scholarly journals Rapid Volume Loss from Two East Greenland Outlet Glaciers Quantified Using Repeat Stereo Satellite Imagery

2014 ◽  
pp. 1-5
Author(s):  
Leigh A. Stearns ◽  
Gordon S. Hamilton
Keyword(s):  
Satellite Imagery ◽  
Volume Loss ◽  
East Greenland

scholarly journals Multi-decadal record of ice dynamics on Daugaard Jensen Gletscher, East Greenland, from satellite imagery and terrestrial measurements

2005 ◽  
Vol 42 ◽  
pp. 53-58 ◽  
Author(s):  
Leigh A. Stearns ◽  
Gordon S. Hamilton ◽  
Niels Reeh
Keyword(s):  
Mass Balance ◽  
Satellite Imagery ◽  
Field Measurements ◽  
Snow Accumulation ◽  
Ice Dynamics ◽  
Outlet Glacier ◽  
East Greenland ◽  
Front Position ◽  
History Of ◽  
Ice Velocity

AbstractThe history of ice velocity and calving front position of Daugaard Jensen Gletscher, a large outlet glacier in East Greenland, is reconstructed from field measurements, aerial photography and satellite imagery for the period 1950-2001. The calving terminus of the glacier has remained in approximately the same position over the past ∼50 years. There is no evidence of a change in ice motion between 1968 and 2001, based on a comparison of velocities derived from terrestrial surveying and feature tracking using sequential satellite images. Estimates of flux near the entrance to the fjord vs snow accumulation in the interior catchment show that Daugaard Jensen Gletscher has a small negative mass balance. This result is consistent with other mass-balance estimates for the inland region of the glacier.

LambdaNet: A Fully Convolutional Architecture for Directional Change Detection

2020 ◽  
Vol 2020 (8) ◽  
pp. 114-1-114-7
Author(s):  
Bryan Blakeslee ◽  
Andreas Savakis
Keyword(s):  
Change Detection ◽  
Satellite Imagery ◽  
Classification Scheme ◽  
Deep Architecture ◽  
Binary Process ◽  
Directional Change ◽  
Class Labels ◽  
Image Pairs

Change detection in image pairs has traditionally been a binary process, reporting either “Change” or “No Change.” In this paper, we present LambdaNet, a novel deep architecture for performing pixel-level directional change detection based on a four class classification scheme. LambdaNet successfully incorporates the notion of “directional change” and identifies differences between two images as “Additive Change” when a new object appears, “Subtractive Change” when an object is removed, “Exchange” when different objects are present in the same location, and “No Change.” To obtain pixel annotated change maps for training, we generated directional change class labels for the Change Detection 2014 dataset. Our tests illustrate that LambdaNet would be suitable for situations where the type of change is unstructured, such as change detection scenarios in satellite imagery.

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