# Normalized Difference Vegetation Index

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 = (NIR - VIS) / (NIR + VIS)

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. This dataset is
spatially identical to the previous NDVI-e product but has less NDVI
signal removed via smoothing. This has resulted in a real-time,
operational NDVI product (called NDVI-rg) that can be compared to the
historical archive for identification of anomalous vegetation trends.
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
now available from the ADDS server in WinDisp and generic BIL formats.
Note that the NDVI data from July dekad 1, 1981 through March dekad 3,
2005 are NDVI-g. The data from April dekad 1, 2005 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 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](../GIMMSdocumentation_NDVIg_8km_10day.doc).

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.

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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., Pinzon J.E., Brown M.E., Slayback D., Pak E.W., Mahoney
R.,

Vermote E., El Saleous N. (2005). \"An Extended AVHRR 8-km NDVI

> Data Set Compatible with MODIS and SPOT Vegetation NDVI Data.\"
> International Journal of Remote Sensing, in press.

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.