Pennsylvania Land Cover Data Set

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Frequently anticipated questions:

What does this data set describe?

Title: Pennsylvania Land Cover Data Set
Abstract:

These data can be used in a geographic information system (GIS) for any
number of purposes such as assessing wildlife habitat, water quality,
pesticide runoff, land use change, etc. The State data sets are provided
with a 300 meter buffer beyond the State border to faciliate combining
the State files into larger regions.


The user must have a firm understanding of how the datasets were compiled
and the resulting limitations of these data. The National Land Cover Dataset
was compiled from Landsat satellite TM imagery (circa 1992) with a spatial
resolution of 30 meters and supplemented by various ancillary data (where
available). The analysis and interpretation of the satellite imagery was
conducted using very large, sometimes multi-state image mosaics (i.e. up to 18
Landsat scenes). Using a relatively small number of aerial photographs for
'ground truth', the thematic interpretations were necessarily conducted from a
spatially-broad perspective. Furthermore, the accuracy assessments (see below)
correspond to 'federal regions' which are groupings of contiguous states. Thus,
the reliability of the data is greatest at the state or multi-state level. The
statistical accuracy of the data is known only for the region.


Important Caution Advisory


With this in mind, users are cautioned to carefully scrutinize the data to
see if they are of sufficient reliability before attempting to use the
dataset for larger-scale or local analyses. This evaluation must be made
remembering that the NLCD represents conditions in the early 1990s.


The Pennsylvania portion of the NLCD was created as part of land cover
mapping activities for Federal Region III that includes the States of
Maryland, Delaware, Pennsylvania, Virginia, West Virginia, and the
District of Columbia. The NLCD classification contains 21 different
land cover categories with a spatial resolution of 30 meters. The NLCD
was produced as a cooperative effort between the U.S. Geological Survey
(USGS) and the U.S. Environmental Protection Agency (US EPA) to produce
a consistent, land cover data layer for the conterminous U.S. using
early 1990s Landsat thematic mapper (TM) data purchased by the
Multi-resolution Land Characterization (MRLC) Consortium. The MRLC
Consortium is a partnership of federal agencies that produce or use land
cover data. Partners include the USGS (National Mapping, Biological
Resources, and Water Resources Divisions), US EPA, the U.S. Forest Service,
and the National Oceanic and Atmospheric Administration.
Supplemental_Information:

The land cover data files are provided as a 'Geo-TIFF'. The land cover
data sets are single band raster images.
  1. How should this data set be cited?

    U.S. Geological Survey (USGS), 19990527, Pennsylvania Land Cover Data Set: U.S. Geological Survey, Sioux Falls, SD USA.

    Online Links:

  2. What geographic area does the data set cover?

    West_Bounding_Coordinate: -80.438
    East_Bounding_Coordinate: -75.083
    North_Bounding_Coordinate: 42.722
    South_Bounding_Coordinate: 38.912

  3. What does it look like?

  4. Does the data set describe conditions during a particular time period?

    Beginning_Date: 1986
    Ending_Date: 1993
    Currentness_Reference: ground condition

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

    Geospatial_Data_Presentation_Form: raster digital data

  6. How does the data set represent geographic features?

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

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

      • Dimensions 11105 x 17370 x 1, type Grid Cell

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

      The map projection used is Albers Conical Equal Area.

      Projection parameters:

      Planar coordinates are encoded using row and column
      Abscissae (x-coordinates) are specified to the nearest 30.0
      Ordinates (y-coordinates) are specified to the nearest 30.0
      Planar coordinates are specified in meters

      The horizontal datum used is North American Datum 1983.
      The ellipsoid used is Geographic Reference System 80.
      The semi-major axis of the ellipsoid used is 6378137.
      The flattening of the ellipsoid used is 1/298.257.

  7. How does the data set describe geographic features?

    Entity_and_Attribute_Overview:

    NOTE - All classes may NOT be represented in a specific state data set.
    The class number represents the digital value of the class in the data
    set.

