Landslides in glacial environments are high-magnitude,
long-runout events, believed to be increasing in frequency as a paraglacial
response to ice retreat and thinning and, arguably, due to warming
temperatures and degrading permafrost above current glaciers. However, our
ability to test these assumptions by quantifying the temporal sequencing of
debris inputs over large spatial and temporal extents is limited in areas
with glacier ice. Discrete landslide debris inputs, particularly in
accumulation areas, are rapidly “lost”, being reworked by motion and
icefalls and/or covered by snowfall. Although large landslides can be
detected and located using their seismic signature, smaller (M≤5.0)
landslides frequently go undetected because their seismic signature is less
than the noise floor, particularly supraglacially deposited landslides, which
feature a “quiet” runout over snow. Here, we present GERALDINE (Google Earth Engine supRaglAciaL Debris INput dEtector): a new free-to-use tool
leveraging Landsat 4–8 satellite imagery and Google Earth Engine. GERALDINE
outputs maps of new supraglacial debris additions within user-defined areas
and time ranges, providing a user with a reference map, from which large
debris inputs such as supraglacial landslides (>0.05 km2)
can be rapidly identified. We validate the effectiveness of GERALDINE
outputs using published supraglacial rock avalanche inventories, and then
demonstrate its potential by identifying two previously unknown, large
(>2 km2) landslide-derived supraglacial debris inputs onto
glaciers in the Hayes Range, Alaska, one of which was not detected
seismically. GERALDINE is a first step towards a complete global
magnitude–frequency of landslide inputs onto glaciers over the 38 years of
Landsat Thematic Mapper imagery.
Introduction
There are currently >200000 glaciers worldwide, covering
>700000 km2, of which 8.2 % are less than 1 km2
(Herreid and Pellicciotti, 2020), excluding the Greenland and Antarctic ice
sheets (RGI Consortium, 2017). Recent estimates suggest supraglacial debris
only covers 7.3 % of the area of these glaciers (Herreid and Pellicciotti, 2020), up from
4.4 % estimated by Scherler et al. (2018). However, for
many glaciers supraglacial debris plays a critical role in controlling a
glacier's response to climate change, due to its influence on surface
ablation and mass loss (e.g. Benn et al., 2012; Mihalcea et al., 2008a, 2008b; Nicholson and Benn,
2006; Østrem, 1959; Reznichenko et al., 2010). Extensive debris coverage
can alter the hydrological regime of a glacier (Fyffe et al., 2019), with the
potential to increase or decrease downstream freshwater availability (Akhtar et al., 2008), and can play a key role in controlling rates of
glacier thinning and/or recession, subsequently contributing to sea level
rise (Berthier et al., 2010). This
supraglacial debris control is thought to be particularly important in the
context of negative glacier mass balance, with retreating glaciers being
characterized by expanding debris cover extents (Kirkbride
and Deline, 2013; Scherler et al., 2011b; Tielidze et al., 2020). The
expansion of supraglacial debris cover is due to (i) glaciological and
climatological controls such as thrusting and meltout of sub- and en-glacial
sediment onto the surface (e.g. Kirkbride
and Deline, 2013; Mackay et al., 2014; Wirbel et al., 2018); (ii) debris
input from surrounding valley walls through bedrock mass movements (Deline et al., 2015; Porter et
al., 2010); (iii) dispersion of medial moraines (Anderson, 2000); and (iv) remobilization of
debris stores, particularly lateral moraines (Van Woerkom et al., 2019). The
relative contributions of “glacially” derived sediment, which may in fact be
the re-emergence of glacially modified mass movements (Mackay et al., 2014), as compared to
direct subaerial inputs, are highly variable and there is complex coupling
between hillslopes and glaciers that varies with relief (Scherler et al., 2011a). However, recent evidence
from the Greater Caucasus region (Eurasia) suggests that supraglacially
deposited rock avalanches (RAs), attributed to processes associated with
climate change, are a key factor in increasing supraglacial debris coverage
(Tielidze et al., 2020). Magnitude–frequency relationships suggest these low-frequency,
high-magnitude events have a disproportionate effect on sediment delivery (Korup and Clague,
2009; Malamud et al., 2004). One of these large events mobilizes enough
debris to dominate overall volumetric production and delivery rates of
debris, exceeding that of the much higher-frequency but lower-magnitude
events. Here, we focus on supraglacial landslide deposits (>0.05 km2). Such deposits are commonly associated with RAs, which are defined
as landslides (a) of high magnitude (>106 m3), (b)
perceived low frequency, (c) long runout, and (d) where there is disparity
between high present-day rates of slope processes above ice (Allen et al., 2011; Coe et al., 2018) and expected rates based on theories
of lagged paraglacial slope responses (Ballantyne,
2002; Ballantyne et al., 2014a).
