Quantification and Change Assessment Benjamin Aubrey Robson 2016 Dissertation date: 31st October 2016 of Debris-Covered Glaciers using Remote Sensing

This thesis investigates how remote sensing data can be used to assess the changing state of debris-covered ice. The principal study areas are the Manaslu Region in Nepal (papers I and III) and the Hohe Tauern National Park, Austria (paper II). Clean glacier ice is straightforward to semi-automatically classify using multi-spectral satellite imagery owing to the strong spectral signature of clean ice in the visible and near-infrared sections of the electromagnetic spectrum. Since the ablation zones of clean ice glaciers are at the pressure melting point, a change in terminus position or glacier area can be directly linked to a change in climate. Debris-covered ice is however more complicated to map and to interpret temporal change. Supraglacial debris is spectrally indistinguishable from the surrounding paraglacial terrain, and requires auxiliary data such as a Digital Elevation Model (DEM), thermal band data, or flow data. Object-Based Image Analysis (OBIA) provides a framework for combining multiple datasets in one analysis, while additionally allowing shape, contextual, hierarchical and textural criteria to be used to classify imagery. Paper I combines optical (Landsat-8), topographic (void-filled SRTM) and SAR coherence (ALOS PALSAR) data within an OBIA workflow to semi-automatically classify both clean ice and debris-covered ice in the challenging area surrounding Mount Manaslu in Nepal. When compared with manually delineated outlines, the classification achieved an accuracy of 91% (93% for clean ice and 83% for debriscovered ice). The classification was affected by seasonal snow and shadows while the debris-covered ice mapping was influenced by the datasets being temporally inconsistent, and the mountainous topography causing inconsistencies in the SAR coherence data. The method compares well with other automated techniques for classifying debris-covered ice, but has two additional advantages: firstly, that SAR coherence data can distinguish active ice from stagnant ice based on whether motion or significant downwasting has occured, and secondly, that the method is applicable over a large study area using just space-borne data. Paper II explores the potential of using high-resolution (10 m) topographic data and an edge detection algorithm to morphologically map the extent of debris-covered ice. The method was applied in the Hohe Tauern National Park, Austria, using a 10 m DEM derived from airborne Light Detection and Radar (LiDAR) acquisitions. Additionally, the end-of-summer transient snowline (TSL) was also mapped, which approximates the annual Equilibrium Line Altitude (ELA). Our classification was applied on three Landsat satellite images from 1985, 2003 and 2013 and compared the results to the Austrian Glacier Inventories from 1969 and 1998 to derive decadal-scale glacial changes. A mean rate of glacier area reduction of 1.4 km2a-1 was calculated between 1969 and 2013 with a total reduction in area of 33%. The TSL rose by 92 m between 1985 and 2013 to an altitude of 3005 m. By comparing our results with manually delineated outlines an accuracy of 97.5% was determined. When a confusion matrix was calculated it could be seen that the results contained few false positives but some false negatives which were attributed to seasonal snow, shadows and misclassified debris. Our results correspond broadly with those found in other areas of the European Alps although a heterogeneity in glacier change is observable. We recommend that future glacier mapping investigations should utilise a combination of both SAR coherence data and high-resolution topographic data in order to delineate the extent of both active and stagnant glacier ice. Paper III investigates decadal scale changes in glacier area, velocity and volume in the previously undocumented Manaslu Region, Nepal. Between 2001 and 2013 the glacier area reduced by 8.2% (-0.68% a-1). Simultaneously, the glaciers lowered by -0.21 ± 0.08 m a-1 and had a slightly negative specific mass balance of -0.05 ± 016 m w.e a-1 although mass balances ranged -2.49 ± 2.24 to +0.27 ± 0.30 m w.e a-1 throughout the region. The geodetic mass balance for select glaciers covered by a Corona DEM between 1970 and 2013 was -0.24 ± 0.12 m w.e a-1 which became more negative (-0.51 ± 0.12 m w.e. a-1) between 2005 and 2013. Rates of surface lowering over debriscovered ice increasing by 168% between 1970 – 2000 (0.40 ± 0.18 m a-1) and 2005 – 2013 (1.07 ±0.48 m a-1). The rate of glacier melt varies due to presumed increases in debris thickness at the upper and lower boundaries of the ablation zone, while an area of enhanced glacier downwasting corresponds to the presence of supraglacial lakes and exposed ice. The glacier velocity varies across the region. Many glaciers have stagnant sections towards the glacier termini, and a trend of ongoing stagnation is observable. No relationship exists between trends in glacier area and glacier volume or velocity, although a weak relationship exists between trends in the changes of volume and velocity. The rates of glacier area and velocity change appear to be similar, although the number of glaciers that had records of area, velocity, and volume was few. Our results are comparable to studies looking at mean surface lowerings and geodetic mass balances in other areas of the Himalayas, and point towards heterogeneous yet pronounced mass losses across the Himalaya region