    Water
    11 Open Water
    12 Perennial Ice/Snow

    Developed
    21 Low Intensity Residential
    22 High Intensity Residential
    23 Commercial/Industrial/Transportation

    Barren
    31 Bare Rock/Sand/Clay
    32 Quarries/Strip Mines/Gravel Pits
    33 Transitional

    Vegetated; Natural Forested Upland
    41 Deciduous Forest
    42 Evergreen Forest
    43 Mixed Forest

    Shrubland
    51 Shrubland

    Non-natural Woody
    61 Orchards/Vineyards/Other

    Herbaceous Upland
    71 Grasslands/Herbaceous

    Herbaceous Planted/Cultivated
    81 Pasture/Hay
    82 Row Crops
    83 Small Grains
    84 Fallow
    85 Urban/Recreational Grasses


    Wetlands
    91 Woody Wetlands
    92 Emergent Herbaceous Wetlands


    NLCD Land Cover Classification System Land Cover Class Definitions:

    Water All areas of open water or permanent ice/snow cover.

    11. Open Water - areas of open water, generally with less
    than 25 percent or greater cover of water (per pixel).

    12. Perennial Ice/Snow - All areas characterized by year-long
    cover of ice and/or snow.

    Developed - areas characterized by high percentage (approximately
    30% or greater) of constructed materials (e.g. asphalt, concrete,
    buildings, etc).

    21. Low Intensity Residential - Includes areas with a mixture of
    constructed materials and vegetation. Constructed materials account
    for 30-80 percent of the cover. Vegetation may account for 20 to 70
    percent of the cover. These areas most commonly include single-family
    housing units. Population densities will be lower than in high intensity
    residential areas.

    22. High Intensity Residential - Includes heavily built up urban
    centers where people reside in high numbers. Examples include
    apartment complexes and row houses. Vegetation accounts for less
    than 20 percent of the cover. Constructed materials account for
    80-100 percent of the cover.

    23. Commercial/Industrial/Transportation - Includes infrastructure
    (e.g. roads, railroads, etc.) and all highways and all developed areas
    not classified as High Intensity Residential.

    Barren - Areas characterized by bare rock, gravel, sad, silt, clay, or
    other earthen material, with little or no "green" vegetation present
    regardless of its inherent ability to support life. Vegetation, if
    present, is more widely spaced and scrubby than that in the
    "green" vegetated categories; lichen cover may be extensive.

    31. Bare Rock/Sand/Clay - Perennially barren areas of bedrock, desert,
    pavement, scarps, talus, slides, volcanic material, glacial debris, and
    other accumulations of earthen material.

    32. Quarries/Strip Mines/Gravel Pits - Areas of extractive mining
    activities with significant surface expression.

    33. Transitional - Areas of sparse vegetative cover (less than 25
    percent that are dynamically changing from one land cover to
    another, often because of land use activities. Examples include
    forest clearcuts, a transition phase between forest and agricultural land,
    the temporary clearing of vegetation, and changes due to natural causes
    (e.g. fire, flood, etc.)

    Forested Upland - Areas characterized by tree cover (natural or
    semi-natural woody vegetation, generally greater than 6 meters tall);
    Tree canopy accounts for 25-100 percent of the cover.

    41. Deciduous Forest - Areas dominated by trees where 75 percent
    or more of the tree species shed foliage simultaneously in response to
    seasonal change.

    42. Evergreen Forest - Areas characterized by trees where 75 percent
    or more of the tree species maintain their leaves all year. Canopy is
    never without green foliage.

    43. Mixed Forest - Areas dominated by trees where neither
    deciduous nor evergreen species represent more than 75 percent
    of the cover present.

    Shrubland - Areas characterized by natural or semi-natural woody
    vegetation with aerial stems, generally less than 6 meters tall
    with individuals or clumps not touching to interlocking. Both evergreen
    and diciduous species of true shrubs, young trees, and trees or shrubs
    that are small or stunted because of environmental conditions are
    included.

    51. Shrubland - Areas dominated by shrubs; shrub canopy accounts
    for 25-100 percent of the cover. Shrub cover is generally greater
    than 25 percent when tree cover is less than 25 percent. Shrub cover
    may be less than 25 percent in cases when the cover of other life forms
    (e.g. herbaceous or tree) is less than 25 percent and shrubs cover
    exceeds the cover of the other life forms.

    Non-natural Woody - Areas dominated by non-natural woody
    vegetation; non-natural woody vegetative canopy accounts for
    25-100 percent of the cover. The non-natural woody classification
    is subject to the availability of sufficient ancillary data to
    differentiate non-natural woody vegetation from natural woody vegetation.

    61. Orchards/Vineyards/Other - Orchards, vineyards, and other areas
    planted or maintained for the production of fruits, nuts, berries, or
    ornamentals.

