Glacier mapping with Sentinel-2 in Svalbard: Challenges when creating a new glacier inventory in the Artic

2020 ◽  
Author(s):  
Frank Paul ◽  
Philipp Rastner
Keyword(s):  
The Arctic ◽  
Minimum Size ◽  
Ratio Method ◽  
Large Area ◽  
Band Ratio ◽  
Glacier Inventory ◽  
Ice Caps ◽  
Global Sea Level ◽  
Sentinel 2 ◽  
Snow And Ice

<p>Svalbard is dominated by large (often calving) glaciers and ice caps with a strong contribution to global sea-level rise. Due to many surge-type glaciers, large changes of glacier extents are common and determination of their mass balance requires a regular update of their outlines. However, frequent cloud cover prevents accurate repeat mapping. In consequence, the last glacier inventory for Svalbard was compiled from satellite scenes acquired over a period of 11 years, making change assessment and other applications difficult. Due to long-lasting seasonal snow and confusion with large perennial snow patches, the minimum size of this inventory has been set to 1 km<sup>2</sup>.</p><p>Here we present a new glacier inventory for Svalbard that has been compiled at 10 m resolution from two Sentinel-2 scenes that were acquired only two days apart. Sea ice, ice-bergs, lakes and turbid water were wrongly classified as glaciers by the applied band ratio method and manually removed. Debris cover, snow and ice under some clouds but also polluted (very dark) clean ice was not mapped as thresholds were optimized to get snow and ice in shadow properly mapped. These missing regions were manually added. Snow patches were removed with a 5 by 5 majority filter applied to the binary glacier map and a minimum size of 0.05 km<sup>2</sup>. Outlines from the previous inventory as available in the RGI were used to guide the corrections. After careful comparison, we used the Arctic DEM to derive surface drainage divides and topographic attributes for all glaciers.</p><p>The largest challenges for accurate glacier delineation are discrimination of debris-covered glaciers from peri-glacial debris and rock glaciers, handling of attached seasonal or perennial snowfields, and identifying disintegrating tongues of down-wasting and often debris-covered ice masses remaining after a surge. Compared to the previous inventory, the large area gains and losses of surge-type glaciers are remarkable, but area differences result also from a different interpretation of debris-covered glaciers, inclusion of snow-filled couloirs and several new glaciers that were excluded in the previous inventory.</p>

Which DEM to use for glacier inventory applications? The example of Svalbard

2020 ◽  
Author(s):  
Philipp Rastner ◽  
Frank Paul
Keyword(s):  
Arctic Region ◽  
The Arctic ◽  
Minor Role ◽  
Large Area ◽  
Glacier Inventory ◽  
Ice Caps ◽  
Topographic Information ◽  
Digital Elevation ◽  
Elevation Model ◽  
A Minor

<p>Creating glacier inventories from satellite images and a digital elevation model (DEM) has become quasi standard. Besides the specific challenges for glacier mapping, also the selection of the ‘best’ DEM can be difficult. When using it to derive surface drainage divides and topographic information for each glacier, one has to consider the date of acquisition, artefacts, spatial completeness (data voids) and resolution. In general, using different DEMs gives different drainage divides and thus other glacier sizes. Moreover, due to widespread glacier retreat and rapid surface lowering, topographic information from older DEMs is increasingly biased towards too high values.</p><p>In this study we analyse seven freely available DEMs for the Arctic region of Svalbard: ALOS AW3D30, two National Elevation Datasets (NEDs), Arctic DEM, TanDEM-X (90 and 30 m products) and the ASTER GDEM2. All individual DEM tiles were mosaicked and re-projected bilinearly to UTM 33 N. Comparisons of topographic data are performed for three test regions: a) stable terrain (off glaciers), b) glaciers in rough topography, and c) flat glaciers and ice caps.</p><p>Overlay of drainage divides indicate large area differences on flat ice caps and small ones in rough topography, where mountain ridges are distinct. On the other hand, different spatial resolution results in large differences in rough topography but plays only a minor role for flat topography. Only 2 m elevation differences on stable terrain in flat valley bottoms were detected between the ALOS DEM (79.9m) and the two NEDs (77.9 m). No differences were found between the TanDEM-X 90 / 30 m and the Arctic DEM (all 109. 9 m). The ellipsoid-geoid difference is thus ~30 m in this region.</p><p>Mean elevations of glaciers with flat topography or ice caps differ only slightly, but in steeper topography they reach 6 to 8 m. These differences are also due to the different resolution of the DEMs. In all test regions, only small gaps are detected in the Arctic DEM and artefacts are especially present in the ALOS DEM. For this region the ‘best’ DEM is the TanDEM-X DEM.</p>

scholarly journals Glacier shrinkage in the Alps continues unabated as revealed by a new glacier inventory from Sentinel-2

