This is a human-readable view of the FGDC XML metadata.
References: Huang, C., L. Yang, C. Homer, B. Wylie, J. Vogelman and T. DeFelice, At-Satellite Reflectance: A First Order Normalization of Landsat & ETM+ Images, USGS White Paper.. Loveland, T. R., and Shaw D. M., 1996, Multi-resolution land characterization: building collaborative partnerships, in GAP Analysis: A Landscape Approach to Biodiversity Planning, J. M. Scott, T. H. Tear, and F. W. Davis, Editors, American Society for Photogrammetry and Remote Sensing, Bethesda, Maryland, p. 79-85. Riitters, K.H., Wickham, J.D., O'Neill, R.V., Jones, K.B., Smith, E.R., Coulston, J.W., Wade, T.G., and Smith, J.H. 2002. Fragmentation of continental United States forests. Ecosystems, 5: 815-822. Riitters, K.H., J.D. Wickham, J.E. Vogelmann, and K.B. Jones. 2000. National land-cover pattern data. Ecology 81: 604; Ecology 81:604. Smith, J.H., Wickham, J.D., Stehman, S.V., and L. Yang, Impacts of patch size and land-cover heterogeneity on thematic image classification accuracy Photogrammetric Engineering and Remote Sensing, Vol. 68, No. 1, 65-70. Smith, J., Stehman, S., Wickham, J., Yang, L, Effects of landscape characteristics on land-cover class accuracy. Remote Sensing of Environment 84 (2003) 342-349. Stehman, S.V., Czaplewski, R.L., Nusser, S.M., Yang, L., and Zhu, Z. Combining accuracy assessment of land -cover maps with environmental monitoring programs. Environmental Monitoring and Assessment, 64: 115-126. Stehman, S., Wickham, J., Smith, J. Yang, L, Thematic accuracy of the 1992 National Land-Cover Data for the eastern United States: Statistical methodology and regional results. Remote Sensing of Environment 86 (2003) 500-516. Stehman, S.V., J.D. Wickham, L. Yang, and J.H. Smith, Assessing the accuracy of large-area land cover maps: Experiences from the Multi-resolution Land-cover Characteristics (MRLC) project. Proceedings of the 4th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, Delft University Press, The Netherlands, 601-608. Vogelmann, J.E., T.L. Sohl, P.V. Campbell, and D.M. Shaw. 1998. Regional land cover characterization using Landsat Thematic Mapper data and ancillary data sources. Environmental Monitoring and Assessment 51: 415-428. Vogelmann, J.E., T. Sohl, and S.M. Howard. 1998. Regional characterization of land cover using multiple sources of data. Photogrammetric Engineering & Remote Sensing 64: 45-57. Vogelmann, J.E. and Wickham, J., 2000, Implementation strategy for production of national land cover data (NLCD) from the Landsat 7 Thematic Mapper Satellite, EPA/600/R-00/051 (NTIS PB2001-101756), Las Vegas, NV.: U.S. EPA. Vogelmann, J.E., S.M. Howard, L. Yang, C. R. Larson, B. K. Wylie, and J. N. Van Driel, 2001, Completion of the 1990s National Land Cover Data Set for the conterminous United States, Photogrammetric Engineering and Remote Sensing 67:650-662. Wickham, J.D., S.V. Stehman, J.H. Smith, L. Yang. 2004. Thematic Accuracy of the 1992 National Land-Cover Data for the Western United States. Remote Sensing of Environment, Vol. 91, pp. 452-468. Wylie, B., C. Huang, L. Yang and C. Homer, "Evaluation of Optimal Sets of Landsat TM Spectral Deriviatives for Land Cover Classification", USGS Draft White Paper, 2001. Yang, L., Stehman, S., Smith, J., Wickham, J. 2001. Thematic accuracy of MRLC land cover for the eastern United States, Remote Sensing of Environment 76, 418-422. Yang, L., Stehman, S. V., Wickham, J.D., Smith, J.H., Van Driel, N.J, Thematic validation of land cover data of the eastern United States using aerial photography: Feasibility and challenges. Proceedings of the 4th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, Delft University Press, The Netherlands, 747-754. Zhu, Z., Yang, L., Stehman, S.V., and Czaplewski, R.L., 1999, Chapter 46 - Designing an accuracy assessment for a USGS regional land cover mapping program, in Lowell, Kim, ed., Spatial Accuracy Assessment Land Information Uncertainty in Natural Resources: Chelsea, Michigan, Sleeping Bear Press/Ann Arbor Press, p. 393-398. Zhu, Z., Yang, L., Y., Stehman, S.V., and Czaplewski, R.L, 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, No. 12: pg 1425-1435.
An accuracy assessment was 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 incorporatesa 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.
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. 1998a and Vogelmann et al. 1998b.
|ObjectID||Internal feature number||Sequential unique whole numbers that are automatically generated.|
|Count||A nominal integer value that designates the number of pixels that have each value in the file; histogram column in ERDAS Imagine raster attributes table||Integer|
|Value||Land Cover Class Code Value.||coded values|