Just How Good is Drone Imagery?
Agribotix founder, Tom McKinnon, wrote an excellent post on Agribotix near infrared imagery, vegetation indices, and how cameras modified to image in the near infrared can be used to reliably monitor within-field crop variations.
I wondered how Agribotix imagery would compare with the gold standard of moderate resolution land imaging sensors, Landsat 8.
To find out, I went to the USGS EarthExplorer and downloaded two Landsat 8 collections (9-20-2014 and 10-06-2014) that were collected near in time to an Agribotix test flight over Anderson Farms, near Erie, CO on September 23rd. Fall of 2014 on the Colorado Front Range was dry, warm and clear for the several weeks over which this imagery was collected: atmospheric conditions were optimal for imaging. See the beautiful Landsat image from September 20th that captured much of the Colorado Front Range, including the Agribotix test plot.
Results for the Agribotix imaging system look great! Check out a plot of the mean modified-NDVI measured by the Agribotix imaging system when compared to m-NDVI calculated from the two dates of Landsat imagery. Normalized vegetation index values range between -1.0 and 1.0, so the fact that Agribotix-measured m-NDVI is within 0.1 of m-NDVI measured from Landsat on two separate dates on either side of the Agribotix acquisition is very encouraging.
Find full details below on how we arrived at the plot of Agribotix vs Landsat m-NDVI.
Landsat 8 images were calibrated to units of radiance (W / (m2 * sr * µm)).
Agribotix images are currently uncalibrated, so this experimental comparison with a well-characterized system like Landsat gives great insight into the radiometric quality of Agribotix imaging.
Agribotix images were acquired with the GoPro HERO3+ Silver Edition, collecting images as 8-bit JPEGs.
Individual Agribotix flight images were stitched using the Agribotix Image Processing Service.
Agribotix image georegistration was improved using the Google Satellite web map service as ‘truth’, and had a geopositioning accuracy within 1 to 2 meters. The geopositioning accuracy that the USGS provides for Landsat is exceptional; images were used as-is.
Last summer, Tom and his team created vegetation index images using the near infrared and green bands from the Agribotix sensor system. Here, we test creation of vegetation index images using the near infrared and blue band.
The blue and green channels have been selected for generating NDVI products from Agribotix imagery because Agribotix sensors don’t image in the red portion of the electromagnetic spectrum. Agribotix installs a custom filter in camera systems that blocks red light, and allows imaging of the near infrared, green and blue (NGB) portions of the spectrum. While the Normalized Difference Vegetation Index (NDVI) has traditionally been created using the near infrared and red channels, it turns out that most any channel in the visible range will work to highlight vegetation abundance and condition in imagery when compared with a near infrared channel. As discussed by Tom in his recent Blog, both green and blue channels offer opportunity for vegetation index creation because of the way visible light is reflected from plants relative to near infrared light. See the figure of the reflectance signatures of three species of shrub and trees (Toyon, Live Oak and Blue Oak), and how each of those plants reflect orders of magnitude more radiation in the near infrared than they do in the blue, green and red bands of the visible spectrum. You can also see in the figure how plants preferentially absorb both blue and red light for use in photosynthesis, and reflect more green light. This is the basis for our using the near infrared and blue channels in the calculation of our ‘modified’ normalized difference vegetation index.
Landsat and Agribotix Images were converted to a modified normalized difference vegetation index (m-NDVI) by applying the following equation:
m-NDVI = NIR – blue / NIR + blue
The Agribotix image was resampled to the 30-meter pixel resolution of Landsat using nearest neighbor resampling.
One hundred randomly sampled points were established for extracting pixel values from a test plot.
We’re happy to see that the Agribotix imaging system performs so well when compared to Landsat 8, one of the highest quality land imaging satellite systems, and gives us great confidence in our system for providing growers with on-demand in-field intelligence for precision agriculture.