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Dynamic Disturbance Processes in Permafrost Regions

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Biogeosciences Remote Sensing".

Deadline for manuscript submissions: closed (15 May 2022) | Viewed by 33254

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Special Issue Editors

Prof. Dr. Mark J. Lara

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Guest Editor

Department of Plant Biology/Department of Geography & Geographic Information Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
Interests: multi/hyperspectral image analysis/classification; land cover change processes; permafrost disturbances; carbon/nitrogen cycling; machine learning and big data applications

Dr. Simon Zwieback

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Guest Editor

Geophysical Institute, University of Alaska Fairbanks, Fairbanks, AK 99775, USA
Interests: permafrost remote sensing; periglacial processes; active and passive microwave remote sensing; inverse problems and uncertainty quantification

Special Issue Information

Dear Colleagues,

Permafrost regions are in transition. Dynamic disturbances such as wildfire and permafrost degradation are restructuring the spatial and temporal distribution of snow, water, vegetation, soil carbon/nutrients, and energy dynamics, with implications for local to global feedbacks. The interdependence of these disturbances makes quantifying their impact challenging, yet paramount for improving our predictive capacity as climate change and disturbance regimes intensify.

In this Special Issue, we aim to advance knowledge of dynamic disturbance processes that impact high-latitude permafrost ecosystems. We welcome submissions on the application of remote sensing to a broad range of disturbances: (1) Thermokarst (vertical surface subsidence) and thermoerosion (lateral transport of sediments via ground ice melt), (2) thermokarst lake dynamics, (3) coastal and fluvial erosion, (4) wildfire–ecosystem interactions, (5) permafrost vegetation interactions, and (6) anthropogenic disturbances. We particularly encourage applications linking two interacting components that influence periglacial ecosystem dynamics (e.g., wildfire and vegetation; thermokarst and hydrology; climate and thermokarst).

Prof. Dr. Mark J. Lara
Dr. Simon Zwieback
Guest Editors

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Keywords

Climate change
Thermokarst
Thermoerosion
Wildfire
Coastal erosion
Shrub expansion
Disturbance

Published Papers (10 papers)

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Research

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21 pages, 36528 KiB

Open AccessArticle

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

by Soraya Kaiser, Julia Boike, Guido Grosse and Moritz Langer

Remote Sens. 2022, 14(23), 6107; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14236107 - 02 Dec 2022

Cited by 5 | Viewed by 1599

Abstract

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

- 0.35

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

- 0.35

m and an alignment root mean square error of

0.99

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. Full article

(This article belongs to the Special Issue Dynamic Disturbance Processes in Permafrost Regions)

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40 pages, 17758 KiB

Open AccessArticle

Automated Detection of Retrogressive Thaw Slumps in the High Arctic Using High-Resolution Satellite Imagery

by Chandi Witharana, Mahendra R. Udawalpola, Anna K. Liljedahl, Melissa K. Ward Jones, Benjamin M. Jones, Amit Hasan, Durga Joshi and Elias Manos

Remote Sens. 2022, 14(17), 4132; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14174132 - 23 Aug 2022

Cited by 11 | Viewed by 2225

Abstract

Retrogressive thaw slumps (RTS) are considered one of the most dynamic permafrost disturbance features in the Arctic. Sub-meter resolution multispectral imagery acquired by very high spatial resolution (VHSR) commercial satellite sensors offer unique capacities in capturing the morphological dynamics of RTSs. The central goal of this study is to develop a deep learning convolutional neural net (CNN) model (a UNet-based workflow) to automatically detect and characterize RTSs from VHSR imagery. We aimed to understand: (1) the optimal combination of input image tile size (array size) and the CNN network input size (resizing factor/spatial resolution) and (2) the interoperability of the trained UNet models across heterogeneous study sites based on a limited set of training samples. Hand annotation of RTS samples, CNN model training and testing, and interoperability analyses were based on two study areas from high-Arctic Canada: (1) Banks Island and (2) Axel Heiberg Island and Ellesmere Island. Our experimental results revealed the potential impact of image tile size and the resizing factor on the detection accuracies of the UNet model. The results from the model transferability analysis elucidate the effects on the UNet model due the variability (e.g., shape, color, and texture) associated with the RTS training samples. Overall, study findings highlight several key factors that we should consider when operationalizing CNN-based RTS mapping over large geographical extents. Full article

(This article belongs to the Special Issue Dynamic Disturbance Processes in Permafrost Regions)

