sUAS vegetation mapping: First successful data collection

After conducting our initial mission, we realized a problem with the images taken from the Tetracam. We were missing images from one of the lenses, which prohibited us from calculating normalized difference vegetation index (NDVI). After trying to resolve the issue with Tetracam support, we had to send the camera back to Tetracam headquarters for repairs. Sending the camera half-way across the country cost us valuable research time as we intended on monitoring vegetation health throughout the growing season.

Once the issues were resolved, I performed pre-flight inspections and took off!

Jeff flying the 3DR Solo

The mission went smooth and I was able to capture our first useful data from the Tetracam. I also collected biomass samples for correlation between data received from the air and live biomass weight once it was dried.  I then uploaded the images and began processing in Pixel Wrench, Pix4D, and ArcMap to calculate NDVI from the orthomosaic.

After images are assigned a GPS position, Pix4D is able to locate the images and create a ray cloud shown here:

RayCloud produced in Pix4D

From the ray cloud, an orthomosaic is created that we can upload to ArcMap and calculate NDVI. NDVI is a measure of live green vegetation in a target and allows us to analyze vegetation health remotely. We are still working on the image calibration procedure, but our preliminary NDVI calculation from the first flight is shown below.

NDVI values range from -1 (red ) to 1 (blue). The blue represents healthy vegetation while red areas correspond with unhealthy vegetation. Anomalies exist within the images and need further explanation.

While these accomplishments were significant, we were still seeing artifacts (straight lines and vignetting) in our imagery and resulting orthomosaic that will require further work.