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RSS FeedsRemote Sensing, Vol. 11, Pages 1151: SenDiT: The Sentinel-2 Displacement Toolbox with Application to Glacier Surface Velocities (Remote Sensing)


15 may 2019 08:01:44

Remote Sensing, Vol. 11, Pages 1151: SenDiT: The Sentinel-2 Displacement Toolbox with Application to Glacier Surface Velocities (Remote Sensing)

Satellite imagery represents a unique opportunity to quantify the spatial and temporal changes of glaciers world-wide. Glacier velocity has been measured from repeat satellite scenes for decades now, yet a range of satellite missions, feature tracking programs, and user approaches have made it a laborious task. To date, there has been no tool developed that would allow a user to obtain displacement maps of any specified glacier simply by establishing the key temporal, spatial and feature tracking parameters. This work presents the application and development of a unique, semi-automatic, open-source, flexible processing toolbox for the retrieval of displacement maps with a focus on obtaining glacier surface velocities. SenDiT combines the download, pre-processing, feature tracking, and postprocessing of the highest resolution Sentinel-2A and Sentinel-2B satellite images into a semi-automatic toolbox, leaving a user with a set of rasterized and georeferenced glacier flow magnitude and direction maps for their further analyses. The solution is freely available and is tailored so that non-glaciologists and people with limited geographic information system (GIS) knowledge can also benefit from it. The system can be used to provide both regional and global sets of ice velocities. The system was tested and applied on a range of glaciers in mainland Norway, Iceland, Greenland and New Zealand. It was also tested on areas of stable terrain in Libya and Australia, where sources of error involved in the feature tracking using Sentinel-2 imagery are thoroughly described and quantified. Digg Facebook Google StumbleUpon Twitter
43 viewsCategory: Geology, Physics
Remote Sensing, Vol. 11, Pages 1153: Domain Adversarial Neural Networks for Large-Scale Land Cover Classification (Remote Sensing)
Remote Sensing, Vol. 11, Pages 1152: Creating a Lowland and Peatland Landscape Digital Terrain Model (DTM) from Interpolated Partial Coverage LiDAR Data for Central Kalimantan and East Sumatra, Indonesia (Remote Sensing)
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