Between late June and late August 2015 I worked with the Antarctic Climate and Ecosystems Co-operative Research Centre (ACE CRC) to tidy up some long running loose ends with an airborne LiDAR project. This project is close to home – my PhD revolves around cracking some of the larger nuts associated with getting a science result from survey flights undertaken between 2007 and 2012. However, I’ve worked on one small subset of data – and the CRC was in need of a way to unlock and use the rest.
Many technical documents exist for the airborne LIDAR system, but the ‘glue’ to tie them together was lacking. In six weeks I provided exactly that. The project now has a strong set of documentation covering the evolution of the system, how navigate the myriad steps involved in turning raw logfiles from laser scanners, navigation instruments and GPS observations into meaningful data, and how to interpret the data that arise from the system. After providing a ‘priority list’ of flight data to work on, ACE CRC also took advantage of my experience to churn out post-processed GPS and combined GPS + inertial trajectories for those flights. The CRC also now has the tools to estimate point uncertainty and reprocess any flights from the ground up – should they wish to.
All of which means ACE CRC are in a position to make meaningful science from the current set of airborne LiDAR observations over East Antarctic sea ice.
Some of this – a part of my PhD work and a small part of the overall project – is shown here. A first-cut of sea ice thickness estimates using airborne LiDAR elevations, empirical models for snow depth, and a model for ice thickness based on the assumption that ice, snow and seawater all exist in hydrostatic equilibrium.
In late 2014 I was contracted by the Antarctic Climate and Ecosystems Cooperative Research Centre to analyse Antarctic shipping patterns from 2000 to 2014. The aim was to extend a planning report first published in 2008, and provide deeper insights into shipping patterns in order to plan for future shipping seasons. Obvious shipping routes arise as a combination of crew experience, average sea conditions, sea ice conditions and logistical constraints. Shipping is expensive, so great effort goes into minimising ship time required for a given task. Days can be saved or lost by the choice of shipping route. Hugging the coast in transit between stations is clearly the shortest route – but seasonally the most risky due to the presence of sea ice.
So what routes are being used most often? and do they work?
Mining data from shipping reports and ship GPS traces, I was able to map where and when ships had difficulty accessing stations. While plenty of maps exist showing ship tracks, there has never been any analysis of where and why ships had difficulty getting to stations. The map presented below is one of the first.
It shows a ‘heatmap’ – a frequency count of hourly ship positions per 25km square grid cell. Overlaid on the map are labelled round indicators of ship ‘stuckness’ due to sea ice conditions (as opposed to delay for operational purposes), and squares where ship to shore helicopter access was forced by ice conditions.
This map says nothing about seasonality, or the times of year which are most risky for ships. It does show a clear preference for routes to and from stations, in particular Casey and Davis. It also shows that generally ships transit between the two stations by heading north to skirt sea ice, or hugging the coast – which is clearly troublesome at times. For the most part, ships get to stations and back with few dramas.
As a final note, a colleague pointed out that this is also a map of bias in our knowledge of the Southern Ocean. That’s a much longer story…
My first job as a new consultancy! In August 2013 I was asked to solve a DTM problem for a local airborne surveying company, Spatial Scientific.
Starting with two collections of ASCII points at different spatial resolution and an orthophoto, my job was to find out which DTM was ‘right’ – or free of unusual artifacts. Given the initial data format, my first instinct is to treat them as point clouds. In this case CloudCompare is the first port of call, and showed a clear pattern of artifacts present when the DTMs were compared. This exercise gave me a spatial context for the DTM issues – but when the gridded DTM points are 3 to 10m apart and the DTM differences are in the order of decimetres, it is very hard to tell where exactly the artifacts come from.
The solution came in the form of an image analysis approach. Initial concerns about the DTMs came from a raster DTM differencing process, so I first reproduced this result using the rasterised ASCII data. Again, this alone did not provide any clues about which DTM was at fault.
It was impossible to detect the DTM problems by close examination, and difficult to know which DTM was the issue by comparison. So I enlisted the help of image processing methods. Detecting edges and slopes in each DTM clearly identified the culprit – edges and steep slopes/steps leaping from the screen where none should exist! Combined with the CloudCompare result, it gave a very clear picture of where the DTM had problems and pointed clearly at the source. Problem solved.
The cover photo for this site shows.. the back of my head, a Leica Viva TS 15, a prism, and a bright yellow, low cost, very effective instrument warming/battery box I’m very proud of! I’m acquiring prism lock using the remote control, before heading out to collect locations on a SIPEX II ice station. The sea ice surveying project was part of my work for the Australian Antarctic Division, and complements my PhD studies. It was a challenging task – nobody knew if the total station would play happily at -20 degrees celcius on drifting sea ice. It performed admirably, and the results will provide much-needed spatial glue for on, over and under ice spatial datasets collected on the voyage. Photo: Polly Alexander
Hi everyone, and welcome to Spatialised! – a consultancy aimed at solving spatial analysis, problems.
I can help you with point cloud analysis and manipulation, photogrammetric modelling methods, airborne LiDAR problem solving, DTM, DEM and DSM production. See my toolkit page for an up-to-date list of my in-house capacity and what I can do using your software and hardware.
With experience drawn from four reseach voyages to the Antarctic pack ice zone, I can also offer logistical and OH & S support for data collection in remote areas.
I’m currently based in Adelaide, South Australia – I’m happy to work locally or for remote clients anywhere in the world. I hope I can help you solve your spatial problems.
Interesting spatial discoveries, news and updates will appear here as they come – you’ll find links to contact information, a bit about myself and my spatial toolkit in the menu bar above.