Figure 12. Histograms of the downslope velocities for (a) all RGs, (b)
DMS-RGs, (c) G-RGs, (d) GF-RGs, and (e) T-RGs, respectively.
5 Discussion
In this section, we firstly summarize the potential and limitations of
using the combined methodology for mapping rock glaciers (Sect. 5.1).
Then we discuss the genetic and evolutional implications carried by the
geomorphic characteristics of the mapped rock glaciers (Sect. 5.2).
5.1 Potential and limitations of the InSAR-Deep learning combined method
for mapping rock glaciers
We used an InSAR-Deep learning combined approach to map rock glaciers
across the West Kunlun Mountains. The advantage of the combined
methodology is twofold: the InSAR-based mapping approach provides
essential information on surface kinematics and accurate manual
delineation for training the deep learning model; whereas the automated
method improves mapping efficiency and more importantly, overcomes the
conservativeness of the former approach and expands the InSAR-based
sub-dataset. More specifically, some rock glaciers cannot be detected by
InSAR due to coherence loss in interferogram, geometric distortions,
their topographic orientations insensitive to InSAR line-of-sight
measurements, or simply their inactive kinematic status (Wang et al.
2017; Robson et al. 2020). As we used the conventional Differential
InSAR method, the smaller amount of interferograms adopted for
identifying rock glaciers could lead to more serious omission in the
dataset compared with using multi-temporal data (Cai et al., 2021; Zhang
et al., 2021; Bertone et al., 2022). By combining the deep learning
method, we can map the landforms that had been omitted due to coherence
loss in the limited number of interferograms. In addition, rock glaciers
moving parallel to the satellite direction, or along a steep slope, or
at a very fast or slow pace, can be mapped as well.
However, our deep learning approach has a limited level of automation:
the results produced by this methodology still requires manual
inspections and modifications to increase the accuracy. Among the
factors controlling the deep learning performance, the amount and
quality of training and validation samples is one primary factor that
affects the mapping accuracy. In this study, the training and validation
datasets consist of the boundaries of active rock glaciers in the
InSAR-based sub-dataset overlying the Sentinel-2 optical images
(examples as shown in Figure 5). The amount of rock glaciers (172) as
training and validation samples is in the same order of magnitude as the
landform amount (338) used by Robson et al. (2020) for training their
deep learning network; yet the training data size can be improved to
fully achieve the potential of the state-of-the-art network
(DeepLabv3+Xception71) we adopted. Quality of the input images is also
moderate, as the Sentinel-2 images have a medium spatial resolution of
~10 m, making it challenging to characterize some rock
glaciers, especially small ones with areas smaller than 30,000
m2, from these optical images and possibly leading to
inaccuracy in the output. Therefore, manual inspection is required in
the post-processing to improve the accuracy of the automatically
delineated boundaries. Additionally, the cloud cover of the images
hinders the compilation of a complete inventory across the large area.
Finally, the Google Earth images (2009–2020) we referred to while
creating the InSAR-based sub-dataset are unsynchronized with the
Sentinel-2 images (Jul–Aug of 2018) used for producing the training
data and for predicting rock glaciers by the trained model. Accordingly,
we conducted additional manual inspections while preparing the input
data and recognized few differences requiring corrections to the
training data because the rock glacier activity is relatively low in the
study area (Sect. 4.3), yet this asynchronization may lead to errors in
areas where rock glaciers have been moving fast in recent decades.
Furthermore, as we evaluated the effectiveness of the deep
learning-based method by applying the trained model to a test area
outside the original study area and the validation IoU, which reached a
value of ~0.8 comparable with the previous milestone
research (Chen et al., 2018), the imperfect metric we achieved (i.e.,
validation IoU < 1) reveals the possibility that some rock
glaciers may still be missed in our inventory. We estimated the
magnitude of landform underestimation by calculating an index from the
validation IoU and a test experiment in a new region (methodology
detailed in Text S1); yet it is challenging to provide a precise
estimate given that no ground truth data is available over the study
region.
In addition, our combined approach is limited to mapping intact
landforms, i.e., active and transitional rock glaciers according to the
updated categorization scheme of rock glacier activity proposed by RGIK
(2021). The InSAR-based sub-inventory contains active rock glaciers, the
surface of which display coherent downslope motion as revealed by the
interferograms. The transitional rock glaciers, on the other hand, show
little movement over the surface, yet their geomorphologic
characteristics are less distinguishable from the active landforms. Our
deep learning model essentially learned the visual features of active
rock glaciers through the optical images in the training dataset, and
thus the model is likely to identify and delineate transitional rock
glaciers as well. In contrast, relict rock glaciers usually develop
distinct geomorphologic features such as subdued topography and
vegetation cover, which cannot be mapped by the deep-learning model.
Considering the above limitations, several improvements can be
implemented in our future research: (1) to increase the amount and
diversity of training samples by including rock glacier boundaries from
other regions; (2) to adopt higher-resolution and more cloud-free
optical images for producing input dataset; and (3) to use generative
adversarial network for translating optical images (for landform
inference) to the domain of training data and include them during
training. Nevertheless, the developed model will be useful for regions
where data gap exists, such as many mountain ranges on the Tibetan
Plateau. The inventory produced by this work will serve as an important
database for scientific investigations such as managing geohazards
(e.g., Kummert and Delaloye, 2018), assessing sediment budget (e.g.,
Kofler et al., 2022), and monitoring permafrost changes (e.g., Thibert
and Bodin, 2022).
