Evaluation of Hexagon Imagery for Regional Mass Balance Study in the Bhutan Himalayas

There is much uncertainty regarding the present and future state of Himalayan glaciers, which supply meltwater for river systems vital to more than 1.4 billion people living throughout Asia. Previous assessments of regional glacier mass balance in the Himalayas using various remote sensing and field-based methods give inconsistent results. In this study, declassified Hexagon stereo imagery is processed to generate a digital elevation model (DEM) in the Bhutan Himalayas. Results indicate that the Hexagon imagery database represents a largely untapped resource for understanding decadal scale patterns of mass balance in the region. Future research will utilize the imagery and DEMs to quantify changes in volume and extent of glaciers in the Bhutan Himalayas by comparing the historical imagery to more recent data and calculating changes in ice volume over an approximately 40 year period

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

A study of decadal scale glacier changes of the Lunana glacier system in Bhutan, Himalaya, with considerations to glacial lake outburst floods (GLOFs)

This study assesses changes in glacier area, velocity, and geodetic mass balance for a selection of glaciers in the Lunana glacier system of Bhutan, Himalaya. It takes considerations to Glacial Lake Outburst Floods (GLOFs) by creating a glacial lake inventory of two important potential dangerous glacial lakes, Raphstreng Tsho and Luggye Tsho. Bhutan is located in the eastern parts of the HKH region and is known for its earlier GLOF events. The precipitation in Bhutan is driven by the Indian monsoon resulting in 60% annual precipitation, the high amount of rainfall results in rockfalls that covers large valley glacier tongues with debris. I studied the glacier area changes between 1976, 1996 and 2018 using freely available Landsat satellite imagery, SAR Sentinel 1&2, the SRTM Digital Elevation Model (DEM) and HMA DEM. The geodetic mass balance was calculated between 1976, 2000 and 2018/9 (for selected glaciers) using DEM constructed from high-resolution stereo images, Pléiades and SPOT, granted from the European Space Agency, as well as using the already accessed SRTM DEM and a Hexagon DEM courtesy of King, et al. (2019). The glacier velocity was generated using SAR TerraSAR-X data from 2016 and shows an average yearly displacement over the Lunana glacier system. The glacial lake time series for Raphstreng Tsho and Luggye Tsho where studied between 1993 and 2018 using a stack of freely available Landsat imagery. The results of this study, show a variety of decadal glacial changes over Lunana glacier system, with glaciers lowering on an average by 0.48± 0.08 m a-1 between 1976 and 2018/9 which calculates to a geodetic mass balance of -0.41 ± 0.068 m w.e. a-1. The system had a total average of 12.73% area of reduction for all glaciers, between the same time period. The Lunana glacier system consists of both debris-covered glaciers in the south and debris-free glaciers in the north, and as a result, the glacier changes vary between the two regions. Between 1976 – 2018/9 the southern region had an average surface melt of 0.76 ± 0.07 m a-1 which calculates to a geodetic mass balance of -0.65 ± 0.06 m w.e. a-1 and a 12.65% area of reduction. For the Northern region, the average surface melt was measured to be 1.26 ± 0.07 m a-1 which calculates to a geodetic mass balance of 1.07 ± 0.06 m w.e. a-1 and a 12.80% area of reduction. The glacier velocity was calculated to be at average of 3.05 ± 0.73 m a-1 over the south region and 3.78 ± 0.73 m a-1 over the north region. The Luggye glacier 1, located in the southern parts of Lunana glacier system, is the main input source for glacier meltwater to Luggye Tsho an ice-moraine dam proglacial lake which outburst in 1994 due to hydrostatic pressure. Between 1976 and 2018/9, the Luggye glacier 1 has had a considerable loss in surface elevation by 1.19 ± 0.07 m a-1 which calculates to a geodetic mass balance of 1.01 ± 0.069 m w.e. a-1. The 1994 GLOF event discharged over 18 million m3 of water, destroying infrastructure, flooding villages and houses which killed 21 humans. Today, Luggye Tsho is classified to yield over 1.41 km2 of water, an increase from its former state of 1.12 km2 in 1993, just before the event. This study cannot affirm if PDGLs such as Luggye Tsho is to outburst in the future, but it does affirm its growth in lake area and its input source from glacier melt over Luggye glacier, and that it should be monitored in case of potential outbreak. This can be done by doing repeated analysis of glacier velocity and calculation of glacier mass balance, as this would calculate the input source amount of meltwater to Luggye Tsho.Masteroppgave i geografiGEO350MASV-PHYGMASV-GEOGMPGEOGRMASV-MEH

