Designing global land cover databases to maximize utility—the US prototype

Abstract

One of the most pressing problems in global climate and ecosystem studies is a lack of adequate land cover data. Staff from the United States Geological Survey's EROS Data Center and the University of Nebraska have developed a United States prototype for a proposed global land cover characteristics database derived from 1-km Advanced Very High Resolution Radiometer satellite data. A total of 159 seasonally distinct spectral I temporal land cover classes were labelled according to their constituent vegetation types rather than forcing the vegetation complexes into a predefined classification scheme. The database contains attributes that characterize each land cover class, including elevation, climate attributes and biophysical parameters derived from the normalized difference vegetation index (ND VI). The database design permits convenient translation to a variety of land cover classification schemes commonly used in global scale models. This approach allows scientists to continue using classification schemes with which they are comfortable, eliminates duplicating land cover database development, and provides a degree of uniformity in the development of parallel but distinct databases.