global

Many investigators need and use global land cover maps for a wide variety of purposes. Ironically, after many years of very limited availability, there are now multiple global land cover maps and it is not readily apparent (1) which is most useful for particular applications or (2) how to combine the different maps to provide an improved dataset. The existing global land cover maps at 1 km spatial resolution have arisen from different initiatives and are based on different remote sensing data and employed different methodologies. Perhaps more significantly, they have different legends. As a result, comparison of the different land cover maps is difficult and information about their relative utility is limited. In an attempt to compare the datasets and assess their strengths and weaknesses we harmonized the thematic legends of four available coarse-resolution global land cover maps (IGBP DISCover, UMD, MODIS 1-km, and GLC2000) using the LCCS-based land cover legend translation protocols. Analysis of the agreement among the global land cover maps and existing validation information highlights general patterns of agreement, inconsistencies and uncertainties. The thematic classes of Evergreen broadleaf trees, Snow and Ice, and Barren show high producer and user accuracy and good agreement among the datasets, while classes of mixed tree types show high commission errors. Overall, the results show a limited ability of the four global products to discriminate mixed classes characterized by a mosaic of trees, shrubs, and herbaceous vegetation. There is a strong relationship between class accuracy, spatial agreement among the datasets, and the heterogeneity of landscapes. Suggestions for future mapping projects include careful definition of mixed unit classes, and improvement in mapping heterogeneous landscapes.

Human actions are altering the terrestrial environment at unprecedented rates, magnitudes, and spatial scales. Landcover change stemming from human land uses represents a major source and a major element of global environmental change. Not only are the global-level data on landuse and land-cover change relatively poor, but we need a much better understanding of the underlying driving forces for these changes. Many forces have been proposed as significant, but single-factor explanations of land transformation have proved to be inadequate. How the human causes interact, and under what circumstances each is important, are questions needing systematic research. An international and interdisciplinary agenda is currently being developed to address these issues, through several closelyconnected foci of study. A division of the world according to common situations of environment, human driving forces, and land-cover dynamics will be followed by detailed study of the processes at work within each situation. The results will form the basis for a concurrent effort to develop a global land model that can offer projections of patterns of land transformation.