Interannual Variability of Lake Ice Backscatter Anomalies on Lake Neyto, Yamal, Russia. GI_Forum|GI_Forum 2019, Volume 2|

• Published in: GI_Forum
• Format: Journal/Newspaper
• Language: English
• Published: oeaw 2020
• Subjects: 912,remote sensing; Arctic; lakes; image processing; synthetic aperture radar,Geography; Yamal Peninsula
• Online Access: http://epub.oeaw.ac.at/?arp=8740-0inhalt/A04_FP_2145_Pointner.pdf; https://doi.org/10.1553/giscience2020_01_s47

Anomalous areas of varying shape and location characterized by low backscatter in Synthetic Aperture Radar (SAR) imagery of lake ice on lake Neyto on the Yamal Peninsula in Russia have been described qualitatively in the literature for many years. Possible suggested causes are the formation of eddies or the release of gas through the lake sediments, which could both lead to local thinning of the ice layer and alter radar backscatter. To date, the phenomenon, its cause, and its spatial and temporal dynamics are poorly understood, and studies from other geographic regions are completely absent. In order to perform first steps towards a better understanding of the phenomenon, we developed a workflow to quantitatively assess the spatial variability of the anomalies in the years 2015 to 2019 for lake Neyto. We introduce a binary image classification algorithm developed with state-of-the-art open-source image processing tools and employ metrics commonly used for describing spatial relationships of vector and raster data. This includes polygon distances, polygon intersections and cumulative pixel counts deduced from the classification results in order to quantify, for the very first time, the dynamics over a number of years. The geospatial analysis reveals large spatial variations, but also some overlap between different years. Locations of anomalies do not seem more similar between consecutive years than when they are compared over the longer period. Some of the spatial properties of the clusters of low backscatter may support the explanation of gas release as the primary cause of the observed patterns.