Mapping the grounding line of Antarctica in SAR interferograms with machine learning techniques

The grounding line marks the transition between ice grounded at the bedrock and the floating ice shelf. Its location is required for estimating ice sheet mass balance, modelling of ice sheet dynamics and glaciers and for evaluating ice shelf stability, which merits its long-term monitoring. The line migrates both due to short term influences such as ocean tides and atmospheric pressure, and long-term effects such as changes of ice thickness, slope of bedrock and variations in sea level. Of the numerous in-situ and remote sensing methods currently in use to map the grounding line, Differential Interferometric Synthetic Aperture Radar (DInSAR) is, by far, the most accurate technique which produces spatially dense delineations. Tidal deformation at the ice sheet-ice shelf boundary is visible as a dense fringe belt in DInSAR interferograms and its landward limit is taken as a good approximation of the grounding line location (GLL). The GLL is usually manually digitized on the interferograms by human operators. This is both time consuming and introduces inconsistencies due to subjective interpretation especially in low coherence interferograms. On a large scale and with increasing data availability a key challenge is the automation of the delineation procedure. So far, a limited amount of studies were published regarding the delineation processes of typical features on the ice sheets using deep neural networks (DNNs). The objectives of this thesis were to further explore the feasibility of using machine learning for mapping the interferometric grounding line, as well as exploring the contributions of complementary features such as coherence estimated from phase, Digital Elevation Model, ice velocity, tidal displacement and atmospheric pressure, in addition to DInSAR interferograms. A dataset composed of manually delineated GLLs generated within ESA’s Antarctic Ice Sheet Climate Change Initiative project and corresponding DInSAR interferograms from ERS-1/2, Sentinel-1 and TerraSAR-X missions over Antarctica together with the above mentioned features was compiled and used for training two DNNs: Holistically-Nested Edge Detection (HED) andUNet. The developed processing chain handles creation of the training feature stack, training the DNNs and performing post processing functions on the resulting predictions. HED outperformed UNet and was able to achieve a median deviation (from manual delineations) of 209.23 m with a median absolute deviation of 152.91 m. Analysis of the individual feature contributions revealed that only the phase and derived features (real and imaginary interferogram components and coherence estimates) substantially influence the predicted GLLs. This finding is advantageous in terms of saving time, computational effort and memory in creating and storing the above mentioned feature stack. Although the delineations generated from HED do not perfectly follow the true GLL in all locations, the gains in efficiency and consistency are considerable, compared to the time and effort spent for manual digitizations. This study shows the potential of DNNs for automating the interferometric GLL delineation process

Error sources and guidelines for quality assessment of glacier area, elevation change, and velocity products derived from satellite data in the Glaciers_cci project

Satellite data provide a large range of information on glacier dynamics and changes. Results are often reported, provided and used without consideration of measurement accuracy (difference to a true value) and precision (variability of independent assessments). Whereas accuracy might be difficult to determine due to the limited availability of appropriate reference data and the complimentary nature of satellite measurements, precision can be obtained from a large range of measures with a variable effort for determination. This study provides a systematic overview on the factors influencing accuracy and precision of glacier area, elevation change (from altimetry and DEM differencing), and velocity products derived from satellite data, along with measures for calculating them. A tiered list of recommendations is provided (sorted for effort from Level 0 to 3) as a guide for analysts to apply what is possible given the datasets used and available to them. The more simple measures to describe product quality (Levels 0 and 1) can often easily be applied and should thus always be reported. Medium efforts (Level 2) require additional work but provide a more realistic assessment of product precision. Real accuracy assessment (Level 3) requires independent and coincidently acquired reference data with high accuracy. However, these are rarely available and their transformation into an unbiased source of information is challenging. This overview is based on the experiences and lessons learned in the ESA project Glaciers_cci rather than a review of the literature