Provided by: grass-doc_7.0.3-1build1_all bug

NAME

       i.vi  - Calculates different types of vegetation indices.
       Uses red and nir bands mostly, and some indices require additional bands.

KEYWORDS

       imagery, vegetation index, biophysical parameters

SYNOPSIS

       i.vi
       i.vi --help
       i.vi   red=name   output=name   viname=type    [nir=name]     [green=name]     [blue=name]
       [band5=name]     [band7=name]     [soil_line_slope=float]      [soil_line_intercept=float]
       [soil_noise_reduction=float]       [storage_bit=integer]       [--overwrite]      [--help]
       [--verbose]  [--quiet]  [--ui]

   Flags:
       --overwrite
           Allow output files to overwrite existing files

       --help
           Print usage summary

       --verbose
           Verbose module output

       --quiet
           Quiet module output

       --ui
           Force launching GUI dialog

   Parameters:
       red=name [required]
           Name of input red channel surface reflectance map
           Range: [0.0;1.0]

       output=name [required]
           Name for output raster map

       viname=type [required]
           Type of vegetation index
           Options: arvi, dvi, evi, evi2, gvi, gari, gemi, ipvi, msavi, msavi2, ndvi, pvi,  savi,
           sr, vari, wdvi
           Default: ndvi
           arvi: Atmospherically Resistant Vegetation Indices
           dvi: Difference Vegetation Index
           evi: Enhanced Vegetation Index
           evi2: Enhanced Vegetation Index 2
           gvi: Green Vegetation Index
           gari: Green Atmospherically Resistant Vegetation Index
           gemi: Global Environmental Monitoring Index
           ipvi: Infrared Percentage Vegetation Index
           msavi: Modified Soil Adjusted Vegetation Index
           msavi2: second Modified Soil Adjusted Vegetation Index
           ndvi: Normalized Difference Vegetation Index
           pvi: Perpendicular Vegetation Index
           savi: Soil Adjusted Vegetation Index
           sr: Simple Ratio
           vari: Visible Atmospherically Resistant Index
           wdvi: Weighted Difference Vegetation Index

       nir=name
           Name of input nir channel surface reflectance map
           Range: [0.0;1.0]

       green=name
           Name of input green channel surface reflectance map
           Range: [0.0;1.0]

       blue=name
           Name of input blue channel surface reflectance map
           Range: [0.0;1.0]

       band5=name
           Name of input 5th channel surface reflectance map
           Range: [0.0;1.0]

       band7=name
           Name of input 7th channel surface reflectance map
           Range: [0.0;1.0]

       soil_line_slope=float
           Value of the slope of the soil line (MSAVI2 only)

       soil_line_intercept=float
           Value of the intercept of the soil line (MSAVI2 only)

       soil_noise_reduction=float
           Value of the factor of reduction of soil noise (MSAVI2 only)

       storage_bit=integer
           Maximum bits for digital numbers
           If  data  is  in  Digital  Numbers  (i.e. integer type), give the max bits (i.e. 8 for
           Landsat -> [0-255])
           Options: 7, 8, 10, 16
           Default: 8

DESCRIPTION

       i.vi calculates vegetation indices based on biophysical parameters.

           •   ARVI: atmospherically resistant vegetation indices

           •   DVI: Difference Vegetation Index

           •   EVI: Enhanced Vegetation Index

           •   EVI2: Enhanced Vegetation Index 2

           •   GARI: Green atmospherically resistant vegetation index

           •   GEMI: Global Environmental Monitoring Index

           •   GVI: Green Vegetation Index

           •   IPVI: Infrared Percentage Vegetation Index

           •   MSAVI2: second Modified Soil Adjusted Vegetation Index

           •   MSAVI: Modified Soil Adjusted Vegetation Index

           •   NDVI: Normalized Difference Vegetation Index

           •   PVI: Perpendicular Vegetation Index

           •   RVI: ratio vegetation index

           •   SAVI: Soil Adjusted Vegetation Index

           •   SR: Simple Vegetation ratio

           •   WDVI: Weighted Difference Vegetation Index

   Background for users new to remote sensing
       Vegetation Indices are often considered the entry point of remote sensing for  Earth  land
       monitoring.  They  are  suffering  from  their success, in terms that often people tend to
       harvest satellite images from online sources and use them directly in this module.

