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

CONCEPTUAL FRAMEWORK OF COMBINED PIXEL AND OBJECT-BASED METHOD FOR DELINEATION OF DEBRIS-COVERED GLACIERS

Delineation of the glacier is an important task for understanding response of glaciers to climate. In Himalayan region, most of the glaciers are covered with debris. Supraglacial debris works as an obstacle for automatic mapping of glacier using remote sensing data. Different methods have been used to reduce this difficulty based on pixel-based and object-based approaches using optical data, thermal data and DEM. Pixel-based glacier mapping is a traditional method for delineation of the glacier but the object-based method has emerged as a new approach in cryosphere application leading to its successful application in different applications. All pixel-based methods require some degree of manual correction because these can’t be delineated automatically, especially in shadow area and debris covered part of the glacier. In the majority of studies, the object-based method has provided higher accuracy to delineate the debris-covered glacier. Spatially high spatial resolution satellite data is best suited for object-based image classification. In future, a combination of pixel-based method and object-based method can be attempted for delineation of the debris-covered glacier along with its critical analysis for suitability. The present paper critically reviews pixel-based and object-based methods as well as provides a framework for combined pixel and object-based method for delineation of debris-covered glacier

Contemporary geomorphological activity throughout the proglacial area of an alpine catchment

Quantification of contemporary geomorphological activity is a fundamental prerequisite for predicting the effects of future earth surface process and landscape development changes. However, there is a lack of high-resolution spatial and temporal data on geomorphological activity within alpine catchments, which are especially sensitive to climate change, human impacts and which are amongst the most dynamic landscapes on Earth. This study used data from repeated laser scanning to identify and quantify the distribution of contemporary sediment sources and the intensity of geomorphological activity within the lower part of a glaciated alpine catchment; Ödenwinkelkees, central Austria. Spatially, geomorphological activity was discriminated by substrate class. Activity decreased in both areal extent and intensity with distance from the glacier, becoming progressively more restricted to the fluvially-dominated valley floor. Temporally, geomorphological activity was identified on annual, seasonal, weekly and daily timescales. Activity became more extensive with increasing study duration but more intense over shorter timescales, thereby demonstrating the importance of temporary storage of sediment within the catchment. The mean volume of material moved within the proglacial zone was 4400m.yr, which suggests a net surface lowering of 34mm.yr in this part of the catchment. We extrapolate a minimum of 4.8mm.yr net surface lowering across the whole catchment. These surface lowering values are approximately twice those calculated elsewhere from contemporary measurements of suspended sediment flux, and of rates calculated from the geological record, perhaps because we measure total geomorphological activity within the catchment rather than overall efflux of material. Repeated geomorphological surveying therefore appears to mitigate the problems of hydrological studies underestimating sediment fluxes on decadal-annual time-scales. Further development of the approach outlined in this study will enable the quantification of geomorphological activity, alpine terrain stability and persistence of landforms

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

GlacierNet2: A Hybrid Multi-Model Learning Architecture for Alpine Glacier Mapping

In recent decades, climate change has significantly affected glacier dynamics, resulting in mass loss and an increased risk of glacier-related hazards including supraglacial and proglacial lake development, as well as catastrophic outburst flooding. Rapidly changing conditions dictate the need for continuous and detailed observations and analysis of climate-glacier dynamics. Thematic and quantitative information regarding glacier geometry is fundamental for understanding climate forcing and the sensitivity of glaciers to climate change, however, accurately mapping debris-cover glaciers (DCGs) is notoriously difficult based upon the use of spectral information and conventional machine-learning techniques. The objective of this research is to improve upon an earlier proposed deep-learning-based approach, GlacierNet, which was developed to exploit a convolutional neural-network segmentation model to accurately outline regional DCG ablation zones. Specifically, we developed an enhanced GlacierNet2 architecture thatincorporates multiple models, automatic post-processing, and basin-level hydrological flow techniques to improve the mapping of DCGs such that it includes both the ablation and accumulation zones. Experimental evaluations demonstrate that GlacierNet2 improves the estimation of the ablation zone and allows a high level of intersection over union (IOU: 0.8839) score. The proposed architecture provides complete glacier (both accumulation and ablation zone) outlines at regional scales, with an overall IOU score of 0.8619. This is a crucial first step in automating complete glacier mapping that can be used for accurate glacier modeling or mass-balance analysis

