This land cover data set was produced as part of a cooperative project
between the U.S. Geological Survey (USGS) and the U.S. Environmental
Protection Agency (USEPA) to produce a consistent, land cover data layer
for the conterminous U.S. based on 30-meter Landsat thematic mapper (TM)
data. National Land Cover Data (NLCD) was developed from TM data acquired
by the Multi-resoultion 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), USEPA, the U.S. Forest Service,
and the National Oceanic and Atmospheric Administration. 21-Class National Land Cover Data Key:
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.
NLCD Land Cover Classification System Key - Rev. July 20, 1999
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
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 - All areas of open water; typically 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 a high percentage (30 percent 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 highly developed areas 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 to100 percent of the cover.
23. Commercial/Industrial/Transportation - Includes infrastructure (e.g.
roads, railroads, etc.) and all highly developed areas not classified as High
Intensity Residential.
Barren - Areas characterized by bare rock, gravel, sand, 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 - Prennially barren areas of bedrock, desert
pavement, scarps, talus, slides, volcanic material, glacial debris, beaches, 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 of
cover) 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 dominated 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 deciduous 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.
General Procedures
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 comprized 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 mosaicking 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 unspervised 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 Aeria l 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-spliting 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.
Accuracy
Assessment:
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
accuracies, and omission and commission error probabilities.
Discussion:
While we believe that the approach taken has
yielded a very good general land cover classification product for a large
region, 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 upbefore 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.
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, Oregon, 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.
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