Glacier-surface velocities in alpine terrain from optical satellite imagery—Accuracy improvement and quality assessment

Scherler, Dirk; Leprince, S.; Strecker, Manfred R.

doi:10.1016/j.rse.2008.05.018

Cited by 304 publications

(321 citation statements)

References 65 publications

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“…In order to overcome the previously mentioned region-specific limitation of InSAR in mapping glacier velocity, we took an alternative approach to monitor glacier velocities utilizing sub-pixel offset tracking on nadir-viewing near infrared (NIR) imagery from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). This technique utilizes cross-correlation between two images to estimate surface velocities, details of which are illustrated in Scherler, Leprince, and Strecker (2008). Pixel offset tracking or sub-pixel correlation is a technique to measure the motion of spatially coherent features through time between two images (Crippen 1992).…”

Section: Velocity Measurements Using Pixel Offset Trackingmentioning

confidence: 99%

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Recent changes in the snout position and surface velocity of Gangotri glacier observed from space

Saraswat

Syed

Famiglietti

et al. 2013

International Journal of Remote Sensing

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Glacier mass variations have a direct impact on some of the key components of the global water cycle, including sea level rise and freshwater availability. Apart from being one of the largest Himalayan glaciers, Gangotri is one of the sources of water for the Ganges river, which has a considerable influence on the socioeconomic structure of a largely over-populated catchment area accounting for ∼26% of India's landmass. In this study, we present the most recent assessment of the Gangotri glacier dynamics, combining the use of interferometric techniques on synthetic aperture radar data and sub-pixel offset tracking on Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite imagery. Results show that on average, the Gangotri glacier snout has receded at a rate of 21.3 ± 3 m year −1 over a period of 6 years (2004-2010). While glacier surface velocity near the snout is estimated to be between 24.8 ± 2.3 and 28.9 ± 2.3 m year −1 , interior portions of the glacier recorded velocities in the range of 13.9 ± 2.3 to 70.2 ± 2.3 m year −1 . Further, the average glacier surface velocity in the northern (lower) portions (28.1 ± 2.3 m year −1 ) is observed to be significantly lower than in the southern (higher) portions (48.1 ± 2.3 m year −1 ) of the Gangotri glacier. These values are calculated with an uncertainty of less than 5 m year −1 . Results also highlight a consistent retreat and non-uniform dynamics of the Gangotri glacier.

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Section: Velocity Measurements Using Pixel Offset Trackingmentioning

confidence: 99%

“…The potential of remote sensing for glacier mass balance and velocity mapping has been demonstrated with optical (e.g. Kääb 2005;Scherler, Leprince, and Strecker 2008), synthetic aperture radar (SAR) (e.g. Luckman, Quincey, and Bevan 2007), and thermal infrared sensors (e.g.…”

Section: Introductionmentioning

confidence: 99%

Recent changes in the snout position and surface velocity of Gangotri glacier observed from space

Saraswat

Syed

Famiglietti

et al. 2013

International Journal of Remote Sensing

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“…1b), where no terrain displacements is to be expected (Berthier et al, 2005;Scherler et al, 2008). Noise in displacements may resemble the Additive White Gaussian Noise.…”

Section: Uncertainty Estimationmentioning

confidence: 99%

“…Advances in methods and techniques for detecting changes in glaciers, such as surface velocities, have been used during the last decades. Currently, three methods are commonly employed to derive glacier-surface velocities: interferometry of SAR imagery, SAR tracking techniques, and cross correlation of optical satellite images (Scherler et al, 2008). Cross-correlation of satellite imagery has been largely utilized for mapping and monitoring glacier velocities in several mountain regions around the globe (Taylor et al, 2008;Heid and Kaab, 2011;Heid, 2011;Herman et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

“…In fact, the orthorectification of images by means of this technique is highly accurate at the sub-pixel scale (Scherler et al, 2008;Ayoub et al, 2009). In the Argentinean Andes, however, glacier velocities determinations relying on this method remain scarce, and limited to a few areas, like Monte Tronador, North Patagonian Andes (Ruiz et al, 2015) and Horcones Inferior Glacier, Mount Aconcagua, Central Andes (Pitte et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

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Velocidades superficiales del glaciar Upsala, Andes Patagónicos Sur, mediante el uso de correlación cruzada en imágenes satelitales: periodo 2013-2014.

