Thomas Schellenberger

Wet Snow Cover Mapping Algorithm Based on Multitemporal COSMO-SkyMed X-Band SAR Images

Multitemporal COSMO-SkyMed (CSK) images are exploited to map wet snow cover in a mountainous area in South Tyrol by using a ratio and a probability of error (POE) approach. Free water in the snowpack attenuates the X-band synthetic aperture radar (SAR) signal and wet snow can be classified by comparing images acquired under wet snow and snow-free conditions. The three steps of the algorithms are: preprocessing of SAR data with particular attention on the potential of speckle filtering to improve the classification, classification of wet snow and postprocessing of the snow cover area (SCA) map. Furthermore, the choice of the snow-free reference and wet snow images on the classification threshold and the SCA is assessed as well as the influence of different landcover classes (blocky scree, grassland, forest). Thresholds to distinguish snow-covered and snow-free pixels are - 2.6 dB for grassland and rocks. To quantify the accuracy of the ratio method, POE maps are calculated. The advantage of the POE method is its independency from auxiliary information on snow cover and the possibility to limit the maximum error. SCA maps derived with a maximum POE of 25% and ratio SCA maps show good overall agreement with total SCA of 66.8% (ratio) and 65.6% (POE) on 26th April 2010. A comparison to SCA derived from Landsat 7 ETM+ reveals that total SCA is similar to SAR SCA when a NDSI threshold of 0.7 is applied, but only 86% of the pixels are detected as snow from both sensors at the same time.

Circum-Arctic Changes in the Flow of Glaciers and Ice Caps from Satellite SAR Data between the 1990s and 2017

We computed circum-Arctic surface velocity maps of glaciers and ice caps over the Canadian Arctic, Svalbard and the Russian Arctic for at least two times between the 1990s and 2017 using satellite SAR data. Our analyses are mainly performed with offset-tracking of ALOS-1 PALSAR-1 (2007–2011) and Sentinel-1 (2015–2017) data. In certain cases JERS-1 SAR (1994–1998), TerraSAR-X (2008–2012), Radarsat-2 (2009–2016) and ALOS-2 PALSAR-2 (2015–2016) data were used to fill-in spatial or temporal gaps. Validation of the latest Sentinel-1 results was accomplished by means of SAR data at higher spatial resolution (Radarsat-2 Wide Ultra Fine) and ground-based measurements. In general, we observe a deceleration of flow velocities for the major tidewater glaciers in the Canadian Arctic and an increase in frontal velocity along with a retreat of frontal positions over Svalbard and the Russian Arctic. However, all regions have strong accelerations for selected glaciers. The latter developments can be well traced based on the very high temporal sampling of Sentinel-1 acquisitions since 2015, revealing new insights in glacier dynamics. For example, surges on Spitsbergen (e.g., Negribreen, Nathorsbreen, Penckbreen and Strongbreen) have a different characteristic and timing than those over Eastern Austfonna and Edgeoya (e.g., Basin 3, Basin 2 and Stonebreen). Events similar to those ongoing on Eastern Austofonna were also observed over the Vavilov Ice Cap on Severnaya Zemlya and possibly Simony Glacier on Franz-Josef Land. Collectively, there seems to be a recently increasing number of glaciers with frontal destabilization over Eastern Svalbard and the Russian Arctic compared to the 1990s.

Exploitation of Cosmo-Skymed image time series for snow monitoring in alpine regions

The main aim of this work is to adapt the ratio-technique for snow cover mapping developed for C-band to the X-band and high resolution COSMO-SkyMed images. This algorithm, aimed at detecting wet snow, is based on the difference in backscattering coefficients between snow-covered areas in winter images and snow-free summer images. For these purposes, a series of COSMO-SkyMed acquisitions (Stripmap PingPong mode, dual polarizations VV-VH) has been planned and acquired over the test site located in South Tyrol (Northern Italy) in correspondence of the melting and winter season. Contemporary to radar passes field campaigns have been performed. The objective is to test the sensitivity of X-band data to different snow conditions. An analysis has been carried out to find the most suitable filtering technique which allows a clearer distinction of distributions of backscattering coefficients of snow-covered and snow-free areas. Based on this analysis a first map of snow from the images acquired on 26–27 April 2010 (wet snow) was derived and compared with snow cover area derived from LANDSAT ETM+ of 20-04-2010 based on NDSI. Further statistical analysis will be carried out also considering the new acquisitions.

