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 10.5281/zenodo.6397029 (Xia et al.,
2021a), with the Chinese version at DOI: 10.11888/Cryos.tpdc.272672 (Xia et al. 2021b). 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 loam
underlay. Our 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.
Introduction
Permafrost is defined as ground that remains at or below 0 ∘C for at
least 2 consecutive years (Van Everdingen, 1998; French, 2017). On the
Qinghai–Tibet Plateau, permafrost covers an area of about 1.06×106 km2 (Zou et al., 2017; Cao et al., 2019) with an
average elevation of more than 4000 m (Liu and Chen, 2000) and latitudes of
26–38∘ N (Wang and French, 1994; Zhang et al.,
2008). Because the underlying permafrost on the plateau is characterized by
shallow thickness and relatively high temperature (Ran et al., 2022; Wu and
Zhang, 2008; Wu et al., 2010; Zhao et al., 2021; Zhou et al., 2000), it is
vulnerable to degradation under climate warming and disturbance due to human
activities. One critical zone suffering accelerated permafrost degradation
is the Qinghai–Tibet Engineering Corridor (QTEC), 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).
As a typical type of thermokarst landform, retrogressive thaw slumps (RTSs)
are caused by the thawing of ice-rich permafrost (Jorgenson, 2013) and thus
serve as vital and visual indicators of permafrost degradation. An RTS
typically consists of a sub-vertical ice-rich headwall and a gentle slump
floor occupied by mudflows (Ballantyne, 2018). The triggering factors and
mechanisms include coastal erosion, high air temperatures, extreme
precipitation, and human disturbance (Balser et al., 2014; French, 2017; Niu
et al., 2005). Once initiated, ablation of the exposed ice-rich permafrost
leads to the upslope retreat of the headwall at a rapid rate and disruption
of vegetation cover. RTSs can significantly disrupt the local environment,
for instance, causing damage to infrastructure (Hjort et al., 2022), changing
ecosystems (Kokelj and Jorgenson, 2013), and triggering the release of
carbon previously stored in the frozen ground (Turetsky et al., 2020).
Compared with the counterparts in the circum-Arctic, there is still a lack
of basic knowledge of RTSs locations on the Qinghai–Tibet Plateau (Mu et
al., 2020), with only limited studies identifying RTSs in subregions of the
QTEC. For instance, 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 the Fenghuo Mountain pass. Luo et al. (2019) manually
interpreted 438 RTSs using a series of satellite images from 2008 to 2017
covering the Beiluhe region. None of the previous works obtained a
comprehensive RTS inventory for this vital area owing to the challenges of
visiting RTSs in the remote and harsh permafrost regions or mapping them
from remote sensing imagery (Huang et al., 2020).
Several methods have been used in mapping RTSs in a large area, including
manual delineation and automatic recognition. Lewkowicz and Way (2019) used
the Google Earth Engine Time-lapse dataset to visually locate and delineate
terrain changes on Banks Island in the Canadian Arctic. However, manual
delineation is time consuming and there is a chance of possible RTSs being missed.
Deep-learning techniques automate several fields, such as identifying
targets and classifying various land covers in remote sensing images. For
permafrost-related landforms identification, Zhang et al. (2018) used Mask
R-CNN to delineate ice-wedge polygons in high-resolution aerial images
covering northern Alaska. Abolt and Young (2020) used deep learning and
50 cm-resolution DEMs to identify ice-wedge polygons near Prudhoe Bay,
Alaska. Nitze et al. (2021) tested the regional transferability and
potential for the deep-learning approach in inferring RTSs in the
pan-Arctic. These studies proved the applicability of deep learning in
mapping permafrost-related landforms in remote sensing images and emphasized
the importance of the quality and quantity of the training dataset. However,
many cryospheric studies, this one included, lacked label data that are
readily used in training. Set against this background, we identified and
delineated RTSs along the whole QTEC by combining the efficiency of the deep-learning model in mapping with the reliability of human input based on the
deep-learning-based mapping method proposed by Huang et al. (2020).
