1i.vi(1) GRASS GIS User's Manual i.vi(1)
2
6 i.vi - Calculates different types of vegetation indices.
7 Uses red and nir bands mostly, and some indices require additional
8 bands.
9
11 imagery, vegetation index, biophysical parameters, NDVI
12
14 i.vi
15 i.vi --help
16 i.vi output=name viname=type [red=name] [nir=name] [green=name]
17 [blue=name] [band5=name] [band7=name] [soil_line_slope=float]
18 [soil_line_intercept=float] [soil_noise_reduction=float] [stor‐
19 age_bit=integer] [--overwrite] [--help] [--verbose] [--quiet]
20 [--ui]
21 Flags:
23 --overwrite
24 Allow output files to overwrite existing files
25 --help
27 Print usage summary
28 --verbose
30 Verbose module output
31 --quiet
33 Quiet module output
34 --ui
36 Force launching GUI dialog
37 Parameters:
39 output=name [required]
40 Name for output raster map
41 viname=type [required]
43 Type of vegetation index
44 Options: arvi, ci, dvi, evi, evi2, gvi, gari, gemi, ipvi, msavi,
45 msavi2, ndvi, ndwi, pvi, savi, sr, vari, wdvi
46 Default: ndvi
47 arvi: Atmospherically Resistant Vegetation Index
48 ci: Crust Index
49 dvi: Difference Vegetation Index
50 evi: Enhanced Vegetation Index
51 evi2: Enhanced Vegetation Index 2
52 gvi: Green Vegetation Index
53 gari: Green Atmospherically Resistant Vegetation Index
54 gemi: Global Environmental Monitoring Index
55 ipvi: Infrared Percentage Vegetation Index
56 msavi: Modified Soil Adjusted Vegetation Index
57 msavi2: second Modified Soil Adjusted Vegetation Index
58 ndvi: Normalized Difference Vegetation Index
59 ndwi: Normalized Difference Water Index
60 pvi: Perpendicular Vegetation Index
61 savi: Soil Adjusted Vegetation Index
62 sr: Simple Ratio
63 vari: Visible Atmospherically Resistant Index
64 wdvi: Weighted Difference Vegetation Index
65 red=name
67 Name of input red channel surface reflectance map
68 Range: [0.0;1.0]
69 nir=name
71 Name of input nir channel surface reflectance map
72 Range: [0.0;1.0]
73 green=name
75 Name of input green channel surface reflectance map
76 Range: [0.0;1.0]
77 blue=name
79 Name of input blue channel surface reflectance map
80 Range: [0.0;1.0]
81 band5=name
83 Name of input 5th channel surface reflectance map
84 Range: [0.0;1.0]
85 band7=name
87 Name of input 7th channel surface reflectance map
88 Range: [0.0;1.0]
89 soil_line_slope=float
91 Value of the slope of the soil line (MSAVI only)
92 soil_line_intercept=float
94 Value of the intercept of the soil line (MSAVI only)
95 soil_noise_reduction=float
97 Value of the factor of reduction of soil noise (MSAVI only)
98 storage_bit=integer
100 Maximum bits for digital numbers
101 If data is in Digital Numbers (i.e. integer type), give the max
102 bits (i.e. 8 for Landsat -> [0-255])
103 Options: 7, 8, 10, 16
104 Default: 8
105
107 i.vi calculates vegetation indices based on biophysical parameters.
108 • ARVI: Atmospherically Resistant Vegetation Index
110 • CI: Crust Index
112 • DVI: Difference Vegetation Index
114 • EVI: Enhanced Vegetation Index
116 • EVI2: Enhanced Vegetation Index 2
118 • GARI: Green atmospherically resistant vegetation index
120 • GEMI: Global Environmental Monitoring Index
122 • GVI: Green Vegetation Index
124 • IPVI: Infrared Percentage Vegetation Index
126 • MSAVI2: second Modified Soil Adjusted Vegetation Index
128 • MSAVI: Modified Soil Adjusted Vegetation Index
130 • NDVI: Normalized Difference Vegetation Index
132 • NDWI: Normalized Difference Water Index
134 • PVI: Perpendicular Vegetation Index
136 • RVI: ratio vegetation index
138 • SAVI: Soil Adjusted Vegetation Index
140 • SR: Simple Vegetation ratio
142 • WDVI: Weighted Difference Vegetation Index
144 Background for users new to remote sensing
146 Vegetation Indices are often considered the entry point of remote sens‐
147 ing for Earth land monitoring. They are suffering from their success,
148 in terms that often people tend to harvest satellite images from online
149 sources and use them directly in this module.
