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

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.

Multiscale Object-Based Classification and Feature Extraction along Arctic Coasts

“…Classifications based on training samples present opportunities to create more information-rich classifications, and potential applications, by introducing more classes and potentially dealing directly with problem areas by classifying the coastal zone into subclasses. Similar work by Nitze et al [29], where a deep learning, segmentation-based approach for mapping retrogressive thaw slumps further recommends the importance of pan-Arctic training data for DL-based applications. We believe a deep learning approach augmented with expert knowledge refinements, such as thresholding, would ultimately provide the most robust approach for wider application.…”

“…The polycyclic retrogressive thaw slump (Figure 8(A1-A4) and Figure 9(D1-D3)), which are unique coastal processes in the Arctic, at Crumbling Point was particularly well identified using DL and supervised classifications suggesting the importance of high-quality training datasets. However, due to the presence of vegetation in stabilized areas, the RTS becomes highly ambiguous and hardly discernable [29], resulting in the threshold-based classification to identify unvegetated areas of the slump, suggesting that a hierarchical approach may be able to distinguish between active and inactive retrogressive thaw slumps.…”

“…In all cases, the tundra and water classes were classified with very hig classifications suggesting the importance of high-quality training datasets. H to the presence of vegetation in stabilized areas, the RTS becomes highly am hardly discernable [29], resulting in the threshold-based classification to iden tated areas of the slump, suggesting that a hierarchical approach may be a guish between active and inactive retrogressive thaw slumps. Overall, feature extraction derived from object-based classifications was effective along coastal types with clear boundary lines but proved less effective along coasts with low-centered ice-wedge polygon networks, intertidal zones, and low plain tundra where there is also uncertainty in the reference dataset due to coastal interpretation.…”

“…Generally, coastal erosion studies and subsequent coastal indicators have been dictated by the availability and resolution of remote sensing data products. The increasing ubiquity of very high resolution data such as RapidEye, PlanetScope, or DigitalGlobal imagery offers opportunities to address the paucity of information regarding short-term coastal dynamics [21], the limited spatiotemporal coverage, and standardize coastal indicators but poses significant challenges for image classification and specifically feature detection [29]. The existing pixel-based paradigm on such very high resolution (VHR) imagery is no longer effective as individual pixels are not necessarily representative of the land cover class they are meant to refer [30], and an object-based image analysis (OBIA) approach represents a significant advancement [31] but is limited in the application to coastal erosion studies and particularly along Arctic coasts.…”

“…The existing pixel-based paradigm on such very high resolution (VHR) imagery is no longer effective as individual pixels are not necessarily representative of the land cover class they are meant to refer [30], and an object-based image analysis (OBIA) approach represents a significant advancement [31] but is limited in the application to coastal erosion studies and particularly along Arctic coasts. Object-based and deep learning applications have been applied to infrastructure detection in the Arctic [32], ice-wedge polygon mapping [33][34][35], and recently in detecting RTS [29]. Broadly, deep learning neural networks have been successfully demonstrated in image classification, segmentation, and object detection, leading to substantial application in remote sensing [36][37][38][39][40][41], coastal erosion [42][43][44][45], and geomorphology [46][47][48].…”