VEGINDEX

Generate vegetation indices


EnvironmentsPYTHON :: EASI :: MODELER
Batch ModeYes
Quick linksDescription :: Parameters :: Parameter descriptions :: Details :: Algorithm :: References :: Related

Back to top

Description


For each pixel in the input data, VEGINDEX computes the vegetation indices that you select.
Back to top

Parameters


Name Type Length Value range
Input1: Blue image channel (0.4-0.52 um) Raster port 0 - 1  
Input2: Green image channel (0.52-0.60 um) Raster port 0 - 1  
Input3: Red image channel (0.62-0.71 um) * Raster port 1 - 1  
Input4: Red edge image channel (0.697-0.713 um) Raster port 0 - 1  
Input5: NIR image channel (0.78-0.89 um) Raster port 0 - 1  
Input6: SWIR image channel (1.565-1.655 um) Raster port 0 - 1  
Input7: SWIR image channel (2.100-2.280 um) Raster port 0 - 1  
Input8: Red edge image channel (0.732-0.748 um) Raster port 0 - 1  
Input9: Red edge image channel (0.773-0.793 um) Raster port 0 - 1  
Input10: NIR image channel (0.855-0.875 um) Raster port 0 - 1  
Output: Generated vegetation indices Raster port 0 - 1024  
Index to calculate * String 2 - 1  
Output type String 0 - 1 8U | 16S | 16U | 32R
Default: 32R
Scaling offset Float 0 - 2  
Scaling factor Float 0 - 2  

* Required parameter
Back to top

Parameter descriptions

Input1: Blue image channel (0.4-0.52 um)

The image channel in the input file that contains the blue band.

This parameter is optional.

Input2: Green image channel (0.52-0.60 um)

The image channel in the input file that contains the green band.

This parameter is optional.

Input3: Red image channel (0.62-0.71 um)

The image channel in the input file that contains the red band.

This parameter is mandatory.

Input4: Red edge image channel (0.697-0.713 um)

The image channel in the input file that contains the RedEdge (0.697-0.713 um) band.

This parameter is optional.

Input5: NIR image channel (0.78-0.89 um)

The image channel in the input file that contains the near-infrared (NIR) band.

This parameter is optional.

Input6: SWIR image channel (1.565-1.655 um)

The image channel in the input file that contains the shortwave-infrared SWIR (1.565-1.655 um) band.

Use this input specifically to calculate the Green Vegetation Index (GVI) on Landsat-7 and Landsat-8 data.

This parameter is optional.

Input7: SWIR image channel (2.100-2.280 um)

The image channel in the input file that contains the shortwave-infrared SWIR (2.100-2.280 um) band.

Use this input specifically to calculate the Green Vegetation Index (GVI) on Landsat-7 and Landsat-8 data.

This parameter is optional.

Input8: Red edge image channel (0.732-0.748 um)

The image channel in the input file that contains the Sentinel-2 band called Vegetation Red Edge 2 (0.732-0.748 um).

Use this input specifically to calculate the CIedgeRed on Sentinel-2 data.

This parameter is optional.

Input9: Red edge image channel (0.773-0.793 um)

The image channel in the input file that contains the Sentinel-2 band called Vegetation Red Edge 3(0.773-0.793 um) band.

Use this input specifically to calculate the CIedgeRed on Sentinel-2 data.

This parameter is optional.

Input10: NIR image channel (0.855-0.875 um)

The image channel in the input file that contains the Sentinel-2 band called NIR (0.855-0.875 um) band.

Use this input specifically to calculate the MCARI2 data.

This parameter is optional.

Output: Generated vegetation indices

The output channel or channels to which to write the calculated vegetation index values.

If the output file is new, you need not specify a value for this parameter. VEGINDEX will write the results to 32-bit channels in the new file.

If the output file is an existing file, VEGINDEX will add new 32-bit channels to the file and write the index results to these channels

Index to calculate

The type of vegetation index to calculate. You can enter a single index or enter a comma-separated list of one or more.

To calculate all indices appropriate to the input data, select ALL.

The available options are as follows:

Output type

The data type of the output channel to create.

Supported data types are:

When the number of output channels is greater than one, the specified data type is used for each output channel.

This parameter is optional.

Scaling offset

The scaling offset to convert the computed index values to digital numbers (DN) in the output image.

If you specify a value for this parameter, you must also specify a scaling-factor value.

