Indices gallery
Image indices are images that are computed from multiband images. The images emphasize a specific phenomenon that is present, while mitigating other factors that degrade the effects in the image. For instance, a vegetation index will show healthy vegetation as bright in the index image, while unhealthy vegetation has lower values and barren terrain is dark. Since shading from terrain variation (hills and valleys) affect the intensity of images, the indices are created in ways that the color of an object is emphasized rather than the intensity or brightness of the object. The value of a vegetation index for a healthy pine tree that is shadowed in a valley will have a similar value as a pine tree that is in full sunlight. These indices are often built by combinations of adding and subtracting bands, thereby making various band ratios. They are tied to specific bands that are in specific parts of the electromagnetic spectrum. As a result, they may only be valid for certain sensors or classes of sensors and it is critical that the proper bands are used in the calculation.
One of the common ways that these indices are used is for comparison of the same object across multiple images over time. For instance, there might be multiple images of an agricultural field that were taken weekly since the field was planted and throughout the growing season. The vegetation index would be computed for each image. When you analyze these weekly vegetation indices, you would expect to see a brightening through the growing season. Then when senescence begins in the fall, you would see the index diminish until the plant is harvested or the leaves are dead at the end of the season. The normalizing effect of the indices makes this comparison practical. By comparing multiple fields in a region, you can identify those that thrive and those that are challenged. This type of analysis might also be used to identify fields that have suffered from storm damage.
Choose the index according to the phenomena you want to analyze. Be certain that the input image is from a sensor that has the proper bands (wavelengths and range) to support the index of choice. The indices read the metadata from the image to check the band names. When they find a match, the index will be automatically applied. ArcGIS Pro generally uses the band names from Landsat 8, but the band names from other sensors may have different names. In this case, you can substitute the appropriate band from the sensor you are using in the index function. For example, Landsat 5 TM Raster Product has a band (7) called mid-infrared (MIR), which is comparable to the Landsat 8 counterpart band (7) called shortwave infrared 2 (SWIR2). In this case, the index you want to apply cannot find the required band name information from the image metadata, and a dialog box appears, prompting you to input the proper band number for the index you want to apply.
Note:
When selecting an index to apply to your imagery, ensure that your source imagery contains the proper band for the index. For example, the Normalized Difference Snow Index (NDSI) requires a shortwave infrared (SWIR) band, and will not work properly with imagery that does not contain a SWIR band.
Vegetation and soils indices
CI Green
The Chlorophyll Index - Green (CI Green) method is a vegetation index for estimating the chlorophyll content in leaves using the ratio of reflectivity in the NIR and green bands.
\(CIg \space = \space (NIR \space / \space Green) - 1\)
NIR = pixel values from the near-infrared band
Green = pixel values from the green band
Using a space-delimited list, you will identify the NIR and green bands in the following order: NIR Green. For example, 7 3.
Reference: Gitelson, A.A., Kaufman, Y.J., Merzlyak, M.N., 1996. "Use of a green channel in remote sensing of global vegetation from EOS-MODIS," Remote Sensing of Environment, Vol. 58, 289–298.
CI Red-Edge
The Chlorophyll Index - Red-Edge (CI Red-Edge) method is a vegetation index for estimating the chlorophyll content in leaves using the ratio of reflectivity in the NIR and red-edge bands.
\(CIre \space = \space [(NIR \space / \space RedEdge)-1]\)
NIR = pixel values from the near-infrared band
RedEdge = pixel values from the red-edge band
Using a space-delimited list, you will identify the NIR and red-edge bands in the following order: NIR RedEdge. For example, 7 6.
References:
- Gitelson, A.A., Merzlyak, M.N., 1994. "Quantitative estimation of chlorophyll using reflectance spectra," Journal of Photochemistry and Photobiology B 22, 247–252.
Green NDVI
The Green Normalized Difference Vegetation Index (Green NDVI) method is a vegetation index for estimating photo synthetic activity and is a commonly used vegetation index to determine water and nitrogen uptake into the plant canopy.
\(GNDVI \space = \space (NIR-Green)/(NIR+Green)\)
NIR = pixel values from the near-infrared band
Green = pixel values from the green band
Using a space-delimited list, you will identify the NIR and green bands in the following order: NIR Green. For example, 5 3.
This index outputs values between -1.0 and 1.0.
