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Multispectral drone data is quietly revolutionizing agriculture and forestry—are you missing out?

Multispectral drone data is quietly revolutionizing agriculture and forestry—are you missing out?

Drones equipped with multispectral cameras are transforming agriculture, forestry, and environmental research by providing detailed, on-demand data that overcomes the limitations of satellites. Before investing, it’s crucial to understand how this technology works to ensure the best results and long-term ROI.

Emily Loosli

Multispectral drone for agriculture and forestry

Multispectral data and its use in vegetation analysis is not a new thing. Agronomists, farmers and forestry professionals as well as environmental researchers have depended on this information for decades, thanks to satellite imagery. However, satellites introduce their limits. Namely: lower resolutions, gated access and interruptions with cloud cover. Drones with multispectral cameras, however, address all of these limits. Suddenly, farmers, foresters and researchers have a powerful, on-demand solution in their hands.

Indeed, the tech revolution is taking the agricultural industry to an entirely new level, since rapidly-collected data can now be fed into automated machines to lower the overhead on irrigation and the application of fertilizers and pesticides. Likewise, forestry and environmental fields are impacted with this highly-detailed, bulk data on large areas of land or sea. As climate change introduces fast shifts in planting zones, water availability and pests, drones emerge as dynamic tools to give everyone a comprehensive view of what’s happening and the means to adjust tactics.

Before running out and getting a UAV multispectral camera, however, it behooves us to know how all of this works so we can get the right equipment for the best multispectral drone ROI over the long run. 

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How does multispectral drone data work, technically?

electromagnetic spectrum and visible light spectrum illustration
Only a fraction of wavelengths along the electromagnetic spectrum are visible to the human eye.

The electromagnetic spectrum is a wide range of wavelengths, each carrying information. Only a fraction of these are visible to the human eye. When we look at anything, what we can see is a reflected color spectrum of reds, greens and blues (RGB) that we interpret as any combination of the colors of the rainbow based on their wavelengths. Normal RGB cameras filter wavelengths for the information that we can see. Multispectral cameras are equipped with lenses and filters that pick up wavelengths beyond the visible spectrum—in the direction of infrared wavelengths.

Why is this important for analyzing crops and soil? Plants and soil absorb and reflect wavelengths from sunlight depending on their contents. For example, when a plant is healthy and engaged in photosynthesis, it will absorb a lot of red and blue light and reflect green and a lot more infrared light.  

When you look at a chlorophyll-rich (green) plant, you are seeing the wavelength of light that the plant reflects, not the ones it absorbs (blue and red). Chlorophyll production in leaves results in a lot of infrared light reflectance, but we can’t see this. Multispectral sensors can record this so we can. This is useful, since reduced infrared reflectance signals lower chlorophyll production before we see it with the naked eye, so we can proactively track plant health via multispectral UAV sensors.

light wavelengths reflecting off leaves
Healthy leaves reflect a lot of near-infrared light, which we can’t detect but multispectral sensors can. So this (red to NIR) part of the spectrum is key to tracking plant health.

Researchers and agronomists have been working with multispectral data for decades, and they’ve organized the reflectance ratios into indices that are used for analyzing crops and detecting anything from irrigation stress to pest infestations to weeds. A couple of common indices are the Ratio Vegetation Index (RVI) and the Normalized Difference Vegetation Index (NDVI), which is the most common index in remote sensing.

Since red and infrared light are right next to each other on the spectrum, and they are treated the opposite way in healthy plants, the zone right between them is called red edge. MicaSense makes sensors that pick up this band as powerful early-detection tools for plant stress. The Normalized Difference Red Edge (NDRE) index is a powerful analytical tool for reading this band and detecting changes in plants at their earliest stages. 

The three kinds of resolution that matter

Spatial resolution is where UAV multispectral information shines, because it refers to the pixel density, resolution and accuracy of the data. This is the key differentiator when comparing spectral data across the two methods of capture, because the resolution of satellite data lingers around a meter, while UAV can pick up details down to a centimeter.

Temporal resolution implies how frequently multispectral information is recorded on the same area. With drones, this resolution can be increased to whatever degree necessary to achieve management aims.

Spectral resolution highlights how much of the spectrum is being captur

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