This study is aimed at obtaining a comprehensive inventory of RTSs with high
accuracy along the QTEC using a semi-automatic method and plenty of
supplementary datasets. Apart from this, using the topographic, soil
properties, and vegetation data, we reveal the spatial distribution
characteristics of RTSs.
Study area
The study area is the permafrost region along the Qinghai–Tibet Engineering
Corridor, defined based on the maps of Tong et al. (2011) and Zou et al. (2017). The study area (Fig. 1a) has a length of ∼ 550 km
along the Qinghai–Tibet railway and highway and a total area of
∼ 54 000 km2 (lying within the coordinates
90.91 to 95.15∘ E and 31.74 to
35.99∘ N, Fig. 1b). The mean annual ground temperature on the
natural ground is -4–0 ∘C (Jin et al., 2008; Wu and Zhang, 2008;
Wu et al., 2012). Around half of the permafrost in the region has relatively
high ground ice content with a thin active layer (Cheng, 2005; Yang et al.,
2010). In many locations, the surface vegetation cover has been destroyed or
removed because of anthropogenic and animal activities, which expose the
bare ground to the air and increase the instability of this region (Jin et
al., 2008; Wu et al., 2012). Thermokarst landforms, including retrogressive
thaw slumps, thermo-erosion gullies, and thermokarst lakes, are widely
distributed across the Qinghai–Tibetan Plateau (Huang et al., 2018; Mu et
al., 2020; Niu et al., 2012). Some RTSs developed in the area are perilously
close to the line of the Qinghai–Tibet highway (Niu et al., 2005). Figure 1c shows a typical example.
(a) Coverage of the study area and the permafrost distribution.
The red boundary is the extent of the study area. The yellow line is the
Qinghai–Tibet highway, and the diced line is the Qinghai–Tibet railway, most
of which runs close to the highway. Blue lines represent other national
roads. The background is the permafrost distribution map produced by Zou et
al. (2017), with white patches representing lakes or glaciers. The black
triangles label the sites where we conducted UAV investigations. (b) The
location of the study area on the Qinghai–Tibet Plateau. (c) A UAV photo of
an RTS near the Qinghai–Tibet railway (centre location: 92.883∘ E,
34.709∘ N).
Data sources
We collected PlanetScope scenes (Planet Team, 2017) with a spatial
resolution of 3 m acquired in July and August during the years 2016 to
2020. In addition to the multi-year PlanetScope images, the following
supplementary data were used for reference in manual inspection: Landsat-5
and 8, Sentinel-2, unmanned aerial vehicle (UAV) images, the “World Imagery”
provided by Esri, and the digital elevation model (DEM) from the Shuttle
Radar Topography Mission (SRTM) (Farr et al., 2007). We downloaded Landsat
and Sentinel-2 images taken before 2016 through the Google Earth Engine
(Gorelick et al., 2017). Landsat-5 carried with sensor Thematic Mapper
provides images with 30 m visible bands. Landsat-8 used the Operational Land
Imager sensor to obtain images with resolutions of 30 m for visible bands
and 15 m for the panchromatic band. Sentinel-2 has achieved images since
2015 and provides images with a resolution of 10 m for the red, green, and
blue bands. We used the flying platform DJI P4 Multispectral to obtain the
UAV images with around 15 cm resolution in 16 near-roads sites where 23 RTSs
candidates are located. We also accessed the high resolution (<1 m)
satellite imagery via Esri Wayback Imagery (Esri Inc., 2018), which archived
all published versions of World Imagery. Moreover, we calculated the slopes
and aspects using the 30 m DEM.
To further analyse the RTSs distribution patterns and associated
environmental factors, we used topo-climatic, hydrological, vegetation, and
soil datasets, including (1) the annual potential incoming solar radiation
(PISR), calculated using the method described by Kumar et al. (1997); (2)
the stream networks simulated by SAGA GIS based on the DEM; (3) vegetation
types (data source: Wang et al., 2016); and (4) soil textures (data source:
FAO, 2019). All the data
are listed in Table 1.
List of the data used for mapping RTSs and analysing their spatial distribution.
