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Beyond NDVI: Hyperspectral Vegetation Indices

Hyperspectral vegetation indices (HVI's) are a common tool for analyzing hyperspectral data of agricultural crops. HVI's have found many applications in research using airborne remote sensing data, and are currently subjects of intense research efforts.

HVI's have been correlated with Leaf Area Index, pigment concentration, canopy cover, leaf nitrogen, light use efficiency, growth rates, and other biochemical characteristics.

HVI's are typically simple arithmetic combinations of sums and differences of spectral reflectance values at particular wavelengths. For example, the Structure Insensitive Pigment Index (SIPI) is defined by the following expression:

equation for hyperspectral vegetation index PSRI

where the values in the equation are shown graphically in the figure below:

graph of spectrum obtained with Resonon hyperspectral imaging camera
Graphical representation of values in SIPI equation.

It is easy to produce HVI maps using Spectronon software.

Calculating Hyperspectral Vegetation Indices with Spectronon

Below are three images of data obtained with a Resonon airborne hyperspectral imaging system. The first image is a true-color RGB rendering, the second image is a map of the Anthocyanin Reflectance Index 1 (ARI1), and the third image is a map of the Modified Chlorophyll Absorption Reflectance Index (MCARI).

Hyperspectral vegetation indices in Spectronon
HVI maps from airborne data, using Spectronon software.

The datacube in the above example, along with Spectronon software, is available for free download at downloads.resonon.com.

Additional References

A complete list of HVI's in Spectronon can be found in the Spectronon manual.

The developers at ENVI have written an excellent description of HVI’s which is available at their website. We at Resonon consider ENVI (owned by Exelis) to be excellent hyperspectral data analysis software. Their HVI webiste is located at this link.

Another very good reference discussing HVI's is this book:
Thenkabail, Lyon, and Huete, Remote Sensing of Vegetation, CRC Press (2011).

See our complete list of publications and articles for more information.