Report a bug, broken link, or incorrect content

Normalized Difference Vegetation Index

Also known as

NDVI

Description

The Normalized Difference Vegetation Index (NDVI) is an index of plant “greenness” or photosynthetic activity, and is one of the most commonly used vegetation indices. Vegetation indices are based on the observation that different surfaces reflect different types of light differently. Photosynthetically active vegetation, in particular, absorbs most of the red light that hits it while reflecting much of the near infrared light. Vegetation that is dead or stressed reflects more red light and less near infrared light. Likewise, non-vegetated surfaces have a much more even reflectance across the light spectrum.


Data for this graph courtesy of the Idaho Chapter of The Nature Conservancy
Reflectance of sunlight from four different land cover types in Hells Canyon, Idaho as measured by a field spectrometer.

By taking the ratio of red and near infrared bands from a remotely-sensed image, an index of vegetation “greenness” can be defined. The Normalized Difference Vegetation Index (NDVI) is probably the most common of these ratio indices for vegetation. NDVI is calculated on a per-pixel basis as the normalized difference between the red and near infrared bands from an image:

where NIR is the near infrared band value for a cell and RED is the red band value for the cell. NDVI can be calculated for any image that has a red and a near infrared band. The biophysical interpretation of NDVI is the fraction of absorbed photosynthetically active radiation.

Many factors affect NDVI values like plant photosynthetic activity, total plant cover, biomass, plant and soil moisture, and plant stress. Because of this, NDVI is correlated with many ecosystem attributes that are of interest to researchers and managers (e.g., net primary productivity, canopy cover, bare ground cover). Also, because it is a ratio of two bands, NDVI helps compensate for differences both in illumination within an image due to slope and aspect, and differences between images due things like time of day or season when the images were acquired. Thus, vegetation indices like NDVI make it possible to compare images over time to look for ecologically significant changes. Vegetation indices like NDVI, however, are not a panacea for rangeland assessment and monitoring. The limitations of NDVI are discussed below.

Similar Methods

Output

The output of NDVI is a new image file/layer. Values of NDVI can range from -1.0 to +1.0, but values less than zero typically do not have any ecological meaning, so the range of the index is truncated to 0.0 to +1.0. Higher values signify a larger difference between the red and near infrared radiation recorded by the sensor - a condition associated with highly photosynthetically-active vegetation. Low NDVI values mean there is little difference between the red and NIR signals. This happens when there is little photosynthetic activity, or when there is just very little NIR light reflectance (i.e., water reflects very little NIR light).

Successful Rangeland Uses

Because of its ease of use and relationship to many ecosystem parameters, NDVI has seen widespread use in rangeland ecosystems. The uses include assessing or monitoring:

  • vegetation dynamics or plant phenological changes over time
  • biomass production
  • grazing impacts or attributes related to grazing management (e.g., stocking rates)
  • changes in rangeland condition
  • vegetation or land cover classification
  • soil moisture
  • carbon sequestration or CO2 flux

Application References

NDVI has been applied to many different aspects of rangeland ecology and management. Below is a partial listing of application references arranged by topic.

Vegetation Dynamics / Phenology change over time

  • Fuller, D.O. 1998. Trends in NDVI time series and their relation to rangeland and crop production in Senegal, 1987-1993. International Journal of Remote Sensing 19(10):2013-2018.
  • Wellens, J. 1997. Rangeland vegetation dynamics and moisture availability in Tunisia: an investigation using satellite and meteorological data. Journal of Biogeography 24:845-855.

Biomass production

  • Anderson, G.L., Hanson, J.D., and R.H. Haas. 1993. Evaluating landsat thematic mapper derived vegetation indices for estimating above-ground biomass on semiarid rangelands. Remote Sensing of the Environment 45(2):165-175.
  • Everitt, J.H., Escobar, D.E., Alaniz, M.A., and M.R. Davis. 1996. Comparison of ground reflectance measurements, airborne video, and spot satellite data for estimating phytomass and cover on rangelands. Geocarto International: 11(2):69-76.
  • Hobbs, T.J. 1995. The use of NOAA-AVHRR NDVI data to assess herbage production in the arid rangelands of Central Australia. International Journal of Remote Sensing 16(7):1289-1302.
  • Moleele, N., Ringrose, S., Arnberg, W., Lunden, B., and C. Vanderpost. 2001. Assessment of vegetation indexes useful for browse (forage) production in semi-arid rangelands. International Journal of Remote Sensing 22(5):741-756.
  • Paruelo, J.M., Epstein, H.E., Lauenroth, W.K., and I.C. Burke. 1997. ANPP ESTIMATES FROM NDVI FOR THE CENTRAL GRASSLAND REGION OF THE UNITED STATES. Ecology 78(3):953-958.
  • Paruelo, J.M. Oesterheld, M., Di Bella, C.M., Arzadum, M., Lafontaine, J., Cahuepe, M., and C.M. Rebella. 2000. Estimation of primary production of subhumid rangelands from remote sensing data. Applied Vegetation Science 3:189-195.
  • Reeves, M.C., Winslow, J.C., and S.W. Running. 2001. Mapping weekly rangeland vegetation productivity using MODIS algorithms. Journal of Range Management 54:A90-A105.
  • Todd, S.W., Hoffer, R.M., and D.G. Milchunas. 1998. Biomass estimation on grazed and ungrazed rangelands using spectral indices. International Journal of Remote Sensing 19(3):427-438.

