scholarly journals Supraglacial debris cover assessment in the Caucasus Mountains, 1986-2000-2014

2017 ◽  
Author(s):  
Levan G. Tielidze ◽  
Roger D. Wheate ◽  
Stanislav S. Kutuzov ◽  
Kate Doyle ◽  
Ivan I. Lavrentiev
Keyword(s):  
Morphological Type ◽  
Ice Age ◽  
Landsat 8 ◽  
Glacier Area ◽  
Greater Caucasus ◽  
Rock Structure ◽  
Debris Cover ◽  
Landsat 7 ◽  
Caucasus Mountains ◽  
Elevation Model

Abstract. Surpaglacial debris cover plays an increasingly important role impacting on glacier ablation, while there have been limited recent studies for the assessment of debris covered glaciers in the Greater Caucasus mountains. We selected 559 glaciers according to the sections and macroslopes in the Greater Caucasus main watershed range and the Elbrus massif to assess supraglacial debris cover (SDC) for the years 1986, 2000 and 2014. Landsat (Landsat 5 TM, Landsat 7 ETM+, Landsat 8 OLI) and SPOT satellite imagery were analysed to generate glacier outlines using manual and semi-automated methods, along with slope information from a Digital Elevation Model. The study shows there is greater SDC area on the northern than the southern macroslope, and more in the eastern section than the western and central. In 1986-2000-2014, the SDC area increased from 6.4 %-8.2 %-19.4 % on the northern macroslope (apart from the eastern Greater Caucasus section), while on the southern macroslope, SDC increased from 4.0 %-4.9 %-9.2 %. Overall, debris covered glacier numbers increased from 122-143-172 (1986-2000-2014) for 559 selected glaciers. Despite the total glacier area decrease, the SDC glacier area and numbers increased as a function of slope inclination, aspect, glacier morphological type, Little Ice Age (LIA) moraines, rock structure and elevation. The datasets are available for public download at https://doi.pangaea.de/10.1594/PANGAEA.880147.

scholarly journals Brief communication: Supraglacial debris-cover changes in the Caucasus Mountains

2019 ◽  
Author(s):  
Levan G. Tielidze ◽  
Tobias Bolch ◽  
Roger D. Wheate ◽  
Stanislav S. Kutuzov ◽  
Ivan I. Lavrentiev ◽  
...  
Keyword(s):  
Morphological Type ◽  
Ice Age ◽  
Slope Aspect ◽  
Glacier Mass Balance ◽  
The Caucasus ◽  
Greater Caucasus ◽  
Time Period ◽  
Covered Area ◽  
Debris Cover ◽  
Caucasus Mountains

Abstract. Debris cover on glaciers can significantly alter melt, and hence, glacier mass balance and runoff. Debris coverage typically increases with shrinking glaciers. Here, we present data on debris cover and its changes for 559 glaciers located in different regions of the Greater Caucasus mountains based on 1986, 2000 and 2014 Landsat and SPOT images. Over this time period, the total glacier area decreased from 691.5 km2 to 590.0 km2 (0.52 % yr−1). Thereby, the debris covered area increased from ~ 11 to ~ 24 % on the northern, and from ~ 4 to 10 % on the southern macro-slope between 1986 and 2014. Overall, we found 18 % debris cover for the year 2014. With the glacier shrinkage, debris-covered area and the number of debris-covered glaciers increased as a function of elevation, slope, aspect, glacier morphological type, Little Ice Age moraines, and lithology.

scholarly journals The Greater Caucasus Glacier Inventory (Russia/Georgia/Azerbaijan)

2017 ◽  
Author(s):  
Levan G. Tielidze ◽  
Roger D. Wheate
Keyword(s):  
Surface Area ◽  
Large Scale ◽  
Morphological Type ◽  
Landsat 8 ◽  
Glacier Change ◽  
Glacier Surface ◽  
Glacier Inventory ◽  
Greater Caucasus ◽  
Rock Structure ◽  
June July