    Herbaceous Upland - Upland areas characterized by natural or
    semi- natural herbaceous vegetation; herbaceous vegetation
    accounts for 75-100 percent of the cover.

    71. Grasslands/Herbaceous - Areas dominated by upland grasses
    and forbs. In rare cases, herbaceous cover is less than 25 percent,
    but exceeds the combined cover of the woody species present.
    These areas are not subject to intensive management, but they are
    often utilized for grazing.

    Planted/Cultivated - Areas characterized by herbaceous vegetation
    That has been planted or is intensively managed for the production
    of food, feed, or fiber; or is maintained in developed settings for
    specific purposes. Herbaceous vegetation accounts for 75-100 percent
    of the cover.

    81. Pasture/Hay - Areas of grasses, legumes, or grass-legume mixtures
    planted for livestock grazing or the production of seed or hay crops.

    82. Row Crops - Areas used for the production of crops, such
    as corn, soybeans, vegetables, tobacco, and cotton.

    83. Small Grains - Areas used for the production of graminoid
    crops such as wheat, barley, oats, and rice

    84. Fallow - Areas used for the production of crops that are
    temporarily barren or with sparse vegetative cover as a result of
    being tilled in a management practice that incorporates prescribed
    alternation between cropping and tillage.

    85. Urban/Recreational Grasses - Vegetation (primarily grasses) planted
    in developed settings for recreation, erosion control, or aesthetic
    purposes. Examples include parks, lawns, golf courses, airport grasses,
    and industrial site grasses.

    Wetlands - Areas where the soil or substrate is periodically saturated
    with or covered with water as defined by Cowardin et al.

    91. Woody Wetlands - Areas where forest or shrubland vegetation
    accounts for 25-100 percent of the cover and the soil or substrate
    is periodically saturated with or covered with water.

    92. Emergent Herbaceous Wetlands - Areas where perennial
    herbaceous vegetation accounts for 75-100 percent of the cover
    and the soil or substrate is periodically saturated with or covered
    with water.
    Entity_and_Attribute_Detail_Citation:

    NLCD Regional Land Cover Classification System Key Rev. 07/99


Who produced the data set?

  1. Who are the originators of the data set? (may include formal authors, digital compilers, and editors)

  2. Who also contributed to the data set?


    This work was performed by the Raytheon STX Corporation under
    U.S. Geological Survey Contract 1434-92-C-40004.



  3. To whom should users address questions about the data?

    U.S. Geological Survey EROS Data Center
    Customer Services Representative
    U.S. Geological Survey, EROS Data Center
    Sioux Falls, SD 57198
    USA

    (605) 594-6151 (voice)
    (605) 594-6589 (FAX)
    CUSTSERV@EDCMAIL.CR.USGS.GOV

Why was the data set created?


The main objective of this project was to generate a generalized
and nationally consistent land cover data layer for the entire
conterminous United States. These data can be used as a layer in
a geographic information system (GIS) for any number of purposes
such assessing wildlife habitat, water quality and pesticide runoff,
land use change, etc.

How was the data set created?

  1. From what previous works were the data drawn?

    image1 (source 1 of 27)
    U.S. Geological Survey EROS Data Center , unknown, Landsat TM scene: EROS Data Center, Sioux Falls, SD 57198 USA .

    Other_Citation_Details: Path/Row 014/031
    Type_of_Source_Media: raster digital data
    Source_Contribution:

    The image provides the base from which the
    land cover classification is determined.

    image2 (source 2 of 27)
    U.S. Geological Survey EROS Data Center , unknown, Landsat TM scene: EROS Data Center, Sioux Falls, SD 57198 USA .

    Other_Citation_Details: Path/Row 014/032
    Type_of_Source_Media: raster digital data
    Source_Contribution:

    The image provides the base from which
    the land cover classification is determined.

    image3 (source 3 of 27)
    U.S. Geological Survey EROS Data Center , unknown, Landsat TM scene: EROS Data Center, Sioux Falls, SD 57198 USA .

    Other_Citation_Details: Path/Row 014/033
    Type_of_Source_Media: raster digital data
    Source_Contribution:

    The image provides the base from which the
    land cover classification is determined.

    image4 (source 4 of 27)
    U.S. Geological Survey EROS Data Center , unknown, Landsat TM scene: EROS Data Center, Sioux Falls, SD 57198 USA .