150 From Digital number to Radiance:
152 Satellite imagery is commonly stored in Digital Number (DN) for storage
153 purposes; e.g., Landsat5 data is stored in 8bit values (ranging from 0
154 to 255), other satellites maybe stored in 10 or 16 bits. If the data is
155 provided in DN, this implies that this imagery is "uncorrected". What
156 this means is that the image is what the satellite sees at its position
157 and altitude in space (stored in DN). This is not the signal at ground
158 yet. We call this data at-satellite or at-sensor. Encoded in the 8bits
159 (or more) is the amount of energy sensed by the sensor inside the sat‐
160 ellite platform. This energy is called radiance-at-sensor. Generally,
161 satellites image providers encode the radiance-at-sensor into 8bit (or
162 more) through an affine transform equation (y=ax+b). In case of using
163 Landsat imagery, look at the i.landsat.toar for an easy way to trans‐
164 form DN to radiance-at-sensor. If using Aster data, try the
165 i.aster.toar module.
166 From Radiance to Reflectance:
168 Finally, once having obtained the radiance at sensor values, still the
169 atmosphere is between sensor and Earth’s surface. This fact needs to be
170 corrected to account for the atmospheric interaction with the sun en‐
171 ergy that the vegetation reflects back into space. This can be done in
172 two ways for Landsat. The simple way is through i.landsat.toar, use
173 e.g. the DOS correction. The more accurate way is by using i.atcorr
174 (which works for many satellite sensors). Once the atmospheric correc‐
175 tion has been applied to the satellite data, data vales are called sur‐
176 face reflectance. Surface reflectance is ranging from 0.0 to 1.0 theo‐
177 retically (and absolutely). This level of data correction is the proper
178 level of correction to use with i.vi.
179 Vegetation Indices
181 ARVI: Atmospheric Resistant Vegetation Index
182 ARVI is resistant to atmospheric effects (in comparison to the NDVI)
184 and is accomplished by a self correcting process for the atmospheric
185 effect in the red channel, using the difference in the radiance between
186 the blue and the red channels (Kaufman and Tanre 1996).
187 arvi( redchan, nirchan, bluechan )
188 ARVI = (nirchan - (2.0*redchan - bluechan)) /
189 ( nirchan + (2.0*redchan - bluechan))
190 CI: Crust Index
191 Advantage is taken of a unique spectral feature of soil biogenic crust
193 containing cyanobacteria. It has been shown that the special phycobilin
194 pigment in cyanobacteria contributes in producing a relatively higher
195 reflectance in the blue spectral region than the same type of substrate
196 without the biogenic crust. The spectral crust index (CI) is based on
197 the normalized difference between the RED and the BLUE spectral values
198 (Karnieli, 1997, DOI: 10.1080/014311697218368).
199 ci ( bluechan, redchan )
200 CI = 1 - (redchan - bluechan) /
201 (redchan + bluechan)
202 DVI: Difference Vegetation Index
204 dvi( redchan, nirchan )
205 DVI = ( nirchan - redchan )
206 EVI: Enhanced Vegetation Index
208 The enhanced vegetation index (EVI) is an optimized index designed to
210 enhance the vegetation signal with improved sensitivity in high biomass
211 regions and improved vegetation monitoring through a de-coupling of the
212 canopy background signal and a reduction in atmosphere influences
213 (Huete A.R., Liu H.Q., Batchily K., van Leeuwen W. (1997). A comparison
214 of vegetation indices global set of TM images for EOS-MODIS. Remote
215 Sensing of Environment, 59:440-451).
216 evi( bluechan, redchan, nirchan )
217 EVI = 2.5 * ( nirchan - redchan ) /
218 ( nirchan + 6.0 * redchan - 7.5 * bluechan + 1.0 )
219 EVI2: Enhanced Vegetation Index 2
221 A 2-band EVI (EVI2), without a blue band, which has the best similarity
223 with the 3-band EVI, particularly when atmospheric effects are in‐
224 significant and data quality is good (Zhangyan Jiang ; Alfredo R. Huete
225 ; Youngwook Kim and Kamel Didan 2-band enhanced vegetation index with‐
226 out a blue band and its application to AVHRR data. Proc. SPIE 6679, Re‐
227 mote Sensing and Modeling of Ecosystems for Sustainability IV, 667905
228 (october 09, 2007) doi:10.1117/12.734933).
