dataset : LBA-ECO LC-18 Hyperion 30-m Surface Reflectance, Mato Grosso, Brazil: July 2004

Oak Ridge National Laboratory Distributed Active Archive Center

nasa-ornldaac-889

LBA-ECO LC-18 Hyperion 30-m Surface Reflectance, Mato Grosso, Brazil: July 2004

ABSTRACT: This image was collected by the Hyperion sensor on 10-July-2004 at 13:16:16 GMT. It was calibrated to apparent surface reflectance using the ACORN atmospheric model. The Hyperion imager has a spectral range of 400-2500 nm, a spectral resolution of 10 nm, spatial resolution of 30 m, and a swath width of 7.8 km. Sampling is scene based (256 samples, 512 lines) (http://eo1.usgs.gov/sensors.php). Through these large number of spectral bands, complex land ecosystems can be imaged and accurately classified. Data from the EO-1 Hyperion imaging spectrometer may greatly increase our ability to estimate the presence and structural attributes of selective logging in the Amazon Basin using four biogeophysical indicators not yet derived simultaneously from any satellite sensor: 1) green canopy leaf area index; 2) degree of shadowing; 3) presence of exposed soil and; 4) non-photosynthetic vegetation material. Airborne, field and modeling studies have shown that the optical reflectance continuum (400-2500 nm) contains sufficient information to derive estimates of each of these indicators. Our ongoing studies in the eastern Amazon basin also suggest that these four indicators are sensitive to logging intensity. Satellite-based estimates of these indicators should provide a means to quantify both the presence and degree of structural disturbance caused by various logging regimes.

daac.ornl.gov

Identified by : LC18HYPERION

This dataset was released on January 01, 2008.

The time range for this dataset is July 10, 2004 to July 10, 2004.

The spatial range for this dataset is -13.1678° to -12.6649° latitude, and -52.4642° to -52.28° longitude. map (center)

This dataset uses data from an instance of the hyperspectral-imager-2 instrument on the new-millenium-program-earth-observing-1 platform.
DOI : 10.3334/ORNLDAAC/889
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