Abstract. While there are a large number of glaciers in the Greater Caucasus, the region is not fully represented in modern glacier databases with previous incomplete inventories. Here, we present an expanded glacier inventory for this region over the 1960–1986–2014 period. Large scale topographic maps and satellite imagery (Landsat 5, Landsat 8 and ASTER) were used to conduct a remote sensing survey of glacier change in the Greater Caucasus mountains. Glacier margins were mapped manually and reveal that, in 1960, the mountains contained 2349 glaciers, with a total glacier surface area of 1674.9 ± 35.2 km2. By 1986, glacier surface area had decreased to 1482.1 ± 32.2 km2 (2209 glaciers), and by 2014, to 1193.2 ± 27.0 km2 (2020 glaciers). This represents a 28.8 ± 2.2 % (481 ± 10.6 km2) reduction in total glacier surface area between 1960 and 2014 and a marked acceleration in the rate of area loss since 1986. Analysis of possible controls suggest that the general decreases in both glacier area and number for the period 1960–2014 are directly due to general increase in temperature, especially in summer (June–July–August), although the response of individual glaciers was modulated by other factors, including glacier size, elevation, rock structure, exposition, morphological type and debris cover. This new glacier inventory can be used as a basis dataset for future studies including glacier change assessment.

scholarly journals Supra-glacial debris cover changes in the Greater Caucasus from 1986 to 2014

2020 ◽  
Vol 14 (2) ◽  
pp. 585-598 ◽  
Author(s):  
Levan G. Tielidze ◽  
Tobias Bolch ◽  
Roger D. Wheate ◽  
Stanislav S. Kutuzov ◽  
Ivan I. Lavrentiev ◽  
...  
Keyword(s):  
High Resolution ◽  
Sea Level ◽  
Google Earth ◽  
Gps Data ◽  
Glacier Area ◽  
Greater Caucasus ◽  
Abstract Knowledge ◽  
Covered Area ◽  
Debris Cover ◽  
Automated Methods

Abstract. Knowledge of supra-glacial debris cover and its changes remain incomplete in the Greater Caucasus, in spite of recent glacier studies. Here we present data of supra-glacial debris cover for 659 glaciers across the Greater Caucasus based on Landsat and SPOT images from the years 1986, 2000 and 2014. We combined semi-automated methods for mapping the clean ice with manual digitization of debris-covered glacier parts and calculated supra-glacial debris-covered area as the residual between these two maps. The accuracy of the results was assessed by using high-resolution Google Earth imagery and GPS data for selected glaciers. From 1986 to 2014, the total glacier area decreased from 691.5±29.0 to 590.0±25.8 km2 (15.8±4.1 %, or ∼0.52 % yr−1), while the clean-ice area reduced from 643.2±25.9 to 511.0±20.9 km2 (20.1±4.0 %, or ∼0.73 % yr−1). In contrast supra-glacial debris cover increased from 7.0±6.4 %, or 48.3±3.1 km2, in 1986 to 13.4±6.2 % (∼0.22 % yr−1), or 79.0±4.9 km2, in 2014. Debris-free glaciers exhibited higher area and length reductions than debris-covered glaciers. The distribution of the supra-glacial debris cover differs between the northern and southern and between the western, central and eastern Greater Caucasus. The observed increase in supra-glacial debris cover is significantly stronger on the northern slopes. Overall, we have observed up-glacier average migration of supra-glacial debris cover from about 3015 to 3130 m a.s.l. (metres above sea level) during the investigated period.

scholarly journals Two decades of dynamic change and progressive destabilization on the Thwaites Eastern Ice Shelf

2021 ◽  
Vol 15 (11) ◽  
pp. 5187-5203
Author(s):  
Karen E. Alley ◽  
Christian T. Wild ◽  
Adrian Luckman ◽  
Ted A. Scambos ◽  
Martin Truffer ◽  
...  
Keyword(s):  
Shear Zone ◽  
Structural Integrity ◽  
Dynamic Change ◽  
Image Data ◽  
Landsat 8 ◽  
Ice Shelf ◽  
Ice Flow ◽  
Landsat 7 ◽  
Rapid Pace ◽  
Elevation Model

