An official website of the United States government
Abstract. Permafrost thaw disrupts ecosystems, hydrology, and biogeochemical cycles, reinforcing climate change through a positive permafrost-carbon feedback loop. Thaw can be gradual, deepening the active layer, or abrupt, triggering thermokarst, thermo-erosion, or thermodenudation. Retrogressive thaw slumps (RTSs) are a key manifestation of abrupt permafrost thaw. Yet, their distribution, scale, and environmental controls in the West Siberian Arctic remain poorly understood, further complicated by their rapid evolution. This study presents an extensive update of the West Siberian RTS inventory through manual mapping using high-resolution, multi-source, multi-year recent (2016–2023) satellite basemaps (ESRI, Google Earth, and Yandex Maps). We developed an RTS classification capturing key environmental parameters, including morphology, spatial organization, terrain position, and associated relief-forming concurrent processes. The dataset comprises 6168 classified RTS landforms, integrating newly mapped sites with previously reported occurrences to provide a comprehensive view of a 445 226 km2 region covering the Yamal, Gydan, and Tazovsky peninsulas. The collected data underwent manual filtering and verification, leveraging local field experience and observations from key sites to reduce uncertainty and minimize false positives. Accuracy analysis, performed by comparing the dataset with various field datasets collected across the peninsulas, confirmed high accuracy (>90 %) for RTS identification. The dataset likely underestimated the distribution of small RTSs due to the resolution limitations of remote sensing data, hence generally providing a conservative estimate. This dataset serves as a valuable resource for diverse research fields, including ecology, biogeochemistry, geomorphology, climatology, permafrost science, and natural hazard assessment. Additionally, it provides a crucial reference dataset for machine learning applications, enhancing upcoming remote sensing classification and predictive modeling approaches. The dataset is available from Nesterova et al. (2025; https://doi.org/10.1594/PANGAEA.974406).
more »
« less
This content will become publicly available on December 1, 2026
A Collaborative and Scalable Geospatial Data Set for Arctic Retrogressive Thaw Slumps with Data Standards
Abstract Arctic permafrost is undergoing rapid changes due to climate warming in high latitudes. Retrogressive thaw slumps (RTS) are one of the most abrupt and impactful thermal-denudation events that change Arctic landscapes and accelerate carbon feedbacks. Their spatial distribution remains poorly characterised due to time-intensive conventional mapping methods. While numerous RTS studies have published standalone digitisation datasets, the lack of a centralised, unified database has limited their utilisation, affecting the scale of RTS studies and the generalisation ability of deep learning models. To address this, we established the Arctic Retrogressive Thaw Slumps (ARTS) dataset containing 23,529 RTS-present and 20,434 RTS-absent digitisations from 20 standalone datasets. We also proposed a Data Curation Framework as a working standard for RTS digitisations. This dataset is designed to be comprehensive, accessible, contributable, and adaptable for various RTS-related studies. This dataset and its accompanying curation framework establish a foundation for enhanced collaboration in RTS research, facilitating standardised data sharing and comprehensive analyses across the Arctic permafrost research community.
more »
« less
- Award ID(s):
- 2052107
- PAR ID:
- 10680273
- Author(s) / Creator(s):
- ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- Scientific Data
- Volume:
- 12
- Issue:
- 1
- ISSN:
- 2052-4463
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract Retrogressive Thaw Slumps (RTS) are widespread mass-wasting hillslope failures triggered by thawing permafrost. While regional studies have provided insights into the spatial distribution and dynamics of RTS, a consistent and unbiased quantification and monitoring remains unsolved at pan-arctic scales. We present the Database of AI-detected Arctic RTS footprints (DARTS), comprising ~43,000 individual footprints of active RTS or active areas within larger RTS landforms. DARTS spans ~1.6 million km2from 2018–2023, with at least annual coverage from 2021–2023 across a ~900,000 km2region. The database is freely available in two processing levels: sub-annual and annually aggregated polygon footprints including spatial and tabular metadata. DARTS uses a highly automated workflow based on deep learning segmentation of PlanetScope multi-spectral satellite imagery (3–5 m resolution) and elevation data. Validation against different regional RTS datasets yielded F1 scores ranging from 0.263 to 0.700, with higher accuracy in areas of intense RTS activity. DARTS provides a valuable resource for systematically mapping, quantifying, and analyzing active hillslope thermokarst distribution and changes over time across the circum-arctic permafrost region.more » « less
-
null (Ed.)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.more » « less
-
ABSTRACT Deep‐learning (DL) models have become increasingly beneficial for the detection of retrogressive thaw slumps (RTS) in the permafrost domain. However, comparing accuracy metrics is challenging due to unstandardized labeling guidelines. To address this, we conducted an experiment with 12 international domain experts from a broad range of scientific backgrounds. Using 3 m PlanetScope multispectral imagery, they digitized RTS footprints in two sites. We evaluated label uncertainty by comparing manually outlined RTS labels using Intersection‐over‐Union (IoU) and F1 metrics. At the Canadian Peel Plateau site, we see good agreement, particularly in the active parts of RTS. Differences were observed in the interpretation of the debris tongue and the stable vegetated sections of RTS. At the Russian Bykovsky site, we observed a larger mismatch. Here, the same differences were documented, but several participants mistakenly identified non‐RTS features. This emphasizes the importance of site‐specific knowledge for reliable label creation. The experiment highlights the need for standardized labeling procedures and definition of their scientific purpose. The most similar expert labels outperformed the accuracy metrics reported in the literature, highlighting human labeling capabilities with proper training, site knowledge, and clear guidelines. These findings lay the groundwork for DL‐based RTS monitoring in the pan‐Arctic.more » « less
-
Delineations of Retrogressive Thaw Slump (RTS) expansion and light detection and ranging (LiDAR) datasets (LAS files) of RTS sites were used to model how rates of RTS change are influenced by topographic and climatic characteristics across northern Alaska. LiDAR data were collected at ten sites, where five were collected from an uncrewed aerial system (UAS) and five were collected from a terrestrial LiDAR systems (TLS). LiDAR datasets were used to bias correct the open-source ArcticDEM (2 meter-resolution) for calculating annual rates of RTS volumetric losses across all sites. RTS Delineations were used to calculate annual rates of RTS areal expansion and summarize topographic characteristics calculated from the corrected ArcticDEM. Two shapefiles were created from historic satellite and aerial imagery (1949-2021) to summarize RTS areal change across 44 slumps: AK_RTS_ExansionDelineations.shp summarizes the area of RTS expansion between two time periods and AK_RTS_Delineations.shp summarizes the total RTS outline in each year where RTS expansion occurs. LiDAR UAS and TLS data are provided as LAS files from 12 slumps (five sites) near Toolik Lake and 9 slumps (5 sites) within the Noatak National Preserve.more » « less