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26 pages, 12524 KiB

Open AccessArticle

Automated Extraction of Annual Erosion Rates for Arctic Permafrost Coasts Using Sentinel-1, Deep Learning, and Change Vector Analysis

by Marius Philipp, Andreas Dietz, Tobias Ullmann and Claudia Kuenzer

Remote Sens. 2022, 14(15), 3656; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14153656 - 30 Jul 2022

Cited by 6 | Viewed by 2205

Abstract

Arctic permafrost coasts become increasingly vulnerable due to environmental drivers such as the reduced sea-ice extent and duration as well as the thawing of permafrost itself. A continuous quantification of the erosion process on large to circum-Arctic scales is required to fully assess the extent and understand the consequences of eroding permafrost coastlines. This study presents a novel approach to quantify annual Arctic coastal erosion and build-up rates based on Sentinel-1 (S1) Synthetic Aperture RADAR (SAR) backscatter data, in combination with Deep Learning (DL) and Change Vector Analysis (CVA). The methodology includes the generation of a high-quality Arctic coastline product via DL, which acted as a reference for quantifying coastal erosion and build-up rates from annual median and standard deviation (sd) backscatter images via CVA. The analysis was applied on ten test sites distributed across the Arctic and covering about 1038 km of coastline. Results revealed maximum erosion rates of up to 160 m for some areas and an average erosion rate of 4.37 m across all test sites within a three-year temporal window from 2017 to 2020. The observed erosion rates within the framework of this study agree with findings published in the previous literature. The proposed methods and data can be applied on large scales and, prospectively, even for the entire Arctic. The generated products may be used for quantifying the loss of frozen ground, estimating the release of stored organic material, and can act as a basis for further related studies in Arctic coastal environments. Full article

(This article belongs to the Special Issue Dynamic Disturbance Processes in Permafrost Regions)

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20 pages, 7476 KiB

Open AccessArticle

Accuracy, Efficiency, and Transferability of a Deep Learning Model for Mapping Retrogressive Thaw Slumps across the Canadian Arctic

by Lingcao Huang, Trevor C. Lantz, Robert H. Fraser, Kristy F. Tiampo, Michael J. Willis and Kevin Schaefer

Remote Sens. 2022, 14(12), 2747; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14122747 - 08 Jun 2022

Cited by 9 | Viewed by 2364

Abstract

Deep learning has been used for mapping retrogressive thaw slumps and other periglacial landforms but its application is still limited to local study areas. To understand the accuracy, efficiency, and transferability of a deep learning model (i.e., DeepLabv3+) when applied to large areas or multiple regions, we conducted several experiments using training data from three different regions across the Canadian Arctic. To overcome the main challenge of transferability, we used a generative adversarial network (GAN) called CycleGAN to produce new training data in an attempt to improve transferability. The results show that (1) data augmentation can improve the accuracy of the deep learning model but does not guarantee transferability, (2) it is necessary to choose a good combination of hyper-parameters (e.g., backbones and learning rate) to achieve an optimal trade-off between accuracy and efficiency, and (3) a GAN can significantly improve the transferability if the variation between source and target is dominated by color or general texture. Our results suggest that future mapping of retrogressive thaw slumps should prioritize the collection of training data from regions where a GAN cannot improve the transferability. Full article

(This article belongs to the Special Issue Dynamic Disturbance Processes in Permafrost Regions)

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23 pages, 23140 KiB

Open AccessArticle

Developing and Testing a Deep Learning Approach for Mapping Retrogressive Thaw Slumps

by Ingmar Nitze, Konrad Heidler, Sophia Barth and Guido Grosse

Remote Sens. 2021, 13(21), 4294; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13214294 - 26 Oct 2021

Cited by 21 | Viewed by 4881

Abstract

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. Full article

(This article belongs to the Special Issue Dynamic Disturbance Processes in Permafrost Regions)

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24 pages, 3093 KiB

Open AccessArticle

A Quantitative Graph-Based Approach to Monitoring Ice-Wedge Trough Dynamics in Polygonal Permafrost Landscapes

by Tabea Rettelbach, Moritz Langer, Ingmar Nitze, Benjamin Jones, Veit Helm, Johann-Christoph Freytag and Guido Grosse

Remote Sens. 2021, 13(16), 3098; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163098 - 05 Aug 2021

