Remote sensing flow velocity of debris-covered glaciers using Landsat 8 data

2015 ◽  
Vol 40 (2) ◽  
pp. 305-321 ◽  
Author(s):  
Lydia Sam ◽  
Anshuman Bhardwaj ◽  
Shaktiman Singh ◽  
Rajesh Kumar
Keyword(s):  
Remote Sensing ◽  
Flow Velocity ◽  
Accuracy Assessment ◽  
Geographical Area ◽  
Surface Flow ◽  
Velocity Estimation ◽  
Surface Velocity ◽  
Landsat 8 ◽  
Glacier Surface ◽  
Glacier Velocity

Changes in ice velocity of a glacier regulate its mass balance and dynamics. The estimation of glacier flow velocity is therefore an important aspect of temporal glacier monitoring. The utilisation of conventional ground-based techniques for detecting glacier surface flow velocity in the rugged and alpine Himalayan terrain is extremely difficult. Remote sensing-based techniques can provide such observations on a regular basis for a large geographical area. Obtaining freely available high quality remote sensing data for the Himalayan regions is challenging. In the present work, we adopted a differential band composite approach, for the first time, in order to estimate glacier surface velocity for non-debris and supraglacial debris covered areas of a glacier, separately. We employed various bandwidths of the Landsat 8 data for velocity estimation using the COSI-Corr (co-registration of optically sensed images and correlation) tool. We performed the accuracy assessment with respect to field measurements for two glaciers in the Indian Himalaya. The panchromatic band worked best for non-debris parts of the glaciers while band 6 (SWIR – short wave infrared) performed best in case of debris cover. We correlated six temporal Landsat 8 scenes in order to ensure the performance of the proposed algorithm on monthly as well as yearly timescales. We identified sources of error and generated a final velocity map along with the flow lines. Over- and underestimates of the yearly glacier velocity were found to be more in the case of slow moving areas with annual displacements less than 5 m. Landsat 8 has great capabilities for such velocity estimation work for a large geographic extent because of its global coverage, improved spectral and radiometric resolutions, free availability and considerable revisit time.

Fusion and mining of glacier surface flow velocity time series

2020 ◽  
Author(s):  
Laurane Charrier ◽  
Yajing Yan ◽  
Elise Koeniguer ◽  
Emmanuel Trouvé ◽  
Romain Millan ◽  
...  
Keyword(s):  
Time Series ◽  
Flow Velocity ◽  
Small Time ◽  
Surface Flow ◽  
Landsat 8 ◽  
Glacier Surface ◽  
Temporal Sampling ◽  
Displacement Maps ◽  
Spatio Temporal ◽  
Sentinel 2

<p>Glacier response to climate change results in natural hazards, sea level rise and changes in freshwater resources. To evaluate this response, glacier surface flow velocity constitutes a crucial parameter to study. Nowadays, more and more velocity maps at regional or global scales issued from satellite SAR and/or optical images tend to be available online or on-demand. Such amount of data requires appropriate data fusion strategies in order to generate displacement time series with improved precision and spatio-temporal coverage. The improved displacement time series can then be used by advanced multi-temporal analysis approaches for further physical interpretations of the phenomenon under observation. In this work, time series of Sentinel-2 (10~m resolution, every 5 days), Landsat-8 (15~m resolution, every 16 days) and Venus (5~m resolution, every 2 days) images acquired between January 2017 and September 2018, over the Fox glacier in the Southern Alps of New Zealand are investigated. Velocities are generated with an offset tracking technique using an automatic processing chain for every possible repeat cycles (2 days-100 days and 300 days to 400 days). Thousands of velocity maps are available, and they are subject to both uncertainty and data gaps. In order to produce a displacement time series as precise/complete as possible , we propose three fusion strategies: 1) use all the available Sentinel-2 displacement maps with different time spans. The goal is to construct a time series of displacement with respect to a common master by means of an inversion 2) take only Sentinel-2 displacement maps with as small time spans as possible, at the same time, keep as much as possible redundancy in the network to be able to construct a common master displacement time series by inversion 3) follow the previous strategy but use all available displacement maps from 3 sensors, with different temporal sampling and measurement precision taken into account. Afterwards, the common master displacement time series will be analysed by a data mining approach in order to extract unusual spatio-temporal patterns in the time series.</p>

scholarly journals Interannual and Seasonal Variability of Glacier Surface Velocity in the Parlung Zangbo Basin, Tibetan Plateau

