This data set reports the results of vegetation field surveys that measured tree height and diameter at breast height (DBH) in defined size classes at three study sites -- Santarem, Para; Paragominas, Para; and Alo Brasil, Mato Grosso, Brazil, from 2001-2003.At each site, plots and transects within plots, were defined that represented different types of logging and fire treatments, each including one primary forest plot used as a control. Along each transect all trees with more than 30 cm DBH were measured. Dead standing trees were also measured and classified in three classes of decomposition. A 4 m wide transect was used to measure individuals between 10 and 30 cm DBH. Six small subplots were set along each transect to measure regeneration individuals from 2-10 cm DBH and 0-2 cm DBH. DBH is also provided for stumps found in each of the logged forest plots. There are ten comma-delimited data files with this data set.

This data set provides a land use/land cover map of the Ji-Parana River Basin in the state of Rondonia, Brazil produced from the digital classification of eight Landsat 7-ETM+ scenes from 1999 acquired from the Tropical Rain Forest Information Center (TRFIC) at Michigan State University. Nine land cover classes covering the Ji-Parana Basin were identified. There is one GeoTiff file with this data set.

This data set provides ~100-m resolution image mosaics of South America acquired during the low flood season between September and December 1995 and during the high flood season between May and July of 1996. The images cover the same areas during both seasons and were obtained from the Japanese Earth Resources Satellite 1 (JERS-1) Synthetic Aperture Radar (SAR) of the National Space Development Agency of Japan (NASDA). The data were mosaicked into 34 tiles for each season, each consisting of about 50 JERS-1 scenes. This data set constitutes the first-ever high-resolution and single season coverage of the entire Amazon River Basin, made possible by the cloud penetrating properties of the radar sensor. The images are from the original JERS-1 SAR Global Rain Forest Mapping Project. This data set contains 66 files in GeoTIFF (.tiff) format. There are 32 files for the low flood season and 34 files for the high flood season.

This data set contains hydrology, soils, radiation, cloud, and vegetation data from the International Satellite Land Surface Climatology Project (ISLSCP) Initiative I. The ISLSCP data sets should provide LBA modelers with many of the fields required to describe boundary conditions, and to initialize and force a wide range of land-biosphere-atmosphere models. All of the data have been processed to the same global spatial resolution (1 deg. x 1 deg.), using the same land/sea mask and steps have been taken to ensure spatial and temporal continuity of the data. The data sets cover the period 1987-1988 at 1-month time resolution for most of the seasonally varying quantities. For this pre-LBA data set, the ISLSCP I data are provided as global coverages. The companion file illustrations were subset over the LBA study area, from 35-85 deg. W longitude and 20 deg. S to 10 deg. N latitude, as shown in Figure 1.The data files and illustrations are organized into the three groups listed below.1. Hydrology and Soils2. Radiation and Clouds3. VegetationThe data within each of these areas were acquired from a variety of sources including model output, satellites, and ground measurements. The individual data sets were provided in a variety of forms. In some cases, this required the data publication team to regrid and reformat data sets and in others to produce monthly averages from finer resolution data. The specific processing for each data set is detailed in the documentation. The processed, quality controlled and integrated data in the documented Pre-LBA Data sets were originally published as a set of three CD-ROMs (Marengo and Victoria, 1998) but are now archived individually.

Surface parameter digital maps of vegetation, soil, and topography were obtained for Rondonia, Brazil, covering the 5x5 degree region bounded by 13-8 degrees S and 65-60 degrees W. Numerical maps of the natural landscape structure were prepared by digitizing existing 1:1,000,000 maps. Satellite data give information about the most recent modifications of the surface due to human activities. This mapping work was the first step of a mesoscale meteorological modeling program (Calvet et al., 1997) in forested and deforested Southwestern Amazonia (Rondonia, Brazil). This work was performed in the framework of a research program (CABARE) supported by the European Union, CEC Environment Program.Data are provided in ArcGIS ArcInfo grid ascii format for the following surface parameters:Elevation of terrain of the Rondonia region (altitude.txt)LANDSAT-derived vegetation classification of the Rondonia region in 1993-1994 (classify.txt)Soil classification of the Rondonia region (soil.txt)Sand and Clay of the Rondonia region (sand.txt and clay.txt)Vegetation classification of the Rondonia region from RADAMBRASIL (Macedo et al., 1979) (vegetation.txt)