    Other_Citation_Details: Path/Row 015/031
    Type_of_Source_Media: raster digital data
    Source_Contribution:

    The image provides the base from which the
    land cover classification is determined.

    image5 (source 5 of 27)
    U.S. Geological Survey EROS Data Center , unknown, Landsat TM scene: EROS Data Center, Sioux Falls, SD 57198 USA .

    Other_Citation_Details: Path/Row 015/032
    Type_of_Source_Media: raster digital data
    Source_Contribution:

    The image provides the base from which the
    land cover classification is determined.

    image6 (source 6 of 27)
    U.S. Geological Survey EROS Data Center , unknown, Landsat TM scene: EROS Data Center, Sioux Falls, SD 57198 USA .

    Other_Citation_Details: Path/Row 015/032
    Type_of_Source_Media: raster digital data
    Source_Contribution:

    The image provides the base from which the
    land cover classification is determined.

    image7 (source 7 of 27)
    U.S. Geological Survey EROS Data Center , unknown, Landsat TM scene: EROS Data Center, Sioux Falls, SD 57198 USA .

    Other_Citation_Details: Path/Row 015/033
    Type_of_Source_Media: raster digital data
    Source_Contribution:

    The image provided the base from which the
    land cover classification is determined.

    image8 (source 8 of 27)
    U.S. Geological Survey EROS Data Center , unknown, Landsat TM scene: EROS Data Center, Sioux Falls, SD 57198 USA .

    Other_Citation_Details: Path/Row 016/031
    Type_of_Source_Media: raster digital data
    Source_Contribution:

    The image provides the base from which the
    land cover classification is determined.

    image9 (source 9 of 27)
    U.S. Geological Survey EROS Data Center , unknown, Landsat TM scene: EROS Data Center, Sioux Falls, SD 57198 USA .

    Other_Citation_Details: Path/Row 016/032
    Type_of_Source_Media: raster digital data
    Source_Contribution:

    The image provides the base from which the
    land cover classification is determined.

    image10 (source 10 of 27)
    U.S. Geological Survey EROS Data Center , unknown, Landsat TM scene: EROS Data Center, Sioux Falls, SD 57198 USA .

    Other_Citation_Details: Path/Row 016/33
    Type_of_Source_Media: raster digital data
    Source_Contribution:

    The image provides the base from which the
    land cover classification is determined.

    image11 (source 11 of 27)
    U.S. Geological Survey EROS Data Center , unknown, Landsat TM scene: EROS Data Center, Sioux Falls, SD 57198 USA .

    Other_Citation_Details: Path/Row 017/031
    Type_of_Source_Media: raster digital data
    Source_Contribution:

    The image provdes the base from which the
    land cover classification is determined.

    image12 (source 12 of 27)
    U.S. Geological Survey EROS Data Center , unknown, Landsat TM scene: EROS Data Center, Sioux Falls, SD 57198 USA .

    Other_Citation_Details: Path/Row 017/032
    Type_of_Source_Media: raster digital data
    Source_Contribution:

    The image provides the base from which the
    land cover classification is determined.

    image13 (source 13 of 27)
    U.S. Geological Survey EROS Data Center , unknown, Landsat TM scene: EROS Data Center, Sioux Falls, SD 57198 USA .

    Other_Citation_Details: Path/Row 017/033
    Type_of_Source_Media: raster digital data
    Source_Contribution:

    The image provides the base from which the
    land cover classification is determined.

    image14 (source 14 of 27)
    U.S. Geological Survey EROS Data Center , unknown, Landsat TM scene: EROS Data Center, Sioux Falls, SD 57198 USA .

    Other_Citation_Details: Path/Row 018/031
    Type_of_Source_Media: raster digital data
    Source_Contribution:

    The image provides the base from which the
    land cover classification is determined.

    image15 (source 15 of 27)
    U.S. Geological Survey EROS Data Center , unknown, Landsat TM scene: EROS Data Center, Sioux Falls, SD 57198 USA .

    Other_Citation_Details: Path/Row 018/032
    Type_of_Source_Media: raster digital data
    Source_Contribution:

    The image provides the base from which the
    land cover classification is determined.

    image16 (source 16 of 27)
    U.S. Geological Survey EROS Data Center , unknown, Landsat TM scene: EROS Data Center, Sioux Falls, SD 57198 USA .

