While our eyes only detect a fraction of all light available, satellite sensors can actually capture – and send back – much more information. Furthermore, this information is relayed back to us in a format quite different from the photographs we are used to. For each band, satellites capture the spectral reflectance of the area within a specific narrow band of the light spectrum.
True-colour composite images use the red, green, and blue bands gathered by satellites to mimic the range of vision for the human eye, showing us images closer to what we would expect to see in a normal photograph.
Satellites also capture information in the non-visible part of the light spectrum. Different features: rock, bare soil, vegetation, burned ground, snow, sediment-rich water, etc. all have different reflectance properties in each band. This a called a 'spectral signature'.
To highlight specific features, one or more of the RGB bands can be substituted for another, such as infrared, or near infrared, which are not visible to the human eye. These images are referred to as false-colour images.
Additionally, to better discriminate between features and highlight changes in time, mathematical models can be applied to the data to produce a new kind of processed image. These are referred to as indexes.
NDVI processed images are created using the following formula:
NDVI = (NIR - R)/(NIR + R)where NIR = pixel values from the infrared band and R = pixel values from the red band. Each pixel will have a value between -1 and +1. The resulting image will be displayed in grayscales, where lower values are darker and higher values are lighter.
Looking at the results of the normalized difference vegetation index, negative values are mainly generated from clouds, water, and snow — which have high reflectance in the red band but low values in the near-infrared band. Low NDVI values correspond to barren areas, mostly rock, sand, and bare soil. Moderate values represent shrubs and grassland, while very high values indicate temperate and tropical rainforests.
There are several sectors that frequently use NDVI. Farmers can use NDVI for precision farming and to measure biomass. Different levels in NDVI values can help assess plant health and stress levels. In turn, this can be used to optimize fertilizer use, identify crops that have been attacked by pests and diseases.
Additionally, farmers can monitor plant growth and plan harvest according to vegetation vigor, as well as help determine the optimal crop allocation for the upcoming year.
Foresters can use NDVI to quantify forest supply and leaf area index and identify tree species and density. NDVI can also be used to monitor the growth of newly planted areas, identify insects and pest in crops and help plan operations.
Comparing recent NDVI data with past NDVI data for the same area in previous years reveals whether the productivity in a given region is typical, or whether the plant growth is significantly altered. By combining data regarding reduced plant growth and precipitation data, it is possible to identify at-risk areas for drought and monitor the severity of a drought.