In formerly glaciated landscapes, dating of RA deposits has shown a lag in
the response of paraglacial slope activity with respect to the timing of
deglaciation (Ballantyne
et al., 2014b; Pánek et al., 2017). Events cluster in deep glacially
eroded troughs and inner gorges at relatively low elevations in the
landscape (Blöthe et al., 2015).
Numerical modelling has shown how considerable rock mass damage is possible
during the first deglaciation cycle (Grämiger et al., 2017), and some of the
largest inventories highlight a close association between the former glacier
limits and the source zones of RAs, particularly in the vicinity of glacial
breaches (Jarman and Harrison, 2019).
However, almost all of our knowledge of past events relies on the presence
of in situ RA deposits. Due to erosional and depositional censoring, such
deposits are heavily biased to ice-free landscapes where preservation
potential is higher, although these are still unlikely to constrain true
magnitude–frequency relationships unless rates of geomorphic turnover are low (Sanhueza-Pino et al., 2011). In
supraglacial settings, landslides, where topography allows, travel much
further than their non-glacial counterparts due to the reduced friction of
the ice surface (e.g. Sosio
et al., 2012). Rapid transportation away from source areas also occurs
because of glacier flow. This removes the simplest diagnostic evidence of a
subaerial mass movement: a linked bedrock source area and debris deposit.
Without the associated deposit, bedrock source areas are easily mistaken for
glacial cirques (Turnbull and Davies, 2006). Fresh
snowfall or wind redistribution of snow can rapidly cover a RA deposit that
is many kilometres square in area (Dunning et al., 2015). If this occurs
within the accumulation zone the deposit is essentially lost to all surface
investigation and non-ice-penetrating remote sensing and ground-based
techniques until its eventual re-emergence in the ablation zone, after
potentially considerable modification by transport processes. If a RA is
deposited in the ablation zone, surficial visibility may be seasonal, but
through time surface transport will disrupt the initially distinctive
emplacement forms (Uhlmann et al., 2013). This
supraglacial debris loading represents a glacier input (Jamieson et al., 2015) and can alter glacier mass
balance and influence localized melt regimes (Hewitt, 2009;
Reznichenko et al., 2011) and glacier velocity (Bhutiyani and Mahto,
2018; Shugar et al., 2012), leading to speed-ups and terminus positions
asynchronous with current climatic conditions. Sometimes this leads to
moraines that are out of phase with climate, due to the reduction in surface
ablation and surging (or the slowing of a retreat) caused by large landslide
inputs (Hewitt,
1999; Reznichenko et al., 2011; Shulmeister et al., 2009; Tovar et al.,
2008; Vacco et al., 2010).
Currently, the detection of large supraglacially deposited landslides –
other than through the most common form of ground-based detection,
eyewitness reporting – is through the application of optical satellite
imagery. This is a labour and previously computationally intensive process,
often involving the downloading, pre-processing, and manual analysis of large
volumes (gigabytes) of satellite imagery. Manual imagery analysis to
identify supraglacial landslide deposits and RAs has principally been
applied in Alaska. This technique enabled the detection of 123 supraglacial
landslide deposits in the Chugach Mountains (Uhlmann et al., 2013), 24 RAs in Glacier Bay
National Park (Coe et al., 2018), and
more recently 220 RAs in the Saint Elias Mountains (Bessette-Kirton and Coe, 2020). These studies
acknowledge that their inventories are incomplete or are underestimates due to
analysis of summer imagery and an inability to detect events that are
rapidly advected into the ice. These are critical drawbacks preventing
accurate magnitude–frequency relationships from being derived, but analysis
of more imagery over larger areas is unfeasible due to time and
computational requirements. Studies of this kind are also typically in
response to a trigger event, e.g. earthquakes or a cluster of large RA events
(e.g. Coe et al. (2018) in Glacier Bay National Park), spatially biasing
inventories into areas with known activity. They therefore provide a
snapshot in time, with no continuous record. Methods are needed which are
accessible, quick and easy to apply, and require no specialist knowledge, to
re-evaluate magnitudes and frequencies in glacial environments. Currently, the
only method capable of identifying a continuous record of such events is
seismic monitoring (Ekström and Stark, 2013). Seismic
detection utilizes the global seismic network to detect long-period surface
waves, characteristic of seismogenic landslides. Seismic methods have
identified some of the largest supraglacially deposited RAs in recent times
(e.g. Lamplugh glacier RA; Dufresne et al., 2019)
which are compiled in a database (IRIS DMC, 2017) and, when combined with
manual analysis of satellite imagery, give information on duration,
momenta, potential energy loss, mass, and runout trajectory. However,
landslides are challenging to detect using seismic methods, and event
positional accuracy is limited to a 20–100 km radius, due to the lack of
high-frequency waves when compared to earthquakes, further inhibited by the
low frequencies and long wavelengths of dominant seismic waves worldwide (Ekström and Stark, 2013). This also results in an
inability to detect landslides that are relatively low in volume, due to
their weak seismic fingerprint (M<5.0), and causes underestimation
of landslide properties (e.g. event size and duration) because their
runouts are seismically “quiet”, likely due to frictional melting of
glacier ice (Ekström and Stark, 2013). Despite these difficulties,
current studies seem to indicate an increase in the rates of rock
avalanching onto ice in rapidly deglaciating regions such as Alaska and the
Southern Alps of New Zealand, where the majority of recent (aseismic) RAs
are associated with glaciers. This increase has been linked to climate
warming (Huggel et al., 2012) and potential
feedbacks with permafrost degradation (Allen
et al., 2009; Coe et al., 2018; Krautblatter et al., 2013). These links,
coupled with the availability of high spatial and temporal resolution
optical satellite imagery, have demonstrated the need for systematic
observations of landslides in mountainous cryospheric environments (Coe, 2020). Five “bellwether” sites have been
suggested for these purposes: the Northern Patagonia Ice Field, the western
European Alps, eastern Karakorum in the Himalayas, the Southern Alps of New
Zealand, and the Fairweather Range in Alaska (Coe,
2020).
