Normalized Difference Vegetation Index (NDVI) evaluates vegetation by estimating the contrast between close infrared (which vegetation strongly reflects) and red light (which vegetation absorbs).

NDVI always ranges from -1 to +1. But there isn’t a distinct boundary for each type of land cover.

For instance, when you have negative qualities, almost certainly, it’s water. Then again, if you have an NDVI esteem near +1, there’s a high chance that it’s thick green leaves.

Be that as it may, when NDVI is near zero, there aren’t green leaves and it could even be an urbanized zone.

How do you calculate NDVI?

As shown below, the Normalized Difference Vegetation Index (NDVI) uses the NIR and red channels in its formula.

How do you calculate NDVI?

Healthy vegetation (chlorophyll) reflects progressively close infrared (NIR) and green light contrasted with different frequencies. Be that as it may, it ingests progressively red and blue light.

This is the reason our eyes consider vegetation to be the shading green. On the off chance that you could see close infrared, at that point, it would be solid for vegetation as well. Satellite sensors like Landsat and Sentinel-2 both have the essential groups with NIR and red.

image courtesy of NASA

The result of this formula creates an incentive between – 1 and +1. If you have low reflectance (or low qualities) in the red direct and high reflectance in the NIR channel, this will yield a high NDVI esteem. What’s more, the other way around.

By and large, NDVI is a normalized approach to quantify sound vegetation. At the point when you have high NDVI values, you have more advantageous vegetation. At the point when you have low NDVI, you have less or no vegetation. For the most part, on the off chance that you need to see vegetation change after some time, at that point you should perform the atmospheric correction.

NDVI Example for Agriculture

Let’s examine NDVI for an agricultural area with center pivot irrigation. Pivot irrigation rotates on a point creating a circular crop pattern.

In true color, here’s how it looks for red, green, and blue bands. We say true color because it is the same as how our eyes see.

NDVI Example for Agriculture

In the formula, you can perceive how NDVI influences close infrared (NIR). So when we put the NIR band to show as red, we get shading infrared. We state shading infrared because close to infrared is in the red channel. As should be obvious beneath, the rotate water system vegetation should as of now be yelling out at you in bright red!

NDVI Example for Agriculture bright red

At the point when you apply the equation, brilliant green demonstrates high NDVI. Though red has low NDVI. So it’s evaluating vegetation by estimating the contrast between close infrared (which vegetation strongly reflects) and red light (which vegetation absorbs).

Normalized Difference Vegetation Index

How do we use NDVI?

In particular, there are several sectors that use NDVI.

For instance, in horticulture, ranchers use NDVI for exactness cultivating and to gauge biomass. Though, in ranger service, foresters use NDVI to evaluate woods supply and leaf area index.

Furthermore, NASA states that NDVI is a good indicator of drought. When water limits vegetation growth, it has a lower relative NDVI and density of vegetation.


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