Vegetation index

Calculate the vegetation index using one of several predefined equations.

For more details and additional information, visit ENVI Vegetation Indices or refer to the inline references.

Parameters

Name & Expression

• NDVI
  Normalized Difference Vegetation Index
  
  This index is a measure of healthy, green vegetation.
  
  
  
  

• NDVI705
  Red Edge Normalized Difference Vegetation Index (RENDVI)
  
  Source: https://www.nv5geospatialsoftware.com/docs/narrowbandgreenness.html#red_edge_NDVI
  
  
  
  

• VOG1e
  Vogelmann Red Edge Index 1 (VREI1)

  This index is a narrowband reflectance measurement that is sensitive to the combined effects of foliage chlorophyll concentration, canopy leaf area, and water content.
  
  
  
  

• VOG2
  Vogelmann Red Edge Index 2 (VREI2)

   

  This index is a narrowband reflectance measurement that is sensitive to the combined effects of foliage chlorophyll concentration, canopy leaf area, and water content.
  
  
  
  

• VOG3
  Vogelmann Red Edge Index 3 (VREI3)
  
  
  
  

• SR
  Simple Ratio

   

  This index is a ratio of (1) the wavelength with highest reflectance for vegetation and (2) the wavelength of the deepest chlorophyll absorption. The simple equation is easy to understand and is effective over a wide range of conditions.
  
  
  
  

• EVI
  Enhanced Vegetation Index

   

  This index was originally developed for use with MODIS data as an improvement over NDVI by optimizing the vegetation signal in areas of high leaf area index (LAI). Source: https://www.nv5geospatialsoftware.com/docs/broadbandgreenness.html#enhanced_VI
  
  
  
  

• WBI
  Water Band Index

   

  This index is a reflectance measurement that is sensitive to changes in canopy water status. As the water content of vegetation canopies increases, the strength of the absorption around 970 nm increases relative to that of 900 nm.
  
  
  
  

• VARI
  Visible Atmospherically Resistant Index

   

  This index is based on the ARVI and is used to estimate the fraction of vegetation in a scene with low sensitivity to atmospheric effects.
  
  
  
  

• VIgreen
  Green Normalized Difference Vegetation Index (GNDVI)

   

  This index is similar to NDVI except that it measures the green spectrum from 540 to 570 nm instead of the red spectrum. This index is more sensitive to chlorophyll concentration than NDVI.
  
  
  
  

• PSSR
  Pigment Specific Simple Ratio
  
  
  
  

• PSRI
  Plant Senescence Reflectance Index

   

  This index maximizes the sensitivity of the index to the ratio of bulk carotenoids (for example, alpha-carotene and beta-carotene) to chlorophyll. An increase in PSRI indicates increased canopy stress (carotenoid pigment), the onset of canopy senescence, and plant fruit ripening.
  
  
  
  

• CARI
  Chlorophyll Absorption Ratio Index
  
  
  
  

• MCARI
  Modified Chlorophyll Absorption Ratio Index

   

  This index is one of several CARI indices that indicates the relative abundance of chlorophyll. 
  
  
  
  

• CRI1
  Carotenoid Reflectance Index 1

   

  Carotenoids function in light absorption processes in plants, as well as in protecting plants from the harmful effects of too much light. Weakening vegetation contains higher concentrations of carotenoids, so this index is one measure of stressed vegetation. Higher CRI1 values mean greater carotenoid concentration relative to chlorophyll.
  
  
  
  

• CRI2
  Carotenoid Reflectance Index 2

   

  This index is a modification to CRI1 that provides better results in areas of high carotenoid concentration. Higher CRI2 values mean greater carotenoid concentration relative to chlorophyll.
  
  
  
  

• ARI1
  Anthocyanin Reflectance Index 1

   

  Anthocyanins are water-soluble pigments abundant in newly forming leaves and those undergoing senescence. Weakening vegetation contains higher concentrations of anthocyanins, so this index is one measure of stressed vegetation.
  
  
  
  

• ARI2
  Anthocyanin Reflectance Index 2

   

  This index is a modification to the ARI1 that detects higher concentrations of anthocyanins in vegetation.
  
  
  
  

• SIPI
  Structure Insensitive Pigment Index

   

  This index is a reflectance measurement designed to maximize the sensitivity of the index to the ratio of bulk carotenoids (for example, alpha-carotene and beta-carotene) to chlorophyll while decreasing sensitivity to variation in canopy structure (for example, leaf area index). Increases in SIPI are thought to indicate increased canopy stress (carotenoid pigment). 
  
  
  
  

• PRI
  Photochemical Reflectance Index

   

  This index is a reflectance measurement that is sensitive to changes in carotenoid pigments (particularly xanthophyll pigments) in live foliage. Carotenoid pigments are indicative of photosynthetic light use efficiency, or the rate of carbon dioxide uptake by foliage per unit energy absorbed. As such, it is used in studies of vegetation productivity and stress. 
  
  
  
  

• RVSI
  Red-edge Vegetation Stress Index
  
  Merton and Huntington [52] proposed the Red-edge Vegetation Stress Index (RVSI), an index that captures variation in shape of the Hyperspectral Vegetation Indices 13 red-edge associated with plant stress. The RVSI is calculated as the average canopy reflectance at 714 and 752 nm, minus reflectance at 733 nm. A concave upward red edge and slightly negative or positive RVSI is found in stressed plants, while a concave downward red-edge, and strongly negative RVSI occurs in unstressed plants [52]. Source: https://www.researchgate.net/profile/Dar-Roberts/publication/288952459_Hyperspectral_Vegetation_Indices/links/571d4ac508ae6eb94d0e50cf/Hyperspectral-Vegetation-Indices.pdf
  
  
  
  

• NDWI
  Normalized Difference Water Index

   

  This index is sensitive to changes in vegetation canopy water content because reflectance at 857 nm and 1241 nm has similar but slightly different liquid water absorption properties. The scattering of light by vegetation canopies enhances the weak liquid water absorption at 1241 nm.
  
