The global vegetation type data of 1 x 1 degree latitude and longitude resolution were designed for use in studies of climate and climate change. Vegetation data were compiled in digital form from approximately 100 published sources. The raw data base distinguished about 180 vegetation types that have been collapsed to 32. The vegetation data were encoded using the UNESCO classification system. Additional information about this data set can be found at http://www.giss.nasa.gov/data/landuse/vegeem.html. ORNL DAAC maintains information on related data sets in the Vegetation Collection. Data Citation The data set should be cited as follows: Matthews, E. 1999. Global Vegetation Types, 1971-1982. Available on-line from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A. [ This document was provided by NASA's Global Change Master Directory. For more information on the source of this metadata please visit http://gcmd.nasa.gov/r/geoss/[GCMD]VEG1X1 ]

This dataset is a 1:2 million scale forest cover map for the land area of the Krasnoyarsk Region, Russia. Thirty-two land cover classes are distinguished. These data were digitized from maps of the Atlas of Forests of the USSR (Anon. 1973). This map should not be strictly viewed as a map of actual forest cover, but rather as a map of dominant tree species. Very few tree species are defined, and generally, each polygon and color has only one tree species assigned to it. [ This document was provided by NASA's Global Change Master Directory. For more information on the source of this metadata please visit http://gcmd.nasa.gov/r/geoss/[GCMD]rlc_forest_cover ]

This dataset is a 1:15 million scale map of forest stand carbon for the land area of Russia (Stone et al., 2000). The objective was to create a first approximation of the forest stand carbon reserves of Russia. Data include continuous estimates of forest stand carbon in units of metric tons/ha of carbon (C) and categorized data depicting rages of forest stand carbon. The resulting maps show forest stand C by region in a spatially explicit form. It is the first map of its type for Russia of which we are aware. The mapped C represents 96% of the total of 26.1 Pg forest tree stand C described by Alexeyev and Birdsey (1994) and Alexeyev et al. (1995). Of the remaining 4%, nearly half was due to bushes, which were assumed not to be mapped in the 1973 forest cover map.The source data for the forest stand carbon map were acquired by map digitization from the Atlas of Forests for the Soviet Union (State Committee on Forests, 1973) and spatial application and arithmetic manipulation of carbon storage data from Alexeyev and Birdsey (1998). [ This document was provided by NASA's Global Change Master Directory. For more information on the source of this metadata please visit http://gcmd.nasa.gov/r/geoss/[GCMD]rlc_forest_carbon ]

This data set consists of a subset for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., longitude 85 deg to 30 deg W, latitude 25 deg S to 10 deg N) 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) [http://glcf.umiacs.umd.edu/data/landcover/index.shtml] 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/lba/land_use_change/land_cover_data_1deg/comp/README]. [ This document was provided by NASA's Global Change Master Directory. For more information on the source of this metadata please visit http://gcmd.nasa.gov/r/geoss/[GCMD]lba_avhrr_1deg ]

This biomass density image covers almost the entire BOREAS SSA. The pixels for which biomass density is computed include areas that are in conifer land cover classes only. The biomass density values represent the amount of overstory biomass (i.e. tree biomass only) per unit area. It is derived from a Landsat-5 TM image collected on 02-Sep-1994. The technique that was used to create this image is very similar to the technique that was used to create the physical classification of the SSA. The data are provided in a binary image file format. Companion files include (1) an image inventory listing to inform users of the images that are available and (2) example thumbnail images. [ This document was provided by NASA's Global Change Master Directory. For more information on the source of this metadata please visit http://gcmd.nasa.gov/r/geoss/[GCMD]BOREAS_BIOMDENS ]

