Why the Bungee Cord!?

Over the past year, many of my clients have been asking me why we use a bungee cord to launch our fixed wing Hornet drone here at Agribotix.  Why not make a drone that can be hand launched? - they ask.  Some have even suggested that a catapult launcher might be more convenient for field operations.  I've had a lot of experience with catapults and even built my own; one of which can be viewed here. Catapults offer consistent, reliable launches but with a price: catapults are expensive because they condense several pounds of tension into about a four foot span that a bungee would normally spread over ±40 yards; catapults need to be well built to withstand the stresses they endure, and even then they often break down.  Hand launches on the other hand are unreliable and often result in crashes due to operator error.  I remember this well from my time in the Army where I saw a lot of this happening.

But assuming we are only working with drone professionals who know how do a hand launch properly (and also know better than to put the propeller on backwards) there is yet another reason not to go with the hand launch: doing so limits the range of your drone!  This is because in order for your drone to be hand launched, it needs to have a wingspan that can produce adequate lift for takeoff at slower (hand launching) speeds.  Once that same drone is in the air at cruising speed, its wingspan will produce excess drag, this is why manned aircraft have flaps.  Flaps produce the extra lift at takeoff and landing needed to maintain flight at lower speeds but retract once the plane is cruising in the air so as to reduce the drag of the wingspan.  Although these work well on larger airframes, they tend to complicate the design of sUAS systems to a point where they become more of a burden than an asset.

Bungee cord launches offer a happy medium between the heavy costs of catapults on the one hand, and the limiting range (and crash potential) for a hand-launched drone on the other.  No matter how much battery weight your hand-launched drone is currently carrying, it could carry more and go farther if it were bungee launched.  We now have a video here showing how to use our bungee cord and how it is put together (1:30) -to be sure, there is a short setup time of about 8-12 minutes, but we feel this is a small drawback when considering the alternatives discussed above.

 

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.

Landsat 8 color infrared composite image acquired over the Colorado Front Range, September 20th, 2014.  The test plot is inside the red box.

Landsat 8 color infrared composite image acquired over the Colorado Front Range, September 20th, 2014.  The test plot is inside the red box.

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.

Agribotix modified-NDVI from September 23rd compared with m-NDVI from Landsat on September 20th and October 6th.  Error bars are standard deviations of 100 randomly sampled pixels.

Agribotix modified-NDVI from September 23rd compared with m-NDVI from Landsat on September 20th and October 6th.  Error bars are standard deviations of 100 randomly sampled pixels.

 

Preprocessing

Calibration

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. 

Image Stitching

Individual Agribotix flight images were stitched using the Agribotix Image Processing Service.

Image Registration

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.  

Modified-NDVI

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

Several vegetation signatures with the blue, green, red and near infrared portions of the spectrum highlighted.

Several vegetation signatures with the blue, green, red and near infrared portions of the spectrum highlighted.

Spatial Resampling

The Agribotix image was resampled to the 30-meter pixel resolution of Landsat using nearest neighbor resampling. 

Sampling

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.