  
  
  

• NDNI
  Normalized Difference Nitrogen Index

  This index is designed to estimate the relative amounts of nitrogen contained in vegetation canopies. Reflectance at 1510 nm is largely determined by nitrogen concentration of leaves, as well as the overall foliage biomass of the canopy. Together, leaf nitrogen concentration and canopy foliar biomass are combined in the 1510 nm range to predict total canopy nitrogen content. This is compared to a reference reflectance at 1680 nm, which should contain a similar signal due to foliar biomass, but without the influence of nitrogen absorption. The NDNI is experimental, but it does show strong sensitivity to changing nitrogen status when the canopy is green (not senescent) and closed in architecture. 
  
  
  
  

• NDLI
  Normalized Difference Lignin Index
  
  (NDLI: [48]) Source: https://www.researchgate.net/profile/Dar-Roberts/publication/288952459_Hyperspectral_Vegetation_Indices/links/571d4ac508ae6eb94d0e50cf/Hyperspectral-Vegetation-Indices.pdf
  
  
  
  

• NDII
  Normalized Difference Infrared Index
  
  (NDII: [12]), in which the SWIR band can either be at a short wavelength (1650 nm) or longwavelength (2200 nm). Source: https://www.researchgate.net/profile/Dar-Roberts/publication/288952459_Hyperspectral_Vegetation_Indices/links/571d4ac508ae6eb94d0e50cf/Hyperspectral-Vegetation-Indices.pdf
  
  
  
  

• MSI
  Moisture Stress Index

   

  This index is a reflectance measurement that is sensitive to increasing leaf water content. As the water content of leaves in vegetation canopies increases, the strength of the absorption around 1599 nm increases. Absorption at 819 nm is nearly unaffected by changing water content, so it is used as the reference. Applications include canopy stress analysis, productivity prediction and modeling, fire hazard condition analysis, and studies of ecosystem physiology. The MSI is inverted relative to the other water VIs; higher values indicate greater water stress and less water content.
  
  
  
  

• CAI
  Cellulose Absorption Index

   

  This index indicates exposed surfaces containing dried plant material. Absorptions in the 2000 nm to 2200 nm range are sensitive to cellulose. Applications include crop residue monitoring, plant canopy senescence, fire fuel conditions in ecosystems, and grazing management.
  
  
  
  

• PPR
  Plant Pigment Ratio
  
  
  
  

• SAVI
  Soil Adjusted Vegetation Index
  
  This index is similar to NDVI, but it suppresses the effects of soil pixels. It uses a canopy background adjustment factor, L, which is a function of vegetation density and often requires prior knowledge of vegetation amounts. Huete (1988) suggests an optimal value of L=0.5 to account for first-order soil background variations. This index is best used in areas with relatively sparse vegetation where soil is visible through the canopy.
  
  
  
  

• ARVI
  Atmospherically Resistant Vegetation Index

   

  This index was originally designed for use with MODIS. It is an enhancement to the NDVI that is relatively resistant to atmospheric factors (for example, aerosol). It uses blue reflectance to correct red reflectance for atmospheric scattering. It is most useful in regions of high atmospheric aerosol content, including tropical regions contaminated by soot from slash-and-burn agriculture.
  
  
  
  

• OSAVI
  Optimized Soil Adjusted Vegetation Index

   

  This index is based on the Soil Adjusted Vegetation Index (SAVI). It uses a standard value of 0.16 for the canopy background adjustment factor. Rondeaux (1996) determined that this value provides greater soil variation than SAVI for low vegetation cover, while demonstrating increased sensitivity to vegetation cover greater than 50%. This index is best used in areas with relatively sparse vegetation where soil is visible through the canopy.
  
  

Smooth

Smooth prediction result using median filter kernel

• None

  • No Smoothing prediction.

• Low

  • Smoothing using median filter kernel with 5x5 pixel box

• Medium

  • Smoothing using median filter kernel with 10x10 pixel box

• High

  • Smoothing using median filter kernel with 15x15 pixel box

References

• Rouse, J.W., Haas, R.H., Schell, J.A., & Deering, D.W. (1974). Monitoring vegetation systems in the Great Plains with ERTS. Proceedings of the Third Earth Resources Technology Satellite-1 Symposium, 301-317.

• Tucker, C.J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8, 127-150. https://doi.org/10.1016/0034-4257(79)90013-0

• Pettorelli, N., Vik, J.O., Mysterud, A., Gaillard, J.M., Tucker, C.J., & Stenseth, N.C. (2005). Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends in Ecology & Evolution, 20(9), 503-510. https://doi.org/10.1016/j.tree.2005.05.011

• Huete, A.R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25, 295-309. https://doi.org/10.1016/0034-4257(88)90106-X

• Jensen, J.R. (2007). Remote Sensing of the Environment: An Earth Resource Perspective. Upper Saddle River, NJ: Prentice Hall.

• Myneni, R.B., & Williams, D.L. (1994). On the relationship between FAPAR and NDVI. Remote Sensing of Environment, 49(3), 200-211. https://doi.org/10.1016/0034-4257(94)90016-7

• NV5 Geospatial. (n.d.). ENVI Vegetation Indices. Retrieved from https://www.nv5geospatialsoftware.com/docs/vegetationindices.html