Regional Glacier Mapping Using Optical Satellite Data Time Series
Winsvold, Solveig Havstad
; Peer reviewed
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Winsvold, Solveig Havstad (2017) Mapping glaciers using time-series of remote sensing data. Doctoral thesis.
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Institutt for geofag
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2016,
The first of two Sentinel-2 satellites, launched mid-2015, has similar characteristics as the Landsat TM/ETM+/OLI satellites. Together, these satellites will produce a tremendous quantity of optical images worldwide for glacier mapping, with increasing temporal coverage toward the more glacierized higher latitudes due to convergence of near-polar orbits. To exploit the potential of such near-future dense time series, methods for mapping glaciers from space should be revisited. Currently, snow and ice are typically classified from an optical satellite image using a multispectral band ratio. For each scene, mapping conditions will vary (e.g., snow, ice, and clouds) and not be equally optimal over the entire scene. The increasing amount of images makes it difficult to manually select the best glacier mapping scene as is the current practice. This work is based on the above robust image ratio method for exploiting the dense temporal image coverage. Four application scenarios using time series of Landsat type data for glacier mapping are presented. First, we synthesize an optimal band ratio image from a stack of images within one season to compensate for regional differences. The second application scenario introduces robust methods to improve automatic glacier mapping by exploiting the seasonal variation in spectral properties of snow. Third, we explore the spatio-temporal variation of glacier surface types. Finally, we show how the synthesized band ratio images from the first application scenario can be used for automatic glacier change detection. In summary, we explore automatic algorithms for glacier mapping applications that exploit the temporal signatures in the satellite data time series.
Published Open Access with IEEE. © 2016 IEEE.
This item's license is: Attribution 3.0 Unported
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