The large archives of optical imagery, coupled with the recent boom in
cloud-computing platforms, now provide the perfect combination of
resources, which can be exploited to identify supraglacially deposited
landslides on a large scale. Since the launch of Landsat 1 in July 1972,
optical satellites have imaged the Earth's surface at increasing temporal
and spatial frequency. Six successful Landsat missions have followed Landsat
1, making it the longest continuous optical imagery data series,
revolutionizing global land monitoring (Wulder et al., 2019).
Analysis-ready Landsat data are available for Landsat 4 (1982–1993), Landsat
5 (1984–2012), Landsat 7 (1999–present), and Landsat 8 (2013–present),
providing 38 years of data at a 30 m spatial resolution and a 16 d
temporal resolution. These data are categorized into three tiers: (1) Tier 1
data that are radiometrically and geometrically corrected (<12 m root-mean-square error), (2) Tier 2 data that are of lower geodetic accuracy
(>12 m root-mean-square error), and (3) real-time imagery that
is available immediately after capture but uses preliminary geolocation data
and thermal bands that require additional processing, before being moved to its
final imagery tier (1 or 2) within 26 d for Landsat 7 and 16 d for
Landsat 8. Traditionally, it has been difficult to exploit extensive
optical imagery collections such as Landsat without vast amounts of
computing resources. However, in the last decade, cloud computing has become
increasingly accessible. This allows a user to manipulate and process data
on remote servers, removing the need for a high-performance personal
computer. Google Earth Engine (GEE) is a cloud platform created specifically
to aid the analysis of planetary-scale geospatial datasets such as Landsat
and is freely available for research and education purposes (Gorelick et
al., 2017).
Here, we utilize Google Earth Engine (GEE) and the Landsat data archive of
38 years of optical imagery to present the Google Earth Engine supRaglAciaL Debris INput dEtector (GERALDINE). A free-to-use tool to automatically
delimit new supraglacial debris inputs over large areas and timescales,
which then allows for rapid user-backed verification of inputs from large
landslides specifically. GERALDINE is designed to allow quantification of
the spatial and temporal underreporting of supraglacial landslides. We
describe the methods behind GERALDINE, verify tool outputs against known
supraglacial rock avalanche inventories, and finally demonstrate tool
effectiveness by using it to find two new supraglacial landslides, one of
which cannot be found in the seismic archives.
Method
GERALDINE exploits the capability and large data archive of GEE (Gorelick et al., 2017), with all processing and data
held in the cloud, removing the need to download raw data. By default, it
utilizes Tier 1 Landsat imagery (30 m pixel resolution) that has been
converted to top-of-atmosphere spectral reflectance (Chander et al., 2009), from 1984–present, incorporating Landsat 4, 5, 7, and 8. GERALDINE also gives the user
the following options: (i) to utilize Tier 2 Landsat imagery and (ii) to
utilize real-time Landsat imagery. Tier 2 imagery is valuable in regions
where Tier 1 imagery is limited, e.g. Antarctica, where there is a lack of
ground control points for imagery geolocation. Real-time imagery is useful
for rapid identification of landslide locations if a seismic signal has been
detected, but an exact location has not been identified. Landsat imagery is
used in conjunction with the Randolph Glacier Inventory (RGI) version 6.0
(RGI Consortium, 2017). The RGI is a global dataset of glacier outlines
excluding those of the Greenland and Antarctic ice sheets, digitized both
automatically and manually based on satellite imagery and local topographic
maps (Pfeffer et al., 2014). RGI glacier
boundaries are delineated from images acquired between 1943 and 2014,
potentially introducing errors into analysis due to outdated boundaries (Herreid and Pellicciotti, 2020;
Scherler et al., 2018) (see Sect. S1 in the Supplement). However,
this database represents the best worldwide glacier inventory available, and
shrinking ice as the dominant global pattern means the tool is occasionally
running over ice-free terrain with null results rather than missing
potential supraglacial debris inputs. Any updated version of the RGI will be
incorporated when available. Additionally, the RGI can be replaced by the
user with shapefiles of the Greenland and Antarctic ice sheets (v1.1 line
536 and 543), if analysis is required in these regions, or higher-resolution
(user-defined) glacier outlines if the RGI is deemed insufficient.
