A Comparative Study of Active Rock Glaciers Mapped from Geomorphic- and Kinematic-Based Approaches in Daxue Shan, Southeast Tibetan Plateau

Abstract

Active rock glaciers (ARGs) are important permafrost landforms in alpine regions. Identifying ARGs has mainly relied on visual interpretation of their geomorphic characteristics with optical remote sensing images, while mapping ARGs from their kinematic features has also become popular in recent years. However, a thorough comparison of geomorphic- and kinematic-based inventories of ARGs has not been carried out. In this study, we employed a multi-temporal interferometric synthetic aperture radar (InSAR) technique to derive the mean annual surface displacement velocity over the Daxue Shan, Southeast Tibet Plateau. We then compiled a rock glacier inventory by synergistically interpreting the InSAR-derived surface displacements and geomorphic features based on Google Earth images. Our InSAR-assist kinematic-based inventory (KBI) was further compared with a pre-existing geomorphic-based inventory (GBI) of rock glaciers in Daxue Shan. The results show that our InSAR-assist inventory consists of 344 ARGs, 36% (i.e., 125) more than that derived from the geomorphic-based method (i.e., 251). Only 32 ARGs in the GBI are not included in the KBI. Among the 219 ARGs detected by both approaches, the ones with area differences of more than 20% account for about 32% (i.e., 70 ARGs). The mean downslope velocities of ARGs calculated from InSAR are between 2.8 and 107.4 mm∙a−1. Our comparative analyses show that ARGs mapping from the InSAR-based kinematic approach is more efficient and accurate than the geomorphic-based approach. Nonetheless, the completeness of the InSAR-assist KBI is affected by the SAR data acquisition time, signal decorrelation, geometric distortion of SAR images, and the sensitivity of the InSAR measurement to ground deformation. We suggest that the kinematic-based approach should be utilized in future ARGs-based studies such as regional permafrost distribution assessment and water storage estimates.

Mapping and Characterizing Rock Glaciers in the Arid Western Kunlun Mountains Supported by InSAR and Deep Learning

“…To address these problems, recent research progress in compiling RG inventories includes (a) integrating Interferometric Synthetic Aperture Radar (InSAR) techniques to facilitate active RG identification and kinematics quantification (e.g., Barboux et al., 2014; Cai et al., 2021; Liu et al., 2013; Reinosch et al., 2021; Wang et al., 2017; E. Zhang et al., 2021; X. Zhang et al., 2021); (b) implementing Convolutional Neural Networks to demonstrate the feasibility of automating RG delineation (Robson et al., 2020) or to improve the consistency of existing RG inventories (Erharter et al., 2022); and (c) establishing widely accepted inventorying guidelines by the international RG research community (RGIK, 2022a, 2022b).…”

“…Manual delineation of RGs based on InSAR and high-resolution optical imagery in this study is guided by the baseline concepts proposed by the International Permafrost Association (IPA) Action Group on RGs to ensure a standard high-quality data set utilized to train the deep learning network, and thus, the final mapping results (RGIK, 2022a(RGIK, , 2022b. We adopted the deep learning method to improve the mapping efficiency by automating the identification and delineation tasks, and more importantly, to generate a more comprehensive geodatabase by overcoming the limitations of the InSAR-based method, such as the coherence loss and the insensitivity to the movement perpendicular to the line-of-sight (LOS) (Cai et al, 2021).…”

“…To address these problems, recent research progress in compiling RG inventories includes (a) integrating Interferometric Synthetic Aperture Radar (InSAR) techniques to facilitate active RG identification and kinematics quantification (e.g., Barboux et al, 2014;Cai et al, 2021;Liu et al, 2013;Reinosch et al, 2021;Wang et al, 2017;E. Zhang et al, 2021;X.…”

Mapping and Characterizing Rock Glaciers in the Arid Western Kunlun Mountains Supported by InSAR and Deep Learning

“…To address these problems, recent research progress in compiling rock glacier inventories includes (1) integrating InSAR techniques to facilitate active rock glacier identification and kinematics quantification (e.g., Liu et al 2013;Barboux et al 2014;Wang et al 2017;Cai et al 2021;Reinosch et al 2021;); (2) implementing Convolutional Neural Networks (CNN) to demonstrate the feasibility of automating rock glacier delineation (Robson et al 2020) or to improve the consistency of existing rock glacier inventories (Erharter et al 2022); and (3) establishing widely accepted inventorying guidelines by the international rock glacier research community (RGIK, 2022a(RGIK, , 2022b.…”

“…Marcer (2020) glaciers to ensure a standard high-quality dataset utilized to train the deep learning network, and thus, the final mapping results (RGIK, 2022a(RGIK, , 2022b. We adopted the deep learning method to improve the mapping efficiency by automating the identification and delineation tasks, and more importantly, to generate a more comprehensive geodatabase by overcoming the limitations of InSAR-based method, such as the coherence loss and the insensitivity to the movement perpendicular to the line-of-sight (Cai et al, 2021).…”