       From Digital number to Radiance:
       Satellite imagery is commonly stored in Digital Number (DN) for  storage  purposes;  e.g.,
       Landsat5  data  is  stored  in 8bit values (ranging from 0 to 255), other satellites maybe
       stored in 10 or 16 bits. If the data is provided in DN, this implies that this imagery  is
       "uncorrected".  What  this  means  is  that  the  image  is what the satellite sees at its
       position and altitude in space (stored in DN).  This is not the signal at ground  yet.  We
       call  this data at-satellite or at-sensor. Encoded in the 8bits (or more) is the amount of
       energy sensed by  the  sensor  inside  the  satellite  platform.  This  energy  is  called
       radiance-at-sensor.  Generally,  satellites  image providers encode the radiance-at-sensor
       into 8bit (or more) through an affine  transform  equation  (y=ax+b).  In  case  of  using
       Landsat  imagery,  look  at  the  i.landsat.toar  for  an  easy  way  to  transform  DN to
       radiance-at-sensor. If using Aster data, try the i.aster.toar module.

       From Radiance to Reflectance:
       Finally, once having obtained the radiance at  sensor  values,  still  the  atmosphere  is
       between  sensor  and  Earth’s  surface. This fact needs to be corrected to account for the
       atmospheric interaction with the sun energy that the vegetation reflects back into  space.
       This  can  be  done in two ways for Landsat. The simple way is through i.landsat.toar, use
       e.g. the DOS correction. The more accurate way is by using i.atcorr (which works for  many
       satellite  sensors).  Once  the  atmospheric  correction has been applied to the satellite
       data, data vales are called surface reflectance.  Surface reflectance is ranging from  0.0
       to  1.0  theoretically (and absolutely). This level of data correction is the proper level
       of correction to use with i.vi.

   Vegetation Indices
       ARVI: Atmospheric Resistant Vegetation Index

       ARVI is resistant to atmospheric effects (in comparison to the NDVI) and  is  accomplished
       by  a  self  correcting  process  for the atmospheric effect in the red channel, using the
       difference in the radiance between the blue and the red channels (Kaufman and Tanre 1996).
       arvi( redchan, nirchan, bluechan )
       ARVI = (nirchan - (2.0*redchan - bluechan)) /
              ( nirchan + (2.0*redchan - bluechan))

       DVI: Difference Vegetation Index
       dvi( redchan, nirchan )
       DVI = ( nirchan - redchan )

       EVI: Enhanced Vegetation Index

       The enhanced vegetation index  (EVI)  is  an  optimized  index  designed  to  enhance  the
       vegetation  signal  with  improved  sensitivity  in  high  biomass  regions  and  improved
       vegetation monitoring through  a  de-coupling  of  the  canopy  background  signal  and  a
       reduction  in  atmosphere  influences  (Huete  A.R., Liu H.Q., Batchily K., van Leeuwen W.
       (1997). A comparison of vegetation indices global set of TM images for  EOS-MODIS.  Remote
       Sensing of Environment, 59:440-451).
       evi( bluechan, redchan, nirchan )
       EVI = 2.5 * ( nirchan - redchan ) /
             ( nirchan + 6.0 * redchan - 7.5 * bluechan + 1.0 )

       EVI2: Enhanced Vegetation Index 2

       A  2-band  EVI  (EVI2), without a blue band, which has the best similarity with the 3-band
       EVI, particularly when atmospheric effects are insignificant  and  data  quality  is  good
       (Zhangyan  Jiang  ;  Alfredo  R.  Huete  ;  Youngwook  Kim and Kamel Didan 2-band enhanced
       vegetation index without a blue band and its application to AVHRR data. Proc.  SPIE  6679,
       Remote Sensing and Modeling of Ecosystems for Sustainability IV, 667905 (october 09, 2007)
       doi:10.1117/12.734933).
       evi2( redchan, nirchan )
       EVI2 = 2.5 * ( nirchan - redchan ) /
              ( nirchan + 2.4 * redchan + 1.0 )

