ALN logo; link to Arid Lands Newsletter Home page No. 53, May/June 2003
Using geospatial technologies to develop
participatory tools for natural resources management

Orr et al.: Sidebar 1: Normalized Difference Vegetation Index (NDVI)

A vegetation index is a quantitative measure used to measure biomass or vegetative vigor, usually formed from combinations of several spectral bands (range of wavelength), whose values are added, divided, or multiplied in order to yield a single value that indicates the amount or vigor of vegetation. The simplest form of vegetation index is a ratio between near-infrared and red reflectance. For healthy living vegetation, this ratio will be high due to the inverse relationship between vegetation brightness in the red and infrared regions of the spectrum.

The NDVI, perhaps the most commonly used vegetation index, provides a standardized method of comparing vegetation greenness between satellite images.

The formula to calculate NDVI is:

NDVI = (near infrared band - red band) / (near infrared band + red band)

The underlying principle of the formula is that radiation from visible red light is considerably absorbed (or poorly reflected) by chlorophyll in green plants, while radiation from near infrared light is strongly reflected by the spongy mesophyll leaf structure (Tucker 1979; Jackson, Slater and Pinter 1983; Tucker et al. 1991).

Index values can range from -1.0 to 1.0, but vegetation values typically range between 0.1 and 0.7. Higher index values are associated with higher levels of healthy vegetation cover. However, clouds and snow will cause index values near zero, making it appear that the vegetation is less green.

Bands from the following satellite sensors can be used to calculate NDVI:

NDVI can be used as an indicator of relative biomass and greenness (Boone et al. 2000; Chen and Brutsaert 1998). If sufficient ground data are available, the NDVI can also be used to calculate and predict primary production, dominant species, and grazing impact and stocking rates (Ricotta, Qvena and Palma 1999; Oesterheld, Dibella and Kerdiles 1998; Paruelo et al. 1997; Peters et al. 1997; Diallo et al. 1991). It is also highly correlated with climatic variables, such as the El Niño Southern Oscillation (ENSO) (Li and Kafatos 2000; Boone et al. 2000) and precipitation (Schmidt and Karnieli 2000).

The above text was modified from the RangeView glossary (, the EO Library ( and the Goddard Space Flight Center (


Boone, R.B., K.A. Galvin, N.M. Smith and S.J. Lynn. 2000. Generalizing El Niño effects upon Maasai livestock using hierarchical clusters of vegetation patterns. Photogrammetric Engineering & Remote Sensing 66(6): 737-744.

Chen, D. and W. Brutsaert. 1998. Satellite-sensed distribution and spatial patterns of vegetation parameters over a tallgrass prairie. Journal of the Atmospheric Sciences 55(7): 1225-1238.

Diallo, O., A. Diouf, N.P. Hannan, A. Ndiaye, and Y. Prevost. 1991. AVHRR monitoring of savanna primary production in Senegal, West Africa: 1987-1988. International Journal of Remote Sensing 12(6): 1259-1279.

Jackson, R.D., P.N. Slater, and P.J. Pinter, 1983. Discrimination of growth and water stress in wheat by various vegetation indices through clear and turbid atmospheres. Remote Sensing of the Environment 15:187-208.

Li, Z. and M. Kafatos. 2000. Interannual variability of vegetation in the United States and its relation to El Niño/Southern Oscillation. Remote Sensing of Environment 71(3): 239-247.

Oesterheld, M., C.M. DiBella, and H. Kerdiles. 1998. Relation between NOAA-AVHRR satellite data and stocking rate of rangelands. Ecological Applications 8(1): 207-212.

Paruelo, J.M., H.E. Epstein, W.K. Lauenroth and I.C. Burke. 1997. ANPP estimates from NDVI for the Central Grassland Region of the United States. Ecology 78(3): 953-958.

Peters, A.J., M.D. Eve, E.H. Holt and W.G. Whitford. 1997. Analysis of desert plant community growth patterns with high temporal resolution satellite spectra. Journal of Applied Ecology 34: 418-432.

Ricotta, C., G. Avena, and A.D. Palma. 1999. Mapping and monitoring net primary productivity with AVHRR NDVI time-series: Statistical equivalence of cumulative vegetation indices. Journal of Photogrammetry and Remote Sensing 54(5): 325-331.

Schmidt, H. and A. Karnieli. 2000. Remote sensing of the seasonal variability of vegetation in a semi-arid environment. Journal of Arid Environments 45(1): 43-60.

Tucker, C.J. 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of the Environment 8:127-150.

Tucker, C.J., W.W. Newcomb, S.O. Los, and S.D. Prince, 1991. Mean and inter-year variation of growing-season normalized difference vegetation index for the Sahel 1981-1989. International Journal of Remote Sensing 12:1113-1115.

[Back to the article text]

Link to ALN home page Link to index page for back web issues Link to index page for pre-web issue archive Link to ALN #53 table of contents