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Schellenberger T.,University of Oslo | Ventura B.,Institute of Applied Remote Sensing | Zebisch M.,Institute of Applied Remote Sensing | Notarnicola C.,Institute of Applied Remote Sensing
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing | Year: 2012

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. © 2012 IEEE.


Wall S.,Jet Propulsion Laboratory | Hayes A.,California Institute of Technology | Bristow C.,University of London | Lorenz R.,Johns Hopkins University | And 18 more authors.
Geophysical Research Letters | Year: 2010

Of more than 400 filled lakes now identified on Titan, the first and largest reported in the southern latitudes is Ontario Lacus, which is dark in both infrared and microwave. Here we describe recent observations including synthetic aperture radar (SAR) images by Cassini's radar instrument (λ= 2 cm) and show morphological evidence for active material transport and erosion. Ontario Lacus lies in a shallow depression, with greater relief on the southwestern shore and a gently sloping, possibly wave-generated beach to the northeast. The lake has a closed internal drainage system fed by Earth-like rivers, deltas and alluvial fans. Evidence for active shoreline processes, including the wave-modified lakefront and deltaic deposition, indicates that Ontario is a dynamic feature undergoing typical terrestrial forms of littoral modification. Copyright © 2010 by the American Geophysical Union.


Rastner P.,University of Zürich | Bolch T.,University of Zürich | Bolch T.,TU Dresden | Notarnicola C.,Institute of Applied Remote Sensing | Paul F.,University of Zürich
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing | Year: 2014

Precise information about the size and spatial distribution of glaciers is needed for many research applications, for example water resources evaluation, determination of glacier specific changes in area and volume, and for calculation of the past and future contribution of glaciers to sea-level change. However, mapping glacier outlines is challenging even under optimal conditions due to time consuming manual corrections of wrongly classified pixels. In the last decades, advantages in computer technologies have led to the development of object-based-image analysis (OBIA), an image classification technique that can be seen as an alternative to the common pixel-based image analysis (PBIA). In this study we compare the performance of OBIA with PBIA for glacier mapping in three test regions with challenging mapping conditions. In both approaches, a ratio image was created to map clean snow and ice while thermal and slope information was used to assist in the identification of debris-covered ice. The mapping results of OBIA have overall a ~3% higher quality than PBIA, in particular in the processing of debris-covered glaciers where OBIA has a 12% higher accuracy. The post-processing possibilities in OBIA (e.g., the application of a processing loop and neighborhood analysis) are especially powerful to improve the final classification. This leads also to a reduction of the workload for the manual corrections, which are still required to achieve a sufficient accuracy. © 2008-2012 IEEE.

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