Overview of processing flow
GERALDINE gathers all Landsat images from the user-specified date range and
all the images in the year preceding this user-specified date range, within
the user-specified region of interest (ROI), creating two image collections
within GEE. Users should note that smaller ROIs and annual or sub-annual date
ranges increase processing speed, with processing slowing considerably with
>800 Landsat images (∼160–1500 GB of data). The
software clips all images to the ROI, applies a cloud mask, and then
delineates supraglacial debris cover from snow and ice. GERALDINE acquires
the maximum debris extent from both image collections, creating two maximum
debris mosaics, then subtracts these mosaics and clips them to the RGI v6.0
(or user-defined area if not using RGI) to output a map. This map highlights
debris within the user-specified time period that was not present in the
preceding year, which we term “new debris additions”. This map is viewable
within a web browser as a layer in the map window. However, as it is
calculated “on the fly” (Gorelick et al., 2017), large areas can be slow to
navigate. All files can be exported in GeoJSON (Georeferenced JavaScript
Object Notation) format for further analysis, including to verify if
detections are discrete landslide inputs. This is recommended for large
ROIs. An overview of the workflow is presented in Fig. 1 and the detail for
each step described in Sect. 2.1.1–2.1.3.
Processing flow of GERALDINE.
Cloud masking
GERALDINE masks cloud cover using the GEE built-in “simple cloud score”
function (Housman et al. 2018).
This pixel-wise cloud probability score allows fast and efficient
identification of clouds, suitable for large-scale analysis (Housman et al., 2018), and has
been previously applied and well-justified for use in glacial environments (Scherler et al., 2018). A 20 % threshold is applied to
every image, thereby excluding any pixel with a cloud score >20 % from the image. We quantitatively evaluated this threshold to ensure
optimum tool performance (see Sect. S2). Cloud
shadow is not masked, as it was found to have a minimal effect on the tool
delineating debris from snow and ice whilst greatly increasing processing time.
NDSI
The Normalized Difference Snow Index (NDSI) is a ratio calculated using the
green (0.52–0.6 λ) and shortwave infrared (SWIR) (1.55–1.75 λ) bands. It helps
distinguish snow and ice from other land cover (Hall et al., 1995) and excels at
detecting ice where topographic shading is commonplace (Racoviteanu et al., 2008), due to
high reflectance in the visible range and strong absorption in the SWIR
range. GERALDINE applies the NDSI to all images and a threshold of 0.4 is
used to create a binary image of supraglacial debris (<0.4) and
snow/ice (≥0.4). This threshold has been utilized by studies in the
Andes (e.g. Burns and Nolin, 2014)
and Himalaya (e.g. Zhang
et al., 2019), but optimum thresholds often vary between 0.5 (Gjermundsen et al., 2011) and 0.2 (Keshri
et al., 2009; Kraaijenbrink et al., 2017). We justify our 0.4 threshold
based on Scherler et al. (2018), who deemed it optimal for
the creation of a global supraglacial debris cover map using Landsat images.
We advise users to use this default threshold but if this appears
sub-optimal in a user-defined region of interest (ROI), the threshold can be
fine-tuned in the code (v1.1 line 244 and 254). We utilize NDSI instead of
newer band ratio techniques (e.g. Keshri et al., 2009) and more complex algorithms (e.g. Bhardwaj et al., 2015) to ensure
transferability between Landsat TM, ETM+, and OLI TIRS sensors as we wish
to harness the full temporal archive.
Retrieving maximum debris extent
To attain a maximum debris extent, GERALDINE reduces each image collection
to an individual image using a pixel-based approach (Fig. 2). Every binary
image (supraglacial debris: 0; snow and ice: 1) in each image collection is
stacked, with pixels in the same geographic location stacked sequentially.
