Description: Remote sensing-based Earth Observation plays an important role in assessing environmental changes throughout our planet. As an image-heavy domain, the evaluation of the data strongly focuses on statistical and pixel-based spatial analysis methods. However, considering the complexity of our Earth system, there are some environmental structures and dependencies that are not possible to accurately describe with these traditional image analysis approaches. One example for such a limitation is the representation of (spatial) networks and their characteristics. In this study, we thus propose a computer vision approach that enables the representation of semantic information gained from images as graphs. As an example, we investigate digital terrain models of Arctic permafrost landscapes with its very characteristic polygonal patterned ground. These regular patterns, which are clearly visible in high-resolution image and elevation data, are formed by subsurface ice bodies that are very vulnerable to rising temperatures in a warming Arctic. Observing these networks’ topologies and metrics in space and time with graph analysis thus allows insights into the landscape’s complex geomorphology, hydrology, and ecology and therefore helps to quantify how they interact with climate change. We show that results extracted with this analytical and highly automated approach are in line with those gathered from other manual studies or from manual validation. Thus, with this approach, we introduce a method that, for the first time, enables upscaling of such terrain and network analysis to potentially pan-Arctic scales where collecting in-situ field data is strongly limited.

Description: Erosion of permafrost coasts has received increasing scientific attention since 1990s because of rapid land loss and the mobilisation potential of old organic carbon. The majority of permafrost coastal erosion studies are limited to time periods from a few years to decades. Most of these studies emphasize the spatial variability of coastal erosion, but the intensity of inter-annual variations, including intermediate coastal aggradation, remains poorly documented. We used repeat airborne Light Detection And Ranging (LiDAR) elevation data from 2012 and 2013 with 1 m horizontal resolution to study coastal erosion and accompanying mass-wasting processes in the hinterland. Study sites were selected to include different morphologies along the coast of the Yukon Coastal Plain and on Herschel Island. We studied elevation and volume changes and coastline movement and compared the results between geomorphic units. Results showed simple uniform coastal erosion from low coasts (up to 10 m height) and a highly diverse erosion pattern along coasts with higher backshore elevation. This variability was particularly pronounced in the case of active retrogressive thaw slumps, which can decrease coastal erosion or even cause temporary progradation by sediment release. Most of the extremes were recorded in study sites with active slumping (e.g. 22 m of coastline retreat and 42 m of coastline progradation). Coastline progradation also resulted from the accumulation of slope collapse material. These occasional events can significantly affect the coastline position on a specific date and can affect coastal retreat rates as estimated in long term by coastline digitalisation from air photos and satellite imagery. These deficiencies can be overcome by short-term airborne LiDAR measurements, which provide detailed and high-resolution information about quickly changing elevations in coastal areas.