A cautionary tale regarding use of the National Land Cover Dataset 1992

Abstract

Digital land‐cover data are among the most popular data sources used in ecological research and natural resource management. However, processes for accurate land‐cover classification over large regions are still evolving. We identified inconsistencies in the National Land Cover Dataset 1992, the most current and available representation of land cover for the conterminous United States. We also report means to address these inconsistencies in a bird‐habitat model. We used a Geographic Information System (GIS) to position a regular grid (or lattice) over the upper midwestern United States and summarized the proportion of individual land covers in each cell within the lattice. These proportions were then mapped back onto the lattice, and the resultant lattice was compared to satellite paths, state borders, and regional map classification units. We observed mapping inconsistencies at the borders between mapping regions, states, and Thematic Mapper (TM) mapping paths in the upper midwestern United States, particularly related to grassland‐herbaceous, emergent‐herbaceous wetland, and small‐grain land covers. We attributed these discrepancies to differences in image dates between mapping regions, suboptimal image dates for distinguishing certain land‐cover types, lack of suitable ancillary data for improving discrimination for rare land covers, and possibly differences among image interpreters. To overcome these inconsistencies for the purpose of modeling regional populations of birds, we combined grassland‐herbaceous and pasture‐hay land‐cover classes and excluded the use of emergent‐herbaceous and small‐grain land covers. We recommend that users of digital land‐cover data conduct similar assessments for other regions before using these data for habitat evaluation. Further, caution is advised in using these data in the analysis of regional land‐cover change because it is not likely that future digital land‐cover maps will repeat the same problems, thus resulting in biased estimates of change.

Full Issue

“…The coarse classifications in the land-use data also may have precluded the ability to discern strong effects relating to shrubland vegetation (Thogmartin et al 2004a).…”

Sediment Accumulation in Prairie Wetlands under a Changing Climate: the Relative Roles of Landscape and Precipitation

“…The cover factor (C) was determined from landcover data. We used state-level landcover data based on 2000-2001 Landsat Thematic Mapper satellite imagery that differentiated grass, hay, and cropland, the primary land uses in the U.S. PPR (Thogmartin et al 2004). We compared land use and 1997 National Resources Inventory sub-county C-factor values (Natural Resources Conservation Service 2000) to assign values of 0.19, 0.02, and 0.01 to cropland, hayland, and grassland, respectively (Online Resource 1, Table S1).…”

Assessing Loss of Biodiversity in Europe Through Remote Sensing: The Necessity of New Methodologies

“…Land cover data are usually derived by using multispectral remotely sensed data and statistical clustering methods. Remotely-sensed land cover data have been used at different scales (local, regional and global) as: i) input variables in biosphere -atmosphere models simulating exchanges of energy and water between land surface and the atmosphere and in terrestrial ecosystem models simulating carbon dynamics at global scales; ii) input variables in terrestrial vegetation change assessments; iii) proxies of biodiversity distribution (DeFries 2008;Hansen et al 2004;Thogmartin et al 2004). On the other hand, habitat is a three-dimensional spatial entity that comprises at least one interface between air, water and ground spaces, it includes both the physical environment and the communities of plants and animals that occupy it, it is a fractal entity in that its definition depends on the scale at which it is considered" (Blondel 1979).…”

“…For example, biodiversity has been frequently studied indirectly through associations with land cover, which represents that mapping land cover has been often used as a surrogate for habitats (Foody 2008). The suitability of these assumptions is a current scientific concern and a dynamic research issue (Glenn and Ripple 2004;McDermid et al 2009;Thogmartin et al 2004). Some studies (McDermid et al 2009) have evaluated the suitability of general-purpose land cover classifications and compared to other data sources like vegetation inventory or specificpurpose maps: they show the constraints of general-purpose remote sensing land cover maps for explain wildlife habitat patterns and recommend the use of specific-purpose databases based on remote sensing along with field measurements.…”