229 evi2( redchan, nirchan )
230 EVI2 = 2.5 * ( nirchan - redchan ) /
231 ( nirchan + 2.4 * redchan + 1.0 )
232 GARI: green atmospherically resistant vegetation index
234 The formula was actually defined: Gitelson, Anatoly A.; Kaufman, Yoram
236 J.; Merzlyak, Mark N. (1996) Use of a green channel in remote sensing
237 of global vegetation from EOS- MODIS, Remote Sensing of Environment 58
238 (3), 289-298. doi:10.1016/s0034-4257(96)00072-7
239 gari( redchan, nirchan, bluechan, greenchan )
240 GARI = ( nirchan - (greenchan - (bluechan - redchan))) /
241 ( nirchan + (greenchan - (bluechan - redchan)))
242 GEMI: Global Environmental Monitoring Index
244 gemi( redchan, nirchan )
245 GEMI = (( (2*((nirchan * nirchan)-(redchan * redchan)) +
246 1.5*nirchan+0.5*redchan) / (nirchan + redchan + 0.5)) *
247 (1 - 0.25 * (2*((nirchan * nirchan)-(redchan * redchan)) +
248 1.5*nirchan+0.5*redchan) / (nirchan + redchan + 0.5))) -
249 ( (redchan - 0.125) / (1 - redchan))
250 GVI: Green Vegetation Index
252 gvi( bluechan, greenchan, redchan, nirchan, chan5chan, chan7chan)
253 GVI = ( -0.2848 * bluechan - 0.2435 * greenchan -
254 0.5436 * redchan + 0.7243 * nirchan + 0.0840 * chan5chan-
255 0.1800 * chan7chan)
256 IPVI: Infrared Percentage Vegetation Index
258 ipvi( redchan, nirchan )
259 IPVI = nirchan/(nirchan+redchan)
260 MSAVI2: second Modified Soil Adjusted Vegetation Index
262 msavi2( redchan, nirchan )
263 MSAVI2 = (1/2)*(2*NIR+1-sqrt((2*NIR+1)^2-8*(NIR-red)))
264 MSAVI: Modified Soil Adjusted Vegetation Index
266 msavi( redchan, nirchan )
267 MSAVI = s(NIR-s*red-a) / (a*NIR+red-a*s+X*(1+s*s))
268 where a is the soil line intercept, s is the soil line slope, and X
269 is an adjustment factor which is set to minimize soil noise (0.08 in
270 original papers).
271 NDVI: Normalized Difference Vegetation Index
273 ndvi( redchan, nirchan )
274 Satellite specific band numbers ([NIR, Red]):
275 MSS Bands = [ 7, 5]
276 TM1-5,7 Bands = [ 4, 3]
277 TM8 Bands = [ 5, 4]
278 Sentinel-2 Bands = [ 8, 4]
279 AVHRR Bands = [ 2, 1]
280 SPOT XS Bands = [ 3, 2]
281 AVIRIS Bands = [51, 29]
282 NDVI = (NIR - Red) / (NIR + Red)
283 NDWI: Normalized Difference Water Index (after McFeeters, 1996)
285 This index is suitable to detect water bodies.
287 ndwi( greenchan, nirchan )
288 NDWI = (green - NIR) / (green + NIR)
289 The water content of leaves can be estimated with another NDWI (after
291 Gao, 1996):
292 ndwi( greenchan, nirchan )
293 NDWI = (NIR - SWIR) / (NIR + SWIR)
294 This index is important for monitoring vegetation health (not imple‐
295 mented).
296 PVI: Perpendicular Vegetation Index
298 pvi( redchan, nirchan )
299 PVI = sin(a)NIR-cos(a)red
300 for a isovegetation lines (lines of equal vegetation) would all be par‐
301 allel to the soil line therefore a=1.
302 SAVI: Soil Adjusted Vegetation Index
304 savi( redchan, nirchan )
305 SAVI = ((1.0+0.5)*(nirchan - redchan)) / (nirchan + redchan +0.5)
306 SR: Simple Vegetation ratio
308 sr( redchan, nirchan )
309 SR = (nirchan/redchan)
310 VARI: Visible Atmospherically Resistant Index VARI was designed to in‐
312 troduce an atmospheric self-correction (Gitelson A.A., Kaufman Y.J.,
313 Stark R., Rundquist D., 2002. Novel algorithms for estimation of vege‐
314 tation fraction Remote Sensing of Environment (80), pp76-87.)
