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Title: CCRS Landcover Maps From Satellite Data

The Canadian Centre for Remote Sensing (CCRS) presents several landcover maps over the SGP CART site area (32-40N, 92-102W) derived from satellite data including AVHRR, MODIS, SPOT vegetation data, and Landsat satellite TM imagery.
Publication Date:
DOE Contract Number:
Product Type:
Research Org(s):
Atmospheric Radiation Measurement (ARM) Archive, Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (US)
Sponsoring Org:
USDOE Office of Science (SC), Biological and Environmental Research (BER)
54 Environmental Sciences; Surface condition
OSTI Identifier:
  1. ARM focuses on obtaining continuous measurements—supplemented by field campaigns—and providing data products that promote the advancement of climate models. ARM data include routine data products, value-added products (VAPs), field campaign data, complementary external data products from collaborating programs, and data contributed by ARM principal investigators for use by the scientific community. Data quality reports, graphical displays of data availability/quality, and data plots are also available from the ARM Data Center. Serving users worldwide, the ARM Data Center collects and archives approximately 20 terabytes of data per month. Datastreams are generally available for download within 48 hours.
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  1. Spatially and temporally complete surface spectral albedo/BRDF products over the ARM SGP area were generated using data from two Moderate Resolution Imaging Spectroradiometer (MODIS) sensors on Terra and Aqua satellites. A landcover-based fitting (LBF) algorithm is developed to derive the BRDF model parameters and albedomore » product (Luo et al., 2004a). The approach employs a landcover map and multi-day clearsky composites of directional surface reflectance. The landcover map is derived from the Landsat TM 30-meter data set (Trishchenko et al., 2004a), and the surface reflectances are from MODIS 500m-resolution 8-day composite products (MOD09/MYD09). The MOD09/MYD09 data are re-arranged into 10-day intervals for compatibility with other satellite products, such as those from the NOVA/AVHRR and SPOT/VGT sensors. The LBF method increases the success rate of the BRDF fitting process and enables more accurate monitoring of surface temporal changes during periods of rapid spring vegetation green-up and autumn leaf-fall, as well as changes due to agricultural practices and snowcover variations (Luo et al., 2004b, Trishchenko et al., 2004b). Albedo/BRDF products for MODIS on Terra and MODIS on Aqua, as well as for Terra/Aqua combined dataset, are generated at 500m spatial resolution and every 10-day since March 2000 (Terra) and July 2002 (Aqua and combined), respectively. The purpose for the latter product is to obtain a more comprehensive dataset that takes advantages of multi-sensor observations (Trishchenko et al., 2002). To fill data gaps due to cloud presence, various interpolation procedures are applied based on a multi-year observation database and referring to results from other locations with similar landcover property. Special seasonal smoothing procedure is also applied to further remove outliers and artifacts in data series. « less
  2. This database offers select carbon monoxide (CO) mixing ratios from eleven field and aircraft measurement programs around the world. Carbon monoxide mixing ratios in the middle troposphere have been examined for short periods of time by using the Measurement of Air Pollution from Satellites (MAPS)more » instrument. MAPS measures CO from a space platform, using gas filter correlation radiometry. During the 1981 and 1984 MAPS flights, measurement validation was attempted by comparing space-based measurements of CO to those made in the middle troposphere from aircraft. Before the 1994 MAPS flights aboard the space shuttle Endeavour, a correlative measurement team was assembled to provide the National Aeronautics and Space Administration (NASA) with results of their CO field measurement programs during the April and October shuttle missions. To maximize the usefulness of these correlative data, team members agreed to participate in an intercomparison of CO measurements. The correlative data presented in this database provide an internally consistent, ground-based picture of CO in the lower atmosphere during Spring and Fall 1994. The data show the regional importance of two CO sources: fossil-fuel burning in urbanized areas and biomass burning in regions in the Southern Hemisphere. « less
  3. Arctic ecosystems have been observed to be warming faster than the global average and are predicted to experience accelerated changes in climate due to global warming. Arctic vegetation is particularly sensitive to warming conditions and likely to exhibit shifts in species composition, phenology and productivitymore » under changing climate. Mapping and monitoring of changes in vegetation is essential to understand the effect of climate change on the ecosystem functions. Vegetation exhibits unique spectral characteristics which can be harnessed to discriminate plant types and develop quantitative vegetation indices. We have combined high resolution multi-spectral remote sensing from the WorldView 2 satellite with LIDAR-derived digital elevation models to characterize the tundra landscape on the North Slope of Alaska. Classification of landscape using spectral and topographic characteristics yields spatial regions with expectedly similar vegetation characteristics. A field campaign was conducted during peak growing season to collect vegetation harvests from a number of 1m x 1m plots in the study region, which were then analyzed for distribution of vegetation types in the plots. Statistical relationships were developed between spectral and topographic characteristics and vegetation type distributions at the vegetation plots. These derived relationships were employed to statistically upscale the vegetation distributions for the landscape based on spectral characteristics. Vegetation distributions developed are being used to provide Plant Functional Type (PFT) maps for use in the Community Land Model (CLM). « less
  4. Color-shaded and contoured images of global, gridded instrumental data have been produced as a computer-based atlas. Each image simultaneously depicts anomaly maps of surface temperature, sea-level pressure, 500-mbar geopotential heights, and percentages of reference-period precipitation. Monthly, seasonal, and annual composites are available in either cylindricalmore » equidistant or northern and southern hemisphere polar projections. Temperature maps are available from 1854 to 1991, precipitation from 1851 to 1989, sea-level pressure from 1899 to 1991, and 500-mbar heights from 1946 to 1991. The source of data for the temperature images is Jones et al.'s global gridded temperature anomalies. The precipitation images were derived from Eischeid et al.'s global gridded precipitation percentages. Grids from the Data Support Section, National Center for Atmospheric Research (NCAR) were the sources for the sea-level-pressure and 500-mbar geopotential-height images. All images are in GIF files (1024 × 822 pixels, 256 colors) and can be displayed on many different computer platforms. Each annual subdirectory contains 141 images, each seasonal subdirectory contains 563 images, and each monthly subdirectory contains 1656 images. The entire atlas requires approximately 340 MB of disk space, but users may retrieve any number of images at one time. « less
  5. This dataset contains maps of deformation covering Raft River, Idaho from 2007 to 2010 calculated from interferometric synthetic aperture radar data. This dataset is used in the study entitled "Inferring geothermal reservoir processes at the Raft River Geothermal Field, Idaho, USA through modeling InSAR-measured surfacemore » deformation" by F. Liu, et al. This dataset was derived from raw SAR data from the Envisat satellite missions operated by the European Space Agency (ESA) that are copyrighted by ESA and were provided through the WInSAR consortium at the UNAVCO facility. All pair directories use the image acquired on 3/11/2007 as a reference image. To view specific information for each grd file, please use the GMT command "grdinfo" - e.g., for grd file In20070311_20071111/drho_utm.grd, use terminal command: grdinfo In20070311_20071111/drho_utm.grd « less