2. NDVI
• The normalized difference vegetation index (NDVI) generates an image
displaying greenness, also known as relative biomass. It is a measure of
vegetation density and vigour
• This index takes advantage of the contrast of characteristics between two
bands from a multispectral raster dataset—the chlorophyll pigment
absorption in the red band and the high reflectivity of plant material in
the near-infrared (NIR) band.
• NDVI = ((NIR - Red)/(NIR + Red))
• NIR = pixel values from the near-infrared band
• Red = pixel values from the red band
• This index outputs values between -1.0 and 1.0.
3. NDVI
• Negative values represent areas with no vegetation at all, such as cloud,
water, or snow.
• Very low values near zero represent rock and bare soil. < 0.2
• Moderate values (0.2 to 0.3) represent shrub and grassland
• High values (0.6 to 0.8) indicate temperate and tropical rainforests.
• NDVI is often used worldwide to monitor drought, monitor and predict
agricultural production, assist in predicting hazardous fire zones, and map
desert encroachment. The NDVI is preferred for global vegetation monitoring
because it helps compensate for changing illumination conditions, surface
slope, aspect, and other extraneous factors (Lillesand 2004).
4. NDVI Function in ArcGIS Pro
• The default equation used to generate the output is as follows:
• NDVI = ((IR - R)/(IR + R)) * 100 + 100
• This results in a value range of 0 to 200. This value range fits within an 8-bit
data structure, which can easily be rendered with a specific color ramp or
color map.
• If you need the scientific pixel values of -1.0 to 1.0, use the Scientific
Output option.
• Red and orange pixels represent areas with no vegetation. Yellow
pixels represent areas with low to moderate vegetation. Green pixels
represent areas with high vegetation density and vigor.
6. MSAVI
• The Modified Soil Adjusted Vegetation Index (MSAVI2)
method minimizes the effect of bare soil on the SAVI.
• MSAVI2 = (1/2)*(2(NIR+1)-sqrt((2*NIR+1)2-8(NIR-Red)))
• NIR = pixel values from the near-infrared band
• Red = pixel values from the red band
• Reference: Qi, J. et al., 1994, "A modified soil vegetation adjusted
index," Remote Sensing of Environment, Vol. 48, No. 2, 119–126.
7. PVI
• The Perpendicular Vegetation Index (PVI) method is similar to a
difference vegetation index; however, it is sensitive to atmospheric
variations. When using this method to compare images, it should
only be used on images that have been atmospherically corrected.
• PVI = (NIR - a*Red - b) / (sqrt(1 + a2))
• NIR = pixel values from the near-infrared band
• Red = pixel values from the red band
• a = slope of the soil line
• b = gradient of the soil line
• This index outputs values between -1.0 and 1.0.
8. SAVI
• The Soil-Adjusted Vegetation Index (SAVI) method is a vegetation index that
attempts to minimize soil brightness influences using a soil-brightness correction
factor. This is often used in arid regions where vegetative cover is low, and it
outputs values between -1.0 and 1.0.
• SAVI = ((NIR - Red) / (NIR + Red + L)) x (1 + L)
• NIR = pixel values from the near infrared band
• Red = pixel values from the near red band
• L = amount of green vegetation cover
• Using a space-delimited list, you will identify the NIR and red bands and enter the L
value in the following order: NIR Red L. For example, 4 3 0.5.
• Reference: Huete, A. R., 1988, "A soil-adjusted vegetation index (SAVI)," Remote
Sensing of Environment, Vol 25, 295–309.
9. TSAVI
• The Transformed Soil Adjusted Vegetation Index (TSAVI) method is a
vegetation index that minimizes soil brightness influences by assuming the
soil line has an arbitrary slope and intercept.
• TSAVI = (s * (NIR - s * Red - a)) / (a * NIR + Red - a * s + X * (1 + s2))
• NIR = pixel values from the near-infrared band
• Red = pixel values from the red band
• s = the soil line slope
• a = the soil line intercept
• X = an adjustment factor that is set to minimize soil noise
• Reference: Baret, F. and G. Guyot, 1991, "Potentials and limits of vegetation
indices for LAI and APAR assessment," Remote Sensing of Environment, Vol.
35, 161–173.
10. VARI
• The Visible Atmospherically Resistant Index (VARI) is designed to
emphasize vegetation in the visible portion of the spectrum, while
mitigating illumination differences and atmospheric effects. It is ideal
for RGB or color images; it utilizes all three color bands.
• VARI = (Green - Red)/ (Green + Red - Blue)
• Green = pixel values from the green band
• Red= pixel values from the red band
• Blue = pixel values from the blue band
• Reference: Gitelson, A., et al. "Vegetation and Soil Lines in Visible Spectral Space:
A Concept and Technique for Remote Estimation of Vegetation Fraction."
International Journal of Remote Sensing 23 (2002): 2537−2562.
11. MNDWI
• The Modified Normalized Difference Water Index (MNDWI) uses
green and SWIR bands for the enhancement of open water features.
It also diminishes built-up area features that are often correlated with
open water in other indices.
• MNDWI = (Green - SWIR) / (Green + SWIR)
• Green = pixel values from the green band
• SWIR = pixel values from the short-wave infrared band
• Reference: Xu, H. "Modification of Normalised Difference Water Index (NDWI) to
Enhance Open Water Features in Remotely Sensed Imagery." International
Journal of Remote Sensing 27, No. 14 (2006): 3025-3033.
