In our new paper we report on some novel tech that uses the sensor in a smartphone for ultraviolet spectroscopy. It is low cost and based entirely on off-the-shelf components plus a 3-D printed case. The system was designed with volcanology in mind – specifically the detection of atmospheric sulphur dioxide, but may also have applications for supraglacial spectroscopy. As far as we know this is the first nanometer resolution UV spectrometer based on smartphone sensor technology and the framework can be easily adapted to cover other wavelengths.
This follows on from a Raspberry-Pi based UV camera reported in Sensors last year which was recently adapted to sense in the visible and near-infra-red wavelengths for use on ice. The plan now is to compare the images from the Pi-cam system to those made using an off-the-shelf multispectral imaging camera that detects the same wavelengths. A report of testing this camera system for detecting volcanic gases is available at Tom Pering’s blog here.
Raspberry-Pi and smartphone based spectroscopy could make obtaining high-spectral resolution data a real possibility for hobbyists and scientists lacking sufficient funds to purchase an expensive field spectrometer. The system is also small and light and therefore more convenient for some field applications than the heavy and cumbersome field specs available commercially and can easily be mounted to a UAV.
A new paper, led by Johnny Ryan, shows that a consumer grade digital camera mounted to a drone can be used to estimate the albedo of ice surfaces with an accuracy of +/- 5%. This is important because albedo measurements are fundamental to predicting melt, but satellite albedo data is limited in its spatial and temporal resolution and ground measurements can only be for small areas. Methods employing UAV technology can therefore bridge the gap between these two scales of measurement. The work demonstrates that this is achievable using a relatively simple workflow and low cost equipment.
The full workflow is detailed in the paper, involving processing, correcting and calibrating raw digital images using a white reference target, and upward and downward shortwave radiation measurements from broadband silicon pyranometers. The method was applied on the SW Greenland Ice Sheet, providing albedo maps over 280 km2 at a ground resolution of 20 cm.
This study shows that albedo mapping from UAVs can provide useful data and as drone technology advances it will likely provide a low cost, convenient method for distinguishing surface contaminants and informing energy balance models.
After testing the UAV performance in Svalbard in March, I realised the original ‘tripod’ landing assembly was not going to cut it for work in the Arctic. To prevent damage from landing in cryoconite holes and to spread the drone’s weight when landing on snow, I have added some ski’s modified from off-the-shelf landing gear for RC helicopters. This also has the added advantage that if one attachment point fails, the UAV is still landable, which is not the case for the tripod design.
As well as the ski’s, I have now added the Red-Edge camera’s down-welling light sensor to the top of the casing. This will automatically correct the images for changes in the ambient light field in each wavelength.
Here are a few things I learned after ten days of field testing the UAV multispectral data acquisition in Svalbard…
Video showing take off in stabilize mode, switch to loiter mode at about 5 m, quick control test then into automatic mission.
1. The UAV is surprisingly robust.
The aircraft was transported to the sites on a sled on the back of a snowmobile, hitting sastrugi and general lumps and bumps in the landscape, was hold baggage on three flights, launched and landed in snow, flown in winds and operated at temperatures down to -25 degrees. It is still is perfect working order and flew every mission without issue. I’m now pretty confident in the flight case and arrangement of kit inside, and trust that there won’t be significant flight issues in Greenland in summer.
2. Very low temperatures rapidly deplete the LiPo batteries
One noticeable, and unsurprising, effect of flying in these conditions was that the battry ran down very quickly. The toughest day was about -19 but with strong winds, and we only managed a 2.5 minute automated flight before the battery voltage dropped low enough for me to get twitchy and land the UAV manually.
3. A dGPS and ground control points will be essential for ground truthing the UAV imagery
It was impossible to accurately pinpoint ground sampling locations using handheld GPS and ground feature ID. This was especially true in Svalbard because of the homogenous snow cover, but will also be an issue in Greenland in the summer. This is not really surprising, but the need for an accurate dGPS location lock has been reinforced by the test flights.
4. The workflow for geotagging multispectral images using Photoscan is not as straightforward as I originally thought.
The red-edge camera does not output GeoTIFFs – they have to be postprocessed with ground control point data before they can be stacked and aligned. This is not a big problem, but good to know in advance of summer data collection.
5. New landing gear needed
The three-leg solution currently used on the UAV is better than I expected, but there is still the real problem that the legs sink into soft ground (e.g. snow) which risks pushing the camera lenses and the internal electronics into the snow. This could scratch the lenses or soak the electronics when the snow melts. Also, with the current model, a problem with any one of the three legs compromises the whole UAV because it becomes impossible to land flat. I’m going to develop something new before Greenland.
Video showing manual landing on a patch of compacted snow after an auto mission. It would be more stable on soft ground with a skid-type landing gear rather than the three legs. Includes heckling by Tedstone…!
