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) affects 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.
MSAVI
The Modified Soil Adjusted Vegetation Index (MSAVI2) is designed to minimize the effect of bare soil on the Soil-Adjusted Vegetation Index (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, also known as relative biomass. 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.
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.
SAVI
The Soil-Adjusted Vegetation Index (SAVI) is a vegetation index designed to minimize soil brightness influences using a soil-brightness correction factor. This is often used in arid regions where vegetative cover is low.
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
NIR and Red refer to the bands associated with those wavelengths. The L value varies depending on the amount of green vegetative cover. Generally, in areas with no green vegetation cover, L=1; in areas of moderate green vegetative cover, L=0.5; and in areas with very high vegetation cover, L=0 (which is equivalent to the NDVI method). This index outputs values between -1.0 and 1.0.
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) is a vegetation index designed to minimize 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
- R = 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.
Green NDVI
The Green Normalized Difference Vegetation Index (GNDVI) 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 = (NIR-Green)/(NIR+Green)- NIR = pixel values from the near-infrared band
- Green = pixel values from the green band
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.
Red-Edge NDVI
The Red-Edge NDVI 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, during which the chlorophyll concentration is relatively higher. It can be used to map the within-field variability of nitrogen foliage to understand the fertilizer requirements of crops.
The Red-Edge NDVI index is calculated using the NIR and red-edge bands.
NDVIre = (NIR-RedEdge)/(NIR+RedEdge)- NIR = pixel values from the near-infrared band
- RedEdge = pixel values from the red-edge band
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.