Morphological exploration of Arctic rivers using Google Earth Engine

Climate change is already altering the hydrological regime of Arctic rivers. However, still little is known about fluvial morphological processes and trajectories in permafrost environments. In such iced floodplains, both hydrological and thermal regimes affect sediment transport and riverine morphological processes. Remote sensing represents a powerful approach to investigate fluvial systems in those isolated areas. Nevertheless, its application presents challenges linked to ice seasonality and the limited time window of the morphological activity, alongside the complex permafrost/river spatial patterns and related spectral signatures, which imply significant computational efforts. Addressing this, we propose an improved integration of existing tools for the spatio-temporal extraction of fluvial morphological indicators, combining in a unique working environment the cloud computing capability of Google Earth Engine (GEE) and a process-based tool for riverine multitemporal planform analysis (PyRIS). Fluvial morphological metrics have been extracted from a set of meandering rivers in the Arctic region, outlining the potential of anisotropic image filtering and image segmentation to enhance active channel detection in complex spatial-pattern areas. A 20-40% refinement in small object removal in river mask detection emerges. The synergy among existing instruments enhances the observation of natural river systems in permafrost environments, setting the basis for further studies on morphological processes and the evolution of such pristine and climatically-sensitive river systems.