       GARI: green atmospherically resistant vegetation index

       The formula was actually defined: Gitelson, Anatoly A.; Kaufman, Yoram J.; Merzlyak,  Mark
       N.  (1996)  Use of a green channel in remote sensing of global vegetation from EOS- MODIS,
       Remote Sensing of Environment 58 (3), 289-298.  doi:10.1016/s0034-4257(96)00072-7
       gari( redchan, nirchan, bluechan, greenchan )
       GARI = ( nirchan - (greenchan - (bluechan - redchan))) /
              ( nirchan + (greenchan - (bluechan - redchan)))

       GEMI: Global Environmental Monitoring Index
       gemi( redchan, nirchan )
       GEMI = (( (2*((nirchan * nirchan)-(redchan * redchan)) +
              1.5*nirchan+0.5*redchan) / (nirchan + redchan + 0.5)) *
              (1 - 0.25 * (2*((nirchan * nirchan)-(redchan * redchan)) +
              1.5*nirchan+0.5*redchan) / (nirchan + redchan + 0.5))) -
              ( (redchan - 0.125) / (1 - redchan))

       GVI: Green Vegetation Index
       gvi( bluechan, greenchan, redchan, nirchan, chan5chan, chan7chan)
       GVI = ( -0.2848 * bluechan - 0.2435 * greenchan -
             0.5436 * redchan + 0.7243 * nirchan + 0.0840 * chan5chan-
             0.1800 * chan7chan)

       IPVI: Infrared Percentage Vegetation Index
       ipvi( redchan, nirchan )
       IPVI = nirchan/(nirchan+redchan)

       MSAVI2: second Modified Soil Adjusted Vegetation Index
       msavi2( redchan, nirchan )
       MSAVI2 = (1/2)*(2(NIR+1)-sqrt((2*NIR+1)^2-8(NIR-red)))

       MSAVI: Modified Soil Adjusted Vegetation Index
       msavi( redchan, nirchan )
       MSAVI = s(NIR-s*red-a) / (a*NIR+red-a*s+X*(1+s*s))
       where a is the soil line intercept, s is the soil line slope, and  X    is  an  adjustment
       factor which is set to minimize soil noise (0.08 in original papers).

       NDVI: Normalized Difference Vegetation Index
       ndvi( redchan, nirchan )
       Data Type Band Numbers ([NIR, Red])
         MSS Bands     = [ 7,  5]
         TM1-5,7 Bands = [ 4,  3]
         TM8 Bands     = [ 5,  4]
         AVHRR Bands   = [ 2,  1]
         SPOT XS Bands = [ 3,  2]
         AVIRIS Bands  = [51, 29]
       NDVI = (NIR - Red) / (NIR + Red)

       PVI: Perpendicular Vegetation Index
       pvi( redchan, nirchan )
       PVI = sin(a)NIR-cos(a)red
       for  a  isovegetation  lines (lines of equal vegetation) would all be parallel to the soil
       line therefore a=1.

       SAVI: Soil Adjusted Vegetation Index
       savi( redchan, nirchan )
       SAVI = ((1.0+0.5)*(nirchan - redchan)) / (nirchan + redchan +0.5)

       SR: Simple Vegetation ratio
       sr( redchan, nirchan )
       SR = (nirchan/redchan)

       VARI:  Visible  Atmospherically  Resistant  Index  VARI  was  designed  to  introduce   an
       atmospheric  self-correction  (Gitelson  A.A., Kaufman Y.J., Stark R., Rundquist D., 2002.
       Novel algorithms for estimation of vegetation fraction Remote Sensing of Environment (80),
       pp76-87.)
       vari = ( bluechan, greenchan, redchan )
       VARI = (green - red ) / (green + red - blue)

       WDVI: Weighted Difference Vegetation Index
       wdvi( redchan, nirchan, soil_line_weight )
       WDVI = nirchan - a * redchan
       if(soil_weight_line == None):
          a = 1.0   #slope of soil line

EXAMPLES

       This  example  is  based  on  a  LANDSAT  TM7  scene included in the North Carolina sample
       dataset.