If any pixel in the temporal image stack is debris, the corresponding pixel
in the final mosaic will be a debris pixel, creating a maximum debris extent
mosaic. GERALDINE is therefore debris biased due to this processing step
(Fig. 2). Calculated maximum debris extent mosaics for both the user-defined
time period and previous year are differenced, the output being new debris
additions. Both the previous year maximum debris extent and new debris
addition mosaics are displayed for user analysis within the GEE interactive
development environment and are easily exportable to Google Drive (included as
part of the sign-up for Google Earth Engine).
Reducer diagram – GEE stacks all images in the collection and
undertakes pixel-wise analysis of debris cover, to create a mosaic of
maximum debris cover extent. If just one pixel in the image stack is debris,
then the corresponding pixel in the maximum debris mosaic will be debris.
White pixels represent snow and ice, and black pixels represent debris.
Validation
A two-part validation was undertaken to assess the effectiveness of
GERALDINE outputs for allowing a user to rapidly identify supraglacially
deposited landslides: a detection validation (i.e. can the user confirm a
supraglacially deposited landslide has occurred from a GERALDINE output?)
and an area validation (i.e. how much of the area of the supraglacial
landslide deposit has GERALDINE detected?). Although areal detection is not
the main purpose of the tool, greater area detection would ultimately help
the user with identification of supraglacially deposited landslides.
Validation was performed against the already-defined RA databases of
Bessette-Kirton and Coe (2016), Deline
et al. (2015), Uhlmann et al. (2013), and the Exotic Seismic Events Catalog
(IRIS DMC, 2017). To provide validation, RAs had to occur after 1984 (onset
of Landsat TM era) and had to deposit debris predominantly onto clean-ice
areas of glaciers in the RGI. A total of 48 events out of 325 met
these criteria, their locations distributed across the European Alps,
Alaska, New Zealand, Canada, Russia, and Iceland (Fig. S5).
GERALDINE was run for the year of the event using Landsat Tier 1 imagery;
the new debris vector output file was exported into a GIS, and after an
initial qualitative step to see if the user would flag the RA from the
GERALDINE output, the area of the deposit it detected was calculated within
the GIS. We utilized the select by location tool in QGIS, to select any
pixels/pixel clusters within or intersecting an outline of the RA
manually digitized from a Landsat image using the Google Earth Engine
Digitization Tool (GEEDiT) (Lea, 2018). We clipped selected pixels to the
manually digitized RA outline and calculated the area of these selected
pixels. The tool-detected area was then compared against the area of the
manually digitized RA outline. These two steps allow for an assessment of
GERALDINE's ability to highlight new debris inputs and if this changes over
the Landsat era.
Results and discussionValidation
Of the 48 validation RAs, the user was able to correctly identify 44 of
these events from GERALDINE output maps, a true positive detection accuracy
of 92 %. False negatives all pre-date 1991 (Fig. 3), giving 100 %
successful user identification post-1991. These false negatives can be
explained by reduced (and insufficient in this case) Tier 1 Landsat image
availability pre-Landsat 7 within the GEE data catalogue, inhibiting
GERALDINE from highlighting the RA as new debris. We note that if just one
image featured the RA, GERALDINE would highlight the deposit as new debris
due to its bias towards debris detection (see Sect. 2.1.3). However, a
true 100 % detection rate for supraglacial landslide deposits on glaciers
is unlikely, due to some deposits running out over existing debris cover,
and some having high snow and ice content or entraining large amounts of
snow and ice during events, which can be common for landslides deposited
supraglacially. This high snow and ice content can mask them as snow and ice during
NDSI delineation from debris, inhibiting detection. However, events of this
kind also pose significant difficulties for user delineation with original
optical imagery. GERALDINE works best when a number of images in the image
stack represent maximal debris cover in the preceding year, reducing false
positives for the time span of interest, i.e. flagging old debris as new
debris, due to a lack of old debris exposure in the previous year. This is
particularly applicable to small (<0.5 km2) glaciers, where
the overall significance of a single pixel increases. The debris bias of
GERALDINE ensures true negative detection is also extremely high, but this
high true negative detection is why user verification of new debris outputs
is needed, as they are flagged as new debris but display no
supraglacial RA characteristics, i.e. lobate and elongated (Deline et al., 2015). To a user familiar with
glacial and landslide processes, the differences in GERALDINE outputs
between true positives and negatives and false positives and negatives are clear
when running the tool to find RA inputs.
GERALDINE rock avalanche (RA) detection accuracy (red line) and RA
area accuracy (boxplots) with different Landsat constellations over time.
L4/5 (1984–1993) – 8 validation RAs; L5 (1993–1999) – 8 validation RAs;
L5/7 (1999–2003) – 9 validation RAs; L5/7 SLC (scan line corrector failure)
(2003–2013) – 11 validation RAs; and L7/8 (2013–present) – 12 validation
RAs. The dashed line represents the mean, the solid line represents the median, the box represents the upper
and lower quartiles, and whiskers represent minimum and maximum area accuracies.