Grazing Impacts / Grazing Management

  • Blanco, L.J., Aguillera, M.O., Paruelo, J.M., and F.N. Biurrun. 2008. Grazing effect on NDVI across an aridity gradient in Argentina. Journal of Arid Environments 72(5):764-776.
  • Harris, A.T., and G.P. Asner. 2003. Grazing gradient detection with airborne imaging spectroscopy on a semi-arid rangeland. Journal of Arid Environments 55(3):391-404.
  • Hunt, E.R. and B.A. Miyake. 2006. Comparison of stocking rates from remote sensing and geospatial data. Rangeland Ecology and Management 59(1):11-18.
  • Kurtz, D.B., Schellberg, J. and M. Braun. 2009. Ground and satellite based assessment of rangeland management in sub-tropical Argentina. Applied Geography doi:10.1016/j.apgeog.2009.01.006
  • Oesterheld, M. C. M. DiBella, H. Kerdiles. 1998. RELATION BETWEEN NOAA-AVHRR SATELLITE DATA AND STOCKING RATE OF RANGELANDS. Ecological Applications 8(1):207-212.
  • Thoma, D.P., Bailey, D.W., Long, D.S., Mielsen, G.A., Henry, M.P., Breneman, M.C., and C. Montagne. 2002. Short-term monitoring of rangeland forage conditions with AVHRR imagery. Journal of Range Management 55:383-389.
  • Todd, S.W., Hoffer, R.M., and D.G. Milchunas. 1998. Biomass estimation on grazed and ungrazed rangelands using spectral indices. International Journal of Remote Sensing 19(3):427-438.

Change Detection

  • Geerken, R., and M. Ilaiwi. 2004. Assessment of rangeland degradation and development of a strategy for rehabilitation. Remote Sensing of the Environment 90(4):490-504.
  • Kurtz, D.B., Schellberg, J. and M. Braun. 2009. Ground and satellite based assessment of rangeland management in sub-tropical Argentina. Applied Geography doi:10.1016/j.apgeog.2009.01.006
  • Minor, T.B., Lancaster, J., Wade, T.G., Wickham, J.D., Whitford, W., and K.B. Jones. 1999. Evaluating Change in Rangeland Condition using Multitemporal AVHRR Data and Geographic Information System Analysis. Environmental Monitoring and Assessment 59(2):211-223.
  • Malmstrom, C. M., H. S. Butterfield, C. Barber, B. Dieter, R. Harrison, J. Qi, D. Riano, A. Schrotenboer, S. Stone, C. J. Stoner, and J. Wirka. 2008. Using remote sensing to evaluate the influence of grassland restoration activities on ecosystem forage provisioning services. Restoration Ecology.
  • Palmer, A.R., and A. Fortescue. 2004. Remote sensing and change detection in rangelands. African Journal of Range and Forage Science 21(2):123-128.
  • Tappan, G.G., Tyler, D. J., Wehde, M. E., and D.G. Moore. 1992. Monitoring rangeland dynamics in Senegal with Advanced Very High Resolution Radiometer data. Geocarto International 7(1):87-98.
  • Thoma, D.P., Bailey, D.W., Long, D.S., Mielsen, G.A., Henry, M.P., Breneman, M.C., and C. Montagne. 2002. Short-term monitoring of rangeland forage conditions with AVHRR imagery. Journal of Range Management 55:383-389.
  • Weiss, E., Marsh, S.E., and E.S. Pfirman. 2001. Application of NOAA-AVHRR NDVI time-series data to assess changes in Saudi Arabia's rangelands. International Journal of Remote Sensing 22(6):1005-1027.

