GSA Annual Meeting in Denver, Colorado, USA - 2016

Paper No. 216-8
Presentation Time: 3:35 PM

THE FUTURE OF LANDSAT DATA PRODUCTS: ANALYSIS-READY DATA AND ESSENTIAL CLIMATE VARIABLES


FOGA, Steve1, DAVIS, Brian1, SAUER, Brian2 and DWYER, John L.2, (1)Stinger Ghaffarian Technologies, Inc., USGS Earth Resources Observation and Science (EROS) Center, 47914 252nd Street, Sioux Falls, SD 57198-0001, (2)U.S. Geological Survey, USGS Earth Resources Observation and Science (EROS) Center, 47914 252nd Street, Sioux Falls, SD 57198-0001, steven.foga.ctr@usgs.gov

The U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center currently hosts Landsat data collected from 1972 to the present day. The large volume of no-cost Level-1 Landsat data products has driven interest from the greater science community for higher-level products containing physical units and thematic descriptions of land surface characteristics. USGS EROS currently provides surface reflectance for Landsat 4 through Landsat 8 data holdings, and in the future will provide land surface temperature and Essential Climate Variables such as surface water extent, burned area and fractional snow covered area. To help alleviate the end-user burden of acquiring and post-processing these data products, USGS EROS will provide analysis-ready data (ARD) stored in a data cube, which will enable users to perform custom spatial and temporal queries through an Application Programming Interface (API) and view pre-compiled land use and land cover change metrics. To store data in a cube, they will be made “analysis ready” by processing all data products to a common spatial grid and extent. The ARD will first be constructed for the conterminous United States (CONUS) in Albers Equal Area map space based on a North American Datum 1983 (NAD83) grid. The advanced processing options available through the EROS Science Processing Architecture (ESPA) ordering interface (https://espa.cr.usgs.gov) offer users the ability to conform data to a common location and spatial transformation which enables seamless ingest of datasets across space and time directly into mapping and modeling applications. Enhanced options for analysis-ready data are evolving through a prototypical phase and are expected to achieve an initial operating capability by November 2017.
Handouts
  • foga_gsa_presentation_FINAL.pdf (2.6 MB)