The Potential of UAV Imagery for the Detection of Rapid Permafrost Degradation: Assessing the Impacts on Critical Arctic Infrastructure

Ground subsidence and erosion processes caused by permafrost thaw pose a high risk to infrastructure in the Arctic. Climate warming is increasingly accelerating the thawing of permafrost, emphasizing the need for thorough monitoring to detect damages and hazards at an early stage. The use of unoccupied aerial vehicles (UAVs) allows a fast and uncomplicated analysis of sub-meter changes across larger areas compared to manual surveys in the field. In our study, we investigated the potential of photogrammetry products derived from imagery acquired with off-the-shelf UAVs in order to provide a low-cost assessment of the risks of permafrost degradation along critical infrastructure. We tested a minimal drone setup without ground control points to derive high-resolution 3D point clouds via structure from motion (SfM) at a site affected by thermal erosion along the Dalton Highway on the North Slope of Alaska. For the sub-meter change analysis, we used a multiscale point cloud comparison which we improved by applying (i) denoising filters and (ii) alignment procedures to correct for horizontal and vertical offsets. Our results show a successful reduction in outliers and a thorough correction of the horizontal and vertical point cloud offset by a factor of 6 and 10, respectively. In a defined point cloud subset of an erosion feature, we derive a median land surface displacement of (Formula presented.) m from 2018 to 2019. Projecting the development of the erosion feature, we observe an expansion to NNE, following the ice-wedge polygon network. With a land surface displacement of (Formula presented.) m and an alignment root mean square error of (Formula presented.) m, we find our workflow is best suitable for detecting and quantifying rapid land surface changes. For a future improvement of the workflow, we recommend using alternate flight patterns and an enhancement of the point cloud comparison algorithm

The impact of lateral heat and water fluxes from thermokarst lakes on tundra landscape dynamics and permafrost degradation

Projected future warming of the Arctic will result in pronounced degradation of permafrost and thereby trigger large-scale landscape and ecosystem changes. In this context, the formation and expansion of thermokarst lakes play a key role as thermokarst dynamics represent a mechanism for abrupt degradation of permafrost soils. Using the process-based model CryoGrid-3 coupled to a model description of lake dynamics (FLake), we explore how the thermal and hydrological state of different permafrost landscapes is affected by an explicit consideration of the interaction between lakes and surrounding permafrost environments. Hereby we especially investigate the role of lateral fluxes in affecting the landscape heat and water budgets

Simulating rapid permafrost degradation and erosion processes under a warming climate

Current model approaches used to simulate the degradation of permafrost under a warming climate are highly simplistic since they only consider one-dimensional (top-down) thawing and ignore lateral processes such as soil erosion and mass wasting which are the most abundant forms of thaw in many regions. Thus, current model assessments are most likely far too conservative in their estimates of permafrost thaw impacts (Rowland & Coon, 2015). It therefore remains uncertain how climate warming and permafrost thaw will affect (i) the intensity of erosion and mass wasting processes and (ii) essential ecosystem functions, landscape characteristics, and infrastructure. It also remains unclear (iii) whether any erosion-induced landscape changes further accelerate permafrost thaw. In order to answer these critical questions, land surface models (LSMs) require a new level of realism in order to adequately project permafrost thaw dynamics. Within the PermaRisk project, the permafrost model CryoGrid3 is extended with an erosion scheme that allows to represent lateral mass movement processes within the limited framework of one dimensional LSMs. The new model will be applied and validated at three Arctic sites in Alaska, Canada, and northern Siberia. Furthermore, 21st century climate impact projections for the key sites are scheduled as a basis for thorough risk analyses concerning potential damages to critical ecosystem functions/services and infrastructure. We will present first simulations on rapid permafrost degradation processes with a special focus on thaw slumps at a test site in northern Canada. We expect the results to demonstrate the capabilities and the limitations of the new model

UNDERCOVEREISAGENTEN - ERSTE EXPEDITION UND AKTUELLER STAND DES ARKTISCHEN PERMAFROSTPROJEKTS

Die Menschen in der Arktis sind seit mehreren Jahrzehnten mit Veränderungen ihres Lebensraums, wie dem Auftauen des Permafrosts durch den globalen Klimawandel, konfrontiert. Ziel dieses Projekts ist es, die Erste Expedition und aktueller Stand des arktischen Permafrostprojekts Auswirkungen des Permafrosttauens durch die Erfassung und Analyse von Bildmaterial von UAVs zusammen mit Schüler*innen (SuS) in Kanada und Deutschland zu untersuchen. Während einer Expedition im September 2022 in Nordkanada durch das AWI, DLR und HeiGIT wurden erste UAV-Daten gemeinsam mit SuS der Moose Kerr School in Aklavik aufgenommen. Neben den rund 30000 Einzelfotos über einer Fläche von ca. 13km² wurden die Grundlagen der Datenerhebung sowie die Projektziele der gemeinschaftlichen Permafrost-Untersuchung vermittelt. Vermittelte Ziele sind die selbstständig fortgeführte Datenaufnahme durch interessierte SuS, sowie die selbstständige Formulierung eigener wissenschaftl. Fragestellungen. Es erfolgte plangemäß die Einarbeitung von lokalem Wissen, um weitere Fragestellungen der lokalen Bevölkerung zu adressieren. Die Daten werden aktuell aufbereitet, um über eine Crowdmapping-Anwendung zur Verfügung gestellt zu werden

Monitoring the Transformation of Arctic Landscapes: Automated Shoreline Change Detection of Lakes Using Very High Resolution Imagery

Water bodies are a highly abundant feature of Arctic permafrost ecosystems and strongly influence their hydrology, ecology and biogeochemical cycling. While very high resolution satellite images enable detailed mapping of these water bodies, the increasing availability and abundance of this imagery calls for fast, reliable and automatized monitoring. This technical work presents a largely automated and scalable workflow that removes image noise, detects water bodies, removes potential misclassifications from infrastructural features, derives lake shoreline geometries and retrieves their movement rate and direction on the basis of ortho-ready very high resolution satellite imagery from Arctic permafrost lowlands. We applied this workflow to typical Arctic lake areas on the Alaska North Slope and achieved a successful and fast detection of water bodies. We derived representative values for shoreline movement rates ranging from 0.40–0.56 m.yr−1 for lake sizes of 0.10 ha–23.04 ha. The approach also gives an insight into seasonal water level changes. Based on an extensive quantification of error sources, we discuss how the results of the automated workflow can be further enhanced by incorporating additional information on weather conditions and image metadata and by improving the input database. The workflow is suitable for the seasonal to annual monitoring of lake changes on a sub-meter scale in the study areas in northern Alaska and can readily be scaled for application across larger regions within certain accuracy limitations.Bundesministerium für Bildung und ForschungPeer Reviewe