interpolation. Other popular algorithms include natural neighbor and
kriging. Click OK and dismiss the popups that follow. Soon you’ll see a
black and white gridded temperature field appear!
We can get the output to look a little nicer by right-clicking on
the gridded layer in the layers panel and selecting “Properties.” In the
popup, set the style render type to “Singleband pseudocolor” and
pick a color pallet that suits you (Figure 19). I chose the YlOrRd; it
naturally gives a sense of increasing temperature.
Spend a while playing with these settings and dressing up your
map (Figure 20). You can find shape files of roads, counties, states,
etc., online and import them to make your map look even better.
Since I was flying over a small park, it did not add a lot to my map.
In the online materials and GitHub repository, you’ll find data
from two small flights and a Python notebook with some more
plotting analysis. The notebook can easily be run on your data to
produce plots of altitude, course, speed, and even colored scatter
plots of temperature (Figure 21).
I also plotted the ambient temperature from the sensor to be
sure that we were not seeing some other variable when looking for
the sidewalk; it appears to be uncorrelated and relatively constant
In my case, I found a sidewalk that admittedly we all knew was
there. With a flight planner, I could easily see running this setup over
a field to check for irrigation leaks or plugged areas; emergency
responders could use similar technology to look for hot spots after
fires; and scientists could measure lake or ocean surface temperatures
during seasonal changes.
Temperature is such a crucial variable to so many processes. Being
able to measure it from the air could be of great utility to many
The color sensor didn’t do as well. It appears that the red, green,
and blue bands have somewhat overlapping and non-linear responses.
My results showed the grass and sidewalk as being varying shades of
brown — not exactly right. My guess is with some careful calibrations,
this could be corrected. There are a few other sensors like the
TCS34725 that could be worth trying out as well.
Now that you’ve got a sensor platform and a set of tools
to look at the data, the only limit is what sensors you can
imagine to fly with.
Go out and collect some data! Analyzing data always
turns up something interesting (though often what you are
looking for and what you find may be very different).
Be sure you post your data online (even as a pull-request
to the GitHub repository for this article) so others can have a
look as well. Until next month, fly safely! SV
Figure 20: The gridded image definitely shows some
gridding artifacts. Some of these may be reduced with
larger grid cells and changing the gridding settings, but a
denser point pattern is the best solution.
Figure 21: The Python notebook included with the article
can produce scatter plots with the points colored by any
variable you like. Here, the sidewalk is easily visible in
the warmer colors.
Figure 22: The ambient temperature measurements didn’t vary much
or in a way that was systematic with the traverses across the
sidewalk. It would appear that the sensor really did pick up a
significant temperature change at the surface.
38 SERVO 08.2017