ALBERT

Description: We present a comprehensive inventory of retrogressive thaw slumps (RTS) for six study sites in the Russian High Arctic covering an area of more than 600 km². The sites are located on the Novaya Zemlya Archipelago, Kolguev Island, Bol'shoy Lyakhovsky Island, and Taymyr Peninsula in ice-rich permafrost characterized by either buried glacial ice or syngenetically formed Yedoma permafrost deposits. This data publication contains geospatial polygon vector files of the individual mapped slumps across multiple time slices. The mapping was performed on multispectral imagery of very high-resolution satellite sensors, including PlanetScope (3m ground resolution), RapidEye (5m), Pléiades (0.5m), and SPOT (1.5m). Cloud free images were acquired between 2011 and 2020 and exist for annual or close-to-annual time steps depending on their availability. Additional data sets such as the ArcticDEM, the Esri Satellite base map, and Tasseled Cap Landsat Trends were used to support the mapping process. The identification and digitization of thaw slumps as polygons (in UTM coordinate reference system) was performed in QGIS 10.3. A total number of 3466 individual RTS were mapped between 2011 or 2013 and 2020. In addition, for the coastal slumps, change distances from headwalls and bluff bases were calculated in ArcMap 10.5 using the Digital Shoreline Analysis System (DSAS) tool version 5 over the study period (2011/2013-2020). Very high-resolution imagery for this study was kindly provided by ESA through Third Party Mission proposal TPM4-ID-54054. We recieved access to the RapidEye imagery via the RapidEye Science Archive (RESA) initiative in the scope of our project 'Thaw Dynamics of Retrogressive Thaw Slumps from High Resolution Images in Siberia (RTStrendr )'. The PlanetScope imagery was recieved in the scope of our project ' Artificial Intelligence for Cold Regions (AI-CORE)'.

Description: During the Arctic Land Expedition Perma-X in West Alaska (2022-07-28 -- 2022-08-21), several LiDAR scans were acquired using a backpack laser scanning system (GreenValley LiBackpack DGC50). The surveys were carried out on the Baldwin Peninsula and on the Seward Peninsula. The goal of the campaign was to quantify permafrost landscape change by mapping various permafrost thaw features such as thaw slumps, gullies, and degraded ice wedge polygons. These features are predominantly less than 1 km2 in size. The 3D point cloud data from the LiDAR backpack were used to generate Digital Elevation Models (DEMs) of the thaw features. The point cloud processing workflow for these DEMs included point cloud georeferencing, filtering, and ground classification. In total, 15 DEMs were derived at different locations during this campaign. In addition to change detection, the accurate field data are suitable for model parameterization and validation from Earth observation data.

Description: In a warming Arctic, permafrost-related disturbances, such as retrogressive thaw slumps (RTS), are becoming more abundant and dynamic, with serious implications for permafrost stability and bio-geochemical cycles on local to regional scales. Despite recent advances in the field of earth observation, many of these have remained undetected as RTS are highly dynamic, small, and scattered across the remote permafrost region. Here, we assessed the potential strengths and limitations of using deep learning for the automatic segmentation of RTS using PlanetScope satellite imagery, ArcticDEM and auxiliary datasets. We analyzed the transferability and potential for pan-Arctic upscaling and regional cross-validation, with independent training and validation regions, in six different thaw slump-affected regions in Canada and Russia. We further tested state-of-the-art model architectures (UNet, UNet++, DeepLabv3) and encoder networks to find optimal model configurations for potential upscaling to continental scales. The best deep learning models achieved mixed results from good to very good agreement in four of the six regions (maxIoU: 0.39 to 0.58; Lena River, Horton Delta, Herschel Island, Kolguev Island), while they failed in two regions (Banks Island, Tuktoyaktuk). Of the tested architectures, UNet++ performed the best. The large variance in regional performance highlights the requirement for a sufficient quantity, quality and spatial variability in the training data used for segmenting RTS across diverse permafrost landscapes, in varying environmental conditions. With our highly automated and configurable workflow, we see great potential for the transfer to active RTS clusters (e.g., Peel Plateau) and upscaling to much larger regions.

Description: In this presentation, we summarize our remote sensing activities in the Lena Delta region that focus on understanding and quantifying landscape changes in recent decades. In particular, we will highlight the great value of Landsat- and Sentinel-2 based trend datasets allowing unique insights into delta-wide permafrost and fluvial landscape dynamics since the 2000s in high spatial detail (30m resolution). Process dynamics that can be observed include thermokarst lake expansion and drainage, channel shore erosion and channel migration, and thaw slumping (Figure 1). We also use high-resolution (~0.5 m) optical imagery from commercial sensors (WorldView-1, WorldView-2, and GeoEye) in combination with historical (1960s-1980s) Corona and Hexagon imagery to quantify erosion and thaw slumping dynamics along lake and river shorelines in the Lena Delta and surrounding areas. Focus areas are the Sobo-Sise, Kurungnakh, and Samoylov islands, as well as Bykovsky Peninsula.

