Many modern satellites collect infrared and ultraviolet light not visible to the human eye. This information can be used to measure vegetation health, to monitor biomass, or to track forest fires. Newer laser and Radar-based sensors precisely scan the earth, eliminating weather dependencies, producing 3D earth models, and enabling better change detection.
In this imagery over rural Uganda, near-infrared sensors augment imagery to highlight vegetation (False Color) and to measure the health of that vegetation (NDVI). These images show a range of imagery resolutions from different growing seasons.
There are several well-known combinations that are optimized to provide maximum contrast between categories of interest under various use cases.
|Combination Name||Red Wavelength||Green Wavelength||Blue Wavelength||LS 8 Bands|
|Natural Color (actual RGB)||0.64-0.67µm||0.53-0.59µm||0.45-0.51µm||4 3 2|
|False Color (urban)||2.11-2.29µm||1.57-1.65µm||0.64-0.67µm||7 6 4|
|Agriculture||0.85-0.88µm||0.64-0.67µm||0.53-0.59µm||5 4 3|
|Atmospheric Penetration||2.11-2.29µm||1.57-1.65µm||0.85-0.88µm||7 6 5|
|Healthy Vegetation||0.85-0.88µm||1.57-1.65µm||0.45-0.51µm||5 6 2|
|Land/Water||0.85-0.88µm||1.57-1.65µm||0.64-0.67µm||5 6 4|
|Natural With Atmospheric Removal||2.11-2.29µm||0.85-0.88µm||0.53–0.59µm||7 5 3|
|Vegetation Analysis||1.57-1.65µm||0.85-0.88µm||0.64-0.67µm||6 5 4|
Mathematic spectral transformations
In addition to false color composition, spectral bands can be combined mathematically to emphasize a particular set of characteristics. These techniques may draw from all relevant bands, rather than the three band limit set by human vision, to draw out very specific characteristics. This generally demands greater processing of the raw data to minimize noise across the deeper image stack. Mathematical transformations can be used to with good ground data and well-developed algorithms to answer more quantitative questions. While false color can be used to detect the subjective health of agriculture, the proper mathematic transformations might be able to measure the health of each field using a transferrable metric. While false color composites can distinguish mud from water, the proper mathematic transformations could measure how wet the mud is.