Abstract
Abstract. Permafrost thaw has been observed at several locations across the Arctic tundra in recent decades; however, the pan-Arctic extent and spatiotemporal dynamics of thaw remains poorly explained. Thaw-induced differential ground subsidence and dramatic microtopographic transitions, such as transformation of low-centered ice-wedge polygons (IWPs) into high-centered IWPs can be characterized using very high spatial resolution (VHSR) commercial satellite imagery. Arctic researchers demand for an accurate estimate of the distribution of IWPs and their status across the tundra domain. The entire Arctic has been imaged in 0.5 m resolution by commercial satellite sensors; however, mapping efforts are yet limited to small scales and confined to manual or semi-automated methods. Knowledge discovery through artificial intelligence (AI), big imagery, and high performance computing (HPC) resources is just starting to be realized in Arctic science. Large-scale deployment of VHSR imagery resources requires sophisticated computational approaches to automated image interpretation coupled with efficient use of HPC resources. We are in the process of developing an automated Mapping Application for Permafrost Land Environment (MAPLE) by combining big imagery, AI, and HPC resources. The MAPLE uses deep learning (DL) convolutional neural nets (CNNs) algorithms on HPCs to automatically map IWPs from VHSR commercial satellite imagery across large geographic domains. We trained and tasked a DLCNN semantic object instance segmentation algorithm to automatically classify IWPs from VHSR satellite imagery. Overall, our findings demonstrate the robust performances of IWP mapping algorithm in diverse tundra landscapes and lay a firm foundation for its operational-level application in repeated documentation of circumpolar permafrost disturbances.
Highlights
Arctic permafrost - unique landscapes comprising the Earth materials that remains at or below 0°C for at least two consecutive years - occupies approximately 24% of the exposed land surface of the northern hemisphere
Ice-rich permafrost can be identified by atypical surface features called ice-wedge polygons (IWPs), which are underlain by several meter-wide and deep ice-wedges that form a network across the tundra
We present some of the automated ice-wedge polygon mapping results while relating to the DLCNN model interoperability across different tundra vegetation types
Summary
Arctic permafrost - unique landscapes comprising the Earth materials that remains at or below 0°C for at least two consecutive years - occupies approximately 24% of the exposed land surface of the northern hemisphere. Ice-rich permafrost can be identified by atypical surface features called ice-wedge polygons (IWPs), which are underlain by several meter-wide and deep ice-wedges that form a network across the tundra. Vegetation and geology maps suggest that about two-thirds or more of the Arctic landscape is occupied by polygonal ground (Raynolds et al 2019) and ice-rich ground, but the exact extent and the prevailing IWP types (i.e. whether the ice wedges experienced melt or not) are largely unknown (Liljedahl et al 2016). The shift from one IWP type to the other is documented to occur in less than a decade (Liljedahl et al 2016) with an unusual warm summer, wildfires, or human activities initiating the onset of ice-wedge degradation. Understanding of spatiotemporal dynamics behind the evolution of ice-wedge polygonal tundra demands for objective and detailed maps consolidating the ice wedge extent and their prevailing successional stages
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