Frequently Asked Questions

At Virginia Ag Drones, we practice regenerative farming, which involves using methods that improve soil health, encourage biodiversity, and reduce our environmental impact.

Multispectral drones are unmanned aerial vehicles equipped with specialized cameras that capture images in different spectral bands, including red, green, blue, near-infrared, and red-edge. The images captured by these cameras provide valuable information about the health, growth, and changes in crops, forests, and other vegetation. This information can be used to optimize agriculture and environmental monitoring practices, leading to more efficient and sustainable outcomes. 

• Improved Crop Management: Multispectral drones can provide farmers with valuable insights into the health and growth of their crops. This information can help farmers make informed decisions about irrigation, fertilization, and pest management, leading to higher crop yields and better use of resources. 
• Enhanced Environmental Monitoring: Multispectral drones can help monitor changes in vegetation and track the health of forests and other ecosystems. This information can be used to make informed decisions about land management, conservation, and reforestation efforts. 
• Accurate Mapping and Surveying: Multispectral drones can be used for mapping and surveying, providing highly accurate and up-to-date information about the topography, land use, and vegetation cover. 

“Normalized Difference Vegetation Index (NDVI) quantifies vegetation by measuring the difference between near-infrared (which vegetation strongly reflects) and red light (which vegetation absorbs).”

What is NDVI (Normalized Difference Vegetation Index)?

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

For example, when you have negative values, it’s highly likely that it’s water. On the other hand, if you have an NDVI value close to +1, there’s a high possibility that it’s dense green leaves.

But when NDVI is close to zero, there are likely no green leaves and it could even be an urbanized area.

NDVI is the most common index that analysts use in remote sensing. But how do you calculate it? What do NDVI values represent? How do Earth scientists use NDVI?

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

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

This is why our eyes see vegetation as the color green. If you could see 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.

The result of this formula generates a value between -1 and +1. If you have 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. When you have high NDVI values, you have healthier vegetation.

When you have low NDVI, you have less or no vegetation. Generally, if you want to see vegetation change over time, then you will have to perform an atmospheric correction.

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 colorbecause it is the same as how our eyes see.

In the formula, you can see how NDVI leverages near-infrared (NIR). So when we put the NIR band to display as red, we get color infrared. We say color infrared because near-infrared is in the red channel. As you can see below, the pivot irrigation vegetation should already be shouting out at you in bright red!

When you apply the formula, 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).

As you can see, it’s easy to pick out the pivot irrigation circles using NDVI. It makes it easier to classify because of NDVI.

We see several sectors using NDVI. For example, in agriculture, farmers use NDVI for precision farming and to measure biomass.

Whereas, in forestry, foresters use NDVI to quantify forest 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.

In reality, there are hundreds of applications where NDVI and other remote sensing applications are being applied in the real world.