Supporting Agriculture with Drones

Nigeria

Drones capture essential data to help farmers take the most out of their crops. The drone data is processed into NDVI to differentiate the areas that are vegetated and doing well from those that are suffering. NDVI always ranges from -1 to +1, and there are negative values. It’s likely water. On the other hand, if NDVI values are close to +1, there’s a high possibility that it’s dense green leaves. But there are likely no green leaves when NDVI is close to zero. 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. NDVI works best with near-infrared data.

Our crowddroner Otete created this map in his hometown in Nigeria. The drone created this map has an optical RGB sensor, so a VARI algorithm is used. It stood for Visible Atmospherically Resistant Index (VARI) and was designed and tested to work with RGB data rather than near-infrared (NIR) data. It is a measure of "how green" an image is. VARI is not a substitute for a NIR camera, but it is meaningful when working with non-NDVI imagery. VARI also measures the reflectance of vegetation versus soil.

In the images below, you can see how we compare the VARI and RGB images from the same location shown above. The red spots are areas that have a smaller amount of trees and greenery.

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* Estimations based on typical resolutions

To me, there's nothing more special than providing food for other families, and that's what we do out here, and I must keep that in mind as I close.

Otete Mandela

Otete Mandela

Drone operator

Otete is a Crowddroning operator based in Nigeria and submitted these images and maps as part of the Drones 4 Sustainable Forestry and Urban Green Challenge by GLOBHE to highlight local community challenges in forests in his home country. Otete is an experienced drone operator and a valuable member of GLOBHE's Crowddroning community.