Monitoring glacial lake outburst flood susceptibility using Sentinel-1 SAR data, Google Earth Engine, and persistent scatterer interferometry

Funding to support this research from the University of St Andrews and the School of Geography and Sustainable Development is gratefully acknowledged.Continuous monitoring of glacial lakes, their parent glaciers and their surroundings is crucial because possible outbursts of these lakes pose a serious hazard to downstream areas. Ongoing climate change increases the risk of this hazard globally due to recession of glaciers leading to formation and expansion of glacial lakes, and permafrost degradation which impacts the stability of glaciers, slopes and moraines. Here, we demonstrate the capability of our approach for monitoring lake outburst susceptibility using time-series of Sentinel-1 Synthetic Aperture Radar (S-1 SAR) data. We selected Lunana in the Bhutanese Himalayas as an example region as it is highly susceptible to glacial lake outburst floods and suitable baseline data were available. We used Google Earth Engine (GEE) to calculate average radar backscatter intensity (ARBI) of glaciers, lakes, basins, and moraines. To determine the periodicity of the highest and the lowest radar backscatter intensity, we denoised the ARBI data using a Fast Fourier Transform and autocorrelated using a Pearson correlation function. Additionally, we determined glacier melt area, basin melt area, lake area, open water area, and lake ice area using radar backscatter intensity data. The Persistent Scatterer Interferometry (PSI) technique was used to investigate the stability of moraines and slopes around glacial lakes. The PSI results were qualitatively validated by comparison with high-resolution digital elevation model differencing results. Our approach showed that glaciers and basins in the region underwent seasonal and periodic changes in their radar backscatter intensity related to changes in ice and snow melt. Lakes also showed seasonal changes in their radar backscatter intensity related to the variation of lake ice and open water area, but the radar backscatter intensity change was not periodic. We could also infer lake area change using a time-series radar backscatter intensity data such as the rapid expansion of Bechung Tsho. The PSI analysis showed that all the terminal moraines were stable except Drukchung Tsho. Its terminal moraine showed subsidence at the rate of –5.18 mm/yr. Sidewalls of lakes were also stable with the exception of Lugge Tsho at site 4. Due to the free availability of S-1 SAR data, the efficiency of processing a large amount of imagery within GEE, and the PSI technique, we were able to understand the outburst susceptibility of glacial lakes in the region at great detail. The regular acquisition of S-1 SAR data enables continuous monitoring of glacial lakes. A similar approach and concept can be transferred to any geographic region on earth that shares similar challenges in glacial lake monitoring.Publisher PDFPeer reviewe

An integrated deep learning and object-based image analysis approach for mapping debris- covered glaciers

Evaluating glacial change and the subsequent water stores in high mountains is becoming increasingly necessary, and in order to do this, models need reliable and consistent glacier data. These often come from global inventories, usually constructed from multi-temporal satellite imagery. However, there are limitations to these datasets. While clean ice can be mapped relatively easily using spectral band ratios, mapping debris-covered ice is more difficult due to the spectral similarity of supraglacial debris to the surrounding terrain. Therefore, analysts often employ manual delineation, a time-consuming and subjective approach to map debris-covered ice extents. Given the increasing prevalence of supraglacial debris in high mountain regions, such as High Mountain Asia, a systematic, objective approach is needed. The current study presents an approach for mapping debris-covered glaciers that integrates a convolutional neural network and object-based image analysis into one seamless classification workflow, applied to freely available and globally applicable Sentinel-2 multispectral, Landsat-8 thermal, Sentinel-1 interferometric coherence, and geomorphometric datasets. The approach is applied to three different domains in the Central Himalayan and the Karakoram ranges of High Mountain Asia that exhibit varying climatic regimes, topographies and debris-covered glacier characteristics. We evaluate the performance of the approach by comparison with a manually delineated glacier inventory, achieving F-score classification accuracies of 89.2%–93.7%. We also tested the performance of this approach on declassified panchromatic 1970 Corona KH-4B satellite imagery in the Manaslu region of Nepal, yielding accuracies of up to 88.4%. We find our approach to be robust, transferable to other regions, and accurate over regional (>4,000 km2) scales. Integrating object-based image analysis with deep-learning within a single workflow overcomes shortcomings associated with convolutional neural network classifications and permits a more flexible and robust approach for mapping debris-covered glaciers. The novel automated processing of panchromatic historical imagery, such as Corona KH-4B, opens the possibility of exploiting a wealth of multi-temporal data to understand past glacier changes.publishedVersio

