Applicability of Landsat 8 data for characterizing glacier facies and supraglacial debris |
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| Institution: | 1. Wadia Institute of Himalayan Geology, 33, GMS Road, Dehradun 248001, India;2. Centre for Glaciology, Wadia Institute of Himalayan Geology, 33 GMS Road, Dehradun 248001, India;3. Post-Graduate Department of Remote Sensing and GIS, University of Jammu, Jammu, 180006, India;1. Universiteit Utrecht, Department of Physical Geography, PO Box 80115, Utrecht, The Netherlands;2. International Centre for Integrated Mountain Development, GPO Box 3226, Kathmandu, Nepal;3. Department of Geography, Northumbria University, Newcastle upon TyneNE1 8ST, United Kingdom;1. Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, 999077, Hong Kong;2. Department of Geography, University of California, Los Angeles, 1255 Bunche Hall, Los Angeles, CA 90095, USA;1. Geophysical Institute, University of Alaska Fairbanks, Fairbanks, AK 99775, USA;2. National Snow and Ice Data Center (NSIDC), University of Colorado at Boulder, Boulder, CO 80303, USA;3. Department of Geological Sciences, University of Oregon, Eugene, OR 97403, USA;4. Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA |
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| Abstract: | The present work evaluates the applicability of operational land imager (OLI) and thermal infrared sensor (TIRS) on-board Landsat 8 satellite. We demonstrate an algorithm for automated mapping of glacier facies and supraglacial debris using data collected in blue, near infrared (NIR), short wave infrared (SWIR) and thermal infrared (TIR) bands. The reflectance properties in visible and NIR regions of OLI for various glacier facies are in contrast with those in SWIR region. Based on the premise that different surface types (snow, ice and debris) of a glacier should show distinct thermal regimes, the ‘at-satellite brightness temperature’ obtained using TIRS was used as a base layer for developing the algorithm. This base layer was enhanced and modified using contrasting reflectance properties of OLI bands. In addition to facies and debris cover characterization, another interesting outcome of this algorithm was extraction of crevasses on the glacier surface which were distinctly visible in output and classified images. The validity of this algorithm was checked using field data along a transect of the glacier acquired during the satellite pass over the study area. With slight scene-dependent threshold adjustments, this work can be replicated for mapping glacier facies and supraglacial debris in any alpine valley glacier. |
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| Keywords: | Glacier facies Supraglacial debris Remote sensing Landsat 8 |
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