When processing remotely sensed data, such as satellite imagery or aerial photography, a range of spectral index equations can be applied to gain useful information which can’t be seen from the ground or using normal visible-spectrum or RGB imagery.
Satellite images and most aerial imagery contains more than just the visible spectrum (red, green and blue) – depending on the sensor, the data may contain additional measurements in parts of the spectrum which are invisible to the human eye. For example, the near-infrared part of the spectrum sits slightly “above” red, with wavelengths in the region of 750-950 nanometres, while the human eye can only respond to wavelengths between about 390nm (blue) and 700nm (red). Measurements from each part of the spectrum (e.g. blue, green, red, near-infrared) are stored in separate bands, or layers, in the image.
Spectral indices can be calculated using the values for two or more of the spectral bands, with the result being a number within a predictable range. For example, the Normalised Difference Vegetation Index (NDVI) is calculated using the red and near-infrared (NIR) bands, and always results in a value between -1 and 1.
Each spectral index has been demonstrated in peer-reviewed scientific research to correlate with a specific physical variable, such as leaf chlorophyll content or soil moisture levels. Using NDVI as an example, high values generally correlate with dense and/or healthy vegetative biomass, with low values indicating bare soil, rock, sand or snow.
The spectral index value across the mapped area can be represented using a colour scale, with NDVI commonly being mapped using red for the highest values, then orange, yellow, green, blue and finally purple for the lowest values.
Below is a brief list of spectral indices which are commonly applied to satellite imagery and other remotely sensed data. All of the below indices are used by Eden PA in satellite analysis and monitoring of crops, pastures, orchards and vineyards, along with a range of satellite environmental management and grounds maintenance applications. Some indices are only available in certain resolutions, depending on which spectral bands Eden PA can acquire from different imagery providers. Other indices are also available in addition to this list, and custom analysis services can also be provided – contact Eden PA to discuss your requirements.
|Index||Description & Equation||Applications & Notes||Source|
|ARVI||Atmospherically Resistant Vegetation Index||Vegetation analysis;|
Resistant to atmospheric effects
|Kaufman & Tanre, 1992|
|ARVI2||Atmospherically Resistant Vegetation Index 2||Vegetation analysis;|
Resistant to atmospheric effects;
Higher dynamic range than NDVI
|Bannari et al., 1995|
|BAI||Burn Area Index||Assessment of crop/pasture damage after fire||Chuvieco et al., 2002|
|BNDVI||Blue Normalised Difference Vegetation Index||Vegetation analysis;|
Strong correlation with LAI
|Wang et al., 2007|
|BWDRVI||Blue Wide Dynamic Range Vegetation Index||Vegetation analysis;|
Strong correlation with LAI;
Wide dynamic range
|Hancock & Dougherty, 2007|
|CCCI||Canopy Chlorophyll Content Index||Vegetation analysis;|
Correlation with chlorophyll content
|Barnes et al., 2000|
|CVI||Chlorophyll vegetation index||Vegetation analysis;|
Correlation with chlorophyll content
|Vincini et al., 2008|
|DVI||Difference Vegetation Index||Vegetation analysis;|
Can be used to distinguish vegetation from soil;
Will produce errors if shadows are present
|EVI||Enhanced Vegetation Index||Vegetation analysis;|
Useful in high LAI areas, where NDVI may saturate
|Heute et al., 2002|
|NDVI||Normalised Difference Vegetation Index||Vegetation analysis;|
Sensitive to chlorophyll;
Correlation with dry total green biomass
|Rouse et al., 1974|