Estimating the volume of the 1978 Rissa quick clay landslide in Central Norway using historical aerial imagery

Quick clay is found across Scandinavia and is especially prominent in south-eastern and central Norway. Quick clay is prone to failure and can cause landslides with high velocities and large run-outs. The 1978 Rissa landslide is one of the best-known quick clay landslides to have occurred in the last century, both due to its size and the fact that it was captured on film. In this article, we utilise Structure from Motion Multi-View Stereo (SfM-MVS) photogrammetry to process historical aerial photography from 1964 to 1978 and derive the first geodetic volume of the Rissa landslide. We found that the landslide covered a total onshore area of 0.36 km2 and had a geodetic volume of 2.53 ± 0.52 × 106 m3 with up to 20 m of surface elevation changes. Our estimate differs profusely from previous estimates by 43–56% which can partly be accounted for our analysis not being able to measure the portion of the landslide that occurred underwater, nor account for the material deposited within the landslide area. Given the accuracy and precision of our analyses, we believe that the total volume of the Rissa landslide may have been less than originally reported. The use of modern image processing techniques such as SfM-MVS for processing historical aerial photography is recommended for understanding landscape changes related to landslides, volcanoes, glaciers, or river erosion over large spatial and temporal scales.publishedVersio

A pan-Himalayan test of predictions on plant species richness based on primary production and water-energy dynamics

Spatial variation in plant species diversity is well-documented but an overarching first-principles theory for diversity variation is lacking. Chemical energy expressed as Net Primary Production (NPP) is related to a monotonic increase in species richness at a macroscale and supports one of the leading energy-productivity hypotheses, the More individuals Hypothesis. Alternatively, water-energy dynamics (WED) hypothesizes enhanced species richness when water is freely available and energy supply is optimal. This theoretical model emphasises the amount and duration of photosynthesis across the year and therefore we include the length of the growing season and its interaction with precipitation. This seasonal-WED model assumes that biotemperature and available water represent the photosynthetically active period for the plants and hence, is directly related to NPP, especially in temperate and alpine regions. This study aims to evaluate the above-mentioned theoretical models using interpolated elevational species richness of woody and herbaceous flowering plants of the entire Himalayan range based on data compiled from databases. Generalized linear models (GLM) and generalized linear mixed models (GLMM) were used to analyse species richness (elevational gamma diversity) in the six geopolitical sectors of the Himalaya. NPP, annual precipitation, potential evapotranspiration (derived by the Holdridge formula), and length of growing season were treated as the explanatory variables and the models were evaluated using the Akaike Information Criterion (AIC) and explained deviance. Both precipitation plus potential evapotranspiration (PET), and NPP explain plant species richness in the Himalaya. The seasonal-WED model explains the species richness trends of both plant life-forms in all sectors of the Himalayan range better than the NPP-model. Despite the linear precipitation term failing to precisely capture the amount of water available to plants, the seasonal-WED model, which is based on the thermodynamical transition between water phases, is reasonably good and can forecast peaks in species richness under different climate and primary production conditions.publishedVersio

Automated detection of rock glaciers using deep learning and object-based image analysis