Acquisition timeSpatial coverageSpectral bandsSpatial resolutionPurposeSource/referencePlanetScope scenesJuly, August 2019QTECred, green, blue3–5 mAutomatically delineatingPlanet Team (2017)July and August during theyears 2016 to 2020Manual inspectionLandSat-82013–2016RTS locations and the surrounding areas within 1 kmPanchromatic band15 mManual inspectionGoogle Earth Enginered, green, blue30 mLandSat-52009–2016red, green, blue30 mSentinel-22015–2016red, green, blue10 mUAV imagesAugust 2020; July 202116 Selected sites along theQinghai–Tibet highwayred, green, blue∼ 15 cmManual inspectionField surveysESRI World ImagerySince 2010QTEC–<1 mManual inspectionEsri Inc. (2018)SRTM DEM2000QTEC–30 mManual inspection and analysing RTS distributionpatternsFarr et al. (2007)Vegetation type–QTEC–1 kmAnalysing RTS distributionpatternsWang et al. (2016)Soil textures2010QTEC–1 kmAnalysing RTS distributionpatternsFAO (2019)MethodologyPre-processing of PlanetScope images
We built an automated pipeline to download and pre-process the PlanetScope
images (Huang et al., 2018), including extracting RGB bands to composite
natural-colour images, converting them from 16 to 8 bit using a linear
transformation, tiling and mosaicking them to cover the entire study region.
We used the images processed in 2019 to train the deep-learning model and
infer RTSs and images from the other years for manual inspection.
Iterative mapping of RTSs
We applied a deep-learning architecture called DeepLabv3+
(https://github.com/tensorflow/models/tree/master/research/deeplab, last access: 17 August 2022) to identify
possible RTSs, and determined RTSs from these potential candidates based on
human knowledge and supplementary datasets. The DeepLabv3+ model
(http://download.tensorflow.org/models/deeplabv3_xception_2018_01_04.tar.gz, last access: 17 August 2022) we
used was pre-trained using the ImageNet dataset (Russakovsky et al., 2015),
making the model parameters effective in extracting general image features.
To make the model feasible for identifying RTSs, we copied the architecture
and parameters of the pre-trained model and fine-tuned all the parameters
using corresponding PlanetScope images and labels as training data. Because
the initial training data were derived from the work of Huang et al. (2021)
and only included 300 RTSs in the Beiluhe region, they were insufficient for
fine-tuning the deep-learning model and would have led to inferior results
containing multiple misidentifications and missing some RTSs. To overcome
this problem and obtain a complete inventory, we adapted an iterative
mapping strategy using optimized training data.
The flowchart of the method is illustrated in Fig. 2. The main steps were
(1) collecting training polygons and preparing training data (Sect. 3);
(2) training and fine-tuning the neural network DeepLabv3+; (3) predicting
RTSs in the whole region using the 2019 PlanetScope images and reserving
newly inferred polygons; (4) manually inspecting 2016–2020 time-lapse
images of each new polygon to determine RTS boundaries; and (5) adding the
newly found RTSs into the positive training dataset, and optionally adding
limited polygons covering representative misidentified RTSs into the
negative training dataset. Then we repeated steps (2)–(5) until no new
RTSs were found. Facing difficulties due to a lack of training polygons,
these iterative experiments succeeded in obtaining a more comprehensive and
representative training dataset by adding newly identified RTSs and a small
number of non-RTS polygons in the next iteration. Further details are
provided below.
Workflow of the deep-learning-aided semi-automatic method.
In every iteration, we trained the model using the confirmed RTS polygons
and negative training data of representative non-RTS polygons together with
the PlanetScope images. The details of preparing training images and label
images are given in Huang et al. (2018). After every iteration, we manually
inspected the newly inferred polygons using the 2019 PlanetScope images,
time-lapse images, as well as other supplementary data listed in Sect. 3. To prepare the time-lapse images, we first extracted sub-images from
PlanetScope images collected in 2016–2020 based on the bounding boxes of
deep-learning-inferred polygons with a buffer size of 300 m. We then used
these chronological sub-images to make time-lapse images, with which we
could visually inspect the temporal changes of RTSs. The manual inspection
was based on the geomorphic features of the RTSs and their annual changes.