GERALDINE RA areal accuracy increases over time from 19 % in the Landsat
4/5 era to 71 % with the current Landsat 7/8 constellation (Fig. 3),
with the latter period characterized by increasingly modern sensors with
greater spectral and temporal resolution. Low areal accuracy in the Landsat
4/5 era is once again a product of the GEE data catalogue having limited
imagery for certain years in glaciated areas, reducing the ability of
GERALDINE to detect the entire area of new debris additions. Areal accuracy
increases after the failure of Landsat 4 in December 1993, at which point
Landsat 5 is the sole data collector of imagery at a frequency of every 16 d. Despite this single functioning satellite, the tool detects all eight
validation events and on average 59 % of the deposit areas between 1993
and the activation of Landsat 7 in 1999. The dual Landsat 5/7 constellation
increases tool area accuracy further to 69 %. However, a decrease in mean
area accuracy is evident after the failure of the Landsat 7 Scan Line
Corrector in May 2003 (Markham et al.,
2004), decreasing tool areal accuracy by 4 %, due to images missing up to
20 %–25 % of their data per image in the stack (Hossain et al., 2015). We find
that a number of Landsat 7 scenes also feature stripes of no data,
pre-dating the scan line corrector failure, and can inaccurately cause
“stripes” of new debris in tool outputs. The current Landsat 7/8
constellation has the highest accuracy for detecting the area of RAs at 71 %. The smallest new debris addition we used for validation was 0.062 km2, of which GERALDINE detected 71 % of the area, so we have
confidence in detection greater than 0.05 km2, equating to
∼56 Landsat pixels. Even with GERALDINE performing well,
additional refinement and/or full automation of landslide deposit
identification would be an interesting (and priority) area for further
investigation. We also envisage development with other higher-resolution and
higher-repeat satellites, e.g. the Sentinel-2 and Planet Lab constellations.
However, we found that current cloud mask algorithms for these data are not
sufficient for accurate global glacial debris delineation.
GERALDINE is frequently affected by the RGI dataset causing
over- and under-estimation of previous-year debris extents and new debris
additions. For example, at tidewater glaciers that have undergone retreat
since their margins were digitized, the tool often detects clean ice and
debris at the tongue. This is dependent on the presence of ice mélange
(NDSI classification as ice and snow) and dark fjord water (NDSI
misclassification as debris) in imagery (see Sect. S1). In addition, we found an instance where a supraglacial
landslide deposit had been misclassified as a nunatak (60∘27′23.7" N, 142∘33′35.7" W), and therefore this section of the
glacier is erroneously missing from the RGI dataset altogether, preventing
tool detection, but this is likely a single case. Topographic shading and/or
bright illumination of debris cover can at times cause pixels to be masked
from Landsat scenes due to misclassification as cloud (see Sect. S2); however, if the tool is run over a sufficiently
long period, this will not influence new debris detection. GERALDINE can
also not detect landslide debris deposition onto an existing debris cover.
Therefore, if a landslide consists of multiple failures, a GERALDINE output
map would only detect one event, with the deposit extent being the combined
total of all failures. In this case, it would be highly beneficial to
combine GERALDINE with seismic detection to help delineate the number of
failures that occur.
New supraglacial landslide input detection example
The Hayes Range, Alaska, has a history of large supraglacial debris additions
(e.g. Jibson et al., 2006), but no events
have been documented in the last decade, in contrast to a recent dense
cluster in the Glacier Bay area of Alaska (Coe et al., 2018), which formed part of
the validation dataset. To test this, we ran GERALDINE for 2018 to highlight
new debris additions on glaciers in the Hayes Range (Fig. 4a). GERALDINE
used a total of 228 Landsat images for analysis: 107 to determine the 2017
debris extent and 121 to determine the 2018 debris extent. Landsat tiles
vary from 200 to 1000 MB when compressed, so if we assume an average
tile is 500 MB, a user would require 114 GB of local storage, a large
bandwidth internet connection to download (which comes with an associated
carbon cost), and a PC capable of processing these data. GEE required none
of these requirements and completed analysis in under 2 min,
extracting information from every available cloud-free pixel to maximize
use of the imagery. The new debris output map produced was 6.5 MB and
contained all relevant “new” debris information from 2018. The output map
highlighted two large supraglacial landslide deposits, which occurred
between 1 January and 31 December 2018. These were manually verified
and the potential window of event occurrence identified using satellite
imagery within GeeDiT (Lea, 2018). The
larger of the two deposits is from a slope collapse on the southern flank of
