The data set consists of a southern African subset of the "Global Soil Profile Data (ISRIC-WISE)" data set. Data files are provided in comma-delimited ASCII format. The International Soil Reference and Information Centre - World Inventory of Soil Emission Potentials (ISRIC-WISE) international soil profile data set consists of a homogenized, global set of 1,125 soil profiles for use by global modelers. These profiles provided the basis for the Global Pedon Database (GPDB) of the International Geosphere-Biosphere Programme - Data and Information System (IGBP-DIS). The data set consists of a selection of 665 profiles originating from the Natural Resources Conservation Service (NRCS, Lincoln, Nebraska, U.S.A.), 250 profiles obtained from the Food and Agriculture Organization (FAO, Rome, Italy), and 210 profiles from the reference collection of the International Soil Reference and Information Centre (ISRIC, Wageningen, The Netherlands). All profiles are georeferenced and classified according to the 1974 Legend of the FAO-UNESCO Soil Map of the World (FAO-UNESCO 1974), as well as the 1988 Revised Legend of FAO-UNESCO (FAO 1990). The data set includes information on soil classification, site data, soil horizon data, source of data, and methods used for determining analytical data.

The data set consists of a southern African subset of the ISRIC-WISE global data set of derived soil properties. The World Inventory of Soil Emission Potentials (WISE) database currently contains data for over 4300 soil profiles collected mostly between 1950 and 1995.

This data set contains a subset for southern Africa of Wilson and Henderson-Sellers' Global Vegetation & Soils 1-degree data. The data are available in both ASCII GRID and binary image files formats. The Wilson, Henderson-Sellers' Global Vegetation and Soils data set is an archive of soil type and land cover data derived for use in general circulation models (GCMs). The data were collated from natural vegetation, forestry, agriculture, land use, and soil maps. The data are archived at 1 degree latitude x 1 degree longitude resolution and include data for soil, soil reliability, primary vegetation, secondary vegetation, and land cover reliability. There are approximately fifty land cover classifications which include categories for agricultural and urban uses. The inclusion of secondary vegetation type is particularly useful is areas with cover types which may have a fragmented distribution, such as urban development. The soil type data are classified using climatically important properties for CGMs and provide color (light, medium, or dark), texture, and drainage quality of the soil. The land cover data are compatible with the soils data forming a coherent and consistent data set. Reliability data rank the land cover data on a 1 to 5 scale from high to low reliability. The soil reliability is ranked as one of the following: high, good, moderate, fair, or poor. Recommendations for the use of these data as well as more detailed information can be found in: Wilson, M.F. and A. Henderson-Sellers, 1985. A Global Archive of Land Cover and Soils Data for Use in General Circulation Climate Models. Journal of Climatology, Vol.5, 119-143. More data set information can be found at: ftp://daac.ornl.gov/data/safari2k/vegetation_wetlands/vegsoils_wilhend/comp/wilhend_readme.pdf.

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 consists of a southern African subset of the University of Maryland (UMD) 1-degree Global Land Cover product in ASCII GRID and binary image formats. The UMD 1-degree Global Land Cover product was produced by researchers at the Laboratory for Global Remote Sensing Studies (LGRSS) at UMD. The product is based on Advanced Very High Resolution Radiometer (AVHRR) maximum monthly composites for 1987 of Normalized Difference Vegetation Index (NDVI) values at approximately 8-km resolution, averaged to one-by-one degree resolution. This coarse- resolution data set was used as the basis for a supervised classification of eleven cover types that broadly represent the major biomes of the world. Because of missing values at high latitudes, the Pathfinder AVHRR data set for 1987 for summer monthly NDVI and red reflectance values were used to distinguish the following cover types: tundra, high latitude deciduous forest and woodland, coniferous evergreen forest and woodland. The 1-degree global land cover product is available for download from the Global Land Cover Facility (GLCF) web site. The data are available as a global coverage in both binary and ASCII format. 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 ) and in the readme file found along with the data [ ftp://daac.ornl.gov/data/safari2k/vegetation_wetlands/land_cover_data_1deg/comp/glcf1deg_readme.pdf].

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.

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.

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.

The data set consists of a southern Africa subset of the ISRIC-WISE global data set of derived soil properties. The World Inventory of Soil Emission Potentials (WISE) database currently contains data for over 4300 soil profiles collected mostly between 1950 and 1995. This database has been used to generate a series of uniform data sets of derived soil properties for each of the 106 soil units considered in the Soil Map of the World (FAO-UNESCO, 1974). These data sets were then linked to a 1/2 degree longitude by 1/2 degree latitude version of the edited and digital Soil Map of the World (FAO, 1995) to generate GIS raster image files for the following variables: total available water capacity (mm water per 1 m soil depth); soil organic carbon density (kg C/m**2 for 0-30 cm depth range); soil organic carbon density (kg C/m**2 for 0-100 cm depth range); soil carbonate carbon density (kg C/m**2 for 0-100 cm depth range); soil pH (0-30 cm depth range); and soil pH (30-100 cm depth range). More information can be found at: ftp://daac.ornl.gov/data/safari2k/soils/ISRIC-WISE_soil_properties/comp/isric_readme.pdf.

This data set contains a subset of the Worldwide Organic Soil Carbon and Nitrogen (Zinke et al. 1986) data set for southern Africa. The data were obtained from soil surveys by Zinke and soil survey literature. The main samples for laboratory analyses were collected at uniform soil increments and included bulk density determinations.&