We manually identified the headwalls based on the annual RTS retreating
direction and direction of uphill and set four criteria for improving
inspection accuracy: (1) the headwall must be located at the highest
elevation inside an RTS; (2) RTSs present a yellowish-brown colour in the
images because of vegetation cover degradation and bare ground emergence;
(3) the headwalls must be arcuate and nearly vertical, and thus tend to be
partially covered by narrow bands of shadows; and (4) the active RTSs
retreat in an upslope direction at a rapid rate, and their retreat can be
identified in the time-lapse images. One example of an RTS is shown in Fig. 3, together with the criteria we identified in the image. Then, for some
inaccurate polygons, we manually modified the boundaries (e.g., Fig. S1 in the Supplement).
Limited by the image resolution of 3 m, we need at least
∼ 55 pixels to identify the features of thaw slumps, so the
minimum mapping unit (MMU) we set is 0.05 ha. In the case of several RTSs
that were stable in 2016–2020, we used multi-source images to extend the
time span. One example of RTS shown in Fig. 4 was larger in 2013 than it was
in 2010, but its area remained almost the same in subsequent years. For
those near-roads polygons that were easy to approach, we went to the field
and collected UAV images, allowing us to further improve the reliability of
the mapping results (e.g., Fig. 1c). Two experts manually inspected the
results independently, costing 2 to 6 h per iteration. The numbers of
training polygons, deep-learning-inferred polygons, and newly identified
RTSs in each iteration are listed in Table 2. To identify RTSs that are near
roads, we measured the distance between the geometric centre of an RTS and
the roads. Considering the sizes of RTSs and their fast retreat rates, which
can sometimes reach 212 m yr-1 (Huang et al., 2021), we set the distance
threshold as 500 m. Using the time-lapse images (data are available from Xia
et al., 2021a), we further subdivided RTSs into four groups: those initiated
before July or August 2016, 2016–2017, 2017–2018, and 2018–2019
(between two summers).
Summary of iterative mapping. The positive polygons are RTS boundaries. The negative polygons outline some non-RTS land forms or land cover that appear similar to RTSs in the PlanetScope images. The deep-learning-inferred polygons are the output of the DeepLabv3+. Newly found RTSs are polygons manually selected from the deep-learning-inferred polygons. We recorded the total number of RTSs for every iteration in the “number of RTSs”.
Iteration numberTraining PredictionManual inspectionNumber of RTSsPositiveNegativeDeep-learning-inferredNewly foundpolygonspolygonspolygonsRTSs1300722064149449244972284219664536457831537371847187810 510868045804904609348386838903362484278429050332186388639036221287598759040310875Uncertainty assessment for RTS inventory
We manually assigned a probability for each mapped RTS as an uncertainty
indicator based on the availability of multi-temporal remote sensing imagery
and coverage of field validation. Owing to the lack of ground truth in the
entire QTEC, we cannot quantify the accuracy of the whole inventory.
Considering the lack of field evidence for each RTS, and the drawbacks of
remote sensing imagery, such as indirect observation and limited spatial
resolution, we assigned low or medium probability for an RTS that does not
strictly meet the four criteria in the manual inspection, for instance, those that retreated abruptly in one year but were stable in other years, or
their changes were too subtle to identify. The numbers of the RTSs with
high, medium, or low probability are 810 (92 %), 33 (4 %), and 32
(4 %), respectively.
