Implementation
Attribute_Definition_Source:
Dobson, J. et al, NOAA Coastal Change Analysis Program (C-CAP):
Guidance for Regional Implementation, NOAA Technical
Report NMFS 123, U.S. Department of Commerce, April
1995
Attribute_Domain_Values:
Enumerated_Domain:
Enumerated_Domain_Value: 1 Unclassified
Enumerated_Domain_Value_Definition:
This class contains no
data due to cloud conditions or data voids.
Enumerated_Domain_Value_Definition_Source: N/A

Enumerated_Domain:
Enumerated_Domain_Value: 2 High Intensity Developed
Enumerated_Domain_Value_Definition:
Contains little or no vegetation. This subclass includes
heavily built-up urban centers as well as large
constructed surfaces in suburban and rural areas. Large
buildings (such as multiple family housing, hangars, and
large barns), interstate highways, and runways typically
fall into this subclass.

Enumerated_Domain_Value_Definition_Source:
Dobson, J. et al, NOAA Coastal Change Analysis Program(C-CAP):
Guidance for Regional Implementation, NOAA Technical
Report NMFS 123, U.S. Department of Commerce, April
1995.

Enumerated_Domain:
Enumerated_Domain_Value: 3 Low Intensity Developed
Enumerated_Domain_Value_Definition:
Contains substantial amounts of constructed surface mixed
with substantial amounts of vegetated surface. Small
buildings (such as single family housing, farm
outbuildings, and sheds), streets, roads, and cemeteries
with associated grasses and trees typically fall into this
subclass.
Enumerated_Domain_Value_Definition_Source:
Dobson, J. et al, NOAA Coastal Change Analysis Program(C-CAP):
Guidance for Regional Implementation, NOAA Technical
Report NMFS 123, U.S. Department of Commerce, April
1995.

Enumerated_Domain:
Enumerated_Domain_Value: 4 Cultivated Land
Enumerated_Domain_Value_Definition:
Includes herbaceous (cropland) and woody (e.g., orchards,
nurseries, and vineyards) cultivated lands.

Enumerated_Domain:
Enumerated_Domain_Value: 5 Grassland
Enumerated_Domain_Value_Definition:
Dominated by naturally occurring grasses and non-grasses
(forbs) that are not fertilized, cut, tilled, or planted
regularly.
Enumerated_Domain_Value_Definition_Source:
Dobson, J. et al, NOAA Coastal Change Analysis Program(C-CAP):
Guidance for Regional Implementation, NOAA Technical
Report NMFS 123, U.S. Department of Commerce, April
1995.

Enumerated_Domain:
Enumerated_Domain_Value: 6 Deciduous Forest
Enumerated_Domain_Value_Definition:

Includes areas dominated by single stemmed, woody
vegetation unbranched 0.6 to 1 meter (2 to 3 feet) above
the ground and having a height greater than 6 meters (20
feet).

Enumerated_Domain:
Enumerated_Domain_Value: 7 Evergreen Forest
Enumerated_Domain_Value_Definition:

Includes areas in which more than 67 percent of the trees
remain green throughout the year. Both coniferous and
broad-leaved evergreens are included in this category.

Enumerated_Domain:
Enumerated_Domain_Value: 8 Mixed Forest
Enumerated_Domain_Value_Definition:
Contains all forested areas in which both evergreen and
deciduous trees are growing and neither predominate.
Enumerated_Domain_Value_Definition_Source:
Dobson, J. et al, NOAA Coastal Change Analysis Program(C-CAP):
Guidance for Regional Implementation, NOAA Technical
Report NMFS 123, U.S. Department of Commerce, April
1995.

Enumerated_Domain:
Enumerated_Domain_Value: 9 Scrub/Shrub
Enumerated_Domain_Value_Definition:

Areas dominated by woody vegetation less than 6 meters in
height. This class includes true shrubs,young trees, and
trees or shrubs that are small or stunted because of
environmental conditions.
Enumerated_Domain_Value_Definition_Source:
Dobson, J. et al, NOAA Coastal Change Analysis Program(C-CAP):
Guidance for Regional Implementation, NOAA Technical
Report NMFS 123, U.S. Department of Commerce, April
1995.