Mt Hayes (4216 m) (63∘35′11.7" N, 146∘42′50.0" W), with
emplacement determined between 10 and 25 February 2018 (Fig. 4b). This
supraglacial landslide was also detected using the seismic method
(Ekström and Stark, 2013; see Sect. 1.0) and confirmed as occurring on
12 February 2018 (Goran Ekström, personal communication, 2019). The
resulting debris deposit covered 9.4 km2 of the surface of the Susitna
Glacier (digitized from Planet Labs Inc. imagery from 31 July 2018). The tool
detected 27.5 % of the area of this deposit, due to emplacement
predominantly in the accumulation area, with the upper half of the deposit
rapidly covered by snow after the event. The second, smaller supraglacial
landslide deposit occurred between 4 and 7 July 2018, on an unnamed glacier
to the east of Maclaren Glacier (63∘20′21.9" N, 146∘26′36.1" W) (Fig. 4c). GERALDINE detected 78 % of this 1.9 km2
supraglacial debris input, which transformed the glacier from 16 % debris
covered to 51 % debris covered, and will have important implications for
glacier melt regime, velocity, and response to atmospheric drivers. Unlike
the larger supraglacially deposited landslide from Mt Hayes, this event was
not automatically detected using seismic methods (Goran Ekström,
personal communication, 2019), suggesting that its seismic signature was
lower than the seismic detection limit (M<5.0) (Ekström and Stark, 2013). Therefore, there is a high
potential to detect all events using GERALDINE, and then provide
time and location filters to seismic records to retrospectively quantify force
histories and precise timings of events not flagged automatically as a
landslide.
We note that new large debris inputs are partially highlighted on the Black
Rapids Glacier for 2018 (Fig. 4d), but these “new” additions were actually
deposited in 2002 during the Denali earthquake (Jibson
et al., 2006; Shugar et al., 2012; Shugar and Clague, 2011). We assign this
discrepancy to minimal cloud-free imagery during summer (a time when
deposits are uncovered by snowmelt), preventing the tool from highlighting
their full summer extent and causing underestimation of the 2017 debris
cover. To a human operator, however, it is clear these debris additions are
erroneous because “new” debris is patchy, with 2017 debris extent and
snow and ice preventing detection of a homogeneous deposit. If GERALDINE is run
annually for multiple years, the user will be able to determine the
emplacement date for these earlier supraglacial landslide deposits.
(a) The 2018 new debris additions in the Hayes Range, Alaska. RA
outlines digitized using Landsat imagery and the GEEDiT tool (Lea, 2018).
The inset map denotes the location of Hayes Range. (b) GERALDINE output of the Mt Hayes
landslide extent and corresponding image courtesy of Planet Labs, Inc.
(31 July 2018). (c) GERALDINE output of landslide extent on a small valley
glacier east of Maclaren glacier and corresponding image courtesy of Planet
Labs, Inc. (13 September 2018). (d) Erroneous 2018 tool detection of Black Rapids
glacier RA deposits, which were deposited as a cause of the 2002 Denali
earthquake (Jibson et al., 2006). Green boxes signify areas of interest and
correspond to magnified areas of (b), (c), and (d). IFSAR DTM
background from the Alaska Mapping Initiative (DOI:~10.5066/P9C064CO).
Tracking new debris transportation
A secondary use of GERALDINE is tracking existing supraglacial landslide
deposits. These deposits are transported down-glacier by ice flow, although
often the initial emplacement geometry is characteristically deformed and
spread due to differential ablation and ice motion (Reznichenko et al., 2011;
Uhlmann et al., 2013). GERALDINE can give an indication of deposit behaviour
and movement by highlighting “new” debris at the lateral and down-glacier
end of the deposit as it moves between image captures (Fig. 5).
Differencing the distance of this new debris from the previous year's
deposit extent can give an approximation of lateral spreading and glacier
velocity over the user-specified time period, the latter of which is often
unknown at the temporal resolution of Landsat and complex to calculate in
high mountain regions (Sam et al., 2015).
Deposition and behaviour of Lituya RA, John Hopkins Glacier, Alaska
(58∘48′54.3" N, 137∘17′40.9" W), detected by GERALDINE
when run for (a) 2012, (b) 2013, and (c) 2014. The Landsat 7 scan line corrector
issue is visible in lower-right section of the 2013 image (b). IFSAR DTM background
from the Alaska Mapping Initiative (DOI:~10.5066/P9C064CO).
To demonstrate the evolution of a RA through time, we ran GERALDINE for
2012, 2013, and 2014 for the Lituya Mountain RA in Alaska. This RA occurred
on 11 June 2012 and was deposited onto a tributary of the John Hopkins
Glacier (Geertsema, 2012). The upper portion of the deposit
was sequestered into the ice after its deposition in 2012, as is common of
debris inputs in glacier accumulation areas (Dunning et al., 2015). However, the deposit toe
remained visible on the surface, likely because it was below the snow line.
