Indices gallery—ArcGIS Pro

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 image 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, so a dialog box opens to ask you to input the proper band number for the index you want to apply.

Note:

When selecting an index to apply to your imagery, make sure 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

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.

NDVI

The Normalized Difference Vegetation Index (NDVI) 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 near-infrared (NIR) band.

The documented and default NDVI equation is as follows:

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.

Learn more about NDVI

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.

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.

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.

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.

Water indices

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 = (Green - SWIR) / (Green + 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.

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.

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.

Geology indices

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.

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.

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

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 = 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 = (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).

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.