VEGINDEX

Generate vegetation indices


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Description


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


vegindex(fili, visirchn, filo, dboc, index, datatype, scaloffs, scalfact, ftype, foptions)

Name Type Caption Length Value range
FILI* str Input image or images for calculation 1 -    
VISIRCHN List[int] Database input channel list 0 - 10 -10000 -
FILO* str Name of the output file 1 -    
DBOC List[int] Generated vegetation indices 0 -    
INDEX* str Vegetation index 2 - 192  
DATATYPE str Output raster type 0 - 3 8U | 16S | 16U | 32R
Default: 32R
SCALOFFS List[float] Scaling offset 0 - 2  
SCALFACT List[float] Scaling factor 0 - 2  
FTYPE str Output file type 0 - 4 Default: PIX
FOPTIONS str Output file options 0 -    

* Required parameter
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Parameter descriptions

FILI

The path and file name of the input imagery to use in the index calculation. The file you specify must be a GDB-supported format.

VISIRCHN

The list of input channels that contain the blue, green, red, red-edge, near-infrared (NIR), and shortwave-infrared (SWIR) bands. This information must be specified correctly to ensure proper computation of the masks.

You specify this parameter as follows:

<Blue>,<Green>,<Red>,<Red edge(705nm)>,<NIR(830nm)>,<SWIR(1610nm)>,<SWIR(2190nm)>,<NIR(860nm)>,<Red edge(740nm)>,<Red edge(783nm)>

If you do not specify a value for this parameter, VEGINDEX checks for the MinWavelength, MaxWavelength, and WavelengthUnits metadata tags at the channel level to properly associate each channel with the correct band.

If the metadata tags are missing for any of the required channels, and no value is specified for VISIRCHN, processing stops, and an error message is displayed.

For example, if using SPOT-5 dataset, and channel 1 contains the NIR band, channel 2 contains the red band, channel 3 contains the green band, and channel 4 contains the SWIR band, the value of VISIRCHN must be:
VISIRCHN = 0,3,2,0,1,0,4,0,0,0

If a required band is missing, you must specify a 0 (zero) for the position of the missing band in the list of input channels.

If using WorldView-2, and the file is set up as a standard WV-2 dataset, the value of VISIRCHN must be:

VISIRCHN = 2,3,5,6,7,8,0,0,0,0
Note: The <swir> and <swir1> bands are used specifically to calculate the Green Vegetation Index (GVI), which is intended for use with only Landsat-7 and Landsat-8 data.

This parameter is optional.

FILO

The name of the output file to which to write the processed data.

If the output file does not exist, one is created based on the type specified for FTYPE and the options specified for FOPTIONS. The file will have the number of channels required and have the same geocoding information and metadata as the input file.

If the output file is an existing file, it must be in a format that can be updated. The file must also contain channels suitable for writing the results, as specified by the value of DBOC.

When you specify an existing file, you need not specify a value for FTYPE and FOPTIONS unless the values of these parameters correspond to the type and options of the file; otherwise, an error may occur.

This parameter is mandatory.

DBOC

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

If the output file is an existing file, you must specify the existing channels to update as the value of this parameter. If the output file and the input file are the same, and the output channels equate to the input channels, the channels are modified in place. The output raster channels must be a depth of 32 bits.

This parameter is optional.

INDEX

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, specify ALL.

The available options are as follows:

DATATYPE

The data type of the output channel to create.

When the output is written to existing channels, this parameter is ignored.

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.

SCALOFFS

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.

SCALFACT

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.

FTYPE

The format of the output file. The format must be a GDB-recognized type.

Supported file formats include, among others:

The default value is PIX.

For a complete list of GDB-recognized file types, see GDB-supported file formats.

FOPTIONS

The file-creation options to apply on creating the output file. These are specific to the format of the file; in each case, the default of no options is allowed. You can specify the compression schemes, file-format subtypes, and other information.

Different options are available for each type, as described for the FTYPE parameter.

For a complete list of GDB-recognized file types, including the available options for each, see GDB-supported file formats.

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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
  • Deimos-1
  • Deimos-2
  • DMC
  • 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
  • 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
  • ZY3
  • ZY3-2
Note: When specifying a sensor, use the exact syntax, as shown in the preceding list.
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Example

The following example calculates all indices for a Landsat-7 image. Because the file has no available metadata, you must enter the channel information.

The number of output indices will be 11, based on the index to calculate being ALL. MCARI indices will not be created, because the required RedEdge band is not of Landsat-8 band structure.

from pci.vegindex import vegindex 

fili	=	"l7_ms_atcor.pix"
visirchn=       [1,2,3,0,4,5,6,0,0,0] # No RedEdge band
filo	=	"vegindex.pix"	# New file to create
dboc	=	[]		# Create 32-bit channels
index	=	"ALL"		# All indices possible for this sensor 
datatype=       '32R'
scaloffs =       []              # Default no scaling
scalfact   =     []              # Default no scaling

ftype	  =	""
foptions  =	""

vegindex(fili, visirchn, filo, dboc, index, datatype, scaloffs, scalfact, ftype, foptions)

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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
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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.

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