The 2015 Surge of Hispar Glacier in the Karakoram

The Karakoram mountain range is well known for its numerous surge-type glaciers of which several have recently surged or are still doing so. Analysis of multi-temporal satellite images and digital elevation models have revealed impressive details about the related changes (e.g., in glacier length, surface elevation and flow velocities) and considerably expanded the database of known surge-type glaciers. One glacier that has so far only been reported as impacted by surging tributaries, rather than surging itself, is the 50 km long main trunk of Hispar Glacier in the Hunza catchment. We here present the evolution of flow velocities and surface features from its 2015/16 surge as revealed from a dense time series of SAR and optical images along with an analysis of historic satellite images. We observed maximum flow velocities of up to 14 m d−1 (5 km a−1) in spring 2015, sudden drops in summer velocities, a second increase in winter 2015/16 and a total advance of the surge front of about 6 km. During a few months the surge front velocity was much higher (about 90 m d−1) than the maximum flow velocity. We assume that one of its northern tributary glaciers, Yutmaru, initiated the surge at the end of summer 2014 and that the variability in flow velocities was driven by changes in the basal hydrologic regime (Alaska-type surge). We further provide evidence that Hispar Glacier has surged before (around 1960) over a distance of about 10 km so that it can also be regarded as a surge-type glacier.

Time series analysis of dual-pol COSMO-SkyMed images for monitoring snow cover in alpine areas

Time series of dual-polarized COSMO-SkyMed (CSK) images are exploited for detection of seasonal snow cover in Alpine areas. For the first time a complete time series of CSK images acquired during snow fall and melt period in winter 2010-2011 is addressed to verify the snow cover mapping capabilities of X-band radar images under different conditions (from dry to wet snow). The algorithm for snow detection is based on a multi-temporal approach with the concept that free water in the snowpack attenuates the X-band synthetic aperture radar (SAR) signal and wet snow can be classified by comparing images acquired under wet snow and snow-free conditions. Thresholds to make this distinction are compared across all the images to check sensitivity to different winter conditions and land-use classes. The impact on the snow cover area (SCA) detected is verified by also exploiting both polarizations in the form of Cross-pol ratio, ratio of VH channel with the reference image, and Depolarization factor, ratio between VH and VV channel of the same image. Snow maps from CSK images compared with LANDSAT ETM+ snow maps indicate a constant underestimation in the detection of snow extent especially during winter season thus showing a scarce sensitivity of X-band signals to snow in dry conditions. The presence of VH polarization indicated, however, an increase in the snow detection variable between 10 and 15%.

Seasonal Snow Cover Mapping in Alpine Areas Through Time Series of COSMO-SkyMed Images

A time series of COSMO-SkyMed (CSK) images is exploited for detection of seasonal snow cover in alpine areas. For the first time, a complete time series of CSK images acquired during snow fall and melt periods in winter 2010-2011 is addressed to verify the snow cover mapping capabilities of X-band radar images under different conditions (from dry to wet snow). The algorithm for snow detection is based on a multitemporal approach with the concept that free water in the snowpack attenuates the X-band synthetic aperture radar signal and wet snow can be classified by comparing images acquired under wet snow and snow-free conditions. Thresholds to make this distinction are compared across all the images to check sensitivity to different winter conditions and land-use classes. The impact of variable and fixed thresholds on the retrieved snow-covered areas is assessed. Snow maps from CSK images compared with Landsat Enhanced Thematic Mapper Plus snow maps indicate a constant underestimation in the detection of snow extent, particularly during winter season, thus showing a scarce sensitivity of X-band signals to snow in dry conditions. Probability of error maps are also calculated for each CSK snow map, thus providing information on the classification error associated to each pixel labeled as snow. The analysis of the snow line variation during spring determines good time consistency in the determination of snow maps from CSK images.