NOTICE -- The LBA-ECO Project website is no longer being supported.  This archive is a snapshot, as it existed in 2013, of the LBA-ECO website, maintained by NASA Goddard Space Flight Center, and now archived at the ORNL DAAC.  Links to external websites may be inactive. Final data products from the LBA project can be found at the ORNL DAAC.
banner
banner banner banner banner banner banner
banner banner banner banner banner banner banner
home aboutlibrarynews archivecontacts banner

spacer
banner
Investigations
Overview
Abstracts & Profiles
Publications
Research Sites
Meetings
Synthesis Groups
LBA-HYDROMET
LBA-Air-ECO
Logistics
Overview
Field Support
Travel
Visa
Shipping
Data
  Overview
Find LBA Data
Investigator Checklist
Process & Policy
Documentation & Archive
Training & Education
  Overview
Activities Summary
T&E Goals
Student Opportunities
  Folha Amazônica
 
spacer

Investigation:

CD-34 (Chambers / Higuchi / J. dos Santos / Camargo)

LBA Dataset ID:

CD34_AMAZON_HYPERION

Originator(s):

CHAMBERS, J.Q.

Point(s) of Contact:

ORNL DAAC User Services Office Oak Ridge National Laboratory Oak Ridge, Tennessee 37 (ornldaac@ornl.gov)

Dataset Abstract:

Hyperion satellite images collected from across the Amazon basin and processed using ENVI (4.1) software as follows: Bad bands were removed leaving a spectral subset of generally 196 bands (some images have fewer). A cloud mask was developed using 2-d scatter plots of variable reflectance bands to highlight clouds as regions of interest (ROIs), allowing clouds and cloud edges to be masked. Next a de-streaking algorithm was applied to the image to reduce variance in balance between the vertical columns which are common in push-broom sensor systems such as Hyperion. Apparent surface reflectance was calculated for this balanced image using the atmospheric correction algorithm ACORN in 1.5pb mode (AIG-LLC, Boulder, CO). This mode is designed specifically for pushbroom sensors, such as Hyperion. Finally, images were georeference using the corresponding ALI satellite images. Each image comes with a header (.hdr file) containing addition image metadata.

Beginning Date:

2002-06-29

Ending Date:

2005-10-01

Metadata Last Updated on:

2012-02-15

Data Status:

Archived

Access Constraints:

PUBLIC

Data Center URL:

http://daac.ornl.gov/

Distribution Contact(s):

ORNL DAAC User Services Office Oak Ridge National Laboratory Oak Ridge, Tennessee 37 (ornldaac@ornl.gov)

Access Instructions:

PUBLIC

Data Access:

IMPORTANT: The LBA-ECO Project website is no longer being supported. Links to external websites may be inactive. Final data products from the LBA project can be found at the ORNL DAAC. Please follow the fair use guidelines found in the dataset documentation when using or citing LBA data.
Datafile(s):

LBA-ECO CD-34 Hyperion 30-m Surface Reflectance, Amazon Basin: 2002-2005:  http://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1064

Documentation/Other Supporting Documents:

LBA-ECO CD-34 Hyperion 30-m Surface Reflectance, Amazon Basin: 2002-2005:  http://daac.ornl.gov/LBA/guides/CD34_Amazon_Hyperion.html

Citation Information - Other Details:

Chambers, J.Q. 2012. LBA-ECO CD-34 Hyperion 30-m Surface Reflectance, Amazon Basin: 2002-2005. Data set. Available on-line [http://daac.ornl.gov] from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A. doi:10.3334/ORNLDAAC/1064

Keywords - Theme:

Parameter Topic Term Source Sensor
REFLECTANCE SPECTRAL/ENGINEERING HYPERSPECTRAL WAVELENGTHS EO-1 (EARTH OBSERVING 1) HYPERION (HYPERSPECTRAL IMAGER)

Keywords - Place (with associated coordinates):

Region
(click to view profile)
Site
(click to view profile)
North South East West
  ENTIRE AMAZON BASIN -0.52840 -13.74100 -51.30640 -75.39550

Related Publication(s):

Chambers, J.Q., A.L. Robertson, V.M.C. Carneiro, A.J.N. Lima, M.L. Smith, L.C. Plourde, and N. Higuchi. 2009. Hyperspectral remote detection of niche partitioning among canopy trees driven by blowdown gap disturbances in the Central Amazon. Oecologia 160(1):107-117.

