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).

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>

Supplementary material to "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):  
Glacier Inventory ◽  
Glacier Shrinkage ◽  
The Alps ◽  
Supplementary Material ◽  
Sentinel 2

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 Updated inventory of glacier ice in New Zealand based on 2016 satellite imagery

2020 ◽  
pp. 1-14
Author(s):  
Sabine Baumann ◽  
Brian Anderson ◽  
Trevor Chinn ◽  
Andrew Mackintosh ◽  
Catherine Collier ◽  
...  
Keyword(s):  
New Zealand ◽  
Satellite Imagery ◽  
Ratio Method ◽  
Landsat 8 ◽  
Band Ratio ◽  
Maximum Likelihood Classification ◽  
The North ◽  
Area Reduction ◽  
Covered Area ◽  
Glacier Ice

Abstract The only complete inventory of New Zealand glaciers was based on aerial photography starting in 1978. While there have been partial updates using 2002 and 2009 satellite data, most glaciers are still represented by the 1978 outlines in contemporary global glacier databases. The objective of this project is to establish an updated glacier inventory for New Zealand. We have used Landsat 8 OLI satellite imagery from February and March 2016 for delineating clean glaciers using a semi-automatic band ratio method and debris-covered glaciers using a maximum likelihood classification. The outlines have been checked against Sentinel-2 MSI data, which have a higher resolution. Manual post processing was necessary due to misclassifications (e.g. lakes, clouds), mapping in shadowed areas, and combining the clean and debris-covered parts into single glaciers. New Zealand glaciers cover an area of 794 ± 34 km2 in 2016 with a debris-covered area of 10%. Of the 2918 glaciers, seven glaciers are >10 km2 while 71% is <0.1 km2. The debris cover on those largest glaciers is >40%. Only 15 glaciers are located on the North Island. For a selection of glaciers, we were able to calculate the area reduction between the 1978 and 2016 inventories.

scholarly journals Updated inventory of glacier ice in New Zealand based on 2016 satellite imagery

2021 ◽  
Author(s):  
S Baumann ◽  
Brian Anderson ◽  
T Chinn ◽  
A MacKintosh ◽  
C Collier ◽  
...  
Keyword(s):  
New Zealand ◽  
Satellite Imagery ◽  
Ratio Method ◽  
Landsat 8 ◽  
Band Ratio ◽  
Maximum Likelihood Classification ◽  
The North ◽  
Area Reduction ◽  
Covered Area ◽  
Glacier Ice

Copyright © The Author(s), 2020. Published by Cambridge University Press. The only complete inventory of New Zealand glaciers was based on aerial photography starting in 1978. While there have been partial updates using 2002 and 2009 satellite data, most glaciers are still represented by the 1978 outlines in contemporary global glacier databases. The objective of this project is to establish an updated glacier inventory for New Zealand. We have used Landsat 8 OLI satellite imagery from February and March 2016 for delineating clean glaciers using a semi-Automatic band ratio method and debris-covered glaciers using a maximum likelihood classification. The outlines have been checked against Sentinel-2 MSI data, which have a higher resolution. Manual post processing was necessary due to misclassifications (e.g. lakes, clouds), mapping in shadowed areas, and combining the clean and debris-covered parts into single glaciers. New Zealand glaciers cover an area of 794 ± 34 km2 in 2016 with a debris-covered area of 10%. Of the 2918 glaciers, seven glaciers are >10 km2 while 71% is <0.1 km2. The debris cover on those largest glaciers is >40%. Only 15 glaciers are located on the North Island. For a selection of glaciers, we were able to calculate the area reduction between the 1978 and 2016 inventories.

scholarly journals Updated inventory of glacier ice in New Zealand based on 2016 satellite imagery

2021 ◽  
Author(s):  
S Baumann ◽  
Brian Anderson ◽  
T Chinn ◽  
A MacKintosh ◽  
C Collier ◽  
...  
Keyword(s):  
New Zealand ◽  
Satellite Imagery ◽  
Ratio Method ◽  
Landsat 8 ◽  
Band Ratio ◽  
Maximum Likelihood Classification ◽  
The North ◽  
Area Reduction ◽  
Covered Area ◽  
Glacier Ice

Copyright © The Author(s), 2020. Published by Cambridge University Press. The only complete inventory of New Zealand glaciers was based on aerial photography starting in 1978. While there have been partial updates using 2002 and 2009 satellite data, most glaciers are still represented by the 1978 outlines in contemporary global glacier databases. The objective of this project is to establish an updated glacier inventory for New Zealand. We have used Landsat 8 OLI satellite imagery from February and March 2016 for delineating clean glaciers using a semi-Automatic band ratio method and debris-covered glaciers using a maximum likelihood classification. The outlines have been checked against Sentinel-2 MSI data, which have a higher resolution. Manual post processing was necessary due to misclassifications (e.g. lakes, clouds), mapping in shadowed areas, and combining the clean and debris-covered parts into single glaciers. New Zealand glaciers cover an area of 794 ± 34 km2 in 2016 with a debris-covered area of 10%. Of the 2918 glaciers, seven glaciers are >10 km2 while 71% is <0.1 km2. The debris cover on those largest glaciers is >40%. Only 15 glaciers are located on the North Island. For a selection of glaciers, we were able to calculate the area reduction between the 1978 and 2016 inventories.

scholarly journals Dual-satellite (Sentinel-2 and Landsat 8) remote sensing of supraglacial lakes in Greenland

2018 ◽  
Vol 12 (9) ◽  
pp. 3045-3065 ◽  
Author(s):  
Andrew G. Williamson ◽  
Alison F. Banwell ◽  
Ian C. Willis ◽  
Neil S. Arnold
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Water Volume ◽  
Landsat 8 ◽  
Total Water ◽  
Small Lakes ◽  
Supraglacial Lakes ◽  
Run Off ◽  
Lake Drainage ◽  
Sentinel 2