   Preparation: DN to reflectance
       As a first step, the original DN (digital  number)  pixel  values  must  be  converted  to
       reflectance  using  i.landsat.toar.  To  do so, we make a copy (or rename the channels) to
       match i.landsat.toar’s input scheme:

       g.copy raster=lsat7_2002_10,lsat7_2002.1
       g.copy raster=lsat7_2002_20,lsat7_2002.2
       g.copy raster=lsat7_2002_30,lsat7_2002.3
       g.copy raster=lsat7_2002_40,lsat7_2002.4
       g.copy raster=lsat7_2002_50,lsat7_2002.5
       g.copy raster=lsat7_2002_61,lsat7_2002.61
       g.copy raster=lsat7_2002_62,lsat7_2002.62
       g.copy raster=lsat7_2002_70,lsat7_2002.7
       g.copy raster=lsat7_2002_80,lsat7_2002.8

       Calculation  of  reflectance  values  from  DN  using   DOS1   (metadata   obtained   from
       p016r035_7x20020524.met.gz):

       i.landsat.toar input=lsat7_2002. output=lsat7_2002_toar. sensor=tm7 \
         method=dos1 date=2002-05-24 sun_elevation=64.7730999 \
         product_date=2004-02-12 gain=HHHLHLHHL
       The resulting Landsat channels are names lsat7_2002_toar.1 .. lsat7_2002_toar.8.

   Calculation of NDVI
       The calculation of NDVI from the reflectance values is done as follows:
       g.region raster=lsat7_2002_toar.3 -p
       i.vi red=lsat7_2002_toar.3 nir=lsat7_2002_toar.4 viname=ndvi \
            output=lsat7_2002.ndvi
       r.colors lsat7_2002.ndvi color=ndvi
       d.mon wx0
       d.rast.leg lsat7_2002.ndvi
       North Carolina dataset: NDVI

   Calculation of ARVI
       The calculation of ARVI from the reflectance values is done as follows:
       g.region raster=lsat7_2002_toar.3 -p
       i.vi blue=lsat7_2002_toar.1 red=lsat7_2002_toar.3 nir=lsat7_2002_toar.4 \
            viname=arvi output=lsat7_2002.arvi
       d.mon wx0
       d.rast.leg lsat7_2002.arvi
       North Carolina dataset: ARVI

   Calculation of GARI
       The calculation of GARI from the reflectance values is done as follows:
       g.region raster=lsat7_2002_toar.3 -p
       i.vi blue=lsat7_2002_toar.1 green=lsat7_2002_toar.2 red=lsat7_2002_toar.3 \
            nir=lsat7_2002_toar.4 viname=gari output=lsat7_2002.gari
       d.mon wx0
       d.rast.leg lsat7_2002.gari
       North Carolina dataset: GARI

NOTES

       Originally from kepler.gps.caltech.edu (FAQ):

       A FAQ on Vegetation in Remote Sensing
       Written by Terrill W. Ray, Div. of Geological and Planetary Sciences, California Institute
       of Technology, email: terrill@mars1.gps.caltech.edu

       Snail Mail:  Terrill Ray
       Division of Geological and Planetary Sciences
       Caltech, Mail Code 170-25
       Pasadena, CA  91125

SEE ALSO

        i.albedo, i.aster.toar, i.landsat.toar, i.atcorr, i.tasscap

REFERENCES

       AVHRR, Landsat TM5:

           •   Bastiaanssen, W.G.M., 1995. Regionalization of surface flux densities and moisture
               indicators  in  composite  terrain; a remote sensing approach under clear skies in
               mediterranean climates. PhD thesis, Wageningen Agricultural Univ., The Netherland,
               271 pp.  (PDF)

           •   Index DataBase: List of available Indices

AUTHORS

       Baburao Kamble, Asian Institute of Technology, Thailand
       Yann Chemin, Asian Institute of Technology, Thailand

       Last changed: $Date: 2015-12-30 14:01:52 +0100 (Wed, 30 Dec 2015) $

       Main index | Imagery index | Topics index | Keywords index | Full index

       © 2003-2016 GRASS Development Team, GRASS GIS 7.0.3 Reference Manual