2000 Land Use Data

Land Use/Land Cover and Impervious Surface

1990 NLCD of Chilton County, Al, compared to 2002 NLCD of the same area.

Process Documentation

One of the primary goals of the project is to generate a current, consistent, seamless, and accurate National Land cover Database (NLCD) circa 2001 for the United States at medium spatial resolution. .

The land cover data sets are single band raster images. All data are projected to Albers Conical Equal Area using the NAD83 Datum, GRS 1980 Spheroid, with a spatial resolution of 30 meters.

The National Land Cover Database 2001 land cover layer was produced through a cooperative project conducted by the Multi-Resolution Land Characteristics (MRLC) Consortium. The information on data quality was generated by the Decision Tree algorithm (CART) that conducts a cross-validation for assessing classification and prediction reliability. No formal independent accuracy assessment of land cover has been made. The regression tree algorithm employed in NLCD 2001 mapping offers a cross-validation option for assessing classification and prediction reliability. The NLCD 2001 database consists of three main data products including: (1) per pixel classified land-cover data (2) sub-pixel percent imperviousness and (3) sub-pixel percent tree canopy density.

The land cover classification was achieved by use of a classification and decision tree method (DT) using a combination of Landsat imagery and ancillary data. The specific DT program employed is called C5, which implements a gain ratio criterion in tree development and pruning (Quinlan, 1993). The completed single pixel product was then generalized to a 1 acre (approximately 5 ETM+ 30 m pixel patch) minimum mapping unit product using a "smart eliminate" algorithm. This aggregation program subsumes pixels from the single pixel level to a 5-pixel patch using a queens algorithm at doubling intervals. The algorithm consults a weighting matrix to guide merging of cover types by similarity, resulting in a product that preserves land cover logic as much as possible.

Compare the greatly improved detail and clarified land cover discrimination in the figures to the right.

1990

2002