Description: Retrogressive thaw slumps (RTS) are typical landforms indicating processes of rapid thawing and degrading permafrost. Their abundance is increasing in many regions and quantifying their dynamics is of high importance for assessing geomorphic, hydrologic, and biogeochemical impacts of climate change in the Arctic. Here we present a deep-learning (DL) based semantic segmentation framework to detect RTS, using high-resolution multi-spectral PlanetScope, topographic (ArcticDEM elevation and slope), and medium-resolution multi-temporal Landsat Trend data. We created a highly automated processing pipeline, which is designed to allow reproducible results and to be flexible for multiple input data types. The processing workflow is based on the pytorch deep-learning framework and includes a variety of different segmentation architectures (UNet, UNet++, DeepLabV3), backbones and includes common data transformation techniques such as augmentation or normalization. We tested (training, validation) our DL based model in six different regions of 100 to 300 km² size across Canada, and Siberia. We performed a regional cross-validation (5 regions training, 1 region validation) to test the spatial robustness and transferability of the algorithm. Furthermore, we tested different architectures, backbones and loss-functions to identify the best performing and most robust parameter sets. For training the models we created a database of manually digitized and validated RTS polygons. The resulting model performance varied strongly between different regions with maximum Intersection over Union (IoU) scores between 0.15 and 0.58. The strong regional variation emphasizes the need for sufficiently large training data, which is representative of the diversity of RTS types. However, the creation of good training data proved to be challenging due to the fuzzy definition and delineation of RTS. We are further continuing to improve the usability and the functionality to add further datasets and classes. We will show first results from the upscaling beyond small test areas towards large spatial clusters of extensive RTS presence e.g. Peel Plateau in NW Canada.

Description: While temperatures are rising globally, they are rising more than twice as fast in the Arctic. Landscapes underlain by permafrost are especially vulnerable to this changing climate and experience increased thaw and degradation. The proceeding warming of organic-rich frozen ground is a highly relevant driver of carbon release into the atmosphere. Retrogressive Thaw Slumps (RTSs) are dynamic thermokarst features which develop when ice-rich permafrost thaws and thus are important indices when it comes to the assessment of potential carbon sources in permafrost landscapes. Thousands of RTSs have been inventoried in northwestern Canada. These inventories showed that thaw slumping modifies terrain morphology and alters the discharge into aquatic systems resulting amongst others in infrastructure instabilities and ecosystem changes. Furthermore, recent studies project that abrupt thermokarst processes contribute significant amounts of greenhouse gas emissions. As observed in most arctic regions, RTS activity has increased in the Russian High Arctic, however, little research has been done on RTSs in this region. The objective of this study is to better understand growth pattern and development rates of RTSs in northern Russia during the last decade. The study area consists of five different sites in the Russian High Arctic covering an area of more than 600 km². The sites are located on the Novaya Zemlya Archipelago, Kolguev Island, Bol’shoy Lyakhovsky Island and Taymyr Peninsula in ice-rich permafrost characterized by either buried glacial ice deposits or syngenetically formed Yedoma permafrost. To assess changes in number and extent, a GIS based inventory of manually mapped RTSs was created. The inventory is based on multispectral imagery of high-resolution satellite sensors, including PlanetScope, RapidEye, Pléiades and SPOT. Cloud free images were acquired between 2011 and 2020 and exist for each or every few years depending on their availability. Additional data sets such as ArcticDEM, Esri Satellite base map and Tasseled Cap Landsat Trends were used to support the mapping process. From the extracted individual RTS objects, changes in number and surface area were calculated. Furthermore, for coastal slumps thermal denudation and thermal abrasion rates were computed. The results show that RTS activity was high at the study sites during the investigation period and that the diverse sites revealed different RTS characteristics, with non-coastal RTSs showing a much larger increase in area. At the non-coastal sites, RTS-affected area increased by a factor of 2 (100 %) in West Taymyr, a factor of 4 (400 %) in Novaya Zemlya, and a factor of 33 (3300 %) in East Taymyr, with particularly large increases in more recent years. At the coastal sites, total RTS area increased by a factor of 1.2 (20%) in North Kolguev, remained the same in South Kolguev, and decreased slightly by a factor of 0.95 (5%) in Bol’shoy Lyakhovsky. Headwall and base of the coastal slumps retreated at different rates. However, at all coastal sites, erosion of the headwall and base progressed, demonstrating that RTS activity cannot be determined by area changes alone because coastal RTSs are strongly influenced by thermal abrasion and thermal denudation which diminishes areal changes. Moreover, the number of RTS did not necessarily increase with increasing RTS activity. At all study sites except East Taymyr, increased RTS activity resulted from RTS growth rather than new RTS initiation. In addition, climate analysis revealed that the mean temperature increased significantly, within the last decade at all sites, potentially favouring RTS initiation and growth. The findings of this study contribute substantially to our understanding of regional permafrost thaw in the Russian High Arctic. Nevertheless, further research is needed to quantify volumetric permafrost loss and associated carbon release comprehensively throughout the Russian High Arctic to better understand RTS dynamics and their impact on greenhouse gas release.

