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Investigation:

LC-15 (Saatchi / Alvala)

LBA Dataset ID:

LC15_SPOT_Metrics

Originator(s):

Saatchi, Dr. Sassan S.

Point(s) of Contact:

Saatchi, Dr. Sassan S. (Saatchi@congo.jpl.nasa.gov)

Dataset Abstract:

NDVI composite images from SPOT VEGETATION. Monthly NDVI data are used to create 5 Metrics: Maximum NDVI, Minimum of 6 greenest months, Range of NDVI between Min and Max, Mean NDVI Dry Months, Mean NDVI Wet Months. 10 day composites of NDVI image data from SPOT VEGETATION over the Amazon basin for two years were reprocessed through several filters for cloud removal. 5 metrics are produced from two years of data or 72 images.

Beginning Date:

1999-01-01

Ending Date:

2000-12-30

Metadata Last Updated on:

2010-03-30

Data Status:

In Preparation for Archive

Access Constraints:

Public

Data Center URL:

http://daac.ornl.gov

Distribution Contact(s):

Dr. Sassan Saatchi saatchi@congo.jpl.nasa.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):

ftp://www-radar.jpl.nasa.gov/projects/carbon/ab/SPOT/:  ftp://www-radar.jpl.nasa.gov/projects/carbon/ab/SPOT/
Access data at ORNL DAAC:  Search at ORNL DAAC

Documentation/Other Supporting Documents:

http://www-radar.jpl.nasa.gov/carbon/
Data Set User's Guide (Draft):  Search at ORNL DAAC

Citation Information - Other Details:

Saatchi S., Steinenger, M., Tucker, C.J., Nelson, B., and Simard, M. 2010. SPOT Vegetation NDVI Metrics over the Amazon Basin. Data set. Available on-line [http://www.daac.ornl.gov] from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A.

Keywords - Theme:

Parameter Topic Term Source Sensor
VEGETATION INDEX RADIANCE OR IMAGERY LAND USE/LAND COVER SPOT (SYSTEME PROBATOIRE POUR L\'OBSERVATION DE LA TERRE) SPOT VEGETATION
VEGETATION COVER RADIANCE OR IMAGERY LAND USE/LAND COVER SPOT (SYSTEME PROBATOIRE POUR L\'OBSERVATION DE LA TERRE) SPOT VEGETATION

Uncontrolled Theme Keyword(s):  Land Cover Land Use, NDVI, Vegetation Types

Keywords - Place (with associated coordinates):

Region
(click to view profile)
Site
(click to view profile)
North South East West
Amazon Basin Amazon Basin 13.85830 -82.72083

Data Characteristics (Entity and Attribute Overview):

Data Characteristics:

NDVI composite images from SPOT VEGETATION. Monthly NDVI data are used to create 7 Metrics: Maximum NDVI, Minimum of 6 greenest months, Range of NDVI between Min and Max, Mean NDVI Dry Months, Mean NDVI Wet Months.



Data are available in 7 .byt files each file is associated with a distinct NDVI metric which is included in the file name.



NDVI_metric1 Maximum NDVI

NDVI_metric2 Minimum NDVI of 6 greenest months

NDVI_metric3 Range of NDVI between Min and Max

NDVI_metric4 Mean NDVI Dry Months

NDVI_metric5 Mean NDVI Wet Months

NDVI_metric6

NDVI_metric7



Data Application and Derivation:

In regional scale analysis of carbon or hydrological cycles, the robustness and realism of these models depend on how precisely the land cover types and their changes are characterized (Running et al., 1995; Cramer et al., 1999).

Over tropical landscapes, satellite observations have been the most important

source of data for documenting and monitoring land cover types and changes (Skole and Tucker, 1993; INPE 1992; DeFries and Belward, 2000). However, most data sets that cover entire regions or continents are at spatial resolutions of 1 km or coarser. Land cover classification derived from such data sets will likely not reveal the subtle changes that are not caused by conversion of very large patches (DeFries et al., 1999). Incorporating remote sensing data from several sensors responding to various attributes of land cover, commonly referred to as data fusion is one way to improve current land classifications in the tropics. These NDVI data were used in a test of data fusion approach for the Amazon Basin land cover mapping in combination with radar data from the JERS-1 satellite.

Quality Assessment (Data Quality Attribute Accuracy Report):

Quality Assessment:

The standard correction method for all VEGETATION products is based on SMAC (Simplified Method for Atmospheric Correction) with the aerosol optical depth at 550 nm and the vertically integrated gaseous contents for water vapor and ozone (Rahman and Dedieu, 1994). The ground surface reflectance was then used to calculate the NDVI for each pixel. The 10-day composite images were used to obtain the monthly NDVI images based on the maximum-NDVI criterion. To further reduce potential cloud contamination, monthly maximum NDVI images from April, 1998 through March, 2001 were used to calculate monthly mean NDVI over 3-year period. The final monthly NDVI time series was further processed for eliminating any outliers using the Fourier-based adjustment technique (Los, 1998).

Process Description:

Data Acquisition Materials and Methods:

We obtained 2 years of 1 km NDVI data from SPOT VEGETATION. The sensor

provides daily 1 km resolution images of nearly all of the terrestrial earth, with 2250 km swath and 101 degrees field of view. The sensor has four spectral channels: blue (0.43-0.47mm), red (0.61-0.68mm), near infrared (0.78-0.89mm), and middle infrared (1.58-1.75mm). The radiometric resolution of surface reflectance is from 0.001 to 0.003 with different adjustments for each band. All pixels are resampled onto a regular grid by taking into account the spectral band registration, satellite location and attitude correction, and the terrain elevation that accounts for parallax distortion. The 10 day composite is obtained by choosing the best measurement of the period from the criteria

that it does not correspond to a blind or interpolated pixel, it is not flagged as cloudy in the status map, and it does correspond to the highest value of Top of Atmosphere NDVI.

References:

Hansen, M.C., DeFries, R. and Townshend, J. (2000). Global land cover classification at 1 km spatial resolution using classification tree. International Journal of Remote Sensing, 21, 1331-1364.





Los, S.O. (1998). Linkages Between Global Vegetation and Climate: An Analysis Based on NOAA Advanced Very High Resolution Radiometer Data. PhD. Dissertation, Vrije Universiteit, NASA/GSFC/CR-1998-206852.





Rahman, H. and Dedieu, G. (1994). SMAC: A Simplified Method of Atmospheric

Correction of satellite measurements in the solar spectrum. International Journal of Remote Sensing, 15, 123-143.





TRFIC (2002). Tropical Rain Forest Information Center. http://bsrsi.msu.edu/trfic/.

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