Abstract. The Thwaites Eastern Ice Shelf (TEIS) buttresses the eastern grounded portion of Thwaites Glacier through contact with a pinning point at its seaward limit. Loss of this ice shelf will promote further acceleration of Thwaites Glacier. Understanding the dynamic controls and structural integrity of the TEIS is therefore important to estimating Thwaites' future sea-level contribution. We present a ∼ 20-year record of change on the TEIS that reveals the dynamic controls governing the ice shelf's past behaviour and ongoing evolution. We derived ice velocities from MODIS and Sentinel-1 image data using feature tracking and speckle tracking, respectively, and we combined these records with ITS_LIVE and GOLIVE velocity products from Landsat-7 and Landsat-8. In addition, we estimated surface lowering and basal melt rates using the Reference Elevation Model of Antarctica (REMA) DEM in comparison to ICESat and ICESat-2 altimetry. Early in the record, TEIS flow dynamics were strongly controlled by the neighbouring Thwaites Western Ice Tongue (TWIT). Flow patterns on the TEIS changed following the disintegration of the TWIT around 2008, with a new divergence in ice flow developing around the pinning point at its seaward limit. Simultaneously, the TEIS developed new rifting that extends from the shear zone upstream of the ice rise and increased strain concentration within this shear zone. As these horizontal changes occurred, sustained thinning driven by basal melt reduced ice thickness, particularly near the grounding line and in the shear zone area upstream of the pinning point. This evidence of weakening at a rapid pace suggests that the TEIS is likely to fully destabilize in the next few decades, leading to further acceleration of Thwaites Glacier.

scholarly journals Spatial patterns in glacier characteristics and area changes from 1962 to 2006 in the Kanchenjunga–Sikkim area, eastern Himalaya

2015 ◽  
Vol 9 (2) ◽  
pp. 505-523 ◽  
Author(s):  
A. E. Racoviteanu ◽  
Y. Arnaud ◽  
M. W. Williams ◽  
W. F. Manley
Keyword(s):  
Spatial Patterns ◽  
Thermal Emission ◽  
Eastern Himalaya ◽  
Slope Aspect ◽  
Glacier Area ◽  
Glacier Surface ◽  
Area Loss ◽  
Debris Cover ◽  
Landsat 7 ◽  
The Himalaya

Abstract. This study investigates spatial patterns in glacier characteristics and area changes at decadal scales in the eastern Himalaya – Nepal (Arun and Tamor basins), India (Teesta basin in Sikkim) and parts of China and Bhutan – based on various satellite imagery: Corona KH4 imagery, Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Advanced Spaceborne Thermal Emission Radiometer (ASTER), QuickBird (QB) and WorldView-2 (WV2). We compare and contrast glacier surface area changes over the period of 1962–2000/2006 and their dependency on glacier topography (elevation, slope, aspect, percent debris cover) and climate (solar radiation, precipitation) on the eastern side of the topographic barrier (Sikkim) versus the western side (Nepal). Glacier mapping from 2000 Landsat ASTER yielded 1463 ± 88 km2 total glacierized area, of which 569 ± 34 km2 was located in Sikkim and 488 ± 29 km2 in eastern Nepal. Supraglacial debris covered 11% of the total glacierized area, and supraglacial lakes covered about 5.8% of the debris-covered glacier area alone. Glacier area loss (1962 to 2000) was 0.50 ± 0.2% yr−1, with little difference between Nepal (0.53 ± 0.2% yr−1) and Sikkim (0.44 ± 0.2% yr−1. Glacier area change was controlled mostly by glacier area, elevation, altitudinal range and, to a smaller extent, slope and aspect. In the Kanchenjunga–Sikkim area, we estimated a glacier area loss of 0.23 ± 0.08% yr−1 from 1962 to 2006 based on high-resolution imagery. On a glacier-by-glacier basis, clean glaciers exhibit more area loss on average from 1962 to 2006 (34%) compared to debris-covered glaciers (22%). Glaciers in this region of the Himalaya are shrinking at similar rates to those reported for the last decades in other parts of the Himalaya, but individual glacier rates of change vary across the study area with respect to local topography, percent debris cover or glacier elevations.

