scholarly journals High-resolution inventory to capture glacier disintegration in the Austrian Silvretta

2021 ◽  
Vol 15 (10) ◽  
pp. 4637-4654
Author(s):  
Andrea Fischer ◽  
Gabriele Schwaizer ◽  
Bernd Seiser ◽  
Kay Helfricht ◽  
Martin Stocker-Waldhuber
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Volume Change ◽  
Ice Age ◽  
Glacier Inventory ◽  
Current State ◽  
Digital Elevation ◽  
Wide Range ◽  
Elevation Data ◽  
Geodetic Mass Balance

Abstract. A new high-resolution glacier inventory captures the rapid decay of the glaciers in the Austrian Silvretta for the years 2017 and 2018. Identifying the glacier outlines offers a wide range of possible interpretations of glaciers that have evolved into small and now totally debris-covered cryogenic structures. In previous inventories, a high proportion of active bare ice allowed a clear delineation of the glacier margins even by optical imagery. In contrast, in the current state of the glacier only the patterns and amounts of volume change allow us to estimate the area of the buried glacier remnants. We mapped the glacier outlines manually based on lidar elevation models and patterns of volume change at 1 to 0.5 m spatial resolution. The vertical accuracy of the digital elevation models (DEMs) generated from six to eight lidar points per square metre is of the order of centimetres. Between 2004/2006 and 2017/2018, the 46 glaciers of the Austrian Silvretta lost −29 ± 4 % of their area and now cover 13.1 ± 0.4 km2. This is only 32 ± 2 % of their Little Ice Age (LIA) extent of 40.9 ± 4.1 km2. The area change rate increased from 0.6 %/yr (1969–2002) to −2.4 %/yr (2004/2006–2017/2018). The Sentinel-2-based glacier inventory of 2018 deviates by just 1 % of the area. The annual geodetic mass balance referring to the area at the beginning of the period showed a loss increasing from −0.2 ± 0.1 m w.e./yr (1969–2002) to −0.8 ± 0.1 m w.e./yr (2004/2006–2017/2018) with an interim peak in 2002–2004/2006 of −1.5 ± 0.7 m w.e./yr. To keep track of the buried ice and its fate and to distinguish increasing debris cover from ice loss, we recommend inventory repeat frequencies of 3 to 5 years and surface elevation data with a spatial resolution of 1 m.

scholarly journals High-resolution inventory to capture glacier disintegration in the Austrian Silvretta

2021 ◽  
Author(s):  
Andrea Fischer ◽  
Bernd Seiser ◽  
Kay Helfricht ◽  
Martin Stocker-Waldhuber
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Ice Age ◽  
Surface Elevation ◽  
Glacier Changes ◽  
Wide Range ◽  
Elevation Data ◽  
Glacier Ice ◽  
Geodetic Mass Balance

Abstract. Eastern Alpine glaciers have been receding since the LIA maximum, but the majority of glacier margins could be delineated unambiguously for the last Austrian glacier inventories. Even debris-covered termini, changes in slope, colour or the position of englacial streams enabled at least an in situ survey of glacier outlines. Today the outlines of totally debris-covered glacier ice are fuzzy and raise the theoretical discussion if these glaciogenic features are still glaciers and should be part of the respective inventory – or part of an inventory of transient cryogenic landforms. A new