For questions about vegetation indices, which are measures of the relative abundance of vegetation. In the context of remote sensing and GIS, vegetation indices are usually encountered as a raster dataset.
Vegetation indices are used to represent the relative abundance of vegetation. In the context of remote sensing, vegetation indices are computed from multispectral or hyperspectral imagery and are therefore are usually in the form of a raster with the same spatial resolution as the input imagery.
Many vegetation indices are normalized, meaning their values are dimensionless and constrained to a certain range such as [0,1] or [-1,1].
Typically, vegetation indices are designed to take advantage of the difference in spectral reflectance between two or more bands in a multispectral or hyperspectral image. For example, the Normalized Difference Vegetation Index (NDVI) leverages the large difference between the red and near infrared (NIR) reflectance of healthy green vegetation.
NDVI is among the most commonly used vegetation indices. Other common vegetation indices include:
- Enhanced Vegetation Index (EVI)
- Soil Adjusted Vegetation Index (SAVI)
- Modified Soil Adjusted Vegetation Index (MSAVI)
Many vegetation indices include additional parameters to correct for confounding factors such soil background or atmospheric effects.
Jensen (2016), citing Running et al., (1994) and Huete and Justice, (1999), lists four things that a vegetation index should do:
- maximize sensitivity to plant bio-physical parameters (ideally, with a linear response);
- normalize (or correct for) external effects (e.g., the solar angle);
- normalize internal effects (e.g., topography); and
- be coupled with measurable biophysical parameter (e.g., biomass).
Vegetation indices are a subset of the broader category of spectral indices. Other types of spectral indices include:
- geology indices, which highlight different minerals;
- landscape indices, which highlight burned areas or built-up areas; and
- water indices, which highlight open water or snow covered areas.
For More Information
The Index DataBase (IDB) provides a database with hundreds of remote sensing spectral indices that can be filtered by bands, sensor, and application.
Jensen, J. R. (2016). Introductory Digital Image Processing: A Remote Sensing Perspective. Pearson Education.