    Other_Citation_Details: Leaf On Path/Row 014/032
    Type_of_Source_Media: raster digital data
    Source_Contribution:

    The image provides the base from which the
    land cover classification is determined.

    image17 (source 17 of 27)
    U.S. Geological Survey EROS Data Center , unknown, Landsat TM scene: EROS Data Center, Sioux Falls, SD 57198 USA .

    Other_Citation_Details: Leaf On Path/Row 014/033
    Type_of_Source_Media: raster digital data
    Source_Contribution:

    The image provides the base from which the
    land cover classification is determined.

    image18 (source 18 of 27)
    U.S. Geological Survey EROS Data Center , unknown, Landsat TM scene: EROS Data Center, Sioux Falls, SD 57198 USA .

    Other_Citation_Details: Leaf On Path/Row 015/031
    Type_of_Source_Media: raster digital data
    Source_Contribution:

    The image provides the base from which the
    land cover classification is determined.

    image19 (source 19 of 27)
    U.S. Geological Survey EROS Data Center , unknown, Landsat TM scene: EROS Data Center, Sioux Falls, SD 57198 USA .

    Other_Citation_Details: Leaf On Path/Row 015/032
    Type_of_Source_Media: raster digital data
    Source_Contribution:

    The image provides the base from which the
    land cover classification is determined.

    image20 (source 20 of 27)
    U.S. Geological Survey EROS Data Center , unknown, Landsat TM scene: EROS Data Center, Sioux Falls, SD 57198 USA .

    Other_Citation_Details: Leaf On Path/Row 015/033
    Type_of_Source_Media: raster digital data
    Source_Contribution:

    The image provides the base from which the
    land cover classification is determined.

    image21 (source 21 of 27)
    U.S. Geological Survey EROS Data Center , unknown, Landsat TM scene: EROS Data Center, Sioux Falls, SD 57198 USA .

    Other_Citation_Details: Leaf On Path/Row 016/031
    Type_of_Source_Media: raster digital data
    Source_Contribution:

    The image provides the base from which the
    land cover classification is determined.

    image22 (source 22 of 27)
    U.S. Geological Survey EROS Data Center , unknown, Landsat TM scene: EROS Data Center, Sioux Falls, SD 57198 USA .

    Other_Citation_Details: Leaf On Path/Row 016/032
    Type_of_Source_Media: raster digital data
    Source_Contribution:

    The image provides the base from which the
    land cover classification is determined.

    image23 (source 23 of 27)
    U.S. Geological Survey EROS Data Center , unknown, Landsat TM scene: EROS Data Center, Sioux Falls, SD 57198 USA .

    Other_Citation_Details: Leaf On Path/Row 016/033
    Type_of_Source_Media: raster digital data
    Source_Contribution:

    The image provides the base from which the
    land cover classification is determined.

    image24 (source 24 of 27)
    U.S. Geological Survey EROS Data Center , unknown, Landsat TM scene: EROS Data Center, Sioux Falls, SD 57198 USA .

    Other_Citation_Details: Leaf On Path/Row 017/031
    Type_of_Source_Media: raster digital data
    Source_Contribution:

    The image provides the base from which the
    land cover classification is determined.

    image25 (source 25 of 27)
    U.S. Geological Survey EROS Data Center , unknown, Landsat TM scene: EROS Data Center, Sioux Falls, SD 57198 USA .

    Other_Citation_Details: Leaf On Path/Row 017/032
    Type_of_Source_Media: raster digital data
    Source_Contribution:

    The image provides the base from which the
    land cover classification is determined.

    image26 (source 26 of 27)
    U.S. Geological Survey EROS Data Center , unknown, Landsat TM scene: EROS Data Center, Sioux Falls, SD 57198 USA .

    Other_Citation_Details: Leaf On Path/Row 017/033
    Type_of_Source_Media: raster digital data
    Source_Contribution:

    The image provides the base from which the
    land cover classification is determined.

    image27 (source 27 of 27)
    U.S. Geological Survey EROS Data Center , unknown, Landsat TM scene: EROS Data Center, Sioux Falls, SD 57198 USA .

    Other_Citation_Details: Leaf On Path/Row 018/032
    Type_of_Source_Media: raster digital data
    Source_Contribution:

    The image provides the base from which the
    land cover classification is determined.

