Identification Information:
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
The New York portion of the NLCD was created as part of land cover mapping
activities for Federal Region II that includes the states of New York and
New Jersey. 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.
Data Quality Information:
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 II,
it is important to indicate to the user where there might be some
potential problems. The biggest concerns for Region II are listed below:
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:
Acknowledgments
References
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.
Spatial Data Organization Information:
Spatial Reference Information:
Entity and Attribute Information:
Water
Developed
Barren
Vegetated; Natural Forested Upland
Shrubland
Non-natural Woody
Herbaceous Upland
Herbaceous Planted/Cultivated
Wetlands
NLCD Land Cover Classification System Land Cover Class Definitions:
Water - All areas of open water or permanent ice/snow cover.
Developed - areas characterized by high percentage (approximately
30% or greater) of constructed materials (e.g. asphalt, concrete,
buildings, etc).
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.
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.
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.
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.
Herbaceous Upland - Upland areas characterized by natural or
semi- natural herbaceous vegetation; herbaceous vegetation
accounts for 75-100 percent of the cover.
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.
Wetlands - Areas where the soil or substrate is periodically saturated
with or covered with water as defined by Cowardin et al.
Metadata Reference Information:
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.
U.S. Department of Commerce, 1977, Countries, dependencies, areas
of special sovereignty, and their principal administrative divisions
(Federal Information Processing Standard 10-3):Washington, D.C.,
National Institute of Standards and Technology.
U.S. Department of Commerce, 1987, Codes for the identification of
the States, the District of Columbia, and the outlying areas of the
United States and associated areas Federal Information Processing
Standard 5-20; Washington, D.C., National Institute of Standards
and Technology.
U.S. Geological Survey
EROS Data Center
This work was performed by the Raytheon STX Corporation under
U.S. Geological Survey Contract 1434-92-C-40004.
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).
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.
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).
U.S. Geological Survey
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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.
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:
This work was performed under contract the U.S. Geological
Survey (Contract 1434-CR-97-CN-40274).
More detailed information on the methodologies and techniques employed
In this work can be found in the following:
U.S. Geological Survey
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U.S. Geological Survey
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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.
11 Open Water
12 Perennial Ice/Snow
21 Low Intensity Residential
22 High Intensity Residential
23 Commercial/Industrial/Transportation
31 Bare Rock/Sand/Clay
32 Quarries/Strip Mines/Gravel Pits
33 Transitional
41 Deciduous Forest
42 Evergreen Forest
43 Mixed Forest
51 Shrubland
61 Orchards/Vineyards/Other
71 Grasslands/Herbaceous
81 Pasture/Hay
82 Row Crops
83 Small Grains
84 Fallow
85 Urban/Recreational Grasses
91 Woody Wetlands
92 Emergent Herbaceous Wetlands
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.
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.
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.)
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.
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.
61. Orchards/Vineyards/Other - Orchards, vineyards, and other areas
planted or maintained for the production of fruits, nuts, berries, or
ornamentals.
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.
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.
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.
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.
U.S. Geological Survey
EROS Data Center