Glacier thinning, recession and advance, and the associated evolution of a glacial lake between 1966 and 2021 at Austerdalsbreen, western Norway

The Jostedalsbreen is the largest ice cap in Norway and mainland Europe. Rapid retreat of many of its outlet glaciers since the 2000s has led to the formation of several glacial lakes. Processes causing the formation and expansion of glacial lakes and their interaction with a glacier and terminal moraine have not been widely addressed yet. In this study, we investigate the degradation of the front of the southeast-facing outlet glacier Austerdalsbreen. Based on a variety of remotely sensed data (UAV-based and airborne orthophotos and DEMs, satellite images), we analyze the coincident glacial and proglacial changes of Austerdalsbreen and quantify the evolution of this transition zone during the last decades. In particular, we focus on the short-term evolution of the glacial lake since 2010, we examine the context of a glacier advance in the 1990s, and we report long-term changes by utilizing 1960s imagery. We discuss the evolution and conditions of Austerdalsbreen compared to other outlet glaciers of Jostedalsbreen. Overall, the glacier terminus has experienced a recession in the last decades. The 1990s terminus advance was more restricted than at other nearby outlet glaciers due to glacier surface debris cover, which is a critical factor for the glacier and lake evolution. However, in the most recent period, since 2012, a distinct expansion of a glacial lake is quantifiable. Since the rates of glacier surface lowering also considerably increased since approximately 2017 and the glacier retreated since the beginning of the 2000s with a clear maximum length decrease in 2015, we interpret the recently formed glacial lake as a contributory factor of glacial changes

Sensing Mountains

Sensing mountains by close-range and remote techniques is a challenging task. The 4th edition of the international Innsbruck Summer School of Alpine Research 2022 – Close-range Sensing Techniques in Alpine Terrain brings together early career and experienced scientists from technical-, geo- and environmental-related research fields. The interdisciplinary setting of the summer school creates a creative space for exchanging and learning new concepts and solutions for mapping, monitoring and quantifying mountain environments under ongoing conditions of change

Short-term geomorphological evolution of proglacial systems

Proglacial systems are amongst the most rapidly changing landscapes on Earth, as glacier mass loss, permafrost degradation and more episodes of intense rainfall progress with climate change. This review addresses the urgent need to quantitatively define proglacial systems not only in terms of spatial extent but also in terms of functional processes. It firstly provides a critical appraisal of prevailing conceptual models of proglacial systems, and uses this to justify compiling data on rates of landform change in terms of planform, horizontal motion, elevation changes and sediment budgets. These data permit us to produce novel summary conceptual diagrams that consider proglacial landscape evolution in terms of a balance of longitudinal and lateral water and sediment fluxes. Throughout, we give examples of newly emerging datasets and data processing methods because these have the potential to assist with the issues of: (i) a lack of knowledge of proglacial systems within high-mountain, arctic and polar regions, (ii) considerable inter- and intra-catchment variability in the geomorphology and functioning of proglacial systems, (iii) problems with the magnitude of short-term geomorphological changes being at the threshold of detection, (iv) separating short-term variability from longer-term trends, and (v) of the representativeness of plot-scale field measurements for regionalisation and for upscaling. We consider that understanding of future climate change effects on proglacial systems requires holistic process-based modelling to explicitly consider feedbacks and linkages, especially between hillslope and valley-floor components. Such modelling must be informed by a new generation of repeated distributed topographic surveys to detect and quantify short-term geomorphological changes