Together, the scaling factor and scaling offset convert the computed radiance values to DNs, as follows:

                            DN = Reflectance × Scaling factor + Scaling offset
                        
Note: The radiometric gain and offset values in the channel metadata are mutually related to the scaling factor and scaling offset values, as follows:
                            Gain = 1 ÷ Scaling factor
                            Bias = -Scaling offset ÷ Scaling factor

                            Scaling factor = 1 ÷ Gain
                            Scaling offset = -Bias ÷ Gain
                        
If you specify the default values of the scaling factor and scaling offset, the values vary according to the type of output data, as follows:

This parameter is optional.

Scaling factor

The scaling factor to convert the computed index values to DNs in the output channels. The value or values you specify must be positive (greater than zero).

If you specify a value for the scaling factor, you must also specify a value for the scaling offset.

For information about scaling offset, including a list of default values according to data type, see the scaling-offset description.

This parameter is optional.

Back to top

Details

VEGINDEX calculates one or more vegetation indices using the reflectance data (top of atmosphere (TOA) or absolute reflectance) in the input file, channels, or both. If the input data is DN values, they will be converted internally to TOA reflectance values for calculation. The results are written to a new file, or to the file and channels you specify.

To avoid scaling of the results produced by VEGINDEX, make sure they are written to 32-bit real channels.

When file and band metadata information is available, VEGINDEX uses this information to select appropriate data for the input index or indices.

Before running VEGINDEX, it is recommended that your input data be corrected atmospherically to ensure your results are due truly to the vegetation, and not changing atmospheric conditions.

Supported sensors

The following sensors are supported:
  • ALI
  • ALOS Avnir-2
  • Aster
  • Cartosat PAN
  • CBERS-4
  • CBERS-4A
  • Deimos-1
  • Deimos-2
  • DMC
  • Dragonette
  • DS-EO
  • FASat-Charlie
  • Formosat-2
  • FORMOSAT-5
  • Geoeye-1
  • GF1
  • GF2
  • GF4
  • GF6
  • GF7
  • Gokturk1
  • IRS-1A
  • IRS-1B
  • IRS-1C
  • IRS-1D
  • IRS-P6
  • Ikonos-2
  • Jilin-1
  • KazEOSat-2
  • KOMPSAT-2
  • KOMPSAT-3
  • KOMPSAT-3A
  • Landsat-4 MSS
  • Landsat-5 MSS
  • Landsat-4 TM
  • Landsat-5 TM
  • Landsat-7 ETM+
  • Landsat-8
  • Maxar-Legion
  • OrbView-3
  • PeruSAT-1
  • PlanetScope
  • Pleiades
  • QuickBird
  • PlanetScope
  • RapidEye
  • Resourcesat-2
  • SAC-C
  • Sentinel-2
  • SPOT-1
  • SPOT-2
  • SPOT-3
  • SPOT-4
  • SPOT-5
  • SPOT-6
  • SPOT-7
  • SuperView
  • Thaichote (THEOS)
  • TripleSat
  • WorldView-2
  • WorldView-3
  • WorldView-4
  • ZY1E
  • ZY3
  • ZY3-2
Note: When specifying a sensor, use the exact syntax, as shown in the preceding list.
Back to top

Algorithm

Typically, indices are used to create output images by combining mathematically the DN values of various bands. These indices can be simplistic, such as the following:
The indices can also be more complex, such as:
In many instances, the indices are ratios of band DN values:

These ratio images are derived from the absorption and reflection spectra of the material of interest. The absorption is based on the molecular bonds in the (surface) material. Therefore, the ratio often provides information on the chemical composition of the target.

The technique of ratioing bands involves separating the spectral-response value of a pixel in one image with that of the corresponding pixel in another. This is done to suppress similarities between bands and to eliminate albedo effects and shadows.

Vegetation indices

An assumption of vegetation indices is that all bare soil in an image will form a line in spectral space. Nearly all of the vegetation indices used commonly are concerned only with red-near-infrared space, so a red-near-infrared line for bare soil is assumed. This line is considered to be the line of zero vegetation.

At this point, there are two divergent lines of thinking about the orientation of lines of equal vegetation (isovegetation lines):
  1. All isovegetation lines converge at a single point.
    The indices that use this assumption are the ratio-based indices, which measure the slope of the line between the point of convergence and the red-NIR point of the pixel. Some examples are: NDVI and RVI.

  2. All isovegetation lines remain parallel to the bare soil line.
    Typically, these indices are called perpendicular, because they measure the perpendicular distance from the soil line to the red-NIR point of the pixel. Examples are: DVI and PVI.

