The data set consists of a southern Africa subset of the 5-min resolution Global Potential Vegetation data set developed by Navin Ramankutty and Jon Foley at the University of Wisconsin. Data are available in both ASCII GRID and binary image file formats. The original map was derived at a 5-min resolution and contains natural vegetation classified into 15 types. This data set is derived mainly from the DISCover land cover data set, with the regions dominated by land use filled using the vegetation data set of Haxeltine and Prentice (1996). The data set represents the world's potential vegetation (i.e., vegetation that would most likely exist now in the absence of human activities), and not necessarily natural pre-settlement vegetation. This is because human activities such as fire suppression have modified the stages of succession at which vegetation communities exist. More information can be found at: ftp://daac.ornl.gov/data/safari2k/vegetation_wetlands/potential_vegetation/comp/Potential_Veg_readme.pdf.

This data set consists of a southern African subset of the University of Maryland (UMD) 8-km Global Land Cover product in ASCII GRID and binary image files formats.

This data set consists of a southern African subset of the 1-km Global Land Cover Data Set Derived from AVHRR developed at the Laboratory for Global Remote Sensing Studies (LGRSS) at the University of Maryland. Both ASCII data and binary image files are available.

This data set consists of a southern African subset of the 1-km Global Tree Cover Data Set developed at the Laboratory for Global Remote Sensing Studies (LGRSS) at the University of Maryland. Data are available in both ASCII GRID and binary image files formats.

This data set contains a subset for southern Africa of Wilson and Henderson-Sellers' Global Vegetation and Soils 1-degree data. The data are available in both ASCII GRID and binary image file formats.

The National Botanical Institute (NBI) has mapped woody plant species distribution to provide estimates of individual species contribution to peak leaf area index for designated vegetation types in southern Africa (Rutherford et al., 2000). The target was to account for 80% of the woody vegetation leaf area in terms of named species, for 80% of the surface area of Africa south of the equator. The data sources include published and unpublished species lists for vegetation types and individual sample plots, with the species contribution estimated by local experts in terms of dominants and subdominants. Source maps include: Low and Rebelo (1998); Giess (1971); Wild and Barbosa (1968); Barbosa (1970); and White (1983). Each source map delineates a wide variety of land cover categories that differ from region to region. Because vegetation discontinuities exist along some of the regional borders and a perfectly continuous regional map could not be achieved within the timeframe and budget of the project, the final map is made up of six independent sub-regional maps. A cross-referenced database of woody plant species, in order of species dominance, associated with all mapped units is provided.The data set contains six GIS shapefile archives, each containing a shape file for a given region in southern Africa on a 5 x 5 degree grid. An accompanying ASCII file contains the species list associated with the map files. The regional NBI Vegetation Map (a compilation of the 6 independent sub-regional coverages) is provided as a JPEG image.

This data set contains leaf area index (LAI), leaf inclination angle, and canopy dimension data from study sites along the Kalahari Transect in southwest Botswana. The data were collected during the 2001 wet season field campaign of the SAFARI 2000 at a total of seven plots of 200 x 150 meter dimensions: two plots each at Tshane and Mabuasehube and three plots at Tsabong. The data set consists of measurements of leaf angle for plot dominant woody species, LAI calculated from overstory and understory photosynthetically active radiation (PAR) measurements, and canopy dimension data (i.e., crown height, crown width, and height to crown) for grass and woody vegetation for use in the parameterization of plant canopy reflectance models. The data files are stored as ASCII table files, in comma-separated-value (.csv) format, with column headers. Photographs (.jpg) are provided of each plot to provide an idea of site conditions. The photographs can be viewed on the S2K Photo Gallery pages.

The data set consists of a southern African subset of the 1-km Global Land Cover Data Set Derived from AVHRR developed at the Laboratory for Global Remote Sensing Studies (LGRSS) at the University of Maryland. Both ASCII data and binary image files are available. Over the past several years, researchers have increasingly turned to remotely sensed data to improve the accuracy of data sets that describe the geographic distribution of land cover at regional and global scales. To develop improved methodologies for global land cover classifications as well as to provide global land cover products for immediate use in global change research, researchers at the Laboratory for Global Remote Sensing Studies (LGRSS) at the University of Maryland have employed the NASA/NOAA Pathfinder Land (PAL) data set with a spatial resolution of 1 km. This data set has a record length of 14 years (1981-1994), providing the ability to test the stability of classification algorithms. Furthermore, this data set includes red, infrared, and thermal bands in addition to the Normalized Difference Vegetation Index (NDVI). Inclusion of these additional bands improves discrimination between cover types. The project aim is to develop and validate global land cover data sets and to develop advanced methodologies for more realistically describing the vegetative land surface based on satellite data. The 1-km global land cover product was created from 1992-93 LAC AVHRR data. The full 1-km global land cover product is available for download from the Global Land Cover Facility (GLCF) web site. Forty-one (41) metrics were developed to describe global vegetation phenology, and these data were used to make the 1-km land cover map. The final product contains 13 land cover classes. More information can be found at: ftp://daac.ornl.gov/data/safari2k/vegetation_wetlands/land_cover_data-1km/comp/glcf1km_readme.pdf.

This data set was collected during February-March 2000 wet season and September 2000 dry season field campaigns of SAFARI 2000. Mongu in Zambia and Pandematenga (aka Kasane) and Tshane in Botswana were visited during the wet season campaign. Dry season data are for Mongu only. Hemispherical photographs, from which Plant Area Index (PAI) estimates are derived, were obtained at the field sites to characterize vegetation structural changes along the Kalahari Transect. The photographs are classified into sky and vegetation (trunk, green and senescent leaves, and branches) using an unsupervised classification scheme.

This data set consists of a southern African subset of the University of Maryland (UMD) 8-km Global Land Cover product in ASCII GRID and binary image files formats. Over the past several years, researchers have increasingly turned to remotely sensed data to improve the accuracy of data sets that describe the geographic distribution of land cover at regional and global scales. To develop improved methodologies for global land cover classifications as well as to provide global land cover products for immediate use in global change research, researchers at the Laboratory for Global Remote Sensing Studies (LGRSS) at the University of Maryland (UMD) have employed the NASA/NOAA Pathfinder Land (PAL) data set with a spatial resolution of 8 km. This data set has a length of record of 14 years (1981-1994), providing the ability to test the stability of classification algorithms. Furthermore, this data set includes red, infrared, and thermal bands in addition to the Normalized Difference Vegetation Index (NDVI). Inclusion of these additional bands improves discrimination between cover types. The project aim is to develop and validate global land cover data sets and to develop advanced methodologies for more realistically describing the vegetative land surface based on satellite data. The 8-km global land cover product was derived by testing several metrics that describe the temporal dynamics of vegetation over an annual cycle. These metrics were applied to 1984 PAL data at 8-km resolution to derive a global land cover classification product using a decision tree classifier. The final product contains 13 land cover classes. The original 8-km global land cover product is available for download from the Global Land Cover Facility (GLCF) web site. Additional information and references on this data set can be found at the GLCF web site as well as at the LGRSS web site (link provided at the GLCF web site ). More information can be found at: ftp://daac.ornl.gov/data/safari2k/vegetation_wetlands/land_cover_data_8km/comp/glcf8km_readme.pdf.