NDVI, VARI, and Other Vegetative Indices for use with Drones

Quantify the health and stress of crops to identify growth, disease, drought, and pests

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By applying a vegetation index to your aerial farm imagery, you can immediately see trends in the relative health of your crops.

Few statistics help a farmer better understand their crops than vegetation indices. NDVI, VARI, GRVI, GLI, NDRE, SAVI, and others like them create farm field health maps from visual and multispectral aerial imagery taken from a drone. These indices apply to a litany of decisions that farmers make when managing crops.


A range of vegetation indices are available in our agriculture mapping software, PrecisionAnalytics Agriculture.

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How do farmers use a vegetation index?

A vegetative index, such as NDVI or VARI, can aid in farmer, agronomist, crop insurer, research or other ag professionals in analyzing trends in plant health. In PrecisionAnalytics Agriculture, our agricultural mapping and analysis tool, users can apply the index at the click of a button, applying a green versus red overlay of their aerial imagery. The green areas show where plants are healthy. Various shades of orange, yellow, and red denote diminishing vigor.

For example, anytime from plant emergence to harvest, a corn farmer might upload multispectral images of their cornfields into PrecisionAnalytics Agriculture and apply NDVI. In observing the multicolored output, they might notice that a part of the field has turned orange and red. This indicates that the plants are browning, yellowing, or becoming pock-marked. The plants in this area could be drought-stressed, flooded, under- or over-fertilized, or wracked with pests, weeds, or disease.

As before, ground-truthing is the best way to diagnose a particular issue. But, the vegetative index in PrecisionAnalytics Agriculture gave the farmer a signal that they needed to focus on that particular zone of their field. They can now go and assess the cause and decide how to mitigate the issue.

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How is a vegetation index calculated?

A vegetation index (VI) is a spectral calculation of two or more bands of light that highlights vegetative properties. In a field of crops, it allows the viewer to make comparisons of the photosynthetic activity across your area of interest.

In calculating a vegetative index, PrecisionAnalytics Agriculture cycles pixel-by-pixel through imagery and to calculate the spectral values of each pixel. It uses this calculation to assign a value representing some version of plant health for each pixel in the image. These values are then turned into the colorized map you see in PrecisionAnalytics agriculture when the vegetative index calculation is complete.

Vegetative indices express the relationship of each pixel’s spectral value to the other. (That’s why, for a field that’s all green and lush or dead and brown, the visual output could appear monochromatic or consisting of a single color.) Because these are relative differences in plant health across your field it is important to apply only to the areas you are interested in.

The methodology behind these calculations is slightly different for each vegetative index that we offer.

Talk to our team about your goals to learn if drone-based aerial intelligence is right for your farming operation.

 

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Which vegetation index should I apply to my drone aerial imagery?

There are credible differences to each of the vegetation indices available in PrecisionAnalytics Agriculture. Below, we explore the purpose of each. 

However, the vast majority of farming users are best-off testing various indices and choosing one or two that they find best reflects their crop health. Regardless of its stated purpose, a vegetation index should reflect the reality “on the ground.” Results of the indices can vary widely depending on the type of crop, quality of soil, and other environmental conditions.

Normalized Difference Vegetation Index (NDVI) is the most commonly used index, due to its versatility and reliability in reporting general biomass. For new drone intelligence users, it’s the best vegetation index to start. But, NDVI does have its limitations, which we discuss below.

Here’s a list of the vegetation indices in PrecisionAnalytics Agriculture, along with some technical detail as to how they differ:

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Normalized Difference Vegetation Index (NDVI)

In this algorithm, the red and near-infrared (NIR) bands of imagery are evaluated to calculate a vegetation index value. It’s designed to detect differences in green canopy area, emphasizing the green color of a healthy plant. It’s commonly used as an indicator of chlorophyll content in several different types of crops, including corn, alfalfa, soybean, and wheat.

The difference between the green and red values of the image differentiates between plants and soil.

Though the reliability and simplicity of NDVI have earned its popular use, it does have limitations. For instance, visual red light used by NDVI is generally absorbed at the top of the plant canopy. So, lower levels of vegetation don’t appear as strongly in NDVI outputs. The more leaves there are, such as in trees or late-stage corn, the more this limitation skews the NDVI. Also, grasses and cereal crops, as well as certain row crops during later growth stages saturate with chlorophyll, making it hard to detect variability in the NDVI output.

Subsequently, we’ll discuss alternatives to NDVI, available in PrecisionAnalytics Agriculture, that help to mitigate some of the popular algorithm’s limitations.

