3 Methodology
The method we adopted consists of two parts and is detailed below.
First, we mapped active rock glaciers manually from interferograms and
Google Earth images. Second, we used the manually labelled images to
train a deep learning network, i.e., DeepLabv3+, for mapping rock
glaciers automatically from Sentinel-2 optical images.
3.1 Mapping active rock glaciers from interferograms and Google Earth
images
In this subsection, we first describe the strategy of delineating rock
glaciers. Then we present the method for quantifying rock glacier
kinematics by InSAR. Finally, we introduce how to determine the
geomorphic attributes of the mapped landforms.
3.1.1 Manual identification and delineation of rock glaciers
We mapped active rock glaciers by combining two imagery sources:
interferograms and Google Earth images. The displacement maps generated
by InSAR allow us to easily recognize moving parts of the ground
surface, meanwhile the high-resolution and multi-temporal Google Earth
images provide geomorphic information to distinguish rock glaciers from
the other active surface units, such as debris-covered glaciers,
solifluction lobes, and slow-moving landslides. Visual identification
was conducted based on the geomorphological criteria proposed by RGIK
(2021) including the frontal and lateral margin morphology, and the
surface ridge-and-furrow topography as an optional indicator. We then
outlined the recognized landforms along their extended geomorphological
footprints, i.e., the frontal and lateral margins are included within
the boundaries. We followed the IPA guidelines because it provides
practical and standardized baseline concepts for identifying and
outlining rock glaciers from remote sensing images and readily
applicable to producing consistent inventories over wide-extent regions.
3.1.2 Kinematic quantification by InSAR
In total, twenty-two interferograms generated from ALOS-1 PALSAR images
covering the West Kunlun Mountains were used for ground movement
detection between 2007–2008 (Table 1). To maintain high interferometric
coherence and reduce topographic error, we selected image pairs with
temporal spans of 46 days and perpendicular baselines smaller than 1,000
m. The topographic phase were estimated and removed by using a digital
elevation model (DEM) produced by the Shuttle Radar Topography Mission
(SRTM) with a spatial resolution of ~30 m over most of
the study region. A tile of TanDEM-X DEM (spatial resolution
~12 m) was adopted for correcting topographic phases for
one interferogram overlapping with the permafrost survey region.
Multi-looking operation and adaptive Goldstein filter (8\(\times\)8
pixels) were applied in the interferometric processing, which was
implemented by the open-source software InSAR Scientific Computing
Environment (ISCE) version 2.2.0 (available athttps://github.com/isce-framework/isce2 ). We then unwrapped the
interferograms with the SNAPHU (Chen and Zebker 2002) and selected one
point located at the flat and stable ground close to each rock glacier
to re-reference the unwrapped phases measured within the boundary of
each landform. By doing so, we managed to remove the long-wavelength
orbital errors and the atmospheric artefacts including the water vapor
delay and ionospheric effects, all of which can be assumed identical
within the extent of a rock glacier (Hanssen 2001).
We determined the surface downslope velocities of rock glaciers as their
kinematic attributes. The surface velocities along the SAR satellite
line-of-sight (LOS) direction were derived from the unwrapped
interferograms and then projected to the downslope direction of each
landform (Hu et al. 2021).
Associated
uncertainties including the InSAR measurements and geometric parameters
were quantified through error propagation (Hu et al. 2021). We used the
spatial mean velocity within a rock glacier to represent its overall
kinematic status. Then we refined the results by selecting data that
fulfilled the following criteria: (1) after masking out the pixels with
low coherence (< 0.3) (Wang et al. 2017), the remaining pixels
account for more than 40% of the entire landform extent; (2) the
relative errors of the spatial mean velocities are lower than 20%.
3.1.3 Determination of geomorphic
attributes
Essential geomorphic attributes such as the elevation range, mean slope
angle, and landform aspect were quantified using the SRTM DEM.
Qualitative attributes including the spatial connection of the rock
glacier to the upslope unit and the activity category were described and
assigned to the dataset following the IPA guideline (RGIK, 2021). We
primarily classified the mapped rock glaciers according to their spatial
connection to the upslope unit because it could provide implications
regarding the landform genesis (Sect. 5.2). We used the Global Land Ice
Measurements from Space (GLIMS) dataset to help recognize the
surrounding glacier units (GLIMS and NSIDC, 2005). Figure 2 presents
examples of rock glaciers that were classified by their upslope units
into four categories. For instance, Figure 2b shows a glacier-connected
rock glacier, the frontal and lateral margins of which are discernible
from the Google Earth image, though the rooting zone is ambiguous. We
separated the rock glacier from the upslope unit from surface structure
in this case. Finally, we created the InSAR-based sub-dataset. The
entire workflow is illustrated in Figure 3 with one example shown in
Figure 4.