Description: Retrogressive thaw slumps (RTS) are typical landscape processes of thawing and degrading permafrost. To this point, their distribution and dynamics are almost completely undocumented across many regions in the permafrost domain, partially due to the lack of data and monitoring techniques in the past. We are tackling this shortcoming by creating a deep learning based semantic segmentation framework to detect RTS, using multi-spectral PlanetScope, derived topographic (ArcticDEM) and multi-temporal Landsat Trend data. We created a highly automated processing pipeline, which is designed to create reproducible results and to be flexible for multiple input features. The processing workflow is based on the pytorch deep-learning framework and includes a variety of different segmentation architectures (UNet, UNet++, DeepLabV3), backbones and includes common data transformation techniques such as augmentation or normalization. We tested (training, validation) our DL based model in six different regions of 100 to 300 km² size across Canada (Banks Island, Tuktoyaktuk, Horton, Herschel Is.), and Siberia (Kolguev, Lena). We performed a regional cross-validation (5 regions training, 1 region validation) to test the spatial robustness and transferability of the algorithm. Furthermore, we tested different architectures backbones and loss-function to identify the best performing and most robust parameter sets. For training the models we created a training database of manually digitized and validated RTS polygons. The resulting model performance varied strongly between different regions with maximum Intersection over Union (IoU) scores between 0.15 and 0.58. The strong regional variation emphasizes the need for sufficiently large training data, which is representative for the massive variety of RTS. However, the creation of good training data proved to be challenging due to the fuzzy definition and delineation of RTS, particularly on the lower part. We have recently expanded our analysis to several RTS-rich regions across the Arctic (Fig.X) for the year 2021 and annual analysis (2018-2021) for RTS hot-spots, e.g. Banks Island, Peel Plateau and others. First model inference runs are promising for detecting RTS, but are still strongly overestimating the number and area of RTS, due to an excessive number of false positives. Model performance however, varies strongly between regions. Due to the strong variability of landscapes with RTS, we expect an improvement in model performance with an increase in the number and spatial distribution of training datasets. The community driven formation of the IPA Action Group RTSIn, which aims to create standardized RTS digitization protocols and training datasets for deep/machine-learning purposes will be a great boost for our purpose. With our standardized processing pipeline (preprocessing, training, inference), which allows to add more features based on user interest and data availability,, we tested our workflow for surface water and pingos with a mixture of publically available (Jones et al) and digitized data (Grosse pingos, Nitze water). These tests produced very good results and showed that the designed workflow is transferrable beyond the segmentation of RTS only. In the near future, we are aiming to integrate the community based training data and further gradually improve our training database. Within the framework of the ML4Earth project, we will create a temporal and pan-arctic monitoring system for RTS based on our highly automated processing chain. This will enable us to better understand pan-arctic RTS dynamics, their influencing factors, and consequences. Combining these spatial-temporal datasets with volumetric change information and carbon stock information will enable us to better quantify the consequences of thaw slumping across the permafrost domain.

Description: While temperatures are increasing on the global scale, the Arctic regions are especially vulnerable to this changing climate and landscapes underlain by permafrost experience increased thaw and degradation. The enhanced warming of organic-rich frozen ground can have severe consequences on infrastructure and ecosystems and is projected to become a highly relevant driver of greenhouse gas fluxes into the atmosphere. Degrading permafrost landscapes occur extensively in vast areas of the North American Arctic, directly affecting communities and ecosystems. To identify and quantify these widespread degradation phenomena over vast areas, we require highest-resolution Earth observation dataset that we collect during aerial imaging campaigns. We here report on observations and first results from three airborne campaigns in 2018, 2019 and 2021. We performed large-scale monitoring of permafrost-affected areas in northern Canada and Alaska, focusing on sites that experienced disturbances in the past or recently. This included sites with vulnerable settlements, coastal erosion, thaw slumping, lake expansion and drainage, ice-wedge degradation and thaw subsidence, fire scars, pingos, methane seeps, and sites affected by beaver activities. All surveys were flown with the Alfred Wegener Institute's Polar-5 and -6 scientific airplanes at 500-1500 m altitude above terrain. The onboard sensor, the Modular Aerial Camera System (MACS), a very-high-resolution multispectral camera developed by the German Aerospace Center, operated in the visible (RGB) and near-infrared (NIR) domain. From the comprehensive collection of multiple TB of gathered data, super-high-resolution (up to 7 cm/px) RGB+NIR image mosaics and stereophotogrammetric digital surface models were derived. By presenting the data and first analyses, we would like to invite the community to discuss best use for maximized benefit of the data, in order to substantially contribute to our understanding of permafrost thaw dynamics.