Vegetation / Land Cover Classification

  • Geerken, R., Batikha, N., Celis, D., and E. Depauw. 2005. Differentiation of rangeland vegetation and assessment of its status: field investigations and MODIS and SPOT VEGETATION data analyses. International Journal of Remote Sensing 26(20).
  • Geerken, R., Zaitchik, B., and J.P. Evans. 2005. Classifying rangeland vegetation type and coverage from NDVI time series using Fourier Filtered Cycle Similarity. International Journal of Remote Sensing 26(24):5535-5554.
  • Evans, J.P. and R. Geerken. 2006. Classifying rangeland vegetation types and coverage using a Fourier component based similarity measure. Remote Sensing of the Environment 105(1):1-8.
  • Knight, J.F., R.L. Lunetta, J. Ediriwickrema, and S. Khorram, 2006. Regional Scale Land-Cover Characterization using MODIS-NDVI 250 m Multi-Temporal Imagery: A Phenology Based Approach. GIScience and Remote Sensing, 43(1), 1-23.

Soil Moisture Estimation

  • Wang, C., Qi, J., Moran, S., and R. Marsett. 2004. Soil moisture estimation in a semiarid rangeland using ERS-2 and TM imagery. Remote Sensing of the Environment 90(2):178-189.

Carbon Sequestration / CO2 flux

  • Gilmanov, T.G., Johnson, D.A., Saliendra, N.Z., Akshalov, K., and B.K. Wylie. 2004. Gross primary productivity of the true steppe in central Asia in relation to NDVI: scaling up CO2 fluxes. Environmental Management 33(Supplement 1):S492-S508.
  • Hunt, E.R., Fahenstock, J.T., Kelly, R.D., Welker, J.M., Reiners, W.A., and W.K. Smith. 2002. Carbon sequestration from remotely-sensed NDVI and net ecosystem exchange. Pp 161-174 in R.S. Muttiah (ed). From Laboratory Spectroscopy to Remotely Sensed Spectra of Terrestrial Ecosystems. Kluwer Academic Publishers, Dordrecht, Netherlands.
  • Hunt, E.R., Kelly, R.D., Smith, W.K., Fahenstock, J.T., Welker, J.M., and W.A. Reiners. 2004. Estimation of Carbon Sequestration by Combining Remote Sensing and Net Ecosystem Exchange Data for Northern Mixed-Grass Prairie and Sagebrush–Steppe Ecosystems. Environmental Management 33:S432-441.
  • Wylie, B.K., Fosnight, E.A., Gilmanov, T.G., Frank, A.B., Morgan, J.A., Haferkamp, M.R., and T.P. Meyers. 2007. Adaptive data-driven models for estimating carbon fluxes in the Northern Great Plains. Remote Sensing of the Environment 106(4):399-413.
  • Wylie, B.K., Johnson, D.A. Laca, E., Saliendra, N.Z., Gilmanov, T.G., Reed, B.C., Tieszen, L.L., and B.B. Worstell. 2003. Calibration of remotely-sensed coarse resolution NDVI to CO2 fluxes. Remote Sensing of Environment, Volume 85(2):243-255.

Technical References

The following technical references describe the theory behind NDVI and how it was developed.

  • Carlson, T.N. and D.A. Ripley. 1997. On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sensing of the Environment 62(3):241-252.
  • Huete, A.R., and R.D. Jackson. 1987. Suitability of spectral indices for evaluating vegetation characteristics on arid rangelands. Remote Sensing of the Environment 23(2):213-232.
  • Huete, A.R., and Jackson, R.D., 1988. Soil and atmosphere influences on the spectra of partial canopies, Remote Sensing of the Environment 25:89-105.
  • Huete, A., Justice, C., and W. van Leeuwen. 1999. MODIS vegetation index (MOD 13) algorithm theoretical basis document, version 3. USGS Land Process Distributed Active Archive Center. http://modis.gsfc.nasa.gov/data/atbd/atbd_mod13.pdf
  • Jensen, J. R. 1996. Introductory digital image processing. Prentice-Hall, Inc., Upper Saddle River, NJ.
  • Lillesand, T. M., and R. W. Kiefer 1994. Remote sensing and image interpretation. John Wiley & Sons, Inc., New York.

