The vegetation indices are visual representations of the reflectance of plants. They work by combining two or more wavelengths of the electromagnetic spectrum (green, red, infrared, near infrared, red edge, blue, etc …).
Each one is designed to accentuate specific properties of the analyzed vegetation: variation of nutrients, water stress, biomass and use of light, are some examples of properties that can be analyzed with vegetation indexes.
There are more than 150 indexes published in the scientific literature and are grouped into the following categories:
- Green reflectance
- Light Efficiency
- Nitrogen in foliage
- Carbon stroke
- Pigmentation
- Water content
Some of the most known vegetation indexes are:
Normalized Difference Vegetation Index
NDVI= (NIR-RED) / (NIR+RED)
Range: -1 a 1
Propiedades: Differentiation of green and healthy vegetation of yellowish and stressed vegetation. Observation of areas with normal percentages of chlorophyll and photosynthesis. Information on relative biomass and foliage.
Green Normalized Difference Vegetation Index
GNDVI= (NIR-GREEN) / (NIR+GREEN)
Range: -1 a 1
Properties: Similar to NDVI with an even greater focus on chlorophyll. Designed for vegetation with more foliage. Emphasize more on poor vegetation in chlorophyll and soil or non-photosynthetic areas.
Normalized Difference Red Edge
NDRE= (NIR-RE) / (NIR+RE)
Range: -1 a 1
Properties: More suitable for making measurement in late stages of a crop because of its focus on the differences between vegetation and soil.
Modified Chlorophyll Absorption Ratio Index
MCARI= [(p700-p670)-0.2(p700-p550)]*(p700/p670)
Range: -1 a 1
Properties: More suitable for crops with greater amount of foliage when focusing on subtle differences of areas with higher and lower chlorophyll.
Structure Intensive Pigment Index 2
SIPI2= (800nm-505nm)/(800nm-690nm)
Range: -1 a 1
Properties: More suitable for forest analysis or crops with high foliage variation.
It is important to understand that the images generated from vegetation indices are visual representations of information; therefore, it’s not the colors that matter, but the numerical range of the indices. The colorimetry of the images can change, the important thing is to understand the scale of the indices and how they relate to a crop and its conditions.