This data set provides annual spatial patterns of cropland, natural pasture, and planted pasture land uses across Amazonia for the period 1940/1950-1995. Two series of 5-minute grid cell historical maps were generated starting from land use classification products for 1995. Annual data are the fraction of natural pasture, planted pasture, and cropland in each 5-min grid cell. The annual maps are provided in two NetCDF (.nc) format file at 5-minute resolution. The AMZ-C.nc file covers the Brazilian portion of Amazon and Tocantins Rivers basins, and is based on the 1995 land use classification of Cardille et al. (2002), generated through the fusion of remote sensing (AVHRR) and agricultural census data. The second file, AMZ-R.nc, covers the entire Legal Amazon region and adjacent areas and is based on the 1995 land use classification by Ramankutty et al. (2008). The land use classification was generated by the fusion of satellite imagery (MODIS and VEGETATION-SPOT) and data from the agricultural census. A historical land-use reconstruction algorithm was used to generate the annual spatial patterns (based on work from Ramunkutty and Foley, 1999).

This data set provides Landsat Enhanced Thematic Mapper Plus (ETM+) imagery, derived classified land cover products, and cloud-water masks for selected Brazilian states (Acre, Amapa, Amazonas, Maranhao, Mato Grosso, Para, Rondonia, and Roraima) for the years 1999-2002. The Landsat ETM+ images were processed to derive fractional land cover types (photosynthetic vegetation [PV], non-photosynthetic vegetation [NPV], and bare substrate) by application of the Carnegie Landsat Analysis System (CLAS) methodology (Asner et al., 2005). CLAS utilizes a quantitative determination of fractional land cover at the subpixel scale (e.g., within each Landsat 30 x 30 m pixel). The resulting images display estimates of subpixel land cover fraction values including free of clouds, cloud shadows, and water. There are 584 *.zip files in this data set which when expanded, contain a total of 1,717 (*.tif) images files (GeoTiff Standard format).

This data set contains two image mosaics of L-band radar backscatter and two image mosaics of first order texture. The two backscatter images are mosaics of L-band Radar Backscatter at Horizontal-Horizontal (HH) Polarization created from 1,500 images collected by the Japanese Earth Resources Satellite-1 (JERS-1) Synthetic Aperture Radar (SAR) over the Amazon River Basin as part of the Global Rainforest Mapping Project (GRMP). These backscatter image mosaics were developed using data collected over 62 days from August to November of 1995 for the peak of the dry season and for 62 days from May to June of 1996 during the peak of the wet season. The two image mosaics are at 3 arc-sec resolution. Data provided under this project are resampled images at 30 arc-sec resolution (or about 1 km resolution). For each radar backscatter image, first order texture statistical information was derived and is distributed along with the image mosaic.This data set contains four images each in both geotiff and ENVI formats, provided in eight zip files. The four files in ENVI file format contain o_envi? in their file name and when extrapolated contain an envi image (*_envi.dat) and an envi image header file (_envi.hdr). The four files in geotiff format contain o_geotiff? in their file name and when extrapolated contain *.tif and *.tfw file pairs. See Section 2 for more information about the characteristics of these data files.

This data set contains proportional estimates for the vegetative cover types of woody vegetation, herbaceous vegetation, and bare ground over the Amazon Basin for the period 2000-2001. These products were derived from all seven bands of the Moderate-resolution Imaging Spectroradiometer (MODIS) sensor onboard NASA's Terra satellite. A set of MODIS 32-day composites were used to create the vegetation cover types using the Vegetation Continuous Fields (VCF) (Hansen et al., 2002) approach which shows how much of a land cover such as "forest" or "grassland" exists anywhere on the land surface. The VCF product may depict areas of heterogeneous land cover better than traditional discrete classification schemes which shows where land cover types are concentrated.The original MODIS products are 500-m spatial resolution and are derived from 2000-2001 data products. The data were resampled to 1-km resolution for the regional study under this project, and provided as 3 separate cover type files in ENVI and GeoTIFF file formats that are provided in six zipped files. These products are registered to the rest of the regional data sets over the Amazon basin. These data are also available for download from the Global Land Cover Facility Website (http://modis.umiacs.umd.edu/).

This data set includes Landsat TM scenes from across the Legal Amazon region. A single image is provided for each spatial tile, representing the most cloud-free retrieval from 9/21/86 to 9/17/94. All files are in a single directory, including one band-sequential (bsq) file and one database (ddr) file for each scene.