Moragues

Lenzano

Vecchio

et al. 2017

andgeo

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ABSTRACT. In this study we present surface velocities estimation for the Upsala glacier catchment, South Patagonian Ice Field (SPI) during the summer season of years 2013 (January-March) and 2014 (March-April), including the Bertacchi, Cono, and Murallón tributaries using satellite images from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). The Cross-Correlation method was applied by COSI-Corr technique with sub-pixel accuracy. In general, it should be noted that the SPI glaciers, and Upsala glacier in particular, are fast-flowing ice bodies, which makes the technique works properly. Results of surface velocities estimation ranged from 0.22 to 2.93 md -1 for January-March 2013 and 0.12 to 5.8 md -1 for March-April 2014. In summary, COSI-Corr can achieved accurate and reliable results for glacier displacements and surface velocities estimation, also contributing in the better knowledge of the velocities change processes in time, taking into account Upsala is one of the most dynamic temperate glaciers of the SPI. RESUMEN. Velocidades superficiales del glaciar Upsala, Andes Patagónicos Sur, mediante el uso de correlación cruzada en imágenes satelitales: periodo 2013-2014. En este estudio se presentan las estimaciones de velocidades superficiales de la cuenca del glaciar Upsala, Campo de Hielo Patagónico Sur (CHPS) durante la temporada de verano de los años 2013 (enero-marzo) y 2014 (marzo-abril), incluyendo los glaciares tributarios Bertacchi, Cono y Murallón utilizando imágenes satelitales ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer). El método de correlación cruzada se aplicó mediante la implementación de la técnica COSI-Corr, con una precisión a nivel de subpixel. Cabe destacar que los glaciares del CHPS, en general, y el glaciar Upsala, en particular, son cuerpos de hielo de flujo rápido, lo que hace que la técnica funcione correctamente. Los resultados de las velocidades superficiales oscilaron entre 0,22 a 2,93 md -1 para el periodo enero-marzo de 2013 y de 0,12 a 5,8 md -1 para el periodo marzo-abril de 2014. En resumen, COSI-Corr alcanzó resultados precisos y confiables para la estimación de desplazamientos y velocidades superficiales de los glaciares, contribuyendo al mejor conocimiento de los procesos de cambio de velocidades en el tiempo, teniendo en cuenta que Upsala es uno de los glaciares templados más dinámicos del CHPS.

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Forecasting the response of Earth's surface to future climatic and land use changes: A review of methods and research needs

et al. 2015

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In the future, Earth will be warmer, precipitation events will be more extreme, global mean sea level will rise, and many arid and semiarid regions will be drier. Human modifications of landscapes will also occur at an accelerated rate as developed areas increase in size and population density. We now have gridded global forecasts, being continually improved, of the climatic and land use changes (C&LUC) that are likely to occur in the coming decades. However, besides a few exceptions, consensus forecasts do not exist for how these C&LUC will likely impact Earth-surface processes and hazards. In some cases, we have the tools to forecast the geomorphic responses to likely future C&LUC. Fully exploiting these models and utilizing these tools will require close collaboration among Earth-surface scientists and Earth-system modelers. This paper assesses the state-of-the-art tools and data that are being used or could be used to forecast changes in the state of Earth's surface as a result of likely future C&LUC. We also propose strategies for filling key knowledge gaps, emphasizing where additional basic research and/or collaboration across disciplines are necessary. The main body of the paper addresses cross-cutting issues, including the importance of nonlinear/threshold-dominated interactions among topography, vegetation, and sediment transport, as well as the importance of alternate stable states and extreme, rare events for understanding and forecasting Earth-surface response to C&LUC. Five supplements delve into different scales or process zones (global-scale assessments and fluvial, aeolian, glacial/periglacial, and coastal process zones) in detail.

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Glacier-surface velocities in alpine terrain from optical satellite imagery—Accuracy improvement and quality assessment

Cited by 304 publications

References 65 publications

Recent changes in the snout position and surface velocity of Gangotri glacier observed from space

Recent changes in the snout position and surface velocity of Gangotri glacier observed from space

Velocidades superficiales del glaciar Upsala, Andes Patagónicos Sur, mediante el uso de correlación cruzada en imágenes satelitales: periodo 2013-2014.

Forecasting the response of Earth's surface to future climatic and land use changes: A review of methods and research needs

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