Most vegetation formulas are designed for use with data of three or more bands. The VEGINDEX algorithms are separated by the type of input data that is expected for each.

Sensors with RGB only

Name Algorithm
GRVI (Green – Red) ÷ (Green + Red)
GI ((2.0 × Green) - (Red + Blue)) ÷ ((2.0 × Green) + Red + Blue)

Sensors with RGB and near-infrared (NIR)

Name Algorithm Reference
VDI NIR – Red 10
RVI NIR ÷ Red 2
NDVI (NIR – Red) ÷ (NIR + Red) 9
TDVI (NIR – Red) ÷ (NIR + Red) + .51/2 1
SAVI ((NIR – Red) ÷ (NIR + Red + L)) × (1 + L)

The L value is based on the amount of green vegetative cover. With VEGINDEX, L is a default of 0.5, which means, generally, areas of moderate green vegetative cover.

5
MSAVI2 (0.5) × (2(NIR + 1) – sqrt((2 × NIR + 1)2 – 8(NIR – Red))) 8
GEMI eta × (1 – 0.25 × eta) – ((Red – 0.125) ÷ (1 – Red))

Where eta = (2 × (NIR2 – Red2) + 1.5 × NIR + 0.5 × Red) ÷ (NIR + Red + 0.5)

7
MTVI 1.2 × [1.2 × (NIR – Green) – 2.5 × (Red – Green)] 4
EVI 2.5 × (NIR – Red) ÷ (1+NIR+(6 ×Red) – (7.5 × Blue) 11
EVI2 2.5 × (NIR – Red) ÷ (NIR+(2.4 ×Red)+1) 12
OSAVI (NIR – Red) ÷ (NIR+Red)+0.16) 13
MCARI2 (1.5 ×[2.5 × (NIR – Red)] – [1.3 ×(NIR – Green)]) ÷ SQRT[((2 × NIR)+1)2 – ((6 ×NIR) – (5 × (SQRT(Red)))) – 0.5)] 4
LAI (3.618 × EVI) – 0.118 22

Sensors with RGB, red edge and near-infrared (NIR) or shortwave infrared (SWIR)

Name Algorithm Reference
GVI (Landsat-7 and Landsat-8 only) (–0.2848*TM1) + (–0.2435*TM2) + (–0.5436*TM3) + (0.7243*TM4) + (0.0840*TM5) + (–1.1800*TM7) 6
MCARI [(RedEdge – Red) – 0.2 × (RedEdge - Green)] × (RedEdge ÷ Red) 3
AFRI16 [(NIR – (0.66 × SWIR1.6)) ÷ (NIR + (0.66 × SWIR1.6)); 14
AFRI21 [(NIR – (0.5 × SWIR2.1)) ÷ (NIR + (0.5 × SWIR2.1)); 14
RENDVI (also known as NDRE) (RedEdge750mm – NIR)) ÷ (RedEdge750mm+NIR); 15
MRENDVI (RedEdge750mm – NIR)) ÷ (RedEdge750mm+NIR – (2 × Blue); 16/17
TCARI 3 × (RedEdge700mm – Red) – (0.2 × (RedEdge700mm – Green) × (RedEdge700mm ÷ Red) 18
NMDI (NIR860mm – (SWIR1.6 – SWIR2.1)) ÷ (NIR860mm + (SWIR1.6 –SWIR2.1)) 19/20
CIRedEdge (RedEdge780mm ÷ RedEdge705mm) – 1 21
NDNI ((log(1 ÷ SWIR) – log(1 ÷ SWIR1)) ÷ (log(1 ÷ SWIR) – log(1 ÷ SWIR1))) 23
PSRI ( (Red – Blue) ÷ RedEdge750mm 24
Back to top

