Normalized Difference Vegetation Index (NDVI) in Remote Sensing

In one of the Geospatial Engineering disciplines, remote sensing, we hear of the word Normalized Difference Vegetation Index. This is pretty much used when investigating plants, ranging from grassland, shrubs, trees etc on the earth surface. A lot of this information is derived from satellite imagery. Satellite technology has different bands in the electromagnetic spectrum that are able to capture different kinds of things on the earth surface such as plants, bare land and everything else that is visible on the specific bands. We shall quickly delve into the science behind the investigation of different types of plants on the earth surface and how differentiating between the different types is achieved with the aid of satellite technology.

NDVI is the most common index that Geospatial analysts use in remote sensing. We shall therefore look at ways to calculate it, what their (NDVI) values represent and how Geospatial Engineers use NDVI in their decision making process.

Normalized Difference Vegetation Index (NDVI) quantifies vegetation by measuring the difference between near-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.

Illustratively, with negative values, it’s highly likely that it’s water. On the other hand, NDVI value close to +1, is indicative that there is a high possibility that it’s dense green leaves. However, when NDVI is close to zero, there isn’t green leaves and hence could be concluded to be an urban area.

How to calculate NDVI

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

NDVI = (NIR-Red)/ (NIR +Red)

Healthy vegetation (with high concentration of chlorophyll) reflects more near-infrared (NIR) and green light compared to other wavelengths. But it absorbs more red and blue light.

Hence our eyes decipher vegetation as the color green. If you decipher near-infrared, then it would be strong for vegetation too. Satellite sensors like Landsat and Sentinel-2 both have the necessary bands with NIR and red.

Image Credits : NASA

The result of this formula generates a value between -1 and +1. With low reflectance (or low values) in the red channel and high reflectance in the NIR channel, this will yield a high NDVI value. And vice versa.

Overall, NDVI is a standardized way to measure healthy vegetation. With high NDVI values, that is a representation of healthier vegetation. The opposite is true for low NDVI, where you end up with less or no vegetation. Generally, if you want to see vegetation change over time, then you will have to perform 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. The connotation true color is because it is the same as the way your eyes see it.

Image Credits : GISGeography

In the formula, it is clear that NDVI leverages near-infrared (NIR). When NIR band is put to display as red, the result is color infrared. This is called so because infrared is near the red channel. On the image displayed below, the pivot irrigation vegetation shows a bright color red.

Image Credits: GISGeography

With the application of the formulae, bright green indicates high NDVI. Whereas red has low NDVI. So it’s quantifying vegetation by measuring the difference between near-infrared (which vegetation strongly reflects) and red light (which vegetation absorbs).

Image Credits : GISGeography

How NDVI is used

There are several sectors that use NDVI. In agriculture, NDVI is used for precision farming and to measure biomass. On the other hand, it is used to quantify forest supply and leaf area index in forestry.

In addition, NASA outlines on how NDVI is a good indicator for drought. When water limits vegetation growth, it has a lower relative NDVI and density of vegetation.

In reality, there are hundreds of applications where NDVI and other remote sensing applications are being applied in the real world.As mentioned before, satellites like Sentinel-2 ,Landsat and SPOT produce red and near infrared images. There are clearly more application to NDVI mentioned besides the given examples.

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