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Enhanced Normalized Difference Vegetation Index (ENDVI)

Traditional NDVI uses only red and near-infrared spectral data. Enhanced Normalized Difference Vegetation Index (ENDVI) is a close equivalent that uses blue and green visible light, instead of solely red, as in the method of the standard NDVI algorithm.

The ENDVI algorithm better isolates plant health indicators, as the plant’s absorption of blue light and high reflectance of green and near-infrared waves is a reliable marker of plant health.

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Visual Atmospheric Resistance Index (VARI)

The Visual Atmospheric Resistance Index is a vegetation index that was originally designed for satellite imagery. It’s minimally sensitive to atmospheric effects, allowing for vegetation to be estimated in a wide variety of environments.

As sunlight reaches the earth’s atmosphere, it is scattered in all directions by the gasses and particles in the air. But, blue light tends to scatter more than all the other colors because it travels in smaller wavelengths than the rest of the visual spectrum. Therefore, we see the sky as blue most of the time. This vegetation index accounts for to presence of blue in its calculation of spectral data.

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Green Difference Vegetation Index (GDVI)

Green Difference Vegetation Index (GDVI) is an index of greenness or photosynthetic activity of living plants. It’s sensitive to the variation of chlorophyll content in plants.

If a plant is taking in the right amount of water or nitrogen, it becomes greener and healthier. These elements fuel the photosynthesis process through the production of chlorophyll. Because of this, the GDVI algorithm is most commonly used to determine water and fertilizer uptake across a field. Use GDVI results to allocate your water and fertilizer.

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Normalized Difference Red Edge (NDRE)

When you’ve captured data using a sensor that features red edge data, you can apply the Normalized Difference Red Edge (NDRE) index. It’s sensitive to chlorophyll content, changes in leaf area, and the effect of soil in the background. For that reason, it’s helpful for determining the relative nitrogen content of your crops in the field, agnostic of the content that’s in the soil.

For mid- and late-season crops, which have accumulated high levels of chlorophyll, NDRE takes advantage of the near-infrared light that has penetrated to lower leaves past the canopy. This makes NDRE superior to NDVI for use in determining vegetative vigor late in the growing season.

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Soil-adjusted Vegetation Index (SAVI)

Where NDVI outputs tend to vary with soil color, soil moisture, and saturation effects from high-density vegetation, Soil-adjusted Vegetation Index (SAVI) accounts for differential red and near-infrared extinction through the vegetation canopy. It minimizes soil brightness and emphasizes data from vegetation.

SAVI is particularly useful in circumstances where soil quality varies substantially within a single given area of interest.

Results

Accelerating agriculture research

An agribusiness research company tested drones on the crops it grew at its research stations. Instead of surveying plots manually it deployed drones. The results:

2.5X more efficient than sampling
25% more accurate than hand counts
Surveyed full plots instead of sampling 10% to 40% of the field

Read the case study →

BASF quantified turf improvements

By quantifiably measuring sprig growth and green quality, BASF Turf and Landscape proved a 24 percent increase in Green Level Index for sprigs treated with Lexicon®. Now, according to Gary Myers, CGCS, BASF Pinehurst Project Lead, BASF is able to “use the data as the foundation of [their] presentations to prospective customers and golf course superintendents.” 

Read the case study →

Why PrecisionHawk?

We’ve built our offerings to scale. From investing in geospatial science expertise to understanding the regulatory environment, we’re able to support a one-time flight or a fully integrated enterprise aerial intelligence program.

 

Vegetation indices, on-demand

PrecisionAnalytics Agriculture pre-processes vegetative indices, giving users the ability to apply an index at the click of a button.

10 years of spectral science

We first started using vegetation indices like NDVI and NDRE over vineyards, when we were WineHawk: an aerial robotics company for vintners. Since then, we’ve built our library of vegetation for a wide range of crops.

Trusted by leading agrochemical and seed companies

Crop scientists and researchers at the world’s largest agronomic enterprises use our vegetation indices to develop their crops.

Consultants to top agriculture technology research institutions

Our vegetation indices were developed in partnership with the USDA, North Carolina State University, and many other organizations that are advancing the science of agronomy.

Compatible with common sensors

We’ve worked with manufacturers MicaSense, Headwall, and DJI, to optimize our vegetative indices for use with a range of visual, thermal, multispectral, and hyperspectral sensors--purpose-built for agriculture.

Ag tech by ag pros

Our industry-leading team of Ph.D. and remote sensing-accredited geospatial technologists have tested and attenuated our vegetation indices using millions of acres of crop data.

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