Data Characteristics (Entity and Attribute Overview):

Data Characteristics:

Each image comes with a header (.hdr file) containing addition image metadata. The following images (21 total) comprise this archive:



EO1H0020682004132110PZ_196b_a15_m_d_geo

EO1H0020692003225110PZ_196b_a15_m_d_geo

EO1H0020692005314110KZ_196b_a15_m_d

EO1H0030572003344110PZ_196b_a15_m_d_geo

EO1H0030652004187110PZ_196b_a15_m_d_geo

EO1H0040632003255110PZ_196b_a15_m_d_geo

EO1H0040692002204110PZ_196b_a15_m_d_geo

EO1H0040692003143110PY_196b_a15_m_d_geo

EO1H0060622003333110PZ_196b_a15_m_d_geo

EO1H0070632003292110PZ_196b_a15_m_d_geo

EO1H0070652003244110PZ_196b_a15_m_d_geo

EO1H2250612002248110PZ_196b_a15_m_d_geo

EO1H2290662002180110PZ_196b_a15_m_d_geo

EO1H2310622002226110PY_196b_a15_m_d_geo

EO1H2310622003174110KT_196b_a15_m_d_geo

EO1H2310622005218110PE_196b_a15_m_d_geo

EO1H2310622005266110PB_196b_a15_m_d_geo

EO1H2316022002315110KV_194b_a15_m_d

EO1H2330602003051110PZ_196b_a15_m_d_geo

EO1H2330622004070110PZ_184b_a15_m_d_geo

EO1H2330632002320110PY_196b_a15_m_d_geo




The files names are coded as follows using the first image in this list as an example:

EO1H0020682004132110PZ - the Hyperion image identifier, including the year (2004) and Julian date (211) acquired; 196 - 196 band subset;

a15 - ACORN algorithm used for atmospheric correction,

m - clouds masked,

d - imaged destreaked as described above,

geo - georeferenced to the corresponding ALI image (two of the images above were not georeferenced, but are still provided for this archive).

Data Application and Derivation:

Hyperion satellite images collected from across the Amazon basin and processed using ENVI (4.1) software as follows:

Bad bands were removed leaving a spectral subset of generally 196 bands (some images have fewer). A cloud mask was developed using 2-d scatter plots of variable reflectance bands to highlight clouds as regions of interest (ROIs), allowing clouds and cloud edges to be masked. Next a de-streaking algorithm was applied to the image to reduce

variance in balance between the vertical columns which are common in push-broom sensor systems such as Hyperion. Apparent surface reflectance was calculated for this balanced image using the atmospheric correction algorithm ACORN in 1.5pb mode (AIG-LLC, Boulder, CO). This mode is designed specifically for pushbroom sensors, such as Hyperion.

Quality Assessment (Data Quality Attribute Accuracy Report):

Quality Assessment:

Assessment of the quality of each image provided below:



EO1H0020692003225110PZ excellent

EO1H0040692003143110PY good

EO1H0070632003292110PZ good

EO1H0040632003255110PZ excellent

EO1H0060622003333110PZ good

EO1H0030572003344110PZ excellent

EO1H0020682004132110PZ excellent

EO1H2250612002248110PZ fair

EO1H0070652003244110PZ excellent

EO1H2330602003051110PZ excellent

EO1H2290662002180110PZ excellent

EO1H2330622004070110PZ fair

EO1H2310622003174110KT excellent

EO1H0030652004187110PZ good

EO1H2330632002320110PY good

EO1H0040692002204110PZ very good

EO1H2310622005218110PE excellent

EO1H2310622002226110PY good

EO1H2316022002315110KV excellent

EO1H2310622005266110PB excellent

EO1H0020692005314110KZ very good

Process Description:

Data Acquisition Materials and Methods:

Data acquired by the Hyperion sensor on NASA\'s EO1 satellite

References:

Chambers, J. Q., A. L. Robertson, V. Carneiro, A. Nogueiro, M. L. Smith, L. Plourde, and N. Higuchi (2009) Hyperspectral remote detection of niche partitioning among canopy trees driven by blowdown gap disturbances in the Central Amazon. Oecologia. In press.

Skip navigation linksHOME | ABOUT | LIBRARY | NEWS ARCHIVE | CONTACTS | INVESTIGATIONS | LOGISTICS | DATA |TRAINING & EDUCATION

NASA logo
ORNL DAAC
Get Acrobat Reader