Cited by 13 | Viewed by 3093

Abstract

In response to increasing Arctic temperatures, ice-rich permafrost landscapes are undergoing rapid changes. In permafrost lowlands, polygonal ice wedges are especially prone to degradation. Melting of ice wedges results in deepening troughs and the transition from low-centered to high-centered ice-wedge polygons. This process has important implications for surface hydrology, as the connectivity of such troughs determines the rate of drainage for these lowland landscapes. In this study, we present a comprehensive, modular, and highly automated workflow to extract, to represent, and to analyze remotely sensed ice-wedge polygonal trough networks as a graph (i.e., network structure). With computer vision methods, we efficiently extract the trough locations as well as their geomorphometric information on trough depth and width from high-resolution digital elevation models and link these data within the graph. Further, we present and discuss the benefits of graph analysis algorithms for characterizing the erosional development of such thaw-affected landscapes. Based on our graph analysis, we show how thaw subsidence has progressed between 2009 and 2019 following burning at the Anaktuvuk River fire scar in northern Alaska, USA. We observed a considerable increase in the number of discernible troughs within the study area, while simultaneously the number of disconnected networks decreased from 54 small networks in 2009 to only six considerably larger disconnected networks in 2019. On average, the width of the troughs has increased by 13.86%, while the average depth has slightly decreased by 10.31%. Overall, our new automated approach allows for monitoring ice-wedge dynamics in unprecedented spatial detail, while simultaneously reducing the data to quantifiable geometric measures and spatial relationships. Full article

(This article belongs to the Special Issue Dynamic Disturbance Processes in Permafrost Regions)

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26 pages, 11402 KiB

Open AccessArticle

Remote Sensing-Based Statistical Approach for Defining Drained Lake Basins in a Continuous Permafrost Region, North Slope of Alaska

by Helena Bergstedt, Benjamin M. Jones, Kenneth Hinkel, Louise Farquharson, Benjamin V. Gaglioti, Andrew D. Parsekian, Mikhail Kanevskiy, Noriaki Ohara, Amy L. Breen, Rodrigo C. Rangel, Guido Grosse and Ingmar Nitze

Remote Sens. 2021, 13(13), 2539; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13132539 - 29 Jun 2021

Cited by 8 | Viewed by 2585

Abstract

Lake formation and drainage are pervasive phenomena in permafrost regions. Drained lake basins (DLBs) are often the most common landforms in lowland permafrost regions in the Arctic (50% to 75% of the landscape). However, detailed assessments of DLB distribution and abundance are limited. In this study, we present a novel and scalable remote sensing-based approach to identifying DLBs in lowland permafrost regions, using the North Slope of Alaska as a case study. We validated this first North Slope-wide DLB data product against several previously published sub-regional scale datasets and manually classified points. The study area covered >71,000 km², including a >39,000 km² area not previously covered in existing DLB datasets. Our approach used Landsat-8 multispectral imagery and ArcticDEM data to derive a pixel-by-pixel statistical assessment of likelihood of DLB occurrence in sub-regions with different permafrost and periglacial landscape conditions, as well as to quantify aerial coverage of DLBs on the North Slope of Alaska. The results were consistent with previously published regional DLB datasets (up to 87% agreement) and showed high agreement with manually classified random points (64.4–95.5% for DLB and 83.2–95.4% for non-DLB areas). Validation of the remote sensing-based statistical approach on the North Slope of Alaska indicated that it may be possible to extend this methodology to conduct a comprehensive assessment of DLBs in pan-Arctic lowland permafrost regions. Better resolution of the spatial distribution of DLBs in lowland permafrost regions is important for quantitative studies on landscape diversity, wildlife habitat, permafrost, hydrology, geotechnical conditions, and high-latitude carbon cycling. Full article

(This article belongs to the Special Issue Dynamic Disturbance Processes in Permafrost Regions)

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14 pages, 4557 KiB

Open AccessFeature PaperArticle

Periglacial Lake Origin Influences the Likelihood of Lake Drainage in Northern Alaska

by Mark Jason Lara and Melissa Lynn Chipman

Remote Sens. 2021, 13(5), 852; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13050852 - 25 Feb 2021

