Impervious Comparison of NLCD versus a Detailed Dataset Over Time
To address accuracy concerns of the National Land Cover Dataset (NLCD), this case study compares impervious surface from the NLCD to a Detailed Land Cover Dataset (DLCD) for the Town of Blacksburg, Virginia
over two time periods (2005/2006 and 2011) at spatial aggregation scales (30 m and 90 m) and scopes (site-specific to area-extent). When comparing the total impervious surface area, the NLCD overestimated significant amounts (12 to 27 percent) for the entire town
and across all specified land use zones (single family, multi-family, and non-residential) for both time periods examined. A binary pixel-wise accuracy assessment of impervious surface revealed that the NLCD performed well for the multi-family and non-residential
land use zones. However, accuracy level was quite low (user's accuracy <40 percent) for the single family land use zone. Percent impervious surface of NLCD and DLCD was further compared at 30 m and 90 m spatial scales. The spatial aggregation
of pixels to 90 m led to improved agreement between the two datasets, although NLCD still showed an underestimate of high values and an overestimate of low values. An empirical normalization equation was successfully applied to the NLCD
to further reduce such data skewness.
Document Type: Research Article
Publication date: 01 June 2017
- The official journal of the American Society for Photogrammetry and Remote Sensing - the Imaging and Geospatial Information Society (ASPRS). This highly respected publication covers all facets of photogrammetry and remote sensing methods and technologies.
Founded in 1934, the American Society for Photogrammetry and Remote Sensing (ASPRS) is a scientific association serving over 7,000 professional members around the world. Our mission is to advance knowledge and improve understanding of mapping sciences to promote the responsible applications of photogrammetry, remote sensing, geographic information systems (GIS), and supporting technologies. - Editorial Board
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