Results
The inventory we compiled includes 875 RTSs along the Qinghai–Tibet
Engineering Corridor (Fig. 5). The largest RTS has an area of 24.03 ha,
and the smallest one is 0.05 ha; whereas 98.5 % of them are smaller than
10 ha (Fig. 6a). Together they affect 1700 ha of land on a 5 400 000 ha study
region. Altitudes in this whole study area vary from ∼ 3300 m
to ∼ 6200 m. Around 90 % of the RTSs were found at medium
elevations (4582–5010 m), and the highest was at an elevation of 5394 m
(Fig. 6b). The RTSs tend to be located on north-facing slopes with gentle
gradients ranging from 1.2 to 18.1∘ (Fig. 6c and
d). Most of them (67 %) are located on slopes with gradients of
4–8∘. They also tend to occur in areas where the annual PISR
ranges from 2900 to 3200 kWh m-2, whereas
the entire study region potentially receives solar radiation from 2500 to
3450 kWh m-2 (Fig. 6e). We also found
209 RTSs adjacent to the simulated stream networks. The main vegetation
types in the study region are swamp meadow, alpine meadow, alpine steppe,
and arid desert meadow. The alpine meadow areas contain ∼ 75 % of the RTSs (Fig. 6f). Soil texture analysis indicates that a large
portion of the surface soil is loam and sandy loam (∼ 23.3 %
and ∼ 71.3 % respectively), and only 5.4 % is clay, sandy
clay loam, and sand. Strikingly, ∼ 55 % of the RTSs are in
areas covered by loam (Fig. 6g). These heterogeneities illustrate that the
development of RTSs needs specific environments, such as regions with
massive ground ice and sloped terrains, thus limiting RTSs to regional
clusters. Our inventory revealed that ∼ 50 % of the RTSs are
densely clustered in the west of the Beiluhe region (e.g. Fig. 5b), whereas
the others are sparsely scattered across the other subregions (Fig. 5a).
The lack of uniformity in their distribution is further shown by the density
maps of the total affected area in 10 km × 10 km grid cells (Fig. 7a).
Statistical summaries of the RTSs' geometric features and terrain
properties. (a) The histogram shows the area of all the RTSs in the research
region. (b) The elevation frequency of the landscape and RTSs. Landscape
means the entire study region. (c) The slope aspects of RTSs, with the
radial axis representing the number of RTSs. (d) The slope frequencies of
the landscape and RTSs. (e) The annual PISR frequencies of the landscape and
RTSs. (f) The vegetation type distribution of the landscape and RTSs. (g)
The soil texture distribution of the landscape and RTSs.
We further identified 38 RTSs that are close to roads. Figure 5c presents an
example whose centre is ∼ 400 m from the highway. The RTSs
near roads are moderate in size, with an average area of 0.97 ha, and around
86.8 % of the RTSs are smaller than 2 ha. The largest one has an area of
24.03 ha and is near the Yaxi Co lake. The smallest one has an area of only
0.128 ha.
Our temporal analysis revealed that there were 306 RTSs before July or
August 2016. From summer 2016 to summer 2017, a total of 455 new RTSs emerged,
constituting more than half of the overall number of RTSs included in the
inventory. Only 21 and 55 RTSs formed during 2017–2018 and 2018–2019
respectively. From the distribution map showing the initiating years of RTSs
in grid cells of 25 km × 25 km (Fig. 7b), we observed that many
of the newly initiated RTSs are located in the west of the Beiluhe region.
(a) Areas affected by RTSs in grid cells of 10 km × 10 km. (b) The distribution map of RTSs with different initiating years in grid
cells. For clear visualization, we set the cell size as 25 km × 25 km in (b). The background is a map elevation based on the Shuttle Radar
Topography Mission DEM (Farr et al., 2007).
DiscussionPossible controlling factors of RTS spatial distributions
Most of the RTSs are in the western part of the Beiluhe region, and a small
portion of them are sparsely distributed along the roads. The uneven
distribution may be controlled by topographic factors (Wang and French,
1994), hydrological factors, soil texture, vegetation, and human activities.
(1) Proven by the statistical analysis of topographic features of RTSs, as
the majority of the RTSs are in the Beiluhe region, the RTSs in this
clustered region dominate the distribution characteristics along the QTEC.
In other words, RTSs prefer to occur on gentle north-facing slopes, at
medium elevations, and in locations receiving relatively low annual PISR.