Enumerated_Domain:
Enumerated_Domain_Value: 10 Palustrine Forested Wetland
Enumerated_Domain_Value_Definition:

Includes all nontidal wetlands dominated by woody
vegetation greater than or equal to 6 meters in height,
and all such wetlands that occur in tidal areas in which
salinity due to ocean-derived salts is below 0.5 parts per
thousand (ppt).
Enumerated_Domain_Value_Definition_Source:
Dobson, J. et al, NOAA Coastal Change Analysis Program(C-CAP):
Guidance for Regional Implementation, NOAA Technical
Report NMFS 123, U.S. Department of Commerce, April
1995.

Enumerated_Domain:
Enumerated_Domain_Value: 11 Palustrine Scrub/Shrub Wetland
Enumerated_Domain_Value_Definition:

Includes all nontidal wetlands dominated by woody
vegetation less than or equal to 6 meters in height, and
all such wetlands that occur in tidal areas in which
salinity due to ocean-derived salts is below 0.5 ppt.

Enumerated_Domain:
Enumerated_Domain_Value: 12 Palustrine Emergent Wetland
Enumerated_Domain_Value_Definition:

Includes all nontidal wetlands dominated by trees, shrubs,
persistent emergents, emergent mosses, or lichens, and all
such wetlands that occur in tidal areas in which salinity
due to ocean- derived salts is below 0.5 ppt.
Enumerated_Domain_Value_Definition_Source:
Dobson, J. et al, NOAA Coastal Change Analysis Program(C-CAP):
Guidance for Regional Implementation, NOAA Technical
Report NMFS 123, U.S. Department of Commerce, April
1995.

Enumerated_Domain:
Enumerated_Domain_Value: 13 Estuarine Forest Wetland
Enumerated_Domain_Value_Definition:
Includes all tidal wetlands dominated by woody vegetation
greater than or equal to 6 meters in height, and all such
wetlands that occur in tidal areas in which salinity due
to ocean-derived salts is above 0.5 parts per thousand
(ppt).
Enumerated_Domain_Value_Definition_Source:
Dobson, J. et al, NOAA Coastal Change Analysis Program(C-CAP):
Guidance for Regional Implementation, NOAA Technical
Report NMFS 123, U.S. Department of Commerce, April
1995.

Enumerated_Domain:
Enumerated_Domain_Value: 14 Estuarine Scrub/Shrub Wetland
Enumerated_Domain_Value_Definition:

Includes all tidal wetlands dominated by woody vegetation
less than or equal to 6 meters in height, and all such
wetlands that occur in tidal areas in which salinity due
to ocean-derived salts is above 0.5 ppt.

Enumerated_Domain:
Enumerated_Domain_Value: 15 Estuarine Emergent
Enumerated_Domain_Value_Definition:

Characterized by erect, rooted, herbaceous hydrophytes
(excluding mosses and lichens) that are present for most
of the growing season in most years. Perennial plants
usually dominate these wetlands. All water regimes are
included except those that are subtidal and irregularly
exposed.
Enumerated_Domain_Value_Definition_Source:
Dobson, J. et al, NOAA Coastal Change Analysis Program(C-CAP):
Guidance for Regional Implementation, NOAA Technical
Report NMFS 123, U.S. Department of Commerce, April
1995.

Enumerated_Domain:
Enumerated_Domain_Value: 16 Unconsolidated Shore
Enumerated_Domain_Value_Definition:

Characterized by substrates lacking vegetation except for
pioneering plants that become established during brief
periods when growing conditions are favorable. Erosion and
deposition by waves and currents produce a number of
landforms, such as beaches, bars, and flats, all of which
are included in this class.