  2. How were the data generated, processed, and modified?

    Date: 27-May-1999 (process 1 of 1)

    Land Cover Characterization:
    The project is being carried out on the basis of 10 Federal Regions
    that make up the conterminous United States; each region is comprised
    of multiple states; each region is processed in subregional units
    that are limited to the area covered by no more than 18 Landsat TM
    scenes. The general NLCD procedure is to: (1) mosaic subregional TM
    scenes and classify them using an unsupervised clustering algorithm,
    (2) interpret and label the clusters/classes using aerial photographs
    as reference data, (3) resolve the labeling of confused clusters/classes
    using the appropriate ancillary data source(s), and (4) incorporate
    land cover information from other data sets and perform manual edits to
    augment and refine the "basic" classification developed above.


    Two seasonally distinct TM mosaics are produced, a leaves-on version
    (summer) and a leaves-off (spring/fall) version. TM bands 3, 4, 5,
    and 7 are mosaicked for both the leaves-on and leaves-off versions.
    For mosaick purposes, a base scene is selected for each mosaic and
    the other scenes are adjusted to mimic spectral properties of the base
    scene using histogram matching in regions of spatial overlap.
    Following mosaicking, either the leaves-off version or leaves-on version
    Is selected to be the "base" for the land cover mapping process. The 4
    TM bands of the "base" mosaic are clustered to produce a single 100-
    class image using an unsupervised clustering algorithm. Each of the
    spectrally distinct clusters/classes is then assigned to one or more
    Anderson level 1 and 2 land cover classes using National High Altitude
    Photography program (NHAP)and National Aerial Photography program
    (NAPP) aerial photographs as a reference. Almost invariably, individual
    spectral clusters/classes are confused between two or more land cover
    classes.


    Separation of the confused spectral clusters/classes into appropriate
    NLCD class is accomplished using ancillary data layers. Standard
    ancillary data layers include: the "non-base" mosaic TM bands and 100-
    class cluster image; derived TM normalized vegetation index (NDVI),
    various TM band ratios, TM date bands; 3-arc second Digital Terrain
    Elevation Data (DTED) and derived slope, aspect and shaded relief;
    population and housing density data; USGS land use and land cover
    (LUDA); and National Wetlands Inventory(NWI) data if available. Other
    ancillary data sources may include soils data, unique state or regional
    land cover data sets, or data from other federal programs such as the
    National Gap Analysis Program (GAP) of the USGS Biological Resources
    Division (BRD). For a given confused spectral cluster/class, digital
    values of the various ancillary data layers are compared to determine:
    (1) which data layers are the most effective for splitting the
    confused cluster/class into the appropriate NLCD class, and (2) the
    appropriate layer thresholds for making the split(s). Models are then
    developed using one to several ancillary data layers to split the
    confused cluster/class into the NLCD class. For example, a population
    density threshold is used to separate high-intensity residential areas
    from commercial/industrial/transportation. Or a cluster/class might be
    confused between row crop and grasslands. To split this particular
    cluster/class, a TM NDVI threshold might be identified and used with an
    elevation threshold in a class-splitting model to make the appropriate
    NLCD class assignments. A purely spectral example is using the
    temporally opposite TM layers to discriminate confused cluster/classes
    such as hay pasture vs. row crops and deciduous forests vs. evergreen
    forests; simple thresholds that contrast the seasonal differences in
    vegetation between leaves-on vs. leaves-off.


    Not all cluster/class confusion can be successfully modeled out.
    Certain classes such as urban/recreational grasses or quarries/strip
    mines/gravel pits that are not spectrally unique require manual editing.
    These class features are typically visually identified and then
    reclassified using on-screen digitizing and recoding. Other classes
    such as wetlands require the use of specific data sets such as NWI to
    provide the most accurate classification. Areas lacking NWI data are
    typically subset out and modeling is used to estimate wetlands in these
    localized areas. The final NLCD product results from the classification
    (interpretation and labeling) of the 100-class "base" cluster mosaic
    using both automated and manual processes, incorporating both spectral
    and conditional data layers. For a more detailed explanation please
    see Vogelmann et al. 1998 and Vogelmann et al. 1998.