The BOREAS HYD-04 work was focused on collecting data during the winter field campaign (FFC-W) to improve the understanding of winter processes within the boreal forest. Snow surveys were conducted at special snow courses throughout the 1993/94, 1994/95, 1995/96, and 1996/97 winter seasons. These snow courses were located in different boreal forest land cover types (i.e., old aspen, old black spruce, young jack pine, forest clearing, etc.) to document snow cover variations throughout the season as a function of different land cover. Measurements of snow depth, density, and water equivalent were acquired on or near the first and fifteenth of each month during the snow cover season. The development and validation of remote sensing algorithms will provide the means to extend the knowledge of these processes and states from the local to the regional scale. A specific thrust of the research is the development and validation of snow cover algorithms from airborne passive microwave measurements. [ This document was provided by NASA's Global Change Master Directory. For more information on the source of this metadata please visit http://gcmd.nasa.gov/r/geoss/[GCMD]BOREAS_H04STSND ]

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 document was provided by NASA's Global Change Master Directory. For more information on the source of this metadata please visit http://gcmd.nasa.gov/r/geoss/[GCMD]s2k_glcf1deg ]

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 geor eferenced 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. [ This document was provided by NASA's Global Change Master Directory. For more information on the source of this metadata please visit http://gcmd.nasa.gov/r/geoss/[GCMD]s2kisricwise_profile ]

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 document was provided by NASA's Global Change Master Directory. For more information on the source of this metadata please visit http://gcmd.nasa.gov/r/geoss/[GCMD]s2k_nbi_veg_maps ]

The BigFoot project gathered leaf area index (LAI) data for nine EOS Land Validation Sites located from Alaska to Brazil from 2000 to 2003. Each site is representative of one or two distinct biomes, including the Arctic tundra; boreal evergreen needleleaf forest; temperate cropland, grassland, evergreen needleleaf forest, and deciduous broadleaf forest; desert grassland and shrubland; and tropical evergreen broadleaf forest. LAI was measured at plots within each site for at least two years using standard direct and optical methods at each site. Direct measurement approaches included periodic area harvest for non-forest sites and application of allometric equations to tree diameter data for forest sites. LAI was also estimated indirectly using the Li-Cor LAI-2000 Plant Canopy Analyzers (Gower et al. 1999). LAI was measured three times each year at the forest sites and four to six times at other sites depending upon the phenology of LAI development for a given ecosystem. To develop LAI surfaces at any given site, the Landsat ETM+ image closest in date to maximum LAI was chosen as a reference and images from other dates radiometrically normalized to it. Each LAI surface has a grain of 25 meters and covers a 7 x 7 km extent. The data set consists of the LAI surface images in standard geotiff format, an accompanying text file which provides metadata specific to the image (such as projection, data type, class names, etc), and associated auxiliary and world files. Additional information on LAI measurements and surface development can be found on the BigFoot website at http://www.fsl.orst.edu/larse/bigfoot/ovr_mthd.html. BigFoot Project Background: Reflectance data from MODIS, the Moderate Resolution Imaging Spectrometer onboard NASA's Earth Observing System (EOS) satellite Terra (http://landval.gsfc.nasa.gov/MODIS/index.php), is used to produce several science products including land cover, leaf area index (LAI) and net primary p roduction (NPP). The overall goal of the BigFoot Project was to provi de validation of these products. To do this, BigFoot combined ground measurements, additional high resolution remote sensing data, and ecosystem process models at nine flux tower sites representing different biomes to evaluate the effects of the spatial and temporal patterns of ecosystem characteristics on MODIS products. BigFoot characterized up to a 7 x 7 km area (49 MODIS pixels) surrounding the CO2 flux towers located at each of the nine sites. We collected multi-year, in situ measurements of ecosystem structure and functional characteristics related to the terrestrial carbon cycle. Our sampling design allowed us to examine scales and spatial pattern of these properties, the inter-annual variability and validity of MODIS products, and provided for a field-based ecological characterization of the flux tower footprint. BigFoot was funded by NASA's Terrestrial Ecology Program. [ This document was provided by NASA's Global Change Master Directory. For more information on the source of this metadata please visit http://gcmd.nasa.gov/r/geoss/[GCMD]bigfoot_lai ]