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i.vi(1)			    GRASS GIS User's Manual		       i.vi(1)

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, NDVI

SYNOPSIS
       i.vi
       i.vi --help
       i.vi output=name	viname=type   [red=name]    [nir=name]	  [green=name]
       [blue=name]     [band5=name]    [band7=name]    [soil_line_slope=float]
       [soil_line_intercept=float]     [soil_noise_reduction=float]	[stor-
       age_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:
       output=nameA [required]
	   Name	for output raster map

       viname=typeA [required]
	   Type	of vegetation index
	   Options: arvi, dvi,	evi,  evi2,  gvi,  gari,  gemi,	 ipvi,	msavi,
	   msavi2, ndvi, ndwi, pvi, savi, sr, vari, wdvi
	   Default: ndvi
	   arvi: Atmospherically Resistant Vegetation Index
	   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
	   ndwi: Normalized Difference Water Index
	   pvi:	Perpendicular Vegetation Index
	   savi: Soil Adjusted Vegetation Index
	   sr: Simple Ratio
	   vari: Visible Atmospherically Resistant Index
	   wdvi: Weighted Difference Vegetation	Index

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

       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 (MSAVI only)

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

       soil_noise_reduction=float
	   Value of the	factor of reduction of soil noise (MSAVI 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.

	   o   ARVI: atmospherically resistant vegetation indices

	   o   DVI: Difference Vegetation Index

	   o   EVI: Enhanced Vegetation	Index

	   o   EVI2: Enhanced Vegetation Index 2

	   o   GARI: Green atmospherically resistant vegetation	index

	   o   GEMI: Global Environmental Monitoring Index

	   o   GVI: Green Vegetation Index

	   o   IPVI: Infrared Percentage Vegetation Index

	   o   MSAVI2: second Modified Soil Adjusted Vegetation	Index

	   o   MSAVI: Modified Soil Adjusted Vegetation	Index

	   o   NDVI: Normalized	Difference Vegetation Index

	   o   NDWI: Normalized	Difference Water Index

	   o   PVI: Perpendicular Vegetation Index

	   o   RVI: ratio vegetation index

	   o   SAVI: Soil Adjusted Vegetation Index

	   o   SR: Simple Vegetation ratio

	   o   WDVI: Weighted Difference Vegetation Index

   Background for users	new to remote sensing
       Vegetation Indices are often considered the entry point of remote sens-
       ing  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  sat-
       ellite  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	trans-
       form   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  en-
       ergy 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  correc-
       tion has	been applied to	the satellite data, data vales are called sur-
       face reflectance.  Surface reflectance is ranging from 0.0 to 1.0 theo-
       retically (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  in-
       significant and data quality is good (Zhangyan Jiang ; Alfredo R. Huete
       ; Youngwook Kim and Kamel Didan 2-band enhanced vegetation index	 with-
       out a blue band and its application to AVHRR data. Proc.	SPIE 6679, Re-
       mote 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 )
       Satellite specific band numbers ([NIR, Red]):
	 MSS Bands	  = [ 7,  5]
	 TM1-5,7 Bands	  = [ 4,  3]
	 TM8 Bands	  = [ 5,  4]
	 Sentinel-2 Bands = [ 8,  4]
	 AVHRR Bands	  = [ 2,  1]
	 SPOT XS Bands	  = [ 3,  2]
	 AVIRIS	Bands	  = [51, 29]
       NDVI = (NIR - Red) / (NIR + Red)

       NDWI: Normalized	Difference Water Index (after McFeeters, 1996)

       This index is suitable to detect	water bodies.
       ndwi( greenchan,	nirchan	)
       NDWI = (green - NIR) / (green + NIR)

       The water content of leaves can be estimated with another  NDWI	(after
       Gao, 1996):
       ndwi( greenchan,	nirchan	)
       NDWI = (NIR - SWIR) / (NIR + SWIR)
       This  index  is	important for monitoring vegetation health (not	imple-
       mented).