Limitations

The NDVI is correlated with a number of attributes that are of interest to rangeland ecologist and managers (e.g., percent cover of bare ground and vegetation, biomass). It is not, however, a direct measure of any of these things - it is a measure of “greenness” produced by the ratio of infrared and red light that is reflected from the surface. While the biophysical interpretation of NDVI is the fraction of absorbed photosynthetically active radiation (see fPAR wiki page) absorbed by the surface, there are a lot of factors that influence the strength of the relationship between NDVI and rangeland ecosystem attributes. These can include: atmospheric conditions, scale of the imagery, vegetation moisture, soil moisture, overall vegetative cover, differences in soil type, management, etc… It is important when using NDVI data in analyses that steps be taken to understand and, to the extent possible, control for factors that might be affecting NDVI values before interpretations of differences in NDVI between areas of within the same area over time can be made.

Light from the soil surface can influence the NDVI values by a large degree. This is of concern in rangeland applications because many semi-arid and arid environments tend to have higher cover of bare ground and exposed rock than other temperate or tropical habitats. Heute and Jackson (1988) found that the soil surface impact on NDVI values was greatest in areas with between 45% and 70% vegetative cover. This limitation was the reason for the development of the several different soil-adjusted vegetation indices (e.g., Soil-adjusted Vegetation Index, Modified Soil-adjusted Vegetation Index), and these indices tend to be preferred for rangeland applications.

In addition to the influence of soil surface at the low-end of vegetation cover, NDVI also suffers from a loss of sensitivity to changes in amount of vegetation at the high-cover/biomass end. This means that as the amount of green vegetation increases, the change in NDVI gets smaller and smaller. So at very high NDVI values, a small change in NDVI may actually represent a very large change in vegetation. This type of sensitivity change is problematic for analysis of areas with a high amount of photosynthetically active vegetation. This could be an issue in rangeland ecosystems if you were interested in assessing changes in riparian areas. In these situations, it may be advisable to use another vegetation index with better sensitivity to high-vegetation cover situations like the Enhanced Vegetation Index or the Wide Dynamic Range Vegetation Index.

Data Inputs

The inputs for NDVI are pretty simple. You need an image with a red band and a near infrared band.

Software/Hardware Requirements

The software and hardware requirements for calculating and working with NDVI data are generally low. To calculate NDVI, you need some sort of image processing program (e.g., ERDAS Imagine, ENVI, IDRISI) or GIS program that can handle raster calculations (e.g., ESRI ArcGIS, GRASS). Many programs have functions specifically designed to calculate NDVI, but if not, it is fairly easy to implement the equation to calculate it manually. The hardware requirements, in terms of processing capability and disk space, will largely be determined by the type and size of imagery you have.

Sample Graphic


Image courtesy of the Idaho Chapter of The Nature Conservancy
Example of an NDVI image calculated from an Ikonos image on an approximately 1 mi2 area in Owyhee County, Idaho. The “true” color image (upper-left) shows encroaching juniper woodlands grading into a mosaic of montane sagebrush and semi-wet meadows. The Red (upper-right) and Near Infrared (lower-left) bands for this area each highlight different aspects of the area. From the NDVI image (lower-right), however, the junipers and semi-wet meadows are easily distinguishable.


Image courtesy of the Idaho Chapter of The Nature Conservancy
An example of the change in NDVI over a growing season with changes in plant phenology. Top images are false-color composites of Landsat TM5 images. Bottom images are NDVI calculated from the Landsat TM5 image (bands 4 and 3). Example is for the 45-Ranch on the South Fork of the Owyhee River, Owyhee County, Idaho.

Additional Information

Existing datasets

NDVI is easy to calculate from a wide range of different image sources. In terms of already prepared NDVI data, MODIS data are processed into several different vegetation indices and made available on 16-day, monthly, and yearly intervals at different resolutions. MODIS NDVI data can be downloaded from NASA's Land Processes Distributed Actice Archive Center or from the Global Land Cover Facility.

Prepared MODIS and AVHRR NDVI datasets are also used in online tools like RangeView. While these NDVI datasets cannot be downloaded directly, they can be viewed and used for simple analyses via the website.

Web Search Results

Loading...
Loading...

Discussion/Comments

You must have an account and be logged in to post or reply to the discussion topics below. Click here to login or register for the site.

, 2011/05/19 14:43

Under limitations, you mention how soil reflectance can affect NDVI. How do you account reflectance in urban areas? What vegetation index is recommended?

 

Home | Toolbox Wiki | Rangeland Methods Guide | Framework | Training | About | Who We Are | Visualization | Contact

A joint project of The Nature Conservancy and the USDA Agricultural Research Service

Terms of Use | Privacy Policy