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February 15, 2005 The Normalized Difference Vegetation Index (NDVI) provides a measure of the amount and vigor of vegetation at the land surface. The magnitude of NDVI is related to the level of photosynthetic activity in the observed vegetation. In general, higher values of NDVI indicate greater vigor and amounts of vegetation. NDVI is derived from data collected by National Oceanic and Atmospheric Administration (NOAA) satellites, and processed by the Global Inventory Monitoring and Modeling Studies group (GIMMS) at the National Aeronautical and Space Administration (NASA). The NOAA-Advanced Very High Resolution Radiometer (AVHRR) collects the data that are used to produce NDVI. The scanning radiometer (comprised of five channels) is used primarily for weather forecasting; however, there are an increasing number of other applications, e.g., drought monitoring. NDVI is calculated from two channels of the AVHRR sensor, the near-infrared (NIR) and visible (VIS) wavelengths, using the following algorithm: NDVI is a nonlinear function that varies between -1 and +1 (undefined when NIR and VIS are zero). Values of NDVI for vegetated land generally range from about 0.1 to 0.7, with values greater than 0.5 indicating dense vegetation. Since the late 1980's, the Famine Early Warning System (FEWS) has used AVHRR data to produce dekadal (10-day) composite NDVI images of Africa, and has built a valuable archive of these data from mid 1981 to present. NDVI-g data background: EROS processes and archives a dekadal (i.e. ~10 days, 36/year) Africa NDVI product from the NASA GIMMS group called NDVI-g. The dataset is inter-calibrated with SPOT Vegetation NDVI, and uses NOAA-17 data since January 2004. The NOAA-17 NDVI data have also been inter-calibrated with NOAA-16 and previous NDVI products. These data are available from the ADDS server in WinDisp and generic BIL formats. Note that the NDVI data from July dekad 1, 1981 through December dekad 3, 2008 are NDVI-g. The data from January dekad 1, 2008 to present are NDVI-rg. NASA has stated that the NDVI-rg data will be updated to the archival NDVI-g product approximately every 6-12 months. For more information about AVHRR data and NDVI processing, please see the references at the end of this document or see the GIMMS documentation. The FEWS-NET NDVI data originates from the NASA GIMMS group. For proper acknowledgement of these data in any report or publication, please cite documents 1 and 2 of the references listed at the end of this document. Copies of these papers can be made available upon request. NDVI-g data characteristics: Source: NASA - GIMMS group Time step: 10-day (dekadal) Resolution: 8km Projection: Albers equal area conic File Format: byte (8 bit); WinDisp image or generic BIL The satellite that acquired the data is noted below: NOAA 7 periods (Jul 81 - Feb 85) NOAA 9 periods (Feb 85 - Nov 88) NOAA 11 periods (Nov 88 - Sep 94) NOAA 9(descend) periods (Sep 94 - Jan 95) NOAA 14 periods (Jan 95 - Oct 00) NOAA 16 periods (Nov 00 - Dec 03) NOAA 17 periods (Jan 04 - present) Processing Details: No correction has been applied to correct for atmospheric effects due to water vapor, Rayleigh scattering or stratospheric ozone. Maximum value compositing has been used, with a forward binning procedure method implemented. A stratospheric aerosol correction has been applied during April 82-Dec 84 and June 91-Dec 93 to correct for stratospheric aerosols due to volcanic eruptions (Tanre, Holben and Kaufman 91). The corrections use a hybrid of retrieved optical thicknesses (Vermott et al. 95) and modeled thicknesses from GISS. Artifacts in NDVI due to satellite drift have been corrected using the empirical mode decomposition (EMD). The correction is especially important in tropical regions. For details see paper Pinzon et al 2004, reference below. The VIg correction has been applied to the GIMMS VId data, that has had the desert calibration applied for NOAA 7-14 (Los 1998). Scaling info: NDVI is archived as byte data files. In the formulas below, the data, once imported, is referred to as the 'raw' data. To recover the -1 to 1 range of NDVI, use the following formula: NDVI = raw/250 After conversion, Water pixels have a value of 1.0200, and 1.0160 are masked pixels, and missing are 1.0120. Africa Continental Details: coordinates for corners: Lower left lat : -42.243 deg Lower left lon : -23.490 deg Upper left lat : 43.711 deg Upper left lon : -24.600 deg Lower right lat : -42.242 deg Lower right lon : 63.414 deg Upper right lat : 43.712 deg Upper right lon : 64.523 deg Image size : 1152 rows x 1152 cols Center lat,lon : 1.000000, 20.000000 Pixel size h x w : 8.000000 km x 8.000000 km Origin of latitudes : 1.000000 deg Central meridian : 20.000000 deg First std parallel : -19.000000 deg Second std parallel : 21.000000 deg projection = ALBERS Conical Equal-area projection uses the clarke ellipsoid Applied Temperature Threshold for cloud screening = 285 K References: 1) Tucker C.J., J.E. Pinzón, M.E. Brown, D. Slayback, E.W. Pak, R. Mahoney, E. Vermote, and N. El Saleous, 2005. "An Extended AVHRR 8-km NDVI Data Set Compatible with MODIS and SPOT Vegetation NDVI Data." International Journal of Remote Sensing 26(20): 4485-4498. 2) Pinzon J., Brown M.E., Tucker C.J. (2004). Satellite time series correction of orbital drift artifacts using empirical mode decomposition. Hilbert-Huang Transform: Introduction and Applications. N. Huang: Chapter 10, Part II. Applications. 3) Tucker, C. J. (1979). "Red and Photographic Infrared Linear Combinations for Monitoring Vegetation." Remote Sensing of Environment 8: 127-150. 4) Los, S. (1998). "Estimation of the Ratio of Sensor Degradation Between NOAA AVHRR Channels 1 and 2 from Monthly NDVI Composites." IEEE Transactions on Geoscience and Remote Sensing 36(1): 206-213. 5) Pinzón, J. E. (2002). "Using HHT to successfully uncouple seasonal and interannual components in remotely sensed data." SCI 2002 Proceedings, July 14 - 18, Orlando, FL. 6) Pinzón, J. E., J. F. Pierce, and C. J. Tucker (2001). "Analysis of remote sensing data using Hilbert-Huang transform." SCI 2001 Proceedings, July 22 - 25, Orlando, FL. 7) Tanre, D., B. N. Holben and Y. J. Kaufman (1992). "Atmospheric Correction Algorithms for NOAA-AVHRR products: Theory and Application." IEEE Transactions Geoscience and Remote Sensing 30: 231-248. 8) Vermote, E. and Y. J. Kaufman (1995). "Absolute calibration of AVHRR visible and near-infrared channels using ocean and cloud views." International Journal of Remote Sensing 16(13): 2317-2340. |
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