12. NDMI
• The Normalized Difference Moisture Index (NDMI) is sensitive to the moisture
levels in vegetation. It is used to monitor droughts as well as monitor fuel
levels in fire-prone areas. It uses NIR and SWIR bands to create a ratio
designed to mitigate illumination and atmospheric effects.
• NDMI = (NIR - SWIR1)/(NIR + SWIR1)
• NIR = pixel values from the near infrared band
• SWIR1 = pixel values from the short-wave infrared 1 band
• References:
• Wilson, E.H. and Sader, S.A., 2002, "Detection of forest harvest type using
multiple dates of Landsat TM imagery." Remote Sensing of Environment, 80 , pp.
385-396.
• Skakun, R.S., Wulder, M.A. and Franklin, .S.E. (2003). "Sensitivity of the thematic
mapper enhanced wetness difference index to detect mountain pine beetle red-
attack damage." Remote Sensing of Environment, Vol. 86, Pp. 433-443.
13. Clay Minerals
• The clay ratio is a ratio of the SWIR1 and SWIR2 bands. This ratio leverages
the fact that hydrous minerals such as the clays, alunite absorb radiation in
the 2.0–2.3 micron portion of the spectrum. This index mitigates
illumination changes due to terrain since it is a ratio.
• Clay Minerals Ratio = SWIR1 / SWIR2
• SWIR1 = pixel values from the short-wave infrared 1 band
• SWIR2 = pixel values from the short-wave infrared 2 band
• Reference: Amro F. Alasta, "Using Remote Sensing data to identify iron
deposits in central western Libya." International Conference on Emerging
Trends in Computer and Image Processing (ICETCIP'2011) Bangkok Dec.,
2011.
14. Ferrous Minerals
• The ferrous minerals ratio highlights iron-bearing materials. It uses ratio
between the SWIR band and the NIR band.
• Ferrous Minerals Ratio = SWIR / NIR
• SWIR= pixel values from the short-wave infrared band
• NIR = pixel values from the near infrared band
• Reference: Segal, D. "Theoretical Basis for Differentiation of Ferric-Iron
Bearing Minerals, Using Landsat MSS Data." Proceedings of Symposium for
Remote Sensing of Environment, 2nd Thematic Conference on Remote
Sensing for Exploratory Geology, Fort Worth, TX (1982): pp. 949-951.
15. Iron Oxide
• The iron oxide ratio is a ratio of the red and blue wavelengths. The
presence of limonitic-bearing phyllosilicates and limonitic iron oxide
alteration cause absorption in blue band and reflectance in red band. This
causes areas with strong iron alteration to be bright. The nature of the
ratio allows this index to mitigate illumination differences caused by terrain
shadowing.
• Iron Oxide Ratio = Red / Blue
• Red = pixel values from the red band
• Blue = pixel values from the blue band
• Reference: Segal, D. "Theoretical Basis for Differentiation of Ferric-Iron
Bearing Minerals, Using Landsat MSS Data." Proceedings of Symposium for
Remote Sensing of Environment, 2nd Thematic Conference on Remote
Sensing for Exploratory Geology, Fort Worth, TX (1982): pp. 949-951.
16. BAI
• The Burn Area Index (BAI) uses the reflectance values in the red and NIR
portion of the spectrum to identify the areas of the terrain affected by fire.
• BAI = 1/((0.1 -RED)^2 + (0.06 - NIR)^2)
• Red = pixel values from the red band
• NIR = pixel values from the near infrared band
• Reference: Chuvieco, E., M. Pilar Martin, and A. Palacios. "Assessment of
Different Spectral Indices in the Red-Near-Infrared Spectral Domain for
Burned Land Discrimination." Remote Sensing of Environment 112 (2002):
2381-2396.
17. NBR
• The Normalized Burn Ratio Index (NBRI) uses the NIR and SWIR bands
to emphasize burned areas, while mitigating illumination and
atmospheric effects. Your images should be corrected to reflectance
values before using this index; see the Apparent Reflectance function
for more details.
• NBR = (NIR - SWIR) / (NIR+ SWIR)
• NIR = pixel values from the near infrared band
• SWIR = pixel values from the short-wave infrared band
• Reference: Key, C. and N. Benson, N. "Landscape Assessment: Remote Sensing of
Severity, the Normalized Burn Ratio; and Ground Measure of Severity, the
Composite Burn Index." FIREMON: Fire Effects Monitoring and Inventory System,
RMRS-GTR, Ogden, UT: USDA Forest Service, Rocky Mountain Research Station
(2005).
18. NDBI
• The Normalized Difference Built-up Index (NDBI) uses the NIR and
SWIR bands to emphasize man-made built-up areas. It is ratio based
to mitigate the effects of terrain illumination differences as well as
atmospheric effects.
• NDBI = (SWIR - NIR) / (SWIR + NIR)
• SWIR = pixel values from the short-wave infrared band
• NIR = pixel values from the near infrared band
• Reference: Zha, Y., J. Gao, and S. Ni. "Use of Normalized Difference
Built-Up Index in Automatically Mapping Urban Areas from TM
Imagery." International Journal of Remote Sensing 24, no. 3 (2003):
583-594.