scholarly journals Radiometric Cross Calibration of Landsat 8 Operational Land Imager (OLI) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+)

2014 ◽  
Vol 6 (12) ◽  
pp. 12619-12638 ◽  
Author(s):  
Nischal Mishra ◽  
Md Haque ◽  
Larry Leigh ◽  
David Aaron ◽  
Dennis Helder ◽  
...  
Keyword(s):  
Thematic Mapper ◽  
Landsat 8 ◽  
Landsat 7 ◽  
Cross Calibration

scholarly journals Compiling a new glacier inventory for southeastern Qinghai–Tibet Plateau from Landsat and PALSAR data

2016 ◽  
Vol 62 (233) ◽  
pp. 579-592 ◽  
Author(s):  
LINGHONG KE ◽  
XIAOLI DING ◽  
LEI ZHANG ◽  
JUN HU ◽  
C. K. SHUM ◽  
...  
Keyword(s):  
Shuttle Radar Topography Mission ◽  
Tibet Plateau ◽  
Landsat 8 ◽  
Glacier Change ◽  
Glacier Area ◽  
Glacier Inventory ◽  
Radar Images ◽  
Water Classification ◽  
Multi Temporal ◽  
Classification Information

ABSTRACTGlacier change has been recognized as an important climate variable due to its sensitive response to climate change. Although there are a large number of glaciers distributed over the southeastern Qinghai–Tibetan Plateau, the region is poorly represented in glacier databases due to seasonal snow cover and frequent cloud cover. Here, we present an improved glacier inventory for this region by combining Landsat observations acquired over 2011–13 (Landsat 8/OLI and Landsat TM/ETM+), coherence images from Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar images and the Shuttle Radar Topography Mission (SRTM) DEM. We present a semi-automated scheme for integrating observations from multi-temporal Landsat scenes to mitigate cloud obscuration. Further, the clean-ice observations, together with coherence information, slope constraints, vegetation cover and water classification information extracted from the Landsat scenes, are integrated to determine the debris-covered glacier area. After manual editing, we derive a new glacier inventory containing 6892 glaciers >0.02 km2, covering a total area of 6566 ± 197 km2. This new glacier inventory indicates gross overestimation in glacier area (over 30%) in previously published glacier inventories, and reveals various spatial characteristics of glaciers in the region. Our inventory can be used as a baseline dataset for future studies including glacier change assessment.

scholarly journals Performance comparisons of particle swarm optimization, echo state neural network and genetic algorithm for vegetation segmentation

2017 ◽  
Vol 7 (1.1) ◽  
pp. 184
Author(s):  
Rincy Merlin Mathew ◽  
S. Purushothaman ◽  
P. Rajeswari
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Soft Computing ◽  
Particle Swarm ◽  
Band 3 ◽  
Landsat 8 ◽  
Swarm Optimization ◽  
Landsat 7 ◽  
Performance Comparisons

This article presents the implementation of vegetation segmentation by using soft computing methods: particle swarm optimization (PSO), echostate neural network(ESNN) and genetic algorithm (GA). Multispectral image with the required band from Landsat 8 (5, 4, 3) and Landsat 7 (4, 3, 2) are used. In this paper, images from ERDAS format acquired by Landsat 7 ‘Paris.lan’ (band 4, band 3, Band 2) and image acquired from Landsat 8 (band5, band 4, band 3) are used. The soft computing algorithms are used to segment the plane-1(Near infra-red spectra) and plane 2(RED spectra). The monochrome of the two segmented images is compared to present performance comparisons of the implemented algorithms.

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