       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 par-
       allel 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 in-
       troduce an atmospheric self-correction (Gitelson	 A.A.,	Kaufman	 Y.J.,
       Stark  R., Rundquist D.,	2002. Novel algorithms for estimation of vege-
       tation 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
   Calculation of DVI
       The calculation of DVI from the reflectance values is done as follows:
       g.region	raster=band.1 -p
       i.vi blue=band.1	red=band.3 nir=band.4 viname=dvi output=dvi
       r.univar	-e dvi

   Calculation of EVI
       The calculation of EVI from the reflectance values is done as follows:
       g.region	raster=band.1 -p
       i.vi blue=band.1	red=band.3 nir=band.4 viname=evi output=evi
       r.univar	-e evi

   Calculation of EVI2
       The calculation of EVI2 from the	reflectance values is done as follows:
       g.region	raster=band.3 -p
       i.vi red=band.3 nir=band.4 viname=evi2 output=evi2
       r.univar	-e evi2

   Calculation of GARI
       The calculation of GARI from the	reflectance values is done as follows:
       g.region	raster=band.1 -p
       i.vi blue=band.1	green=band.2 red=band.3	nir=band.4 viname=gari output=gari
       r.univar	-e gari

   Calculation of GEMI
       The calculation of GEMI from the	reflectance values is done as follows:
       g.region	raster=band.3 -p
       i.vi red=band.3 nir=band.4 viname=gemi output=gemi
       r.univar	-e gemi

   Calculation of GVI
       The calculation of GVI (Green Vegetation	Index -	Tasseled Cap) from the
       reflectance values is done as follows:
       g.region	raster=band.3 -p
       # assuming Landsat-7
       i.vi blue=band.1	green=band.2 red=band.3	nir=band.4 band5=band.5	band7=band.7 viname=gvi	output=gvi
       r.univar	-e gvi

   Calculation of IPVI
       The calculation of IPVI from the	reflectance values is done as follows:
       g.region	raster=band.3 -p
       i.vi red=band.3 nir=band.4 viname=ipvi output=ipvi
       r.univar	-e ipvi

   Calculation of MSAVI
       The  calculation	 of  MSAVI from	the reflectance	values is done as fol-
       lows:
       g.region	raster=band.3 -p
       i.vi red=band.3 nir=band.4 viname=msavi output=msavi
       r.univar	-e msavi

   Calculation of NDVI
       The calculation of NDVI from the	reflectance values is done as follows:
       g.region	raster=band.3 -p
       i.vi red=band.3 nir=band.4 viname=ndvi output=ndvi
       r.univar	-e ndvi

   Calculation of NDWI
       The calculation of NDWI from the	reflectance values is done as follows:
       g.region	raster=band.2 -p
       i.vi green=band.2 nir=band.4 viname=ndwi	output=ndwi
       r.colors	ndwi color=byg -n
       r.univar	-e ndwi

   Calculation of PVI
       The calculation of PVI from the reflectance values is done as follows:
       g.region	raster=band.3 -p
       i.vi red=band.3 nir=band.4 viname=pvi output=pvi
       r.univar	-e pvi

   Calculation of SAVI
       The calculation of SAVI from the	reflectance values is done as follows:
       g.region	raster=band.3 -p
       i.vi red=band.3 nir=band.4 viname=savi output=savi
       r.univar	-e savi

   Calculation of SR
       The calculation of SR from the reflectance values is done as follows:
       g.region	raster=band.3 -p
       i.vi red=band.3 nir=band.4 viname=sr output=sr
       r.univar	-e sr

   Calculation of VARI
       The calculation of VARI from the	reflectance values is done as follows:
       g.region	raster=band.3 -p
       i.vi blue=band.2	green=band.3 red=band.4	viname=vari output=vari
       r.univar	-e vari

   Landsat TM7 example
       The following examples are 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.cal-
       tech.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:

	   o   Bastiaanssen,  W.G.M.,  1995.  Regionalization  of surface flux
	       densities and moisture indicators in composite terrain;	a  re-
	       mote  sensing  approach under clear skies in mediterranean cli-
	       mates. PhD thesis, Wageningen Agricultural Univ.,  The  Nether-
	       land, 271 pp.  (PDF)

	   o   Index DataBase: List of available Indices

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

SOURCE CODE
       Available at: i.vi source code (history)

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

       A(C) 2003-2020 GRASS Development	Team, GRASS GIS	7.8.4 Reference	Manual

GRASS 7.8.4							       i.vi(1)

NAME | KEYWORDS | SYNOPSIS | DESCRIPTION | EXAMPLES | NOTES | SEE ALSO | REFERENCES | AUTHORS | SOURCE CODE

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