Abstract
The important Qinghai Tibet Engineering Corridor (QTEC) covers the part of the Highway and Railway underlain by permafrost. The permafrost on the QTEC is sensitive to climate warming and human disturbance and suffers accelerating degradation. Retrogressive thaw slumps (RTSs) are slope failures due to the thawing of ice-rich permafrost. They typically retreat and expand at high rates, damaging infrastructure, and releasing carbon preserved in frozen ground. Along the critical and essential corridor, RTSs are commonly distributed but remain poorly investigated. To compile the first comprehensive inventory of RTSs, this study uses an iteratively semi-automatic method built on deep learning to delineate thaw slumps in the 2019 PlanetScope CubeSat images over a ~54,000 km2 corridor area. The method effectively assesses every image pixel using DeepLabv3+ with limited training samples and manually inspects the deep-learning-identified thaw slumps based on their geomorphic features and temporal changes. The inventory includes 875 RTSs, of which 474 are clustered in the Beiluhe region, and 38 are near roads or railway lines. The dataset is available at https://doi.org/10.1594/PANGAEA.933957 (Xia et al., 2021), with the Chinese version at https://data.tpdc.ac.cn/zh-hans/disallow/50de2d4f-75e1-4bad-b316-6fb91d915a1a/. These RTSs tend to be located on north-facing slopes with gradients of 1.2°–18.1° and distributed at medium elevations ranging from 4511 to 5212 m. a.s.l. They prefer to develop on land receiving relatively low annual solar radiation (from 2900 to 3200 kWh m−2), alpine meadow covered, and silt loam underlay. The results provide a significant and fundamental benchmark dataset for quantifying thaw slump changes in this vulnerable region undergoing strong climatic warming and extensive human activities.
Highlights
We identified and delineated retrogressive thaw slumps (RTSs) along the whole Qinghai-Tibet Engineering Corridor (QTEC) by combing the efficiency of the deep learning model in mapping with the reliability of human input based on the deeplearning-based mapping method proposed by Huang et al (2020)
Around 90% of the RTSs were found at medium elevations (4582–5010 m), and the highest was at an elevation of 5394 m (Figure 6b)
Our inventory revealed that ~50% of the RTSs are densely clustered in the west of the Beiluhe region (e.g., Figure 5b), while the others are sparsely scattered across the other subregions (Figure 5a)
Summary
Permafrost is normally defined as ground that remains at or below 0°C for at least two consecutive years (French, 2018). One critical zone suffering accelerated permafrost degradation is the Qinghai-Tibet Engineering Corridor (QTEC), 40 which contains the Qinghai-Tibet Railway and Qinghai-Tibet Highway. This corridor is 1120 km long, and almost half its length (531 km) is underlain by permafrost (Jin et al, 2008; Wu and Zhang, 2010). Niu et al (2016) identified 42 slope failures (some of them are RTSs) by manually interpreting SPOT-5 imagery and field investigations within a 10 km lateral zone along the Qinghai-Tibet Highway from Wudaoliang to Fenghuo Mountain pass. Manual delineation is time-consuming and has a chance of missing possible 60 RTSs. Deep learning techniques automate several fields, such as identifying targets and classifying various land covers in remote sensing images.
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