6. Some a priori knowledge of image area can be useful
High resolution imagery is not available for all locations – for some of our field sites there was not sufficient google earth coverage to orient ourselves or draw a polygon by eye in Mission Planner. It is possible to estimate using the compass and the map scale, but with some existing knowledge of the GPS coordinates of the grid perimeter would help to plan an accurate mission.
7. It’s amazing how small the UAV looks, even when flying quite close by
Even flying the UAV at 30 or 40 m elevation, it quickly becomes difficult to keep track of its orientation when it flies a hundred metres or so on a mission. This does have implications for the length of mission I’d be comfortable flying, since I want to be able to rescue it manually if there are any GPS or autopilot issues – maybe I’m soft but my comfortable range is less than the CAA ‘dronecode’ distance limits- even though these do not currently apply in Svalbard/Greenland. Of course, the conditions (esp. visibility) affect what feels comfortable.
8. The controller is awkward in gloves
At -25 C gloves were pretty essential, but it is also difficult to have fine control over the switches and sticks on the controller. I was flying in a very thin pair of gloves or gloveless, which meant my fingers quickly went numb, especially when there was any wind. This will be less of a problem in Greenland in summer, but I will still get some warmer, thinner gloves with rubber finger pads to help with the UAV control in the cold.
9. Pre-flight checklists are invaluable
It’s so easy to overlook or forget something in harsh conditions or when rushed or excited. The written checklists developed before we went out to Svalbard were extremely useful for making sure everything went smoothly. These are a condition of CAA compliant flights in the UK and our experience in Svalbard demonstrates why! I will probably add a few additional checks or reorder a few things before our Greenland deployment – site specific things like take off and landing zone preparation (in Svalbard it was often a compacted snow platform, in Greenland I intend to use plyboard to avoid melt ponds and cryoconite holes).
Having made successful UAV test flights at home in the Peak District, we have relocated to Svalbard for a week to test the equipment in the most challenging possible conditions. We are flying in temperatures as low as -10 C, in gusty wind and after pulling the UAV to the field site in its flight case on a sled behind a snow scooter. It is taking off and landing on platforms of compacted snow.
Nevertheless, so far it has performed magnificently. There has been no damage even to the delicate props during transport, suggesting the flight case is sufficient, and the autopilot seems to be deftly dealing with the gusty wind. It interfaces quickly with the laptop and it is easy to load a preprogrammed flight, even in these very cold conditions. It was quite difficult to take of and land in ‘stabilize’ mode with the wind though.
A few modifications I’d still like to make include a new landing gear design. The three legs are OK for flat, hard ground, but for ice and snow I think it would be much better to have something with skids, or possibly a circular base, as this would protect it from landing with a leg in a cryoconite hole and prevent the camera touching the snow if the legs sink. Also, at the moment any problem with a single leg compromises the UAV’s ability to land, whereas a unit that joins to the UAV in more than one place would give more redundancy.
The battery life is also significantly reduced in these temperatures. That’s to be expected, but I’m wondering if there are more mods we can make to extend the flight time for surveying big areas in Greenland.
This performance gives me hope for a successful mapping mission in Greenland, where the UAV will be used to map the ice surface in much less demanding conditions than we are experiencing here, but will be worked on more ambitious missions (higher, further, more frequent). We are continuing to test the UAV protocol in the field for a few more days, but will now focus on our methodology for pairing UAV flights with ground sampling rather than flight tech and UAV control.
As a bonus, we were treated to a spectacular aurora on the way in…
The past few weeks have been spent working down in the robotics department at the University of Sheffield building a UAV (unmanned aerial vehicle, a.k.a drone). Ultimately, it will be used to make measurements of spectral reflectance of the ice surface in Greenland. It’s been great fun working in robotics – entering the lab is like walking onto the set of Robot Wars! UAV expert Owen McAree has been a huge help in developing the hardware and software for the drone – affectionately known as ‘albedrone’ in recognition of the albedo work it will enable – and we have now made successful test flights.
It began as an off-the-peg Steadidrone Mavrik quadcopter. However, we have made several modifications. We added a new brushless gimbal powered from the autopilot, machined a new mount that allowed us to better balance the camera and minimize the power being drawn by the gimbal, and added a GPS that can be used to trigger the image capture. We have also invested significant time into tuning the flight parameters and making it as stable and easy to fly as possible.
Flying can still be quite challenging, so I also invested in flight simulator software that interfaces with the real UAV controller, meaning I have been able to get the hang of flying safely without endangering the UAV. Significant time and effort has also gone in to writing a flight manual and logbooks for the batteries, build modifications and flight records.
We have been flight-testing the UAV at the University of Sheffield’s High Bradfield site and have now successfully made a pre-programmed flight and captured overlapping images in five spectral bands. Some examples are shown below. These are interesting as they were captured over an area with a thick cover of green vegetation, perfect for NDVI analysis. Next jobs are to a) keep modifying the UAV to extend the flight time and perhaps add some additional sensors, and b) test the software that will stitch the images and analyse the spectral information…