References

  1. Bannari, A., H. Asalhi, and P. Teillet. "Transformed Difference Vegetation Index (TDVI) for Vegetation Cover Mapping" in Proceedings of the Geoscience and Remote Sensing Symposium, IGARSS '02, IEE International, Volume 5 (2002).
  2. Birth, G., and G. McVey. "Measuring the Color of Growing Turf with a Reflectance Spectrophotometer." Agronomy Journal 60 (1968): 640-643.
  3. Daughtry, C. et al. "Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance." Remote Sensing Environment 74 (2000): 229-239.
  4. Haboudane, D. et al. "Hyperspectral Vegetation Indices and Novel Algorithms for Predicting Green LAI of Crop Canopies: Modeling and Validation in the Context of Precision Agriculture." Remote Sensing of Environment 90 (2004): 337-352.
  5. Huete, A. "A Soil-Adjusted Vegetation Index (SAVI)." Remote Sensing of Environment 25 (1988): 295-309.
  6. Kauth, R., and G. Thomas. "The Tasselled Cap - A Graphic Description of the Spectral-Temporal Development of Agricultural Crops as Seen by Landsat" in Proceedings of the LARS 1976 Symposium of Machine Processing of Remotely-Sensed Data, West Lafayette, IN: Purdue University, pp. 41-451.
  7. Pinty, B., and M. Verstraete. GEMI: A Non-Linear Index to Monitor Global Vegetation from Satellites. Vegetation 101 (1992): 15-20.
  8. Qi, J. et al, 1994, "A modified soil vegetation adjusted index." Remote Sensing of Environment, Vol. 48, No. 2, 119-126.
  9. 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.
  10. Tucker, C. "Red and Photographic Infrared Linear Combinations for Monitoring Vegetation." Remote Sensing of Environment 8 (1979): 12-150.
  11. Huete, A. et al. "Overview of the Radiometric and Biophysical Performance of the MODIS Vegetation Indices." Remote Sensing of Environment 83 (2002): 195-213.
  12. Jiang, Z., Huete, A. et al. "Development of a two-band enhanced vegetation index without blue band." Remote Sensing of Environment 112 (2008): 3833-3845.
  13. Rondeaux, G., M. Steven and F. Baret. "Optimization of Soil-Adjusted Vegetation Indices." Remote Sensing of Environment 55 (1996): 95-107.
  14. Karnieli, A. Kaufman, Y.J., Wald, A. (2001). AFRI-Aerosol free vegetation index. Remote Sensing of Environment 77 (2001): 10-21.
  15. Gitelson, A., and M. Merzlyak. "Spectral Reflectance Changes Associated with Autumn Senescence of Aesculus Hippocastanum L. and Acer Planoides L. Leaves." Journal of Plant Physiology 142 (1994): 286-292.
  16. Datt, B. "A New Reflectance Index for Remote Sensing of Chlorophyll Content in Higher Plants: Test Using Eucalyptus Leaves." Journal of Plant Physiology 154 (1999): 30-36.
  17. Sims. D., and J. Gamon. "Relationships Between Leafs Pigment Content and Spectral Reflectance Across a Wide Range of Species, Leaf Structures and Developmental Stages." Remote Sensing of Environment 81 (2002): 337-354.
  18. Haboudane, D. et al. "Hyperspectral Vegetation Indices and Novel Algorithms for Predicting Green LAI of Crop Canopies: Modeling and Validation in the Context of Precision Agriculture." Remote Sensing of Environment 90 (2004): 337-352.
  19. Wang, L., and J. Qu. "NMDI: A Normalized Multi-Band Drought Index for Monitoring Soil and Vegetation Moisture with Satellite Remote Sensing." Geophysical Research Letters 34 (2007): L20405.
  20. Wang, L., and J. Qu. "Forest Fire Detection using the Normalized Multi-band Drought Index (NMDI) with Satellite Measurements." Agricultural and Forest Meteorology 148 No. 11 (2008): 1767-1776.
  21. Clevers, J.G.P.W., Gitelson, Anatoly (2012). Using the red-edge bands on Sentinel-2 for retrieving canopy chlorophyll and nitrogen content. European Space Agency. (Special Publication) ESA. SP (2012): 707.
  22. Boegh,E H. Soegaard, N. Broge, C. Hasager, N. Jensen, K. Schelde, and A. Thomsen. (2002). Airborne Multi-spectral Data for Quantifying Leaf Area Index, Nitrogen Concentration and Photosynthetic Efficiency in Agriculture. Remote Sensing of Environment 81, no. 2-3 (2002).
  23. Serrano, L., J. Penuelas, and S. Ustin. "Remote Sensing of Nitrogen and Lignin in Mediterranean Vegetation from AVIRIS Data: Decomposing Biochemical from Structural Signals." Remote Sensing of Environment 81 (2002):355-364.
  24. Ren, Shilong and Chen, Xiaoqiu and An, Shuai. (2016). Assessing plant senescence reflectance index-retrieved vegetation phenology and its spatiotemporal response to climate change in the Inner Mongolian Grassland. International Journal of Biometeorology 61. 10.1007/s00484-016-1236-6.

© PCI Geomatics Enterprises, Inc.®, 2026. All rights reserved.