Lateral Thermokarst Lake Dynamics: A Remote Sensing Based Analysis of Spatial Variabilities and Erosion Processes

• Main Authors: Kaiser, Soraya, Grosse, Guido, Boike, Julia, Langer, Moritz
• Format: Conference Object
• Language: unknown
• Published: AGU 2019
• Subjects: Ice; permafrost; Thermokarst
• Online Access: https://epic.awi.de/id/eprint/50809/; https://hdl.handle.net/10013/epic.6f378caf-0598-482b-a0cb-c37731360d04

Thermokarst lakes are one of the most abundant landforms in periglacial landscapes. They develop in regions underlain by permafrost as a consequence of soil subsidence triggered by the melting of excess ground ice. As a result of further permafrost degradation and shoreline erosion, thermokarst lakes increase in size, expanding vertically and laterally. This growth process has strong impacts on local to regional hydrological networks and ecological functions of the surrounding landscape. Previous research on the lateral growth of thermokarst lakes usually focused on decadal time scales which results in averaged expansion rates. These averages mask the temporal and spatial variations of lateral thermokarst expansion that occur over shorter time periods of only a few years. The short-term variability results from complex interactions between local erosion processes and meteorological and permafrost conditions. The aim of our study is to quantify these short-term changes of lake shorelines to provide a better understanding of permafrost landscape processes using multi-temporal high-resolution satellite imagery. The images are in the visible and near-infrared spectrum with a resolution of 0.3 to 0.7 m. They cover the period from 2006 to 2017 with acquisitions every 2 to 4 years. In order to map the lake shoreline changes we developed a fully-automated, open-source workflow for analyzing the changes of waterbodies larger than 1000 m². First, all necessary pre-processing steps are implemented such as pansharpening and smoothing of any speckle over waterbodies. Then, the normalized difference water index (NDWI) is applied to extract waterbodies from the imagery and derive their shoreline geometry. After filtering for potentially misclassified elements that originate from infrastructure, shoreline movement rates are calculated using a nearest point analysis. The workflow is independent of scale, image spatial resolution, and waterbody geometry. Preliminary findings demonstrate that the approach provides reliable shoreline recognition for every time step in the different study areas even under difficult light conditions. Changes can be detected on a sub-meter scale. Finally, we discuss the influence of the waterbody’s size and geometry on the shoreline change processes.