315 vari = ( bluechan, greenchan, redchan )
316 VARI = (green - red ) / (green + red - blue)
317 WDVI: Weighted Difference Vegetation Index
319 wdvi( redchan, nirchan, soil_line_weight )
320 WDVI = nirchan - a * redchan
321 if(soil_weight_line == None):
322 a = 1.0 #slope of soil line
323
325 Calculation of DVI
326 The calculation of DVI from the reflectance values is done as follows:
327 g.region raster=band.1 -p
328 i.vi blue=band.1 red=band.3 nir=band.4 viname=dvi output=dvi
329 r.univar -e dvi
330 Calculation of EVI
332 The calculation of EVI from the reflectance values is done as follows:
333 g.region raster=band.1 -p
334 i.vi blue=band.1 red=band.3 nir=band.4 viname=evi output=evi
335 r.univar -e evi
336 Calculation of EVI2
338 The calculation of EVI2 from the reflectance values is done as follows:
339 g.region raster=band.3 -p
340 i.vi red=band.3 nir=band.4 viname=evi2 output=evi2
341 r.univar -e evi2
342 Calculation of GARI
344 The calculation of GARI from the reflectance values is done as follows:
345 g.region raster=band.1 -p
346 i.vi blue=band.1 green=band.2 red=band.3 nir=band.4 viname=gari output=gari
347 r.univar -e gari
348 Calculation of GEMI
350 The calculation of GEMI from the reflectance values is done as follows:
351 g.region raster=band.3 -p
352 i.vi red=band.3 nir=band.4 viname=gemi output=gemi
353 r.univar -e gemi
354 Calculation of GVI
356 The calculation of GVI (Green Vegetation Index - Tasseled Cap) from the
357 reflectance values is done as follows:
358 g.region raster=band.3 -p
359 # assuming Landsat-7
360 i.vi blue=band.1 green=band.2 red=band.3 nir=band.4 band5=band.5 band7=band.7 viname=gvi output=gvi
361 r.univar -e gvi
362 Calculation of IPVI
364 The calculation of IPVI from the reflectance values is done as follows:
365 g.region raster=band.3 -p
366 i.vi red=band.3 nir=band.4 viname=ipvi output=ipvi
367 r.univar -e ipvi
368 Calculation of MSAVI
370 The calculation of MSAVI from the reflectance values is done as fol‐
371 lows:
