The associated tabular file "LandCover_key" contains the land cover class definitions. This table can be related using the attribute "VALUE".
The analysis and interpretation of the satellite imagery was conducted using very large, sometimes multi-state image mosaics (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 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.
Additional information about the National Land Cover Dataset (NLCD) can found at URL: <http://www.epa.gov/mrlc/nlcd.html>
These data can be used in a geographic information system (GIS) for many purposes such as assessing wildlife habitat, water quality, pesticide runoff, and land use change.
PSUs are selected from a sampling grid based on NAPP flight lines and photograph centers, each grid cell measures 15' x 15' (minutes of latitude and longitude) and consists of 32 photographs from the National High Altitude Program (NHAP). A geographically stratified random sampling is performed with one photograph from the National Aerial Photography Program photo being randomly selected from each cell (geographic strata), if a sampled photograph falls outside of the regional boundary it is not used. Second stage sampling is accomplished by selecting SSUs (pixels) within each PSU (NAPP photograph) to provide the actual locations for the reference land cover classification.
The SSUs 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.
Major factors that have contributed to disagreements between mapped land cover and reference land cover labels include:
1) Landsat TM data quality and mapping error, 2) time difference in source imagery and reference data acquisition (hay/pasture, row crop, wetland, transitional), 3) definition related to land use (high intensity residential and urban built-up, and the two barren classes), and 4) spatial uncertainty, such as geo-registration error.
An example of mapping error is the limited success in discriminating hay/pasture from row crops using leaf-off season (spring or fall) Landsat TM data. The data analyst assumes that there is a temporal window during which hay and pasture green up before most other annual or perennial vegetation. However, if leaf-off data acquisition is not temporally ideal (e.g., the greenness level of hay/pasture areas is low), it may result in misclassification between hay/pasture and other agricultural lands.
Another source of error is the discrepancy between satellite imagery and NAPP photograph acquisition time. Acquisition dates of the NAPP photographs range from the late 1980s to 1997, whereas the satellite data were mostly acquired from 1991 to 1993. Any changes that took place across the landscape over this time period complicate interpretation and comparison between reference and mapped land cover. One class that suffers most is the transitional barren, a class that is designed for conditions such as temporary clearing and regeneration of forest cover. Similar problems exist within agricultural classes due to crop rotations.
Low accuracy for classes related to land use is understandable. Despite the extensive use of ancillary data, such as the census data, it is very difficult to unambiguously separate high intensity residential from other urban uses, either during the mapping or photo-interpretation process. The same is true for the land use related differences between the quarry/strip mine class and the sandy/gravel class.
1) Some of the TM data sets are not temporally ideal. Leaves-off data sets are used for discriminating between hay/pasture and row crops, and for discriminating between deciduous, coniferous, and mixed forest classes. The success of discriminating between these classes using leaves-off data sets depends on the time of data acquisition. When hay and pasture areas are non-green, those areas 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 and 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 and hay/pasture and hay/pasture and row crops.
2) The data sets used cover a range of years (see data sources), so changes that have occurred 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 (for example, hay one year, row crop the next). More information about the NLCD can be found at URL: <http://landcover.usgs.gov/nationallandcover.html>
3) Wetlands classes are extremely difficult to extract from Landsat TM spectral information alone. The use of ancillary information such as National Wetlands Inventory data is highly desirable. Much emphasis was put 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 the Gap Analylsis Program on these classes.
Selected References:
Cowardin, L.M., Carter, V., Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: U.S. Fish and Wildlife Service, Department of the Interior, Washington, D.C., 191 p.
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, p. 24-30.
Vogelmann, J.E., Sohl, T., and Howard, S.M., 1998, Regional characterization of land cover using multiple sources of data: Photogrammetric Engineering and Remote Sensing, v. 64, no. 1, p. 45-57.
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, v. 51, pp. 415-428.
Zhu, Z.hiliang, Yang, Limin., Stehman, S.V., and Czaplewski, R.L., 1999, Accuracy assessment for the U.S. Geological Survey regional land cover mapping program: New York and New Jersey Region: Photogrammetric Engineering and Remote Sensing 66:1425-1435.
The raster spatial layer was projected and land cover information was selected using the 8-digit hydrologic unit code (HUC8) and the ARC/INFO GRIDCLIP command. The land cover values were reclassified and numbered 1 to 20. The spatial layer was imported in the geodatabase and metadata written.
NOTE - All classes may NOT be represented in a specific state data set.
The class numbers shown below represent the original digital values of the classes in the NLCD data set. The class numbers were assigned to numbers from 1 to 20.
1. Open Water - areas of open water, generally with less than 25 percent or greater cover of water (per pixel).
3. Low Intensity Residential - Includes areas with a mixture of constructed materials and vegetation. Constructed materials account for 30 to 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.
4. High Intensity Residential - Includes heavily built up urban centers where people reside in large numbers. Examples include apartment complexes and row houses. Vegetation accounts for less than 20 percent of the cover. Constructed materials account for 80 to 100 percent of the cover.
5. Commercial, Industrial, or Transportation - Includes infrastructure (roads, railroads) and all highways and all developed areas not classified as High Intensity Residential.
6. Bare Rock, Sand, or Clay - Perennially barren areas of bedrock, desert, pavement, scarps, talus, slides, volcanic material, glacial debris, and other accumulations of earthen material.
7. Quarries, Strip Mines, or Gravel Pits - Areas of extractive mining activities with significant surface expression.
8. 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 (such as fire or flood).
9. Deciduous Forest - Areas dominated by trees where 75 percent or more of the tree species shed foliage simultaneously in response to seasonal change.
10. Evergreen Forest - Areas characterized by trees where 75 percent or more of the tree species maintain leaves all year. Canopy is never without green foliage.
11. Mixed Forest - Areas dominated by trees where neither deciduous nor evergreen species represent more than 75 percent of the cover present.
12. Shrubland - Areas dominated by shrubs; shrub canopy accounts for 25 to 100 percent of land 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 herbaceous or tree cover is less than 25 percent.
13. Grasslands or 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 are often utilized for grazing.
14. Pasture or Hay - Areas of grasses, legumes, or grass-legume mixtures planted for livestock grazing or the production of seed or hay crops.
15 Row Crops - Areas used for the production of crops, such as corn, soybeans, vegetables, tobacco, and cotton.
16. Small Grains - Areas used for the production of graminoid crops such as wheat, barley, oats, and rice.
18. Urban or 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.
19. Woody Wetlands - Areas where forest or shrubland vegetation accounts for 25 to 100 percent of the cover and the soil or substrate is periodically saturated with or covered with water.
20. Emergent Herbaceous Wetlands - Areas where perennial herbaceous vegetation accounts for 75 to 100 percent of the cover and the soil or substrate is periodically saturated with or covered with water.