    Discussion:


    While we believe that the approach taken has yielded a very good general
    land cover classification product for the nation, it is important to
    indicate to the user where there might be some potential problems. The
    biggest concerns are listed below:


    1) Some of the TM data sets are not temporally ideal. Leaves-off data
    sets are heavily relied upon for discriminating between hay/pasture and
    row crop, and also for discriminating between forest classes. The
    success of discriminating between these classes using leaves-off data
    sets hinges on the time of data acquisition. When hay/pasture areas are
    non-green, they are not easily distinguishable from other agricultural
    areas using remotely sensed data. However, there is a temporal window
    during which hay and pasture areas green up before most other vegetation
    (excluding evergreens, which have different spectral properties); during
    this window these areas are easily distinguishable from other crop
    areas. The discrimination between hay/pasture and deciduous forest is
    likewise optimized by selecting data in a temporal window where
    deciduous vegetation has yet to leaf out. It is difficult to acquire a
    single-date of imagery (leaves-on or leaves-off) that adequately
    differentiates between both deciduous/hay and pasture and hay pasture
    /row crop.


    2) The data sets used cover a range of years (see data sources), and
    changes that have taken place across the landscape over the time period
    may not have been captured. While this is not viewed as a major problem
    for most classes, it is possible that some land cover features change
    more rapidly than might be expected (e.g. hay one year, row crop the
    next).


    3) Wetlands classes are extremely difficult to extract from Landsat TM
    spectral information alone. The use of ancillary information such as
    National Wetlands Inventory (NWI) data is highly desirable. We relied
    on GAP, LUDA, or proximity to streams and rivers as well as spectral
    data to delineate wetlands in areas without NWI data.


    4) Separation of natural grass and shrub is problematic. Areas observed
    on the ground to be shrub or grass are not always distinguishable
    spectrally. Likewise, there was often disagreement between LUDA and GAP
    on these classes.


    Acknowledgments


    This work was performed under contract the U.S. Geological
    Survey(Contract 1434-CR-97-CN-40274).


    References


    More detailed information on the methodologies and techniques employed
    In this work can be found in the following:


    Kelly, P.M., and White, J.M., 1993. Preprocessing remotely sensed data
    for efficient analysis and classification, Applications of Artificial
    Intelligence 1993: Knowledge-Based Systems in Aerospace and Industry,
    Proceeding of SPIE, 1993, 24-30.


    Cowardin, L.M., V. Carter, F.C. Golet, and E.T. LaRoe, 1979.
    Classification of Wetlands and Deepwater Habitats of the United States,
    Fish and Wildlife Service, U.S. Department of the Interior, Washington,
    D.C.


    Vogelmann, J.E., Sohl, T., and Howard, S.M., 1998. "Regional
    Characterization of Land Cover Using Multiple Sources of Data."
    Photogrammetric Engineering & Remote Sensing, Vol. 64, No. 1,
    pp. 45-47.


    Vogelmann, J.E., Sohl, T., Campbell, P.V., and Shaw, D.M., 1998.
    "Regional Land Cover Characterization Using Landsat Thematic Mapper
    Data and Ancillary Data Sources." Environmental Monitoring and
    Assessment, Vol. 51, pp. 415-428.


    Zhu, Z., Yang, L., Stehman, S., and Czaplewski, R., 1999. "Designing an
    Accuracy Assessment for USGS Regional Land Cover Mapping Program."
    (In review) Photogrametric Engineering & Remote Sensing.


    Person who carried out this activity:

    U.S. Geological Survey EROS Data Center
    Customer Services Representative

    U.S. Geological Survey

    EROS Data Center

    Sioux Falls, SD 57198
    USA

    (605) 594 - 6551 (voice)
    (605) 594 - 6589 (FAX)
    CUSTSERV@EDCMAIL.CR.USGS.GOV

    Data sources used in this process:
    • Landsat thematic mapper (TM)

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

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

  1. How well have the observations been checked?


    An accuracy assessment is done on all NLCD on a Federal Region basis
    following a revision cycle that incorporates feedback from MRLC
    Consortium partners and affiliated users. The accuracy assessments
    are conducted by private sector vendors under contract to the USEPA.
    A protocol has been established by the USGS and USEPA that incorporates
    a two-stage, geographically stratified cluster sampling plan (Zhu et
    al., 1999)utilizing National Aerial Photography Program (NAPP)
    photographs as the sampling frame and the basic sampling unit. In
    this design a NAPP photograph is defined as a 1st stage or primary
    sampling unit (PSU), and a sampled pixel within each PSU is treated
    as a 2nd stage or secondary sampling unit (SSU).