2020 ◽  
Vol 12 (3) ◽  
pp. 1805-1821 ◽  
Author(s):  
Frank Paul ◽  
Philipp Rastner ◽  
Roberto Sergio Azzoni ◽  
Guglielmina Diolaiuti ◽  
Davide Fugazza ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Uncertainty Assessment ◽  
Ratio Method ◽  
Band Ratio ◽  
Glacier Inventory ◽  
Glacier Shrinkage ◽  
Run Off ◽  
Glacier Ice ◽  
The Alps ◽  
Sentinel 2

Abstract. The ongoing glacier shrinkage in the Alps requires frequent updates of glacier outlines to provide an accurate database for monitoring, modelling purposes (e.g. determination of run-off, mass balance, or future glacier extent), and other applications. With the launch of the first Sentinel-2 (S2) satellite in 2015, it became possible to create a consistent, Alpine-wide glacier inventory with an unprecedented spatial resolution of 10 m. The first S2 images from August 2015 already provided excellent mapping conditions for most glacierized regions in the Alps and were used as a base for the compilation of a new Alpine-wide glacier inventory in a collaborative team effort. In all countries, glacier outlines from the latest national inventories have been used as a guide to compile an update consistent with the respective previous interpretation. The automated mapping of clean glacier ice was straightforward using the band ratio method, but the numerous debris-covered glaciers required intense manual editing. Cloud cover over many glaciers in Italy required also including S2 scenes from 2016. The outline uncertainty was determined with digitizing of 14 glaciers several times by all participants. Topographic information for all glaciers was obtained from the ALOS AW3D30 digital elevation model (DEM). Overall, we derived a total glacier area of 1806±60 km2 when considering 4395 glaciers >0.01 km2. This is 14 % (−1.2 % a−1) less than the 2100 km2 derived from Landsat in 2003 and indicates an unabated continuation of glacier shrinkage in the Alps since the mid-1980s. It is a lower-bound estimate, as due to the higher spatial resolution of S2 many small glaciers were additionally mapped or increased in size compared to 2003. Median elevations peak around 3000 m a.s.l., with a high variability that depends on location and aspect. The uncertainty assessment revealed locally strong differences in interpretation of debris-covered glaciers, resulting in limitations for change assessment when using glacier extents digitized by different analysts. The inventory is available at https://doi.org/10.1594/PANGAEA.909133 (Paul et al., 2019).

scholarly journals Glacier shrinkage in the Alps continues unabated as revealed by a new glacier inventory from Sentinel-2

2019 ◽  
Author(s):  
Frank Paul ◽  
Philipp Rastner ◽  
Roberto Sergio Azzoni ◽  
Guglielmina Diolaiuti ◽  
Davide Fugazza ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Uncertainty Assessment ◽  
Ratio Method ◽  
Band Ratio ◽  
Glacier Inventory ◽  
Glacier Shrinkage ◽  
Run Off ◽  
Glacier Ice ◽  
The Alps ◽  
Sentinel 2

Abstract. The on-going glacier shrinkage in the Alps requires frequent updates of glacier outlines to provide an accurate database for monitoring or modeling purposes (e.g. determination of run-off, mass balance, or future glacier extent) and other applications. With the launch of the first Sentinel-2 (S2) satellite in 2015, it became possible to create a consistent, Alpine-wide glacier inventory with an unprecedented spatial resolution of 10 m. Fortunately, already the first S2 images acquired in August 2015 provided excellent mapping conditions for most of the glacierised regions in the Alps. We have used this opportunity to compile a new Alpine-wide glacier inventory in a collaborative team effort. In all countries, glacier outlines from the latest national inventories have been used as a guide to compile a consistent update. However, cloud cover over many glaciers in Italy required including also S2 scenes from 2016. Whereas the automated mapping of clean glacier ice was straightforward using the band ratio method, the numerous debris-covered glaciers required in-tense manual editing. The uncertainty in the outlines was determined with a multiple digitising experiment of 14 glaciers by all participants. Topographic information for all glaciers was derived from the ALOS AW3D30 DEM. Overall, we derived a total glacier area of 1806 ± 60 km2 when considering 4394 glaciers > 0.01 km2. This is 14 % (−1.2 %/a) less than the 2100 km2 derived from Landsat scenes acquired in 2003 and indicating an unabated continuation of glacier shrinkage in the Alps since the mid-1980s. Due to the higher spatial resolution of S2 many small glaciers were additionally mapped in the new inventory or increased in size compared to 2003. An artificial reduction to the former extents would thus result in an even higher overall area loss. Still, the uncertainty assessment revealed locally considerable differences in interpretation of debris-covered glaciers, resulting in limitations for change assessment when using glacier extents digitised by different analysts. The inventory is available at: https://doi.pangaea.de/10.1594/PANGAEA.909133 (Paul et al., 2019).