2020 ◽  
Vol 13 (1) ◽  
pp. 80
Author(s):  
Jing Zhang ◽  
Li Jia ◽  
Massimo Menenti ◽  
Shaoting Ren
Keyword(s):  
Seasonal Variability ◽  
Global Climate ◽  
Meteorological Data ◽  
Transverse Velocity ◽  
Surface Velocity ◽  
Landsat 8 ◽  
Velocity Change ◽  
Glacier Surface ◽  
Glacier Velocity ◽  
Glacier Flow

Monitoring glacier flow is vital to understand the response of mountain glaciers to environmental forcing in the context of global climate change. Seasonal and interannual variability of surface velocity in the temperate glaciers of the Parlung Zangbo Basin (PZB) has attracted significant attention. Detailed patterns in glacier surface velocity and its seasonal variability in the PZB are still uncertain, however. We utilized Landsat-8 (L8) OLI data to investigate in detail the variability of glacier velocity in the PZB by applying the normalized image cross-correlation method. On the basis of satellite images acquired from 2013 to 2020, we present a map of time-averaged glacier surface velocity and examined four typical glaciers (Yanong, Parlung No.4, Xueyougu, and Azha) in the PZB. Next, we explored the driving factors of surface velocity and of its variability. The results show that the glacier centerline velocity increased slightly in 2017–2020. The analysis of meteorological data at two weather stations on the outskirts of the glacier area provided some indications of increased precipitation during winter-spring. Such increase likely had an impact on ice mass accumulation in the up-stream portion of the glacier. The accumulated ice mass could have caused seasonal velocity changes in response to mass imbalance during 2017–2020. Besides, there was a clear winter-spring speedup of 40% in the upper glacier region, while a summer speedup occurred at the glacier tongue. The seasonal and interannual velocity variability was captured by the transverse velocity profiles in the four selected glaciers. The observed spatial pattern and seasonal variability in glacier surface velocity suggests that the winter-spring snow might be a driver of glacier flow in the central and upper portions of glaciers. Furthermore, the variations in glacier surface velocity are likely related to topographic setting and basal slip caused by the percolation of rainfall. The findings on glacier velocity suggest that the transfer of winter-spring accumulated ice triggered by mass conservation seems to be the main driver of changes in glacier velocity. The reasons that influence the seasonal surface velocity change need further investigation.

scholarly journals A comparison of tools and techniques for stabilising UAS imagery for surface flow observations

2021 ◽  
Author(s):  
Robert Ljubičić ◽  
Dariia Strelnikova ◽  
Matthew T. Perks ◽  
Anette Eltner ◽  
Salvador Peña-Haro ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Computational Complexity ◽  
Visual Information ◽  
Rapid Development ◽  
General Purpose ◽  
Surface Flow ◽  
Velocity Estimation ◽  
Field Conditions ◽  
Sensing Applications ◽  
Selection Of

Abstract. While the availability and affordability of unmanned aerial systems (UASs) has led to the rapid development of remote sensing applications in hydrology and hydrometry, uncertainties related to such measurements are still to be quantified and mitigated. Physical instability of the UAS platform inevitably induces motion in the acquired videos and can have a significant impact on the accuracy of camera-based measurements such as velocimetry. A common practice in the data preprocessing stages is the compensation of platform-induced motion by means of digital image stabilisation (DIS) methods, which use the visual information from the captured videos – in the form of physically static features – to first estimate and then to compensate such motion. Most existing stabilisation approaches rely either on in-house built tools based on different algorithms, or on general-purpose commercial software. Intercomparison of different stabilisation tools for UAS remote sensing purposes that could serve as a basis for a selection of a particular tool in given conditions has not been found in the available literature. In this paper we have attempted to summarise and describe several freely available DIS tools applicable to UAS velocimetry purposes. A total of seven tools – six aimed specifically at velocimetry and one general purpose software – were investigated in terms of their (1) stabilisation accuracy in various conditions, (2) robustness, (3) computational complexity, and (4) user experience, using three case study videos with different flight and ground conditions. In attempt to adequately quantify the accuracy of the stabilisation using different tools, we have also presented a comparison metric based on root-mean-squared differences (RMSD) of interframe pixel intensities for selected static features. The most apparent differences between the investigated tools have been found with regards to the method for identifying and selecting static features in videos – manual selection of features or automatic. State-of-the-art methods which rely on automatic selection of features require fewer user-provided parameters and are able to select a significantly higher number of potentially static features (by several orders of magnitude) when compared to the methods which require manual identification of such features. This allows the former to achieve a higher stabilisation accuracy, but manual feature selection methods have demonstrated lower computational complexity and better robustness in complex field conditions. While this paper does not intend to identify the optimal stabilisation tool for UAS-based velocimetry purposes, it does aim to shed a light on implementational details which can help engineers and researchers choose the tool suitable for their needs and specific field conditions. Additionally, the RMSD comparison metric presented in this paper can also be used in order to measure the velocity estimation uncertainty induced by UAS motion.