    PSU's are selected from a sampling grid based on NAPP flight-lines and
    photo centers, each grid cell measures 15' X 15' (minutes of
    latitude/longitude) and consists of 32 NHAP photographs. A geographically
    stratified random sampling is performed with 1 NAPP photo being randomly
    selected from each cell (geographic strata), if a sampled photo falls
    outside of the regional boundary it is not used. Second stage sampling
    is accomplished by selecting SSU's (pixels) within each PSU (NAPP photo)
    to provide the actual locations for the reference land cover
    classification.


    The SSU's are manually interpreted and misclassification errors are
    estimated and described using a traditional error matrix as well as a
    number of other important measures including the overall proportion of
    pixels correctly classified, user's and producer's accuracy's, and
    omission and commission error probabilities.


    At the time of CD release (Summer 2000), the accuracy assessment was not
    complete. For the Region III accuracy assessment please check the NLCD
    Website: <http://edcwww.usgs.gov/programs/lccp/nationallandcover.html>.
    The accuracy assessment numbers will be posted there around September,
    2000.


    While we believe that the approach taken has yielded a very good
    general land cover classification product for Region III,
    it is important to indicate to the user where there might be some
    potential problems. The biggest concerns for Region III are listed below:


    1) Accurate definition of the transitional barren class was extremely
    difficult. The majority of pixels in this class correspond to clear-cut
    forests in various stages of regrowth. Spectrally, fresh clear-cuts are
    very similar to row-crops in the leaves-off data. Manual correction of
    coding errors was performed to improve differentiation between row-crops
    and clear-cuts, but some errors may still be found. As regrowth occurs
    in a clear-cut region, the definition of transitional barren versus a
    forested class becomes problematic. An attempt was made to classify only
    fresh clear-cuts or those in the earliest stages of regrowth, but there
    are likely forested regions classed as transitional barren and vice versa.


    2) Due to the confusion between clear-cuts, regrowth in clear-cuts,
    Forested areas, and shrublands, no attempts were made to populate the
    shrubland classes. Any shrubland areas that exist in this area are
    classed in their like forest class, i.e. deciduous shrubland is classed as
    deciduous forest, etc.


  2. How accurate are the geographic locations?


    Each Landsat Thematic Mapper image used to create the NLCD was
    precision terrain-corrected using 3-arc-second digital terrain
    elevation data (DTED), and georegistered using ground control
    points. This resulted in a root mean square registration error
    of less than 1 pixel (30 meters).

  3. How accurate are the heights or depths?

  4. Where are the gaps in the data? What is missing?

    All photo-interpretable data are mapped.

  5. How consistent are the relationships among the observations, including topology?


    An unsupervised classification algorithm was used to classify the
    mosaicked multiple leaf-off TM scenes. Aerial photographs were
    used to interpret and label classes into land cover categories and
    ancillary data sources resolved the class confusion. Further land
    cover information from leaf-on TM data, NWI data, and other sources
    were incorporated to refine and augment the "basic" classification.

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. Acknowledgement of the U.S. Geological Survey would be
appreciated in products derived from these data.

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

    U.S. Geological Survey, EROS Data Center
    Customer Services Representative
    Sioux Falls
    SD, SD 57198
    USA

    605-594-6551 (voice)
    605-594-6589 (FAX)
    custserv@edcmail.cr.usgs.gov

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

    Pennsylvania Land Cover

  3. What legal disclaimers am I supposed to read?


    Although these data have been processed successfully on
    a computer system at the USGS, no warranty expressed or
    implied is made by the USGS regarding the use of the data
    on any other system, nor does the act of distribution
    constitute any such warranty.

  4. How can I download or order the data?

  5. What hardware or software do I need in order to use the data set?


    Geo-TIFF viewing software. Some examples are ESRI's ARC/EXPLORER and
    USGS's DLGV32. The DLGV32 viewer is available free for download at
    the MidContinent Mapping Center web site (<http://mcmcweb.er.usgs.gov/>).
    Digital image processing software or geographic information system
    software is required to analyze or otherwise manipulate the data.


Who wrote the metadata?

Dates:
Last modified: 13-Jan-2000
Metadata author:
U.S. Geological Survey, EROS Data Center
Customer Services Representative

U.S. Geological Survey

EROS Data Center

Sioux Falls, SD 57198
USA

605-594-6551 (voice)
605-594-6589 (FAX)
custserv@edcmail.cr.usgs.gov

Metadata standard:
Federal Geographic Data Committee. Content standard for digital geospatial metadata (revised June 1998). Federal Geographic Data Committee. Washington, D.C. (FGDC-STD-001-1998)

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