scholarly journals Uma abordagem metodológica de Sensoriamento Remoto para o monitoramento da contaminação do rio Paraopeba pós-desastre de Brumadinho-MG

2021 ◽  
Vol 43 ◽  
pp. e36
Author(s):  
Neison Cabral Ferreira Freire ◽  
Admilson Da Penha Pacheco ◽  
Vinícius D'Lucas Bezerra Queiroz
Keyword(s):  
Remote Sensing ◽  
Spectral Band ◽  
Ratio Method ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Band Ratio ◽  
Hydrographic Basin ◽  
Search Area ◽  
Brazilian History ◽  
Sentinel 2

The following article aims to present and discuss the monitoring, through Remote Sensing, of the dirt displacement caused by the collapse of the Córrego do Feijão’s dam I of mining waste, which occurred on January 25, 2019, in the rural area of Brumadinho, a city located in the state of Minas Gerais, Brazil. This event is considered one of the greatest technoindustrial disasters in Brazilian history, placing in danger one of the largest hydrographic basin in Brazil: the São Francisco river basin. The search area comprises from where the sludge mud got in contact with the Paraopeba’s right bank to its mouth into the Três Marias Dam, adding up to approximately 315 km. For this monitoring the spectral band ratio method was utilized,  using images from the sensors MSI/Sentinel-2 and OLI/Landsat-8 captured at different dates, employing standardization of means and variances to harmonize the range of the surface reflectance values in each image.

scholarly journals A new programme for monitoring the mass loss of the Greenland ice sheet

2008 ◽  
Vol 15 ◽  
pp. 61-64 ◽  
Author(s):  
Andreas P. Ahlstrøm ◽  
* PROMICE project team
Keyword(s):  
Mass Loss ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Arctic Region ◽  
Decision Makers ◽  
The Arctic ◽  
National Space ◽  
Ice Caps ◽  
Political Concern ◽  
Global Sea Level

The Greenland ice sheet has been losing mass at a dramatic rate in recent years, raising political concern worldwide due to the possible impact on global sea level rise and climate dynamics (Luthcke et al. 2006; Rignot & Kanagaratnam 2006; Velicogna & Wahr 2006; IPCC 2007; Shepherd & Wingham 2007). The Arctic region as a whole is warming up much more rapidly than the globe at large (ACIA 2005) and it is desirable to quantify these changes in order to provide the decision-makers with a firm knowledge base. To cover this need, the Danish Ministry of Climate and Energy has now launched a new Programme for Monitoring of the Green- land Ice Sheet (PROMICE), designed and operated by the Geological Survey of Denmark and Greenland (GEUS) in collaboration with the National Space Institute at the Tech nical University of Denmark and Asiaq (Greenland Survey). The aim of the programme is to quantify the annual mass loss of the Greenland ice sheet, track changes in the extent of local glaciers and ice caps, and track changes in the position of the ice-sheet margin.

scholarly journals A new satellite-derived glacier inventory for western Alaska

2011 ◽  
Vol 52 (59) ◽  
pp. 135-143 ◽  
Author(s):  
R. Le Bris ◽  
F. Paul ◽  
H. Frey ◽  
T. Bolch
Keyword(s):  
Line Graph ◽  
Strong Dependence ◽  
Regional Scale ◽  
Band Ratio ◽  
Kenai Peninsula ◽  
Glacier Inventory ◽  
The Us ◽  
Change Impact ◽  
Mapping Techniques ◽  
Global Sea Level

