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Bioalbedo: new model and TCD paper

I’m very pleased to report our new paper is now in open discussion in The Cryosphere. The paper presents a new model for predicting the spectral bioalbedo of snow and ice, which confirms that ice algae on ice surfaces can change its colour and by doing so enhance its melt rate (“bioalbedo”). We also used the model to critique the techniques used to measure bioalbedo in the field. The model is based on the SNow ICe and Atmosphere Radiative model (SNICAR), but adapted to interface with a mixing model for pigments in algal cells. We refer to the coupled models as BioSNICAR.

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The darkening effect of algal growth (bioalbedo) in the visible wavelengths can be seen in this UAV image of our 2016 field camp at S6 on the Greenland Ice Sheet

The model uses Mie theory to work out the optical properties of individual algal cells with refractive indices calculated using a pigment mixing model. The user can decide how much of each pigment the cell contains, the cell size, the biomass concentration in each of n vertical layers, the snow/ice optical properties, angle and spectral distribution of incoming sunlight and the mass concentration, optical properties and distribution of inorganic impurities including mineral dusts and black carbon (soot). From this information, the model predicts the albedo of the surface for each wavelength in the solar spectrum. This can then be used to inform an energy balance model to see how much melt results from changes to any of the input values, including growth or pigmentation of algae.

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This figure shows the effect of algal cells with different pigmentation on spectral albedo of snow/ice. In A) the cells have 1.5% chlorophyll a and 10% photoprotective carotenoids, B) 1.5% chlorophyll a and 5% photoprotective carotenoids, C) 1.5% chlorophyll a and 1% photoprotective carotenoids and D) 1.5% chlorophyll a only. These percentages are % total cell dry mass. The biomass is shown in the legend, which applies to all four subplots.

 

The model shows that smaller cells with photoprotective pigments have the greatest albedo-reducing effect. The model experiments suggest that in most cases algal cells have a greater albedo-reducing effect than mineral dusts (depending upon optical properties) but less than soot.

 

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A) An equal biomass concentration with varying vertical distribution in the snow/ice; B) mineral dusts in varying mass concentrations doing a good job of recreating the ‘red-edge’ (see previous post); ) mineral dusts obscuring the spectral signature of algal cells; D) the effect of water in interstitial pore spaces. Ice grains are 1000 microns in diameter and the legend refers to the thickness of liquid water coating around the grains (microns). Note the shift of the absorption feature centred at 1030 nm towards shorter wavelengths when more water is present.

 

As well as making predictions about albedo change, the modelling is useful for designing field experiments, as it can quantify the error resulting from certain practises, such as using devices with limited wavelength ranges, or neglecting to characterise the vertical distribution of cells. I’ll cover this in some further posts. The most important thing is metadata collection, since standardising this enables the measurement conditions to be as transparent as possible and encourages complementarity between different projects. Importantly, following a protocol for albedo measurements and collecting sufficient metadata will make it easier to couple ground measurements to satellite data. We outline two key procedures: hemispheric albedo measurement, and hemispherical-conical reflectance factor measurement. To accompany the discussion in our paper, we’ve produced some metadata collection sheets that might be useful to other researchers making albedo measurements in the field (download here: metadata sheets) and made our code and data available in an open repository.

Weston Park Museum: Everyday Wonders

The Western Park Museum recently got in touch to talk about their excellent Arctic World exhibition. I know the museum well as it is a two-minute stroll across the park from my office in Sheffield, so I was really pleased to offer some thoughts. The idea was to produce a new book (‘Everyday Wonders: 50 objects from Weston Park Museum’) that gives a whistle-stop journey through the museum, stopping by at 50 of the most iconic and interesting artifacts on display. I was asked to comment on Snowy, the polar bear – the centre-piece of the Arctic World exhibition. A photo of my contribution to the book is below, but I encourage anyone who is interested to visit the museum and perhaps purchase a copy for themselves.

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The Musueum can be found at:

@MuseumsSheffield

http://www.museums-sheffield.org.uk/

Video: Alive and Well: Microbes Add to Melting of Greenland Ice Sheet

Peter Sinclair and Yale Climate Connections have released an excellent video detailing the role of microbial life in driving Greenland Ice Sheet melt, featuring several researchers from the Dark Snow Project and Black and Bloom. We were lucky enough to have Peter with us for a couple of days at the beginning of our trip in 2016.

Svalbard UAV: Lessons learned

 

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.

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Taking off on a particularly cold flight, near Telbreen, Svalbard

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).

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One of the field sites at Reiperbreen, Svalbard

Svalbard UAV tests

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.

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Day 1 field site, Svalbard, Norway

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.

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Day 1 Field Site, Svalbard, Norway

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…

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A picture that doesn’t do it justice…!

 

High bradfield: UAV test flights

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.

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Overview of the UAV and camera

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.

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A close up of the UAV set-up with the red-edge camera in downwards-looking position

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…

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The same area imaged in five spectral bands using the red-edge camera on the UAV. 1 = blue (475nm) 2 = green (560 nm) 3 = red (668 nm) 4 = NIR (840 nm) 5 = red-edge (717nm)