Cited by 7 | Viewed by 2710

Abstract

Nearly 25% of all lakes on earth are located at high latitudes. These lakes are formed by a combination of thermokarst, glacial, and geological processes. Evidence suggests that the origin of periglacial lake formation may be an important factor controlling the likelihood of lakes to drain. However, geospatial data regarding the spatial distribution of these dominant Arctic and subarctic lakes are limited or do not exist. Here, we use lake-specific morphological properties using the Arctic Digital Elevation Model (DEM) and Landsat imagery to develop a Thermokarst lake Settlement Index (TSI), which was used in combination with available geospatial datasets of glacier history and yedoma permafrost extent to classify Arctic and subarctic lakes into Thermokarst (non-yedoma), Yedoma, Glacial, and Maar lakes, respectively. This lake origin dataset was used to evaluate the influence of lake origin on drainage between 1985 and 2019 in northern Alaska. The lake origin map and lake drainage datasets were synthesized using five-year seamless Landsat ETM+ and OLI image composites. Nearly 35,000 lakes and their properties were characterized from Landsat mosaics using an object-based image analysis. Results indicate that the pattern of lake drainage varied by lake origin, and the proportion of lakes that completely drained (i.e., >60% area loss) between 1985 and 2019 in Thermokarst (non-yedoma), Yedoma, Glacial, and Maar lakes were 12.1, 9.5, 8.7, and 0.0%, respectively. The lakes most vulnerable to draining were small thermokarst (non-yedoma) lakes (12.7%) and large yedoma lakes (12.5%), while the most resilient were large and medium-sized glacial lakes (4.9 and 4.1%) and Maar lakes (0.0%). This analysis provides a simple remote sensing approach to estimate the spatial distribution of dominant lake origins across variable physiography and surficial geology, useful for discriminating between vulnerable versus resilient Arctic and subarctic lakes that are likely to change in warmer and wetter climates. Full article

(This article belongs to the Special Issue Dynamic Disturbance Processes in Permafrost Regions)

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30 pages, 91172 KiB

Open AccessArticle

Geomorphological and Climatic Drivers of Thermokarst Lake Area Increase Trend (1999–2018) in the Kolyma Lowland Yedoma Region, North-Eastern Siberia

by Alexandra Veremeeva, Ingmar Nitze, Frank Günther, Guido Grosse and Elizaveta Rivkina

Remote Sens. 2021, 13(2), 178; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13020178 - 06 Jan 2021

Cited by 43 | Viewed by 6377

Abstract

Thermokarst lakes are widespread in Arctic lowlands. Under a warming climate, landscapes with highly ice-rich Yedoma Ice Complex (IC) deposits are particularly vulnerable, and thermokarst lake area dynamics serve as an indicator for their response to climate change. We conducted lake change trend analysis for a 44,500 km² region of the Kolyma Lowland using Landsat imagery in conjunction with TanDEM-X digital elevation model and Quaternary Geology map data. We delineated yedoma–alas relief types with different yedoma fractions, serving as a base for geospatial analysis of lake area dynamics. We quantified lake changes over the 1999–2018 period using machine-learning-based classification of robust trends of multi-spectral indices of Landsat data and object-based long-term lake detection. We analyzed the lake area dynamics separately for 1999–2013 and 1999–2018 periods, including the most recent five years that were characterized by very high precipitation. Comparison of drained lake basin area with thermokarst lake extents reveal the overall limnicity decrease by 80% during the Holocene. Current climate warming and wetting in the region led to a lake area increase by 0.89% for the 1999–2013 period and an increase by 4.15% for the 1999–2018 period. We analyzed geomorphological factors impacting modern lake area changes for both periods such as lake size, elevation, and yedoma–alas relief type. We detected a lake area expansion trend in high yedoma fraction areas indicating ongoing Yedoma IC degradation by lake thermokarst. Our concept of differentiating yedoma–alas relief types helps to characterize landscape-scale lake area changes and could potentially be applied for refined assessments of greenhouse gas emissions in Yedoma regions. Comprehensive geomorphological inventories of Yedoma regions using geospatial data provide a better understanding of the extent of thermokarst processes during the Holocene and the pre-conditioning of modern thermokarst lake area dynamics. Full article

(This article belongs to the Special Issue Dynamic Disturbance Processes in Permafrost Regions)

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19 pages, 14693 KiB

Open AccessTechnical Note

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

by Soraya Kaiser, Guido Grosse, Julia Boike and Moritz Langer

Remote Sens. 2021, 13(14), 2802; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13142802 - 16 Jul 2021

Cited by 5 | Viewed by 3437

Abstract

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. Full article

(This article belongs to the Special Issue Dynamic Disturbance Processes in Permafrost Regions)

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