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
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.
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
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
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.
This work was performed under contract the U.S. Geological
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,
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,
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.