AbstractGlacier inventories provide the baseline data to perform climate-change impact assessment on a regional scale in a consistent and spatially representative manner. In particular, a more accurate calculation of the current and future contribution to global sea-level rise from heavily glacierized regions such as Alaska is much needed. We present a new glacier inventory for a large part of western Alaska (including Kenai Peninsula and the Tordrillo, Chigmit and Chugach mountains), derived from nine Landsat Thematic Mapper scenes acquired between 2005 and 2009 using well-established automated glacier-mapping techniques (band ratio). Because many glaciers are covered by optically thick debris or volcanic ash and partly calve intowater, outlineswere manually edited in these wrongly classified regions during post-processing. In total we mapped ~8830 glaciers (>0.02 km2) with a total area of ~16 250 km2. Large parts of the area (47%) are covered by a few (31) large (>100 km2) glaciers, while glaciers less than 1 km2 constitute only 7.5% of the total area but 86% of the total number. We found a strong dependence of mean glacier elevation on distance from the ocean and only a weak one on aspect. Glacier area changes were calculated for a subset of 347 selected glaciers by comparison with the Digital Line Graph outlines from the US Geological Survey. The overall shrinkage was ~23% between 1948–57 and 2005–09.

scholarly journals A new glacier inventory on southern Baffin Island, Canada, from ASTER data: II. Data analysis, glacier change and applications

2009 ◽  
Vol 50 (53) ◽  
pp. 22-31 ◽  
Author(s):  
Frank Paul ◽  
Felix Svoboda
Keyword(s):  
Strong Dependence ◽  
Baffin Island ◽  
Automated Classification ◽  
Inventory Data ◽  
Large Area ◽  
Glacier Inventory ◽  
Ice Caps ◽  
Digital Elevation ◽  
Elevation Model ◽  
Remote Sensing Methods

AbstractDespite its large area covered by glaciers and ice caps, detailed glacier inventory data are not yet available for most parts of Baffin Island, Canada. Automated classification of satellite data could help to overcome the data gaps. Along-track stereo sensors allow the derivation of a digital elevation model (DEM) and glacier outlines from the same point in time, and are particularly useful for this task. While part I of this study describes the remote-sensing methods, in part II we present an analysis of the derived glacier inventory data for 662 glaciers and an application to glacier volume and volume-change calculations. Among other things, the analysis reveals a mean glacier elevation of 990 m, with a weak dependence on aspect and a close agreement of the arithmetic mean with the statistical mean elevation as derived from the DEM. A strong scatter of mean slope is observed for glaciers <1 km2, and the derived glacier thickness differs by a factor of two for glaciers of the same size. For the period from about 1920 to 2000 the relative area change is –12.5% (264 glaciers), with a strong dependence on glacier size. Mean mass loss as derived from volume changes is about –0.15 mw.e. a–1.

scholarly journals Perspectives on the production of a glacier inventory from multispectral satellite data in Arctic Canada: Cumberland Peninsula, Baffin Island

2005 ◽  
Vol 42 ◽  
pp. 59-66 ◽  
Author(s):  
Frank Paul ◽  
Andreas Kääb
Keyword(s):  
Satellite Data ◽  
Three Dimensional ◽  
Ice Age ◽  
The Arctic ◽  
Baffin Island ◽  
Inventory Data ◽  
Arctic Canada ◽  
Glacier Inventory ◽  
Ice Caps ◽  
Time Required

AbstractThe consequences of global warming on land ice masses are difficult to assess in detail, as two-dimensional glacier inventory data are still missing for many remote regions of the world. As the largest future temperature increase is expected to occur at high latitudes, the glaciers and ice caps in the Arctic will be particularly susceptible to the expected warming. This study demonstrates the possibilities of space-borne glacier inventorying at a remote site on Cumberland Peninsula, a part of Baffin Island in Arctic Canada, thereby providing glacier inventory data for this region. Our approach combines Landsat ETM+ and Terra ASTER satellite data, an ASTER-derived digital elevation model (DEM) and Geographic Information System-based processing. We used thresholded ratio images from ETM+ bands 3 and 5 and ASTER bands 3 and 4 for glacier mapping. Manual delineation of Little Ice Age trimlines and moraines has been applied to calculate area changes for 225 glaciers, yielding an average area loss of 11%. A size distribution has been obtained for 770 glaciers that is very different from that for Alpine glaciers. Numerous three-dimensional glacier parameters were derived from the ASTER DEM for a subset of 340 glaciers. The amount of working time required for the processing has been tracked, and resulted in 5 min per glacier, or 7 years for all estimated 160 000 glaciers worldwide.

scholarly journals Static mass-balance sensitivity of Arctic glaciers and ice caps using a degree-day approach

