A initial approach on how to understand spectral indices
Continuing our previous article on "Spectral Indices with multispectral satellite data", here we continue with this article on how to interpret them.
So, first things first.
What is a spectral index?
A spectral index is a mathematical equation that is applied on the various spectral bands of an image per pixel.
So image a Sentinel-2 image. It covers an area of 100Km by 100Km.
This translates to approximately 10.000 by 10.000 pixels for the bands that have 10 meters spatial resolution. Half by half for the bands that have 20 meters spatial resolution, etc.
If you want to learn more on the specifics of Sentinel-2 image processing, you can enroll to "ESA Sentinel Application Platform Tutorial".
So, each pixel is described by n numbers, where n is the number of spectral bands. A spectral index is calculated using some of these values (depends on the specific index) in a mathematical formula.
The most common mathematical formulas that are used is the normalized difference:
(Bx - By)/(Bx + By)
In practical terms, it is the difference between two selected bands normalized by their sum. This type of calculus is very useful to minimize (as much as possible) the effects of illumination (shadows in mountainous regions, cloud shadows, etc) and enhance spectral features that are not visible initially.
Of course, there numerous of different equations that can be applied on the spectral to produce a spectral index.
The Normalized Difference Vegetation Index (NDVI) enhances the vegetation and more specifically the healthy vegetation.
The spectral response of vegetation (crops, forests, bushes, etc) shows a huge increase of the reflection percentage from 700nm to 1000nm.
The main ingredient for this increase in reflection is the chlorophyll mostly located in the plant leafs.
On the contrary, land (soil, urban structures) without vegetation, according to the type of surface, has a continuous linear behaviour.
Beside the determination between of the vegetation and other objects it allows to detect the vitality of the vegetation.
The NDVI is calculated using the following equation:
NDVI = (Bnear_IR - Bred) / (Bnear_IR + Bred)
where Bnear_Ir is the value of the pixel at the near infrared band and Bred is the value of the pixel located at the red part of the spectrum.
The value range of an NDVI is -1 to 1. Negative values of NDVI (values approaching -1) correspond to water. Values close to zero (-0.1 to 0.1) generally correspond to barren areas of rock, sand, or snow.
Values greater than 0.2 are vegetation (the higher the value the healthier/denser the vegetation).
Similar to NDVI, the Normalized Difference Water Index (NDWI), as it name suggests, is highly related to water bodies.
It is highly correlated to being a measure of liquid water molecules in vegetation canopies that interacted with the incoming solar radiation.
NDWI is sensitive to changes in liquid water content of vegetation canopies.
It is less sensitive to atmospheric effects than NDVI. NDWI does not remove completely the underlying effects of soil.
NDWI is considered to be complentary to NDVI and usually they are both used in order to extract conclusions.
The NDWI is calculated using the following equation:
NDWI = (Bnear_IR - Bmiddle_IR) / (Bnear_IR + Bmiddle_IR)
where Bnear_Ir is the value of the pixel at the near infrared band and Bmiddle_IR is the value of the pixel located at the mid infrared part of the spectrum.
It has also a range from -1 to 1. Values higher than 0.2 are considered either water saturated soils, flooded areas or water bodies.
NDWI can be used to quickly detect water presence by just applying a threshold.
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