Key points are not available for this paper at this time.
cover maps. This paper makes a preliminary comparison of the methodologies and results of the two products. The DISCover methodology employed an unsu-pervised clustering classi cation scheme on a per-continentbasis using 12 monthly maximum NDVI composites as inputs. The UMd approach employed a super-vised classi cation tree method in which temporal metrics derived from all AVHRR bands and the NDVI were used to predict class membership across the entire globe. The DISCover map uses the IGBP classi cation scheme, while the UMd map employs a modi ed IGBP scheme minus the classes of permanent wetlands, cropland/natural vegetation mosaic and ice and snow. Global area totals of aggregated vegetation types are very similar and have a per-pixel agree-ment of 74%. For tall versus short/no vegetation, the per-pixel agreement is 84%. For broad vegetation types, core areas map similarly, while transition zones around core areas di er signi cantly. This results in high regional variability between the maps. Individual class agreement between the two 1 km maps is 49%. Comparison of the maps at a nominal 0.5ß resolution with two global ground-basedmaps shows an improvementof thematic concurrencyof 46%when viewing average class agreement. The absence of the croplandmosaic class creates a di culty in comparing the maps, due to its signi cant extent in the DISCover map. The DISCover map, in general, has more forest, while the UMd map has considerably more area in the intermediate tree cover classes of woody savanna/ woodland and savanna/wooded grassland. 1.
Hansen et al. (2000) studied this question.