2005 ◽  
Vol 42 ◽  
pp. 217-224 ◽  
Author(s):  
Mattias De Woul ◽  
Regine Hock
Keyword(s):  
Mass Balance ◽  
Temperature Increase ◽  
The Arctic ◽  
Balance Model ◽  
Ice Caps ◽  
Degree Day ◽  
Ice Cap ◽  
Temperature And Precipitation ◽  
Air Temperatures ◽  
Global Sea Level

AbstractFuture climate warming is predicted to be more pronounced in the Arctic where approximately two-thirds of all small glaciers on Earth are located. A simple mass-balance model was applied to 42 glaciers and ice caps north of 60° N to estimate mass-balance sensitivities to a hypothetical climate perturbation. The model is based on daily temperature and precipitation data from climate stations in the vicinity of each glacier and ice cap. A regression analysis was made using a degree-day approach where the annual sum of positive daily air temperatures was correlated to measured summer mass balance, and the total annual snow precipitation was correlated to measured winter mass balance. The net mass-balance sensitivity to a hypothetical temperature increase of +1 K ranged from -0.2 to -2.0 m a-1, and an assumed increase in precipitation of +10% changed the mass balance by <+0.1 to +0.4 m a-1, thus on average offsetting the effect of a temperature increase by approximately 20%. Maritime glaciers showed considerably higher mass-balance sensitivities than continental glaciers, in agreement with similar previous studies. The highest sensitivities were found in Iceland, exceeding those reported in previous studies. Extrapolating our results, glaciers and ice caps north of 60° N are estimated to contribute ∼0.6 mm a–1 K–1 to global sea-level rise. Our results highlight the value of long-term mass-balance records and meteorological records in remote areas.

A new glacier inventory for Svalbard from Sentinel-2 and Landsat 8 for improved calculation of climate change impacts

2021 ◽  
Author(s):  
Frank Paul ◽  
Franz Goerlich ◽  
Philipp Rastner
Keyword(s):  
Climate Change Impacts ◽  
Weather Conditions ◽  
Landsat 8 ◽  
Related Area ◽  
Band Ratio ◽  
Glacier Inventory ◽  
Swir Band ◽  
Specific Calculations ◽  
Glacier Mapping ◽  
Sentinel 2

&lt;p&gt;&lt;span&gt;Svalbard is famous for its numerous surge-type glaciers as well as for the harsh weather conditions of a highly maritime Arctic island, making regular observations of its glaciers challenging. However, the rapid changes of glacier geometry require a frequent update of their extent to perform accurate glacier-specific calculations such as their mass balance or contribution to sea level. The last inventory for Svalbard has been compiled by Nuth et al. (2013) from about 40 satellite scenes acquired by three different sensors (ASTER, Landsat, SPOT) on 30 unique days over a period of 10 years. Accordingly, any change assessment or other time dependent calculations are difficult to perform and a temporarily more consistent dataset is urgently required.&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;In this study we present the results of a new glacier inventory for Svalbard that has been derived from two Sentinel-2 swaths acquired for the main island within 3 days of 2017 and on 1 day in 2016 from Landsat 8 for Nordaustlandet. The images had overall very good snow conditions but in some regions late seasonal snow was hiding glaciers. Glacier mapping under local clouds in the very north and south could be performed by using further scenes from 2017 processed with GEE. We applied a simple red/SWIR band ratio to map clean ice and corrected wrong classifications (sea ice, lakes) or missing parts (debris cover) manually. New drainage divides and topographic parameters were derived from the ArcticDEM. &lt;/span&gt;&lt;/p&gt;&lt;p&gt;The new inventory counts 3136 glaciers &gt;0.01 km&lt;sup&gt;2&lt;/sup&gt; covering an area of 32,948 km&lt;sup&gt;2&lt;/sup&gt;. Of these, glaciers &lt; 1 km&lt;sup&gt;2&lt;/sup&gt; cover 1.3% of the area but nearly 44% of the number whereas glac-iers &gt;10 km&lt;sup&gt;2&lt;/sup&gt; cover 91% of the area and 10% by number. Compared to the previous inventory we have 1468 glaciers more and 2.5% area less. However, when excluding the 2025 glaciers &lt;1 km&lt;sup&gt;2&lt;/sup&gt;, we only identified 1111 glaciers, i.e. 557 less than in the previous inventory. The differences are mostly due to newly considered entities, different drainage divides, glacier retreat and advance/surging. By excluding surge-type glaciers, a more meaningful determination of climate-related area changes can be performed. The presentation will discuss the differences of the new inventory to the RGI dataset, the specific glacier mapping challenges and our approach to solve them.&lt;/p&gt;

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