Enumerated_Domain:
Enumerated_Domain_Value: 17 Bare Land
Enumerated_Domain_Value_Definition:
Composed of bare soil, rock, sand, silt, gravel, or other
earthen material with little or no vegetation.

Enumerated_Domain:
Enumerated_Domain_Value: 18 Water
Enumerated_Domain_Value_Definition:
Includes all areas of open water with less than 30 percent
cover of trees, shrubs, persistent emergent plants,
emergent mosses, or lichens.

Enumerated_Domain:
Enumerated_Domain_Value: 19 Tundra
Enumerated_Domain_Value_Definition:
Includes treeless cover beyond the latitudinal limit of
the boreal forest in poleward regions and above the
elevation range of the boreal forest in high mountains.
Enumerated_Domain_Value_Definition_Source:
Dobson, J. et al, NOAA Coastal Change Analysis Program(C-CAP):
Guidance for Regional Implementation, NOAA Technical
Report NMFS 123, U.S. Department of Commerce, April
1995.

Enumerated_Domain:
Enumerated_Domain_Value: 20 Snow/Ice
Enumerated_Domain_Value_Definition:

Includes persistent snow and ice persist for greater
portions of the year.

Enumerated_Domain:
Enumerated_Domain_Value: 21 Palustrine Aquatic Bed
Enumerated_Domain_Value_Definition:
Includes wetlands and deepwater habitats dominated by
plants that grow principally on or below the surface of
the water for most of the growing season in most years.

Enumerated_Domain:
Enumerated_Domain_Value: 22 Estuarine Aquatic Bed
Enumerated_Domain_Value_Definition:
Includes widespread and diverse Algal Beds in the Marine
and Estuarine Systems, where they occupy substrates
characterized by a wide range of sediment depths and
textures. They occur in both the Subtidal and Intertidal
Subsystems and may grow to depths of 30 m (98 feet).
Enumerated_Domain_Value_Definition_Source:
Dobson, J. et al, NOAA Coastal Change Analysis Program(C-CAP):
Guidance for Regional Implementation, NOAA Technical
Report NMFS 123, U.S. Department of Commerce, April
1995.

Distribution_Information:
Distributor:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: NOAA Coastal Services Center
Contact_Person: Clearinghouse Manager
Contact_Position: Clearinghouse Manager
Contact_Address:
Address_Type: mailing and physical address
Address: 2234 South Hobson Avenue
City: Charleston
State_or_Province: SC
Postal_Code: 29405-2413
Country: USA
Contact_Voice_Telephone: (843)740-1210
Contact_Facsimile_Telephone: (843)740-1224
Contact_Electronic_Mail_Address: clearinghouse@csc.noaa.gov
Hours_of_Service: Monday-Friday, 8-5 EST
Resource_Description: CCAP Rough Change Analysis and Classification Mosaic
Distribution_Liability: This dataset is not for redistribution.
Standard_Order_Process:
Digital_Form:
Digital_Transfer_Information:
Format_Name: ERDAS Imagine image file (.img)
Digital_Transfer_Option:
Offline_Option:
Offline_Media: CD-ROM
Recording_Format: ISO 9660
Compatibility_Information:
ISO 9660 format allows the CDROM
to be read by most computer operating systems.
Fees: none

Metadata_Reference_Information:
Metadata_Date: 20020712
Metadata_Review_Date: 20020712
Metadata_Contact:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: NOAA Coastal Services Center
Contact_Person: Metadata Specialist
Contact_Position: Metadata Specialist
Contact_Address:
Address_Type: mailing and physical address
Address: 2234 S Hobson Ave.
City: Charleston
State_or_Province: SC
Postal_Code: 29405
Country: USA
Contact_Voice_Telephone: 843-740-1210
Contact_Facsimile_Telephone: 843-740-1224
Contact_Electronic_Mail_Address: csc@csc.noaa.gov
Hours_of_Service: 8:00 am to 5:00 pm EST.
Metadata_Standard_Name: FGDC CSDGM
Metadata_Standard_Version: FGDC-STD-001-1998