Reference: Buschmann, C., and E. Nagel. 1993. "In vivo spectroscopy and internal optics of leaves as basis for remote sensing of vegetation," International Journal of Remote Sensing, Vol. 14, 711–722.
MSAVI
The Modified Soil Adjusted Vegetation Index (MSAVI2) method minimizes the effect of bare soil on the SAVI.
\(MSAVI2 \space = \space (1/2) * (2(NIR+1)-sqrt((2 * NIR+1) ^{2} -8(NIR-Red)))\)
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.
MTVI2
The Modified Triangular Vegetation Index (MTVI2) method is a vegetation index for detecting leaf chlorophyll content at the canopy scale while being relatively insensitive to leaf area index. It uses reflectance in the green, red, and NIR bands.
\(MTVI2 \space = \space [1.5(1.2(NIR-Green)-2.5(Red-Green))√((2NIR+1)²-(6NIR-5√(Red))-0.5)]\)
NIR = pixel values from the near-infrared band
Red = pixel values from the red band
Green = pixel values from the green band
Using a space-delimited list, you will identify the NIR, red, and green bands in the following order: NIR Red Green. For example, 7 5 3.
Reference: Haboudane, D., Miller, J.R., Tremblay, N., Zarco-Tejada, P.J., Dextraze, L., 2002. "Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture," Remote Sensing of Environment, Vol. 81, 416–426.
NDVI
The Normalized Difference Vegetation Index (NDVI) method is a standardized index allowing you to generate an image displaying greenness (relative biomass). This index takes advantage of the contrast of the characteristics of two bands from a multispectral raster dataset—the chlorophyll pigment absorptions in the red band and the high reflectivity of plant materials in the NIR band.
The documented and default NDVI equation is as follows:
\(NDVI \space = \space ((NIR \space - \space Red)/(NIR \space + \space Red))\)
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.
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.
\(PVI \space = \space (NIR \space - \space a * Red \space - \space b) \space / \space (sqrt(1 \space + \space a ^{2} ))\)
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.
Red-Edge NDVI
The Red-Edge NDVI method is a vegetation index for estimating vegetation health using the red-edge band. It is especially useful for estimating crop health in the mid to late stages of growth, when the chlorophyll concentration is relatively higher. Also, NDVIre can be used to map the within-field variability of nitrogen foliage to understand the fertilizer requirements of crops.
The NDVIre index is calculated using the NIR and red-edge bands.
\(NDVIre \space = \space (NIR \space - \space RedEdge)/(NIR \space + \space RedEdge)\)
NIR = pixel values from the near-infrared band
RedEdge = pixel values from the red-edge band
Using a space-delimited list, you will identify the NIR and red-edge bands in the following order: NIR RedEdge. For example, 7 6.
This index outputs values between -1.0 and 1.0.
Reference: Gitelson, A.A., Merzlyak, M.N., 1994. "Quantitative estimation of chlorophyll using reflectance spectra," Journal of Photochemistry and Photobiology B 22, 247–252.
Red-Edge SR
The Red-Edge Simple Ratio (Red-Edge SR) method is a vegetation index for estimating the amount of healthy and stressed vegetation. It is the ratio of light scattered in the NIR and red-edge bands, which reduces the effects of atmosphere and topography.
Values are high for vegetation with high canopy closure and healthy vegetation, lower for high canopy closure and stressed vegetation, and low for soil, water, and nonvegetated features. The range of values is from 0 to approximately 30, where healthy vegetation generally falls between values of 1 to 10.
\(SRre \space = \space NIR \space / \space RedEdge\)
NIR = pixel values from the near-infrared band
RedEdge = pixel values from the red-edge band
Using a space-delimited list, you will identify the NIR and red-edge bands in the following order: NIR RedEdge. For example, 7 6.
Reference: Anatoly A. Gitelson, Yoram J. Kaufman, Robert Stark, and Don Rundquist, 2002, "Novel algorithms for remote estimation of vegetation fraction," Remote Sensing of Environment, Vol. 80, 76–87.
RTVIcore
The Red-Edge Triangulated Vegetation Index (RTVICore) method is a vegetation index for estimating leaf area index and biomass. This index uses reflectance in the NIR, red-edge, and green spectral bands.
