What is a Vegetation Index?

A Vegetation Index (VI) is a spectral calculation that is done between two or more bands of the source data designed to enhance the contribution of vegetation properties and allow for comparisons of photosynthetic activity across your area of interest.


When the VI calculation takes place, it goes pixel by pixel through your entire dataset and runs a calculation on the spectral values of that pixel. This will assign a value representing some version of plant health for each pixel in the image. These values are then generalized to different ranges and turned into the colorized map you see on screen when the calculation is complete.


VI’s allow you to see relative differences in the general plant health across your entire field. Because these are relative differences in plant health across your field it is important to apply only to the areas you are interested in. For an example if you include a gravel road in the bounds of your vegetation index run the lower side of your VI range is going to be thrown off by this non living material included in your area of interest.


The methodology behind these calculations are slightly different for each VI that we offer. Below we have reviewed each Vegetation Index applicable to Multispectral data in PrecisionMapper (BGNIR and RGNIR).




Vegetation Indices for Multispectral Data

In PrecisionMapper we accept two types of Multispectral data for Vegetation Index application- BGNIR and RGNIR. 


Healthy vegetation will absorb more Blue and Red light to fuel photosynthesis and create chlorophyll in the plant. A plant with more chlorophyll will reflect more NIR energy than an unhealthy plant. Thus, by comparing the absorption of this blue and red light to the reflectance of the NIR wavelength we are able to provide valid information about a plants health. 



BGNIR

  • ENDVI- Enhanced Normalized Difference Vegetation Index

  • GNDVI- Green Normalized Difference Vegetation Index

  • GDVI- Green Difference Vegetation Index

  • GSAVI- Green Soil Adjusted Vegetation Index


Supported Sensors
BGNIR
Supported Resolution
20 cm/pixel or less
Other Requirements
NA
Estimated Processing Time
2 Hours or less
Outputs    
Georeferenced Image, PDF Map, KML


RGNIR

  • NDVI- Normalized Difference Vegetation Index

  • DVI- Difference Vegetation Index

  • OSAVI- Optimized Soil Adjusted Vegetation Index

  • RDVI- Renormalized Difference Vegetation Index

  • SAVI- Soil Adjusted Vegetation Index


Supported Sensors
RGNIR
Supported Resolution
20 cm/pixel or less
Other Requirements
NA
Estimated Processing Time
2 Hours or less
Outputs    
Georeferenced Image, PDF Map, KML

BGNIR


ENDVI- Enhanced Normalized Difference Vegetation Index


The traditional NDVI uses only red and near infrared data. ENDVI is a close equivalent and modified version of NDVI but since it is used with BGNIR data instead of RGNIR the calculation is done differently.


ENDVI uses Blue and Green visible light instead of the Red only method of the standard NDVI algorithm. This allows for better isolation of plant health indicators.


A normal healthy plant will reflect both visible Green and NIR light while absorbing Blue visible light. In the ENDVI measurement we are calculating the relationship between high absorption of this Blue light and high reflectance of Green and NIR waves as an indicator of plant health.



ENDVI= ((NIR+Green)-(2*Blue))  /  ((NIR+Green)+(2*Blue))



With these normalized values you will see a range of -1 to 1 in the output ENDVI file.



Reference: https://maxmax.com/endvi.htm



GNDVI- Green Normalized Difference Vegetation Index


As you could assume from the name the Green Normalized Difference Vegetation Index is an index of greenness or photosynthetic activity of living plants. It is specifically sensitive to the variation of chlorophyll content in plants.


You can see below this algorithm is also very similar NDVI with the substitution of Green instead of Red in the equation. Because of this change the GNDVI is more sensitive to the concentration of chlorophyll in plants.



GNDVI = (NIR - Green) / (NIR + Green)



If a plant is taking in the right amount of water or nitrogen it becomes greener and healthier. These elements fuel the photosynthesis process through the production of chlorophyll. Because of this the GNDVI algorithm is most commonly used to determine water and fertilizer uptake across a field. With good source data and GNDVI results you will be able to allocate your water and fertilizer in a more effective way.




Reference: Gitelson, A., and M. Merzlyak. "Remote Sensing of Chlorophyll Concentration in Higher Plant Leaves." Advances in Space Research 22 (1998): 689-692.



GDVI- Green Difference Vegetation Index


This algorithm is based on some of the same concepts as the GNDVI. It was originally designed with color infrared photography to predict nitrogen requirements for corn.



