Remote Sensing of Permafrost Landscape Dynamics
Alexandra Runge#*1, 2, Ingmar Nitze*1, Annett Bartsch3,4, Sebastian Westermann5
1.Alfred Wegener Institute for Polar and Marine Research, Telegrafenberg A45, 14473 Potsdam, Germany, email@example.com
2.Institute of Geosciences, University of Potsdam, Potsdam-Golm, Germany
3.Austrian Polar Research Institute, Vienna, Austria,firstname.lastname@example.org
4.b.geos, Korneuburg, Austria
5.University of Oslo, Oslo, Norway,email@example.com
* PYRN Member # session organizer and corresponding author
Session description (250 word maximum in English):
The degradation of permafrost and ground-ice impacts ecosystem properties and services and poses hazards to Arctic communities and infrastructure. Understanding current permafrost conditions, and the synergistic impact of warming and permafrost disturbance processes is necessary for predicting and detecting permafrost landscape dynamics. The combination of gradual, warming-induced regional-scale changes together with abrupt, small-scale disturbances are highly important for understanding current trends. Until recently the permafrost state and degradation were assessed only locally with varying methods and sensors or at too coarse resolution. This limited their comparibility and usability for global-scale climate models.
The variety of remote sensing sensors, emerging techniques, data covering a wide range of spatio-temporal scales, and the latest advances in computational resources (e.g. High-Performance Clusters, Google Earth Engine) have greatly improved possibilities assessing change across climate environmental gradients. Analyses with high to moderate spatial resolution data allow to compare permafrost-related landscape processes across space and time and bridging the gap between local field-based observations, landscape-scale remote sensing, and global-scale land surface models.
This session aims to solicit methods and approaches that characterize permafrost landscape dynamics, spanning scales of space (local to pan-arctic) and time (short to longterm). We welcome studies that present active and passive sensors, big data processing and deep learning, or field-modelling applications. We are interested in studies focused on permafrost landscape conditions and permafrost degradation in a variety of settings including thermokarst, lake dynamics, changes in vegetation, thaw slumps, active-layer detachment slides, thermoerosional gullies, coastal areas, pingos, polygonal landscapes, wildfires, snow and ice.
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