The main reason is that water tends to accumulate on gentle slopes,
resulting in high soil moisture contents and decreased internal friction of
the soil mass (McRoberts and Morgenstern, 1974). Moreover, the north-facing
slopes with relatively low PISR have a thinner active layer than their
south-facing counterparts. As a thin active layer is easier to be removed by
thermokarst processes, the possibility of exposing the permafrost underneath
will increase. The soil moisture content is also higher on land receiving
low PISR (Lin et al., 2019). All the topographically controlled moisture
availability is highly related to the formation of excess ground ice near
the top of permafrost (Lin et al., 2020). (2) The ground near streams tends
to contain a higher water content. (3) The vegetation types also impact the
distribution of RTSs, as we have shown that many RTSs are in alpine meadows.
As alpine meadows grow on land with more water content than alpine steppe
(Yin et al., 2017), permafrost underneath may contain more ice. (4) The
results show that the RTSs tend to develop on the land covered by loam. Silt
fraction, which influences the frost susceptibility of the host sediment, is
higher for loam than for sandy loam, and the ground consequently has a
higher ice content (Gilbert et al., 2016). In sum, all these terrain
factors, potentially related to the ice content, may exert a confounding
influence on RTS formation. (5) We found 38 RTSs that are near roads, with
only 7 of them in the Beiluhe region, a vulnerable area where 474 RTSs are
located. It proves that engineering can minimize the impact that
infrastructure has on permafrost. Excavation for soils and gravel during
road construction damaged the vegetation cover in the 1980s, which led to
the thawing of the exposed ice-rich permafrost and resulted in the
initiation of many RTSs (Luo et al., 2019). Engineers began to realize that
human activities accelerated permafrost degradation, and after 1980 adopted
various methods to protect the permafrost (Luo et al., 2019). Moreover, the
limited RTSs near roads indicate that it is possible, even in a vulnerable
region, to select relatively stable ground for the construction of
facilities and minimize the damage caused by permafrost degradation. As the
distribution of the RTSs helps to pinpoint unstable ground, it should be
possible to plan the alignment of a new highway along the QTEC to avoid such
sensitive areas.
Comparison with other inventories
Our inventory is the first comprehensive one along the entire corridor
region. Compared with the existing RTS datasets in the subregions (Niu et
al., 2016; Luo et al., 2019), our inventory has advantages in its
comprehensiveness, novelty, and being open source. Based on manual
interpretation from SPOT-5 imagery and field investigations, Niu et al.'s results
contain 42 slope failures (some are RTSs) in 2016 in a 10 km lateral zone of
the Qinghai–Tibet highway from Wudaoliang in the north to the Fenghuo
Mountain pass in the south. In this same subregion, our method detected 47
RTSs in 2019, with 4 of them having low or medium probability. Luo et al.'s 2017
results contain 438 RTSs but only cover the Beiluhe region, within which our
inventory found 459 RTSs in 2019. In total, our inventory obtains 875 RTSs
in the entire study area, including the part where the critical
transportation infrastructure is underlain by permafrost. We also labelled
RTSs near roads and provided the initiation periods, areas, probabilities,
and locations of RTSs. The deep-learning model and multi-source and
multi-temporal images were performed in tandem to provide a more accurate
inventory than the results obtained from manual inspection alone.
Necessity and limitations of iterative mapping
Our method combines the efficiency of the deep-learning neural network with
the invaluable interpretative experience of experts. Manual delineation is
labour-intensive and not feasible for a large area. Deep-learning-based
mapping outperforms many other automated mapping methods by a large margin,
although it still produced lots of false positives and missed a few RTSs, as
shown in the first few iterations in Table 2. The newly found RTSs in every
iteration indicate that one-time training and predicting has a high chance
of missing some RTSs owing to the bias between training data and the images
covering the rest of the study area. As proved by our iterative mapping
(Table 2), by adding more training data, each new iteration successfully
inferred some RTSs missed in the previous mapping iterations.
The main disadvantage is that this method is still time consuming compared with a fully automated process. In each iteration, the deep-learning
model inferred 2000 to 5000 polygons that need to be manually inspected.
Another problem is that we may still miss some small RTSs and misidentify
other landforms, for instance, drained ponds and artificial pits. Although
we have already used multi-source images to guarantee the accuracy of the
RTS polygons, the imagery resolution limitation still exists, which
restricts the MMU to 0.05 ha. Moreover, some RTSs that have re-vegetated on
the surface cannot be identified using remote sensing images alone.