372 g.region raster=band.3 -p
373 i.vi red=band.3 nir=band.4 viname=msavi output=msavi
374 r.univar -e msavi
375 Calculation of NDVI
377 The calculation of NDVI from the reflectance values is done as follows:
378 g.region raster=band.3 -p
379 i.vi red=band.3 nir=band.4 viname=ndvi output=ndvi
380 r.univar -e ndvi
381 Calculation of NDWI
383 The calculation of NDWI from the reflectance values is done as follows:
384 g.region raster=band.2 -p
385 i.vi green=band.2 nir=band.4 viname=ndwi output=ndwi
386 r.colors ndwi color=byg -n
387 r.univar -e ndwi
388 Calculation of PVI
390 The calculation of PVI from the reflectance values is done as follows:
391 g.region raster=band.3 -p
392 i.vi red=band.3 nir=band.4 viname=pvi output=pvi
393 r.univar -e pvi
394 Calculation of SAVI
396 The calculation of SAVI from the reflectance values is done as follows:
397 g.region raster=band.3 -p
398 i.vi red=band.3 nir=band.4 viname=savi output=savi
399 r.univar -e savi
400 Calculation of SR
402 The calculation of SR from the reflectance values is done as follows:
403 g.region raster=band.3 -p
404 i.vi red=band.3 nir=band.4 viname=sr output=sr
405 r.univar -e sr
406 Calculation of VARI
408 The calculation of VARI from the reflectance values is done as follows:
409 g.region raster=band.3 -p
410 i.vi blue=band.2 green=band.3 red=band.4 viname=vari output=vari
411 r.univar -e vari
412 Landsat TM7 example
414 The following examples are based on a LANDSAT TM7 scene included in the
415 North Carolina sample dataset.
416 Preparation: DN to reflectance
418 As a first step, the original DN (digital number) pixel values must be
419 converted to reflectance using i.landsat.toar. To do so, we make a copy
420 (or rename the channels) to match i.landsat.toar’s input scheme:
421 g.copy raster=lsat7_2002_10,lsat7_2002.1
423 g.copy raster=lsat7_2002_20,lsat7_2002.2
424 g.copy raster=lsat7_2002_30,lsat7_2002.3
425 g.copy raster=lsat7_2002_40,lsat7_2002.4
426 g.copy raster=lsat7_2002_50,lsat7_2002.5
427 g.copy raster=lsat7_2002_61,lsat7_2002.61
428 g.copy raster=lsat7_2002_62,lsat7_2002.62
429 g.copy raster=lsat7_2002_70,lsat7_2002.7
430 g.copy raster=lsat7_2002_80,lsat7_2002.8
431 Calculation of reflectance values from DN using DOS1 (metadata obtained
433 from p016r035_7x20020524.met.gz):
434 i.landsat.toar input=lsat7_2002. output=lsat7_2002_toar. sensor=tm7 \
436 method=dos1 date=2002-05-24 sun_elevation=64.7730999 \
437 product_date=2004-02-12 gain=HHHLHLHHL
438 The resulting Landsat channels are names lsat7_2002_toar.1 ..
439 lsat7_2002_toar.8.
440 Calculation of NDVI
442 The calculation of NDVI from the reflectance values is done as follows:
443 g.region raster=lsat7_2002_toar.3 -p
444 i.vi red=lsat7_2002_toar.3 nir=lsat7_2002_toar.4 viname=ndvi \
445 output=lsat7_2002.ndvi
446 r.colors lsat7_2002.ndvi color=ndvi
447 d.mon wx0
448 d.rast.leg lsat7_2002.ndvi
449 North Carolina dataset: NDVI
450 Calculation of ARVI
452 The calculation of ARVI from the reflectance values is done as follows:
453 g.region raster=lsat7_2002_toar.3 -p
454 i.vi blue=lsat7_2002_toar.1 red=lsat7_2002_toar.3 nir=lsat7_2002_toar.4 \
455 viname=arvi output=lsat7_2002.arvi
456 d.mon wx0
457 d.rast.leg lsat7_2002.arvi
458 North Carolina dataset: ARVI
459 Calculation of GARI
461 The calculation of GARI from the reflectance values is done as follows:
462 g.region raster=lsat7_2002_toar.3 -p
463 i.vi blue=lsat7_2002_toar.1 green=lsat7_2002_toar.2 red=lsat7_2002_toar.3 \
464 nir=lsat7_2002_toar.4 viname=gari output=lsat7_2002.gari
465 d.mon wx0
466 d.rast.leg lsat7_2002.gari
467 North Carolina dataset: GARI
468
470 Originally from kepler.gps.caltech.edu (FAQ):
471 A FAQ on Vegetation in Remote Sensing
473 Written by Terrill W. Ray, Div. of Geological and Planetary Sciences,
474 California Institute of Technology, email: terrill@mars1.gps.cal‐
475 tech.edu
476 Snail Mail: Terrill Ray
478 Division of Geological and Planetary Sciences
479 Caltech, Mail Code 170-25
480 Pasadena, CA 91125
481
483 AVHRR, Landsat TM5:
484 • Bastiaanssen, W.G.M., 1995. Regionalization of surface flux
486 densities and moisture indicators in composite terrain; a re‐
487 mote sensing approach under clear skies in mediterranean cli‐
488 mates. PhD thesis, Wageningen Agricultural Univ., The Nether‐
489 land, 271 pp. (PDF)
490 • Index DataBase: List of available Indices
492
494 i.albedo, i.aster.toar, i.landsat.toar, i.atcorr, i.tasscap
495
497 Baburao Kamble, Asian Institute of Technology, Thailand
498 Yann Chemin, Asian Institute of Technology, Thailand
499
501 Available at: i.vi source code (history)
502 Accessed: Mon Jun 20 16:47:38 2022
504 Main index | Imagery index | Topics index | Keywords index | Graphical
506 index | Full index
507 © 2003-2022 GRASS Development Team, GRASS GIS 8.2.0 Reference Manual
509GRASS 8.2.0 i.vi(1)