Optical Remote sensing of the Cryosphere: Focus on velocity mapping
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1 Optical Remote sensing of the Cryosphere: Focus on velocity mapping Dr. Alexandra Messerli Norwegian Polar Institute ESA Remote Sensing of the Cryosphere Training Course 12 th June 2018 Longyearbyen, Svalbard
2 Overview Optical satellites and data Velocity: Why and How Methods Examples Summary
3 What are optical satellites? Sun synchronous Visible bands of EM spectrum
4 Optical Sensors Examples Freely available Sentinel-2 Landsat MODIS Commercial WorldView Pleiades SPOT RapidEye MicroSatellites Doves SkySat
5 Optical Sensors Examples Freely available Sentinel-2 Landsat MODIS Commercial WorldView Pleiades SPOT RapidEye MicroSatellites Doves SkySat
6 Optical sensors: Time-lapse Helheim Glacier Iceberg calving event Greenland James et al. (2014) Nature Geosciences
7 Optical sensors: Sentinel-2 6 or more passes over Svalbard every 5 days! Double with Sentinel-2A and 2B Bas Altena, Univ. Oslo 1
8 Uses for Optical Data in the Cryosphere Area Length Terminus Changes Outlines ELA (equilibrium line altitude) Facies (Zones) Hydrology Calving Ice flow Geohazards (e.g. lakes)
9 Uses for Optical Data in the Cryosphere Area Length Terminus Changes Outlines ELA (equilibrium line altitude) Facies (Zones) Hydrology Calving Ice flow Geohazards (e.g. lakes) Jack Kohler, NPI
10 Glaciers come in all shapes and sizes
11 Glaciers come in all shapes and sizes
12 Glaciers come in all shapes and sizes
13 Choosing optical data Cloud free Shadows Illumination
14 Considerations for Velocity mapping Time interval between acquisitions Spatial resolution of the image Features e.g. crevasses Conditions e.g. snow/no snow Stable ground e.g. rock outcrops
15 Considerations for Velocity mapping
16 Considerations for Velocity mapping
17 Orbits, artefacts and conditions Same orbit A. Kääb et al. 2016
18 Orbits, artefacts and conditions A. Messerli
19 Orbits, artefacts and conditions Tile 33XVH (R52 )09/08/2016 (R67) 10/08/2016
20 Velocity: Why? Glacier velocity is important to understand changes in glacier dynamics Mass balance (dynamic mass loss, calving, SLR)
21 NASA/Goddard Space Flight Center Scientific Visualization Studio
22 Velocity: Why? Feedback examples Changes in ocean temperature and circulation Changes in air temperature and extent of the melt season Changes at the glacier terminus leading to more calving Increased ice flow can lead to draw down of inland ice to the coast Thinning Lowering of overall elevation upstream Increase in area of the ablation zone Increase sensitivity to hydrologically induced speed up Increased drawdown ice to ocean.
23 Velocity: Why? Ice velocity is a major control on the dynamic mass loss from glaciers and ice sheets worldwide. To date no solid estimate of dynamic mass loss Therefore it is hard to include into predictions of future sea level rise estimates under the different RCPs Focus on areas of large rapid change Focus on glaciers with potential to mobilise large catchments ~6m ~60m
24 Velocity: How? Numerous methods: Ground based: GPS Stakes Terrestrial time-lapse cameras Satellites: Optical Radar
25 Velocity: Optical Methods Not a new method Many existitng methods using a range of algorithms. Most comonly used NCC Massive advancements due to increased computing power and image processing techniques Platforms almost soley satellite/aerial imagery Manual tracking Finsterwalder 1931, Voigt 1966 repeat terrestrial photogrammetry One of the oldest known examples: Flotron 1973, Unteraargletscher
26 Velocity: Optical Methods ImGRAFT (Messerli and Grinsted, 2015) Pointcatcher (James et al. 2016) OpenCV matchtemplate (2011) COSI- Corr (Caltech, Leprince et al. 2007) CIAS (Kääb and Vollmer, 2000) ImCorr (NSIDC Scambos et al., 1992) PyTrx (How et al. In rev) Many others...