The August 2020 jökulhlaup from a marginal lake at Langjökull, W-Iceland: Course of events, discharge and volume estimates, future monitoring

2021 ◽  
Author(s):  
Thorsteinn Thorsteinsson ◽  
Kristjana G. Eythórsdóttir ◽  
Esther H. Jensen ◽  
Ingibjörg Jónsdóttir ◽  
Finnur Pálsson ◽  
...  
Keyword(s):  
Flow Velocity ◽  
Water Level ◽  
Satellite Image ◽  
Remote Sensing Data ◽  
Early Warning Systems ◽  
Landsat 8 ◽  
Time Data ◽  
Glacier Surface ◽  
Ice Cap ◽  
Sentinel 2

<p>Jökulhlaups from marginal and subglacial lakes are a considerable hazard in Iceland and the rapid retreat of glaciers and ice caps is leading to hydrological changes in many locations at or near the glaciers. This calls for careful monitoring of glaciers and proglacial areas.</p><p>On August 17 2020, increased discharge was observed in Hvítá, a glacial river originating in the ice cap Langjökull. Sediment-laden jökulhlaup waters filled a narrow gorge of the river near the farm and tourist resort Húsafell and dead salmon were found strewn over fields 30–40 km downstream.</p><p>Reconnaissance trips, overflights and satellite image studies revealed the following course of events:</p><p>A marginal glacial lake (current size: 1.3 km<sup>2</sup>) started forming at 890 m elevation at the western margin of Langjökull after the turn of the century. Sentinel-2 satellite images indicate that subglacial outflow from the lake had started in the morning of August 17. The exact path of the 2 km long subglacial water course can be inferred from a Landsat-8 image taken on November 11 2020. The image shows a narrow surface depression resulting from lowering of the glacier surface when the subglacial tunnel carrying the water was formed. The ice thickness averages 70 m along the flowpath.</p><p>Emerging from beneath the ice cap, the water flowed 13 km through the Svartá river canyon, eroding sediment from the river bed and canyon walls. Fresh colouring and sediment deposition was observed on sandur plains where Svartá joins the Geitá and Hvítá rivers.</p><p>Observations of the jökulhlaup (water level and flow velocity) as it passed beneath a bridge near Húsafell help constrain discharge levels and flood volume at a location 18 km from the outlet at Langjökull. In addition, real-time data on Hvítá river water level are available from the Kljáfoss hydrometric station 35 km further downstream, discharge started rising from a background value of 90 m<sup>3</sup>/s on August 17 at 16:00. The flood peaked there at 260 m<sup>3</sup>/s at 01:45 in the early morning of August 18 and had subsided again at noon on that day.</p><p>Using imagery from the Sentinel-2 satellites the area of the marginal lake is estimated to have diminished from 1.29 km<sup>2</sup> to 0.46 km<sup>2</sup> during the jökulhlaup. A lowering of 4 m has been determined from aerial imagery and the total volume released was 3.4 million m<sup>3</sup> according to preliminary estimates. We estimate an average flow velocity of 3±1 m/s for the entire distance from the outlet at the glacier to Kljáfoss.</p><p>The glacier margin in the region has retreated by 500-1000 m and thinned by 3 m/a in the period 2004-2019 leading to the formation of the proglacial lake. Flooding events occurring in 2014 and 2017 have now been detected in hydrometric and remote sensing data. The lake is likely to become larger when retreat continues and further thinning of the ice may lead to more frequent jökulhlaups in coming years. Plans to monitor the lake level and install early warning systems will be outlined in the presentation.</p>