Abstract. Remote sensing is commonly used to monitor supraglacial lakes on the Greenland Ice Sheet (GrIS); however, most satellite records must trade off higher spatial resolution for higher temporal resolution (e.g. MODIS) or vice versa (e.g. Landsat). Here, we overcome this issue by developing and applying a dual-sensor method that can monitor changes to lake areas and volumes at high spatial resolution (10–30 m) with a frequent revisit time (∼3 days). We achieve this by mosaicking imagery from the Landsat 8 Operational Land Imager (OLI) with imagery from the recently launched Sentinel-2 Multispectral Instrument (MSI) for a ∼12 000 km2 area of West Greenland in the 2016 melt season. First, we validate a physically based method for calculating lake depths with Sentinel-2 by comparing measurements against those derived from the available contemporaneous Landsat 8 imagery; we find close correspondence between the two sets of values (R2=0.841; RMSE = 0.555 m). This provides us with the methodological basis for automatically calculating lake areas, depths, and volumes from all available Landsat 8 and Sentinel-2 images. These automatic methods are incorporated into an algorithm for Fully Automated Supraglacial lake Tracking at Enhanced Resolution (FASTER). The FASTER algorithm produces time series showing lake evolution during the 2016 melt season, including automated rapid (≤4 day) lake-drainage identification. With the dual Sentinel-2–Landsat 8 record, we identify 184 rapidly draining lakes, many more than identified with either imagery collection alone (93 with Sentinel-2; 66 with Landsat 8), due to their inferior temporal resolution, or would be possible with MODIS, due to its omission of small lakes <0.125 km2. Finally, we estimate the water volumes drained into the GrIS during rapid-lake-drainage events and, by analysing downscaled regional climate-model (RACMO2.3p2) run-off data, the water quantity that enters the GrIS via the moulins opened by such events. We find that during the lake-drainage events alone, the water drained by small lakes (<0.125 km2) is only 5.1 % of the total water volume drained by all lakes. However, considering the total water volume entering the GrIS after lake drainage, the moulins opened by small lakes deliver 61.5 % of the total water volume delivered via the moulins opened by large and small lakes; this is because there are more small lakes, allowing more moulins to open, and because small lakes are found at lower elevations than large lakes, where run-off is higher. These findings suggest that small lakes should be included in future remote-sensing and modelling work.

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;

Automatic glacier outlines extraction from Sentinel-1 and Sentinel-2 time series

2020 ◽  
Author(s):  
Riccardo Barella ◽  
Mattia Callegari ◽  
Carlo Marin ◽  
Claudia Notarnicola ◽  
Marc Zebisch ◽  
...  
Keyword(s):  
Time Series ◽  
Snow Cover ◽  
Short Wave ◽  
Support Vector ◽  
Glacier Inventory ◽  
Multi Temporal ◽  
Manual Selection ◽  
The Alps ◽  
Loss Of Coherence ◽  
Sentinel 2

&lt;p&gt;Glaciers represent an important part of the hydrologic cycle in the Alps and they are very sensitive to climate change. Satellite remote sensing is an efficient tool for glacier monitoring because it provides a synoptic view over large areas. In literature, well-established methods for glacier delineation based on the Red and Short Wave Infrared (SWIR) ratio have been presented. These methods depend on a manual selection for each glacier of the &amp;#8220;best scene&amp;#8221;, i.e. absence of cloud coverage and minimum snow cover. A further manual refinement step is needed to handle possible errors, mainly due to cloud cover or shadows, and to include debris covered ice.&lt;/p&gt;&lt;p&gt;A manual approach for glacier outline extraction, especially if applied over large areas and beside the respective extraordinary amount of work, may be inadequate for at least two reasons:&lt;/p&gt;&lt;p&gt;1) The increased amount of available satellite data provided by the recently launched Sentinel-2 mission, which ensure at least one acquisition every 5 days on a given area;&lt;/p&gt;&lt;p&gt;2) The need for a more frequent update of the glacier outlines i.e. few years, due to the faster changes affecting glaciers during the last years.&lt;/p&gt;&lt;p&gt;In this work, we present an automatic method for glacier mapping, including bare ice and debris covered ice through the synergetic use of Sentinel-1 and Sentinel-2. The information of the Sentinel-2 time series is first classified with a Support Vector Machine (SVM) to detect cloud and snow. The snow and cloud masks are then used to select the non-cloudy pixels with the lowest snow coverage in the surrounding area. This is done by applying a moving window on the entire multi-temporal classified stack. The selected pixels for each band compose a multi-temporal cloud free mosaic, which represents the glaciers with the minimum snow cover for the current ablation season i.e., the &amp;#8220;best scene&amp;#8221;. If we compose the mosaic with classified pixels instead of the reflectance, we obtain the glacier &amp;#8211; non glacier map that we use for outlines extraction. On the other hand, the Sentinel-1 coherence is used to detect the debris-covered ice over the areas classified as non-glacier from Sentinel-2. In detail, the Sentinel-1 time series is exploited to generate a multi-temporal coherence mosaic, which is representative of the loss of coherence due to the movement of the debris only. By properly thresholding this mosaic and considering the topographic information, the outlines of debris covered glaciers can be extracted.&lt;/p&gt;&lt;p&gt;The results obtained with the proposed method are compared with the recent official glacier inventory of South Tyrol (Italy) and Tyrol (Austria), which was derived from the manual interpretation of aerial orthophotos and lidar data by glacier experts.&lt;/p&gt;

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