Data availability
The PlanetScope CubeSat images are copyrighted by Planet Labs Inc.,
restricted by commercial policies and are not open to the public. The
Landsat 5/8 and Sentinel 2 images are publicly available through the U.S.
Geological Survey and the European Space Agency, respectively, and can be
downloaded via the Google Earth Engine. The Esri World Imagery can be
accessed via the Esri Wayback Imagery:
https://livingatlas.arcgis.com/wayback/ (Esri Inc., 2018). The thaw slump inventory is
accessible through Xia et al. (2021a), Zenodo,
10.5281/zenodo.6397029. The Chinese version is in the
National Tibetan Plateau/Third Pole Environment Data Center (Pan et al.,
2021; Li et al., 2020), with link DOI: 10.11888/Cryos.tpdc.272672 (Xia et al. 2021b).
Conclusions
This study successfully used deep learning to infer possible retrogressive
thaw slumps and temporal multi-source images to visually inspect
retrogressive thaw slumps over a large area. This inventory of 875 thaw
slumps fills the gaps in the RTS data along the corridor and provides a
diverse and representative training dataset for automatically delineating
thaw slumps in even larger areas. Through statistical analysis of the
terrain properties, we found that (1) the RTSs along the QTEC tend to
develop on north-facing slopes with gentle degrees and tend to appear at
medium elevations or areas receiving less solar radiation; (2) 209 RTSs are
near stream networks; (3) a large portion of the RTSs are located on the
ground covered with alpine meadows; (4) RTSs develop more frequently in
areas covered by loam soil. The inventory of 38 RTSs that are near roads
indicates the human impact on permafrost and provides us with data to assess
the ground stability while planning a new highway. The abnormal increase
between 2016 and 2017 is worth further investigation. For instance, we can
lengthen the time span and explore the relationship between the number of
newly initiated RTSs and meteorological variables such as temperature and
precipitation. As the first attempt at mapping RTSs in the Qinghai–Tibet
Engineering Corridor from high-resolution images, the results we obtained
can potentially serve the policymakers and stakeholders with the information
necessary to pursue sustainable socio-economic development on the
Qinghai–Tibet Plateau.
The supplement related to this article is available online at: https://doi.org/10.5194/essd-14-3875-2022-supplement.
Author contributions
LH and LL designed the study and received funding from the Hong Kong
Research Grants Council. LH and ZX processed the data, obtained the
inventory, and wrote the manuscript. LL, LH, TZ, and FN revised the
manuscript. CF and SJ contributed to the field investigation. LH, ZL, JL,
and FN provided the initial training data.
Competing interests
The contact author has declared that none of the authors has any competing interests.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Special issue statement
This article is part of the special issue “Extreme environment datasets for the three poles”. It is not associated with a conference.
Acknowledgements
This research was supported by the Hong Kong Research Grants Council (CUHK14303119) and CUHK
Direct Grant for Research (4053426 and 4053481), the Second Tibetan Plateau Scientific Expedition and Research (STEP)
programme (2019QZKK0905), the National Natural Science Foundation of China (42071097), and the Strategic Priority
Research Program of the Chinese Academy of Sciences (XDA2010030805). We thank Jie Chen and Defu Zou from
Northwest Institute of Eco-Environment and Resources, CAS, for valuable assistance in the fieldwork in July 2021. This
paper is dedicated to the memory of Tingjun Zhang.
Financial support
This research has been supported by the Research Grants Council, University Grants Committee (grant
no. CUHK14303119), the CUHK Direct Grant for Research (grant nos. 4053426, 4053481), the Second Tibetan Plateau
Scientific Expedition and Research (STEP) programme (grant no. 2019QZKK0905), the National Natural Science Foundation of
China (grant no. 42071097), and the Strategic Priority Research Program of the Chinese Academy of Sciences (grant no.
XDA2010030805).
Review statement
This paper was edited by Xin Li and reviewed by Ingmar Nitze, Tianjie Zhao, Yili Yang, and two anonymous referees.
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