B Robson was supported by the Meltzer foundation and a University of Bergen grant. S MacDonell was supported by CONICYT-Programa Regional (R16A10003) and the Coquimbo Regional Government via FIC-R(2016)BIP 40000343. D. Hölbling has been supported by the Austrian Science Fund through the project MORPH (Mapping, Monitoring and Modeling the Spatio-Temporal Dynamics of Land Surface Morphology; FWF-P29461-N29). N Schaffer was financed by CONICYT-FONDECYT (3180417) and P Rastner by the ESA Dragon 4 programme (4000121469/17/I-NB).Rock glaciers are an important component of the cryosphere and are one of the most visible manifestations of permafrost. While the significance of rock glacier contribution to streamflow remains uncertain, the contribution is likely to be important for certain parts of the world. High-resolution remote sensing data has permitted the creation of rock glacier inventories for large regions. However, due to the spectral similarity between rock glaciers and the surrounding material, the creation of such inventories is typically conducted based on manual interpretation, which is both time consuming and subjective. Here, we present a novel method that combines deep learning (convolutional neural networks or CNNs) and object-based image analysis (OBIA) into one workflow based on freely available Sentinel-2 optical imagery (10 m spatial resolution), Sentinel-1 interferometric coherence data, and a digital elevation model (DEM). CNNs identify recurring patterns and textures and produce a prediction raster, or heatmap where each pixel indicates the probability that it belongs to a certain class (i.e. rock glacier) or not. By using OBIA we can segment the datasets and classify objects based on their heatmap value as well as morphological and spatial characteristics. We analysed two distinct catchments, the La Laguna catchment in the Chilean semi-arid Andes and the Poiqu catchment in the central Himalaya. In total, our method mapped 108 of the 120 rock glaciers across both catchments with a mean overestimation of 28%. Individual rock glacier polygons howevercontained false positives that are texturally similar, such as debris-flows, avalanche deposits, or fluvial material causing the user's accuracy to be moderate (63.9–68.9%) even if the producer's accuracy was higher (75.0–75.4%). We repeated our method on very-high-resolution Pléiades satellite imagery and a corresponding DEM (at 2 m resolution) for a subset of the Poiqu catchment to ascertain what difference image resolution makes. We found that working at a higher spatial resolution has little influence on the producer's accuracy (an increase of 1.0%), however the rock glaciers delineated were mapped with a greater user's accuracy (increase by 9.1% to 72.0%). By running all the processing within an object-based environment it was possible to both generate the deep learning heatmap and perform post-processing through image segmentation and object reshaping. Given the difficulties in differentiating rock glaciers using image spectra, deep learning combined with OBIA offers a promising method for automating the process of mapping rock glaciers over regional scales and lead to a reduction in the workload required in creating inventories.Publisher PDFPeer reviewe

Glacier and rock glacier changes since the 1950s in the La Laguna catchment, Chile

Benjamin Aubrey Robson was supported by a University of Bergen mobility grant for this work. This work was also supported by ANID and Concurso de Fortalecimiento al Desarrollo Científico de Centros Regionales (grant no. 2020-R20F0008-CEAZA), and Álvaro Ayala was supported by ANID and FONDECYT (grant no. 3190732).Glaciers and rock glaciers play an important role in the hydrology of the semi-arid northern Chile. Several studies show that glaciers have rapidly lost mass in response to climate change during the last decades. The response of rock glaciers to climate change in this region is, however, less known. In this study we use a combination of historical aerial photography, stereo satellite imagery, airborne lidar, and the Shuttle Radar Topography Mission (SRTM) DEM to report glacier changes for the Tapado Glacier-rock glacier complex from the 1950s to 2020 and to report mass balances for the glacier component of the complex, Tapado Glacier. Furthermore, we examine high-resolution elevation changes and surface velocities between 2012 and 2020 for 35 rock glaciers in the La Laguna catchment. Our results show how Tapado Glacier has shrunk by -25.2 +/- 4.6 % between 1956 and 2020, while the mass balance of Tapado Glacier has become steadily more negative, from being approximately in balance between 1956 and 1978 (-0.04 +/- 0.08 m w.e. a(-1)) to showing increased losses between 2015 and 2020 (-0.32 +/- 0.08 m w.e. a(-1)). Climatological (re-)analyses reveal a general increase in air temperature, decrease in humidity, and variable precipitation since the 1980s in the region. In particular, the severe droughts starting in 2010 resulted in a negative mass balance of -0.54 +/- 0.10 m w.e. a(-1) between 2012 and 2015. The rock glaciers within the La Laguna catchment show heterogenous changes, with some sections of landforms exhibiting pronounced elevation changes and surface velocities exceeding that of Tapado Glacier. This could be indicative of high ice contents within the landforms and also highlights the importance of considering how landforms can transition from more glacial landforms to more periglacial features under permafrost conditions. As such, we believe high-resolution (sub-metre) elevation changes and surface velocities are a useful first step for identifying ice-rich landforms.Publisher PDFPeer reviewe