\(RTVICore \space = \space [100(NIR-RedEdge)-10(NIR-Green)]\)
NIR = pixel values from the near-infrared band
RedEdge = pixel values from the red-edge band
Green = pixel values from the green band
Using a space-delimited list, you will identify the NIR, red-edge, and green bands in the following order: NIR RedEdge Green. For example, 7 6 3.
Reference: Haboudane, D., Miller, J.R., Pattey, E., Zarco-Tejada, P.J., Strachan, I.B., 2004. "Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: modeling and validation in the context of precision agriculture," Remote Sensing of Environment, Vol. 90, 337–352.
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.
\(SAVI \space = \space ((NIR \space - \space Red) \space / \space (NIR \space + \space Red \space + \space L)) \space x \space (1 \space + \space L)\)
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.
SR
The Simple Ratio (SR) method is a common vegetation index for estimating the amount of vegetation. It is the ratio of light scattered in the NIR and absorbed in red bands, which reduces the effects of atmosphere and topography.
Values are high for vegetation with a large leaf area index, or high canopy closure, and low for soil, water, and nonvegetated features. The range of values is from 0 to approximately 30, where healthy vegetation generally falls between values of 2 to 8.
\(SR \space = \space NIR \space / \space Red\)
NIR = pixel values from the near-infrared band
Red = pixel values from the red band
Using a space-delimited list, you will identify the NIR and red bands in the following order: NIR Red. For example, 4 3.
Reference: Birth, G.S., and G.R. McVey, 1968. "Measuring color of growing turf with a reflectance spectrophotometer," Agronomy Journal Vol. 60, 640-649.
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.
\(TSAVI = \Large \frac {( s\ *\ (NIR\ -\ (s \ *\ Red)\ -\ a) )}{( \ (a\ *\ NIR)\ +\ Red\ -\ (a\ *\ s)\ +\ ({X\ *\ (1\ +\ s^{2})) \ )} }\)
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.
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.
\(VARI \space = \space (Green \space - \space Red)/ \space (Green \space + \space Red \space - \space Blue)\)
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.
Water indices
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.
\(NDMI \space = \space (NIR \space - \space SWIR1)/(NIR \space + \space SWIR1)\)
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.
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.
\(MNDWI \space = \space (Green \space - \space SWIR) \space / \space (Green \space + \space SWIR)\)
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.
NDSI
The Normalized Difference Snow Index (NDSI) is designed to use MODIS (band 4 and band 6) and Landsat TM (band 2 and band 5) for identification of snow cover while ignoring cloud cover. Since it is ratio based, it also mitigates atmospheric effects.
\(NDSI \space = \space (Green \space - \space SWIR) \space / \space (Green \space + \space SWIR)\)
Green = pixel values from the green band
SWIR = pixel values from the shortwave infrared band
Reference: Riggs, G., D. Hall, and V. Salomonson. "A Snow Index for the Landsat Thematic Mapper and Moderate Resolution Imaging Spectrometer." Geoscience and Remote Sensing Symposium, IGARSS '94, Volume 4: Surface and Atmospheric Remote Sensing: Technologies, Data Analysis, and Interpretation (1994), pp. 1942-1944.
Geology indices
Clay Minerals
The clay minerals 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.
\(Clay \space Minerals \space Ratio \space = \space SWIR1 \space / \space SWIR2\)
SWIR1 = pixel values from the short-wave infrared 1 band
SWIR2 = pixel values from the short-wave infrared 2 band
Reference: Dogan, H., 2009. "Mineral composite assessment of Kelkit River Basin in Turkey by means of remote sensing," Journal of Earth System Science, Vol. 118, 701-710.
Ferrous Minerals
The ferrous minerals ratio highlights iron-bearing materials. It uses ratio between the SWIR band and the NIR band.
\(Ferrous\ Minerals\ Ratio\ =\ SWIR \space / \space NIR\)
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.
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.
\(Iron \space Oxide \space Ratio \space = \space Red \space / \space Blue\)
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.
Landscape indices
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.
\(BAI = \Large \frac {1}{((0.1\ -\ RED)^2 \ + \ (0.06\ -\ NIR)^2)}\)
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.
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.
\(NBR \space = \space (NIR \space - \space SWIR) \space / \space (NIR+ \space SWIR)\)
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).
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.
\(NDBI \space = \space (SWIR \space - \space NIR) \space / \space (SWIR \space + \space NIR)\)
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.