5.2 Genetic and evolutional implications from the geomorphic
characteristics of rock glaciers
We classified the mapped rock glaciers into glacier-connected (G-RGs),
glacier-forefield-connected (GF-RGs), debris-mantled slope-connected
(DMS-RGs), and talus-connected rock glaciers (T-RGs). This
classification scheme was adopted firstly for a practical reason:
spatial connection of the rock glacier to its upslope unit is mostly
well discernible from the optical images (as illustrated in Figure 2).
Moreover, we take the distinction as an indication of the evolution of
rock glaciers in terms of their ice origin, sediment source, and debris
transfer process. In this subsection, we interpret the genetic and
evolutional implications held by the characteristics of rock glaciers in
the regional geomorphologic context.
Nearly half (~49%) of the mapped rock glaciers are
spatially connected to glaciers. The amount appears to be reasonable
because much of the West Kunlun Mountains (~12,500
km2) is occupied by modern glaciers (Kääb et al.
2015), constituting one of the most prominent glacierization centers on
the Tibetan Plateau (Shi 2006). G-RGs occurring at the immediate
downslope of the modern glaciers are likely to have the ice core
embedded within the landforms, representing the transitional process
from glacier (or debris-covered glacier) to rock glacier (Potter 1972;
Whalley and Azizi 1994). However, we postulate that such transition is
not actively ongoing given that glaciers in the West Kunlun are in mass
balance or even slightly gaining mass in recent decades (Bao et al.
2015; Kääb et al. 2015; Wang et al. 2018; Zhou et al. 2018). The G-RGs
are likely to gradually evolve from glaciers since the last cold period,
i.e., the Little Ice Age (LIA, 200–600 aBP), and this transitional
process tends to slow down in the past several decades (Shi 2006).
Although the landform transition is currently not active in our study
area, we propose that the glacier-to-rock glacier continuum, as one
classical theory about rock glacier genesis (Berthling 2011), can be
adopted to interpret the evolution of the GF-RGs in our inventory. The
GF-RGs are spatially disconnected from the upslope modern glaciers
(Figure 2c), occurring at the lowest altitudes among all the categories
in the study area (Sect. 4.2). Interactions between the GF-RGs and the
glacier units are likely to take place during the glacier advance phases
in geologic history. Anderson et al. (2018) modelled the glacier –
debris-covered glacier – rock glacier evolutional process by simulating
the rise of environmental equilibrium line altitude in response to
climate warming: a pure glacier melts and separates from the emerging
debris-covered terminus, which preserves its ice core due to insulation
effect produced by the surface sediment and finally transforms into a
rock glacier. Accordingly, we postulate that the GF-RGs in our study
area once were part of the upslope glaciers during the Neoglaciation
(3000–4000 aBP), when glaciers extended to altitudes hundreds of meters
lower than the present glacier termini in the West Kunlun (Li and Shi
1992; Shi 2006).
Interactions between glaciers and rock glaciers are highlighted in the
West Kunlun Mountains by the occurrence of abundant surge-type glaciers,
whose flow velocities peaking at 0.2–1 km yr-1 during
their active phases (Quincey et al. 2015; Yasuda and Furuya 2015).
Excess materials consisting of ice and debris are carried downslope to
areas far beyond the normal termini of the surge-type glaciers and may
deliver sediments to the nearby glaciers (or debris-covered glaciers),
whereby the surge events tend to contribute to the glacier-to-rock
glacier transition provided that glaciers in the West Kunlun will
retreat in the future as the glaciers in other alpine regions worldwide
nowadays. A comparable case is the ongoing glacier-to-rock glacier
transition in the Himalayas: based on field observation and
sedimentologic analysis, Jones et al. (2019) elaborated that debris
supply from the environmental sediment sources (in addition to the
sediment derived from glaciation of the transitional landform per se)
drives the evolution as an important factor. Moreover, the ice-debris
body transferred and deposited at the far end of a surge-type glacier
may gradually evolve into a rock glacier under favorable climatic and
topographic conditions. Figure 13 presents an example of the potential
evolutional process: the terminus of a surge-type glacier is covered by
debris (Chudley and Willis, 2019) and many thermokarst lakes develop on
the surface of the ice-debris mixture, which is likely to transform into
a rock glacier in a warming climate. Two rock glaciers (wkl019 and
wkl020) are situated in the surroundings and may receive debris and ice
input during the surge events.
The genesis of two categories of rock glaciers, namely the T-RGs and the
DMS-RGs, are related to periglacial processes. The T-RGs are
conventionally considered as features originated in the periglacial
domain: the rock glaciers contain interstitial ice developed by various
processes such as burial of surface snow that typically occur in the
formation of frozen ground (Humlum 1988; Haeberli 2000; Berthling 2011).
The DMS-RGs are seldomly reported in the literature (Hu et al. 2021),
yet display unique geomorphologic characteristics and constitute the
second largest category (~35%) in the study area. In
the absence of an upslope glacial system, we suggest that the DMS-RGs
also represent the periglacial processes controlling the landform
genesis. In comparison with the other three categories, the DMS-RGs
occupy the highest and steepest slopes, where mechanical weathering
dominates and produces sufficient sediments transferred and accumulated
to the base of the slopes. During the glacial period, interstitial ice
is formed within the deposits. The ice-debris mixture gradually develops
and at one point overcomes the friction and starts to creep as an active
rock glacier. Considering the lack of a headwall and the very small
dimension (~one fifth of the average size of all mapped
landforms, 0.05 km2 vs. 0.26 km2),
it is likely that the DMS-RGs began to emerge during the Little Ice Age
and are still at their embryonic stage.