Early 21st-century glacier surface mass balance across High Mountain Asia derived from remote sensing

2020 ◽  
Author(s):  
Evan Miles ◽  
Michael McCarthy ◽  
Amaury Dehecq ◽  
Marin Kneib ◽  
Stefan Fugger ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Mass Balance ◽  
Surface Velocity ◽  
High Mountain ◽  
Surface Mass Balance ◽  
Equilibrium Line Altitude ◽  
Glacier Surface ◽  
Surface Mass ◽  
Early 21St Century ◽  
Debris Cover

<p>Glaciers in High Mountain Asia have experienced intense scientific scrutiny in the past decade due to their hydrological and societal importance. The explosion of freely-available satellite observations has greatly advanced our understanding of their thinning, motion, and overall mass losses, and it has become clear that they exhibit both local and regional variations due to debris cover, surging and climatic regime. However, our understanding of glacier accumulation and ablation rates is limited to a few individual sites, and altitudinal surface mass balance is essentially unknown across the vast region.</p><p>Here we combine recent assessments of ice thickness and surface velocity to correct observed glacier thinning rates for mass redistribution in a flowband framework to derive the first estimates of altitudinal glacier surface mass balance across the region. We first evaluate our results at the glacier scale with all available glaciological field measurements (27 glaciers), then analyze 4665 glaciers (we exclude surging and other anomalous glaciers) comprising 43% of area and 36% of mass for glaciers larger than 2 km<sup>2</sup> in the region. The surface mass balance results allow us to determine the equilibrium line altitude for each glacier for the period 2000-2016.  We then aggregate our altitudinal and hypsometric surface mass balance results to produce idealised profiles for distinct subregions, enabling us to consider the subregional heterogeneity of mass balance and the importance of debris-covered ice for the region’s overall ablation.</p><p>We find clear patterns of ELA variability across the region.  9% of glaciers accumulate mass over less than 10% of their area on average for the study period. These doomed  glaciers are concentrated in Nyainqentanglha, which also has the most negative mass balance of the subregions, whereas accumulation area ratios of 0.7-0.9 are common for glaciers in the neutral-balance Karakoram and Kunlun Shan. We find that surface debris extent is negatively correlated with ELA, explaining up to 1000 m of variability across the region and reflecting the importance of avalanching as a mass input for debris-covered glaciers at lower elevations. However, in contrast with studies of thinning rates alone, we find a clear melt reduction for low-elevation debris-covered glacier areas, consistent across regions, largely resolving the ‘debris cover anomaly’.  </p><p>Our results provide a comprehensive baseline for the health of the High Asian ice reservoirs in the early 21<sup>st</sup> Century. The estimates of altitudinal surface mass balance and ELAs will additionally enable novel strategies for the calibration of glacier and hydrological models. Finally, our results emphasize the potential of combined remote-sensing observations to understand the environmental factors and physical processes responsible for High Asia’s heterogeneous patterns of recent glacier evolution.</p>

scholarly journals Surface Velocity Analysis of Surge Region of Karayaylak Glacier from 2014 to 2020 in the Pamir Plateau

2021 ◽  
Vol 13 (4) ◽  
pp. 774
Author(s):  
Yanfei Peng ◽  
Zhongqin Li ◽  
Chunhai Xu ◽  
Hui Zhang ◽  
Weixiao Han
Keyword(s):  
Accuracy Assessment ◽  
Meteorological Data ◽  
Maximum Velocity ◽  
Google Earth ◽  
Meteorological Station ◽  
Surface Velocity ◽  
Control Mechanisms ◽  
The West ◽  
Glacier Surface ◽  
Study Region

