i.vi(1grass) GRASS GIS User's Manual i.vi(1grass)
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=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, 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.
• 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
• NDWI: Normalized Difference Water 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 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:
• 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)
• 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)
Accessed: unknown
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