The seasonal evolution of subglacial drainage pathways beneath a soft-bedded glacier

Subglacial hydrology is a key element in glacier response to climate change, but investigations of this environment are logistically difficult. Most models are based on summer data from glaciers resting on rigid bedrocks. However a significant number of glaciers rest on soft (unconsolidated sedimentary) beds. Here we present a unique multi-year instrumented record of the development of seasonal subglacial behavior associated with an Icelandic temperate glacier resting on a deformable sediment layer. We observe a distinct annual pattern in the subglacial hydrology based on self-organizing anastomosing braided channels. Water is stored within the subglacial system itself (till, braided system and ‘ponds’), allowing the rapid access of water to enable glacier speed-up events to occur throughout the year, particularly in winter.publishedVersio

Recent Evolution of Glaciers in the Manaslu Region of Nepal From Satellite Imagery and UAV Data (1970–2019)

Glacierized mountain ranges such as the Himalaya comprise a variety of glacier types, including clean and debris-covered glaciers. Monitoring their behaviour over time requires an assessment of changes in area and elevation along with surface features and geomorphology. In this paper we quantify the surface evolution of glacier systems in the Manaslu region of Nepal over the last five decades using 2013/2019 multi-sensor imagery and elevation data constructed from 1970 declassified Corona imagery and 1970 declassified Corona imagery. We investigate area changes, glacier thickness, geodetic glacier mass balance and surface velocity changes at regional scales and focus on the Ponkar Glacier and Thulagi Glacier and Lake for an in-depth assessment of surface geomorphology and surface feature dynamics (ponds, vegetation and ice cliffs). The time series of surface elevation changes for the lower ablation area of Ponkar Glacier is extended using 2019 UAV-based imagery and field-based ablation rates measured over the period 2016–2019. Glaciers in the Manaslu region experienced a mean area loss of −0.26 ± 0.0001% a−1 between 1970 and 2019. The mean surface lowering was −0.20 ± 0.02 ma−1 over the period 1970 to 2013, corresponding to a regional geodetic mass balance of −0.17 ± 0.03 m w. e.a−1. Overall, debris-covered glaciers had slightly higher thinning rates compared to clean ice glaciers; lake-terminating glaciers had double thinning rates compared to land-terminating glaciers. Individual glacier mass balance was negatively controlled by glacier slope and mean glacier elevation. During the period 1970 to 2013, Ponkar Glacier had a geodetic mass balance of −0.06 ± 0.01 m w. e.a−1, inversely correlated with parts of the central trunk thickening. Between 2013 and 2019 there was a nine-fold increase in the thinning rates over the lower parts of the glacier tongue relative to the period 1970–2013. Ice-surface morphology changes between 1970 and 2019 on Ponkar Glacier include a decrease in ogives and open crevasses, an increase in ice cliffs and ponds and the expansion of the supraglacial debris and ice-surface vegetation. These changes point to reduced ice-dynamic activity and are commensurate with the observed recession and negative glacier mass balance over the last five decades.publishedVersio

Dimethyl fumarate in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial

Dimethyl fumarate (DMF) inhibits inflammasome-mediated inflammation and has been proposed as a treatment for patients hospitalised with COVID-19. This randomised, controlled, open-label platform trial (Randomised Evaluation of COVID-19 Therapy [RECOVERY]), is assessing multiple treatments in patients hospitalised for COVID-19 (NCT04381936, ISRCTN50189673). In this assessment of DMF performed at 27 UK hospitals, adults were randomly allocated (1:1) to either usual standard of care alone or usual standard of care plus DMF. The primary outcome was clinical status on day 5 measured on a seven-point ordinal scale. Secondary outcomes were time to sustained improvement in clinical status, time to discharge, day 5 peripheral blood oxygenation, day 5 C-reactive protein, and improvement in day 10 clinical status. Between 2 March 2021 and 18 November 2021, 713 patients were enroled in the DMF evaluation, of whom 356 were randomly allocated to receive usual care plus DMF, and 357 to usual care alone. 95% of patients received corticosteroids as part of routine care. There was no evidence of a beneficial effect of DMF on clinical status at day 5 (common odds ratio of unfavourable outcome 1.12; 95% CI 0.86-1.47; p = 0.40). There was no significant effect of DMF on any secondary outcome