GDVI = NIR - Green



Values are not normalized to a -1.0-1.0 range like the GNDVI version of this algorithm. Instead you should expect a range of -255 to 255 in the output file.




Reference: Sripada, R., et al. "Aerial Color Infrared Photography for Determining Early In-season Nitrogen Requirements in Corn." Agronomy Journal 98 (2006): 968-977




GSAVI- Green Soil Adjusted Vegetation Index


When surveying young crops, you are including a lot of bare ground in the bounds of your vegetation index calculation. Because these Vegetation Indices are based around detection of healthy living plants there is always the chance of strange readings over soil between each row.


The GSAVI algorithm suppresses the effects of soil pixels by using a canopy background adjustment factor, L, which is a function of vegetation density. The index is best used in areas with relatively sparse vegetation where soil is visible through the canopy.



GSAVI= (1+L)*(NIR-Green) / (NIR+Green+ L)

L=0.16



The optimal value of L= 0.5 to account for the first order soil background variations.






NDVI- Normalized Difference Vegetation Index


Normalized Difference Vegetation Index is one of the oldest Vegetation Indices out there. Most of the Indices around are based from this original NDVI equation. This VI is a robust measure of healthy green vegetation.


The equation uses the highest absorption (Red Light) and reflectance (Green Light) waves of the visual spectrum to calculate the Vegetation Index value. It is a good indicator of chlorophyll content (Greenness) over a wide range of conditions. It can however saturate in dense vegetation conditions when Leaf Area in the survey becomes high.

NDVI= (NIR-Red)  /  (NIR+Red)


Values range from -1 to 1. Common range for green vegetation is 0.2-0.8



Reference: Rouse, J., R. Haas, J. Schell, and D. Deering. Monitoring Vegetation Systems in the Great Plains with ERTS. Third ERTS Symposium, NASA (1973): 309-317.



DVI- Difference Vegetation Index


This simple vegetation Index can distinguish between soil and vegetation. However due to its simplicity it does not account for any atmospheric effects or shadows.



DVI= NIR-Red



The equation is not normalized like many of our other vegetation indices. So resulting values will range from -255 to 255 in the final output.




Reference: Tucker, C. "Red and Photographic Infrared Linear Combinations for Monitoring Vegetation. Remote Sensing of Environment 8 (1979): 127–150.


SAVI- Soil Adjusted Vegetation Index


The soil adjusted vegetation index can suppress the effects of soil pixels in the final output. It does this by using a factor to account for the canopy background in the source data (L). This L value is a function of vegetation density. For this reason, you select the growth stage of your crop when you are applying the SAVI algorithm. You will have Early or Mid growth stage options in this, each option will change the L value in used in the equation.


SAVI= (1+L)*(NIR-Red)/(NIR+Red+L)


Early Growth Stage- L= 0.1

Mid Growth Stage- L= 0.25



The index is best used in areas with relatively sparse vegetation where soil is visible through the canopy.



Reference: Huete, A. "A Soil-Adjusted Vegetation Index (SAVI)." Remote Sensing of Environment 25 (1988): 295-309.




OSAVI- Optimized Soil Adjusted Vegetation Index


When surveying young fields there will be a lot of soil/ bare earth included in the calculation of your Vegetation Indices. The OSAVI algorithm uses a canopy background adjustment factor of 0.16 to account for this visible soil in your survey.


This is a variation of the standard Soil Adjusted Vegetation Index offered for RGNIR data. Because of the difference in the canopy adjustment factor the OSAVI algorithm demonstrates increased sensitivity to vegetation cover greater than 50%.


OSAVI= (NIR-Red)/ (NIR+Red+0.16)



The expected values from this algorithm will be -1 to 1. This index is best used in areas with relatively sparse vegetation where soil is visible through the canopy.






Reference: Rondeaux, G., M. Steven, and F. Baret. "Optimization of Soil-Adjusted Vegetation Indices." Remote Sensing of Environment 55 (1996): 95-107.



RDVI- Renormalized Difference Vegetation Index


This algorithm is another variation of the NDVI. It uses the difference between NIR and Red wavelengths to highlight healthy vegetation.


This algorithm is not built for survey areas with sparse vegetation. For best results apply to heavily vegetated areas. It is insensitive to the effects of soil and sun viewing geometry.

 


RDVI= (NIR-Red)  /  sqrt(NIR+Red)



The expected values of this algorithm are also -1 to 1.




Reference: Roujean, J., and F. Breon. "Estimating PAR Absorbed by Vegetation from Bidirectional Reflectance Measurements." Remote Sensing of Environment 51 (1995): 375-384.