Multisource Synthesized Inventory of CRitical Infrastructure and HUman-Impacted Areas in AlaSka (SIRIUS)

The Arctic region has undergone warming at a rate more than 3 times higher than the global average. This warming has led to the degradation of near-surface permafrost, resulting in decreased ground stability. This instability not only poses a primary hazard to Arctic infrastructure and human-impacted areas but can also lead to secondary ecological hazards from infrastructure failure associated with hazardous materials. This development underscores the need for a comprehensive inventory of critical infrastructure and human-impacted areas. The inventory should be linked to environmental data to assess their susceptibility to permafrost degradation as well as the ecological consequences that may arise from infrastructure failure. Here, we provide such an inventory for Alaska, a vast state covering approximately 1.7 × 106 km2, with a population of over 733 000 people and a history of industrial development on permafrost. Our Synthesized Inventory of CRitical Infrastructure and HUman-Impacted Areas in AlaSka (SIRIUS) integrates data from (i) the Sentinel-1/2-derived Arctic Coastal Human Impact dataset (SACHI); (ii) OpenStreetMap (OSM); (iii) the pan-Arctic Catchment Database (ARCADE); (iv) a dataset of permafrost extent, probability and mean annual ground temperatures; and (v) the Contaminated Sites Database and reports to create a unified new dataset of critical infrastructure and human-impacted areas as well as permafrost and watershed information for Alaska. The integration process included harmonizing spatial references, extents and geometries across all the datasets as well as incorporating a uniform usage type classification scheme for the infrastructure data. Additionally, we employed text-mining techniques to generate complementary geospatial data from textual reports on contaminated sites, including details on contaminants, cleanup duration and the affected media. The combination of SACHI and OSM enhanced the detail of the usage type classification for infrastructure from 5 to 13 categories, allowing the identification of elements critical to Arctic communities beyond industrial sites. Further, the new inventory integrates the high spatial detail of OSM with the unbiased infrastructure detection capability of SACHI, accurately representing 94 % of the polygonal infrastructure and 78 % of the linear infrastructure, respectively. The SIRIUS dataset is presented as a GeoPackage, enabling spatial analysis and queries of its components, either as a function of or in combination with one another. The dataset is available on Zenodo at https://doi.org/10.5281/zenodo.8311243 (Kaiser et al., 2023).Bundesministerium für Bildung und ForschungHumboldt-Universität zu BerlinHorizon 2020Peer Reviewe

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

Detecting Land Surface Changes and Threats to Infrastructure in Alaskan Permafrost Regions

Die Arktis erwärmt sich mehr als 3x so schnell wie der globale Durchschnitt, was zu Permafrostdegradation führt. Permafrostdegradation führt zu einer Absenkung des Bodens mit erheblichen Veränderungen der Landoberfläche (VdL), die tiefgreifende ökologische Folgen haben und Infrastruktur-Stabilität bedrohen. Fernerkundungsdaten ermöglichen die Erkundung von VdL und Störungen in weiten Regionen. Erste Anzeichen von Permafrostdegradation zu detektieren, bleibt jedoch eine Herausforderung auf Grund ihrer kleinen räumlichen Skalen und hohen zeitlichen Variabilität. Die zunehmende Verfügbarkeit hochauflösender Bilddaten erfordert zudem nachhaltige Ansätze für deren effiziente Verarbeitung. Auch ist es wichtig, die Anfälligkeit von Infrastruktur im Kontext dieser VdL und die potenziellen ökologischen Folgen im Falle eines Infrastruktur-Versagens zu verstehen. Meine Dissertation widmete sich diesen Herausforderungen am Beispiel Alaskas (U.S.A.). Die drei Studien hatten folgende Ziele: (i) Erkennung und Quantifizierung von VdL, im Kontext von Permafrostdegradation, unter Nutzung hochauflösender Fernerkundungsdaten und Bewertung ihrer Bedrohung für Infrastruktur und (ii) Identifizierung von Infrastrukturelementen mit entscheidender Bedeutung für die Bevölkerung Alaskas, um deren Anfälligkeit für Permafrostdegradation einschätzen zu können. Das Ergebnis sind (i) zwei skalierbare, weitgehend automatisierte, leicht zugängliche methodische Rahmen, die erfolgreich VdL und Erosionsprozesse an Seeufern erkennen und quantifizieren. Außerdem erstellte ich (ii) ein umfassendes Inventar kritischer Infrastruktur und vom Menschen beeinflusster Gebiete, das über industrielle und wirtschaftliche Bedeutung hinausgeht. Dieses Inventar beruht auf der Integration verschiedener Quellen, wodurch eine eingehende Analyse der Anfälligkeit der Infrastruktur für Permafrostdegradation und ökologischen Folgen möglich wird, die im Falle eines Versagens der Infrastruktur entstehen können.The Arctic is warming more than 3x faster than the global average, leading to permafrost degradation. When permafrost thaws, it results in ground subsidence and causes substantial land surface changes, which have profound ecological consequences and pose a threat to infrastructure stability. Remote sensing data allows us to explore land surface changes and disturbances across regions, yet early detection of permafrost degradation remains challenging due to its small-scale occurrence and high temporal variability. Further, the increasing availability of high-resolution imagery requires a sustainable framework to efficiently process these data. Also, it is essential to understand the vulnerability of infrastructure in context of these land surface changes and the potential ecological consequences that may arise in the event of infrastructure failure. In my thesis, I addressed these challenges focusing on the U.S. state of Alaska. I conducted three studies with the objectives to (i) detecting and quantifying the trajectories of land surface changes attributed to permafrost degradation using very high-resolution remote sensing data and assessing their threat to infrastructure, and (ii) identifying infrastructure elements critical to the Alaskan population to allow an estimation of their vulnerability to permafrost degradation. As a result of my research, I developed (i) two scalable, widely automated and easily accessible frameworks that successfully detect and quantify land surface displacements and shoreline erosion processes attributed to permafrost degradation. Additionally, (ii) I have compiled a comprehensive inventory of critical infrastructure and human-impacted areas, extending beyond economic and industrial importance. I created this inventory by integrating different data sources, allowing for an in-depth analysis of infrastructure vulnerability to permafrost degradation and the ecological consequences that may arise in the event of infrastructure failure

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