27 Imgraft.glaciology.net Overview Using three main algorithms: 1. Camera: Used to project between pixel and world coordinates 2. Viewshed: calculates viewshed from a given point on the DEM 3. Template matching: Tracks displacement between two images using NCC NCC- From a defined template in one image, it searches for the same pattern/structure in a second image. The match with the highest correlation is returned as the positive match. From this you can determine the displacement of the template, providing the images are co-registered.
28 Overview Input for terrestrial application: DEM GCPs Images Input for aerial/satellite application Imagery (georectified)
29 Schematic Legend: =Template Image A Image B = Search region = Point on original grid(centre of Template) = Centre location of the search region = whtemplate (half width of template) = whsearch (half width of search region) =dxyo (Initial guess for x, y displacement) A. Messerli
30 Template Matching
31 Terrestrial Application A. Messerli
32 Terrestrial application A. Messerli
33 Satellite: Landsat-8 Stacked and averaged velocity map of Engabreen produced using ImGRAFT templatematch on Landsat-8 panchromatic band. Note some limitations Resolution (compared to glacier size) Speed v s time separation Shadowing Annual average at Engabreen 2013 A. Messerli
34 Applications: Antarctica Tweetin Ice Shelf Roi Baudouin Ice shelf East Antarctica run by ULB Brussels ~0.83md -1 At the location of the GPS ImGRAFT velocity indicates ~0.85md -1 Very similar values from two independent estimates Landsat-8 over 16 days A. Grinsted
35 Greenland A. Messerli, A. Grinsted, N. Karlsson Joughin et al 2017
36 Nioghalvfjerds (79 Glacier) Hogg et al.
37 Svalbard Landsat-8 Landsat velocity Mosaic A. Messerli
38 Svalbard Sentinel-2 A B C 20.Jun-09.Jul Jul-19.Aug Aug-21.Sept 16 Diff. B-A Diff. C-B Diff. C-A A. Messerli
39 Summary We monitor ice flow to understand glacier dynamics and help us to better constrain glacier and ice sheet contributions to Sea level rise Lots of freely available optical imagery of the cryosphere, satellite and terrestrial Can be used for a range of applications Caveats CLOUDS, Sun-Synchronous, acquisition frequency Need to select images with similar conditions Lots of different methods, most commonly used is NCC Different toolboxes with different platforms, strengths, adaptability and applications
40 References : data visualisation Greenland Ice Flow Khan, S. A., Kjær, K. H., Bevis, M., Bamber, J. L., Wahr, J., Kjeldsen, K. K., Bjørk, A. A., Korsgaard, N. J., Stearns, L. A., van den Broeke, M. R., Liu, L., Larsen, N. K., and Muresan, I. S.: Sustained mass loss of the northeast Greenland ice sheet triggered by regional warming, Nat. Clim. Change, 4, , doi: /nclimate2161, Nagler, Thomas & Rott, Helmut & Hetzenecker, Markus & Wuite, Jan & Potin, Pierre. (2015). The Sentinel-1 Mission: New Opportunities for Ice Sheet Observations. Remote Sensing /rs Joughin, I., B. Smith, and I. Howat A complete map of Greenland ice velocity derived from satellite data collected over 20 years, Journal of Glaciology Messerli and Grinsted (2015). Image GeoRectification And Feature Tracking toolbox: ImGRAFT, Geoscientific Instrumentation, Methods and Data Systems, 4(1). Messerli (2015) Surface velocities and hydrology at Engabreen: Observations from feature tracking and hydrometeorological measurements. (PhD thesis) Kääb, A. et al. Glacier Remote Sensing Using Sentinel-2. Part I: Radiometric and Geometric Performance, and Application to Ice Velocity. Remote Sensing 8, 598 (2016). Hogg, A., A. Shepherd, N. Gourmelen (2015) A first look at the performance of Sentinel-1 over the West Antarctic Ice Sheet, FRINGE 2015, Frascati, Italy, March 2015.
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