The west branch of Karayaylak Glacier (eastern Pamir Plateau) surged in May 2015, significantly impacting on local socio-economic development. This event was also of great significance for studies of surging glaciers. Using Sentinel-1 imagery analyzed by offset tracking, based on normalized cross-correlation (NCC), and with the support of the Google Earth Engine (GEE) platform, we quantified the ice surface velocity of the west branch and terminus of Karayaylak Glacier from 13 October 2014 to 17 October 2020. Sentinel-1 images were acquired at intervals of 12 or 24 days. We also used a three-dimensional (3-D) laser scanner to measure the velocity of 3 ablation stakes and 56 feature points in the study region from 15 August to 6 October 2015, for the purpose of accuracy assessment. We set up an automatic meteorological station to record the air temperature in the same period and combined this with data from Tashkurgan meteorological station from 1957 to 2015. Analysis of this dataset provided insights into the glacier surge mechanism, with the following conclusions. (1) Surface velocity of the west branch and terminus of Karayaylak Glacier increased sharply after October 2014. The velocity then dropped significantly in the two months after the surge, and stayed at low values for nearly a year. After 2017, the velocity was slightly higher than in the previous period. (2) The surge event occurred from 11 April to 17 May 2015; the average surface velocity in this phase attained 2395 m a−1 with a maximum velocity of 4265 m a−1 at the west branch terminus. (3) From 2017 to 2020, the velocity showed periodic annual changes. (4) Based on the meteorological data analysis, we conclude that this surge resulted from the interaction between thermal and hydrological control mechanisms. Simultaneously, we demonstrate the high potential of the GEE platform and Sentinel-1 data to extract glacier surface velocity.

scholarly journals MAPPING SURFACE FLOW VELOCITIES OF SIACHEN AND GANGOTRI GLACIERS USING TERRASAR-X AND SENTINEL-1A DATA BY INTENSITY TRACKING

2018 ◽  
Vol IV-5 ◽  
pp. 325-329 ◽  
Author(s):  
P. Lal ◽  
D. S. Vaka ◽  
Y. S. Rao
Keyword(s):  
Surface Flow ◽  
Velocity Estimation ◽  
Accurate Estimation ◽  
Velocity Mapping ◽  
Tracking Method ◽  
X Band ◽  
Gangotri Glacier ◽  
Glacier Velocity ◽  
Offset Tracking ◽  
Band Image

<p><strong>Abstract.</strong> Glaciers are melting at an alarming rate due to global warming. Two major glaciers of India viz. Gangotri and the Siachen are chosen for the velocity mapping. The line-of-sight (LOS) velocity fields are derived using X-band TerraSAR-X and C-band Sentinel-1A datasets. An intensity-based offset tracking method is used to generate LOS velocities of the glaciers. The single look complex (SLC) images of the TerraSAR-X are converted into intensity before applying the offset tracking method, whereas the ground range detected (GRD) products from Sentinel-1A are directly used to estimate the glacier velocities. The Siachen glacier velocity is mapped using three X-band images from 2011 to 2017 and a C-band image between 2017 and 2018. The X-band images in the case of Siachen are available with the long-time interval between the master and slave images. The velocity of the glacier is observed to be around 30&amp;ndash;40<span class="thinspace"></span>cm<span class="thinspace"></span>day<sup>&amp;minus;1</sup> from X-band and around 45&amp;ndash;50<span class="thinspace"></span>cm day<sup>&amp;minus;1</sup> from C-band. Three X-band images in the year 2012 and a C-band image in the year 2018 are used for the Gangotri glacier velocity estimation. These images are very closely separated in time, and the velocity of the glacier is found to be 15&amp;ndash;20<span class="thinspace"></span>cm<span class="thinspace"></span>day<sup>&amp;minus;1</sup>. A dataset with a temporal gap of approximately three years is also used for the Gangotri glacier velocity estimation and observed a large difference in velocity (&amp;sim;10<span class="thinspace"></span>cm<span class="thinspace"></span>day<sup>&amp;minus;1</sup>) from that of shorter interval data. Therefore, for a slow-moving glacier like Gangotri, a dataset with a high temporal gap may not give a reliable result. It is also observed that X-band TerraSAR-X results are more accurate than the C-band Sentinel-1A results. The penetration depth of X-band is less compared to C-band, which might result in accurate estimation of glacier surface flow. According to the results, the velocity of the Siachen glacier is increasing at a very high rate.</p>

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