We estimate the down-glacier transport velocity of this RA by tracking and
measuring the movement of the deposit toe, to measure the displacement of
the deposit leading edge. Using this method, estimates of down-glacier
transportation of the deposit leading edge between 2012 and 2013 are
∼575±30 and ∼328±30 m between 2013 and 2014 (Fig. 5), the latter in agreement with glacier
velocity calculated by Burgess et al. (2013) between
2007 and 2010 (250–350 m a-1) and ITS_LIVE velocity
from 2013 (300–400 m a-1) (Gardner et
al., 2018; Gardner et al., 2019). We suggest that the higher RA deposit
velocities between 2012 and 2013 are a result of the immediate response of
the glacier to reduced ablation rates directly beneath the debris, causing
an ice pedestal to form from which debris is redistributed through
avalanching off the pedestal sides, expanding debris coverage (Reznichenko et al., 2011). We note other
areas are flagged as “new debris” in 2013 and 2014. These are typically
where glacier downwasting has occurred, exposing more of the valley walls, or
where there has been temporal evolution of the debris cover, e.g. glacier
flow line instabilities. These flow instabilities can cause double-counting
of debris when larger time windows are specified (see Herreid and Truffer, 2016). Both processes
subsequently cause false classification as “new debris”. However, neither
glacier downwasting nor evolution of the debris cover displays supraglacial
landslide characteristics, so it is highly unlikely that a user would
mistake them for one.
Conclusions
GERALDINE is the first free-to-use resource that can rapidly highlight new
supraglacial landslide deposits onto clean ice for a user-specified time and
location. It can aggregate hundreds of Landsat images, utilizing every
available cloud-free pixel, to create maps of new supraglacial debris
additions. Using the output maps produced, GERALDINE gives an objective
starting point from which a user can identify new debris inputs, eliminating
the time-intensive process of manually downloading, processing, and
inspecting numerous satellite images. The method allows user identification
of mass movements deposited in glacier accumulation zones, which have very
short residence times due to rapid advection into the ice. This is a process
that has not previously been quantified. We demonstrate its effectiveness by
verifying it against 48 known, large supraglacially deposited rock
avalanches that occurred in North America, Europe, Asia, and New Zealand.
GERALDINE outputs helped identify 92 % of all 48 events, with 100 %
successful identification post-1991 when image quality and availability
increases. We showcase how GERALDINE does not suffer from the traditional
disadvantages of current manual and seismic detection methods that can cause
supraglacial landslides to go undetected, by identifying two new
supraglacial landslides in 2018, in the Hayes Range of Alaska. One of these
events was not detected using existing methods; therefore, the frequency of
large supraglacial debris inputs is likely historically underestimated. We
suggest that users apply GERALDINE at standardized time intervals in
recently identified “bellwether sites” in glaciated high mountain areas
undergoing rapid change, i.e. Greenland, Alaska, Patagonia, the European
Alps, New Zealand Alps, and the Himalaya, to investigate annual rates of
these large debris inputs. GERALDINE can become part of the repertoire of
tools that enable glacial landslides and rock avalanches to be identified in the
past, present, and future. It will improve remote detection and
characterization of these events, to help quantify and evaluate their
frequency, spatial distribution, and long-term behaviour in a changing
climate.
Code availability
GERALDINE code and the validation dataset are available at 10.5281/zenodo.3524414 (Smith et al., 2020). All other results can be recreated
by running GERALDINE in the respective example areas. A guide on how to use
GERALDINE is provided in Sect. S4.
The supplement related to this article is available online at: https://doi.org/10.5194/esurf-8-1053-2020-supplement.
Author contributions
WDS developed the tool and wrote the manuscript. SAD made substantial
contributions to the conception and functionality of the tool, as well as
manuscript editing. NR, SB, and JT provided useful guidance on tool
functionality and contributed to the manuscript.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
We would like to thank Gioachino Roberti, Michelle Koutnik, and Sam Herreid
for their constructive comments, which greatly improved the manuscript. We acknowledge the freely available Landsat
datasets provided by the USGS and hosted in the Google Earth Engine data
catalogue, as well as the Randolph Glacier Inventory v6.0 (RGI Consortium, 2017).
Hillshade IFSAR DTMs used for figure production were collected as part of
the Alaska Mapping Initiative (10.5066/P9C064CO) and are available
through the USGS EarthExplorer data portal. We would also like to thank Ryan
Dick for his feedback on early versions of GERALDINE.
Financial support
This research has been supported by a Newcastle
University Research Excellence Academy studentship awarded to William D. Smith and a postdoctoral position to Stephen Brough.
Review statement
This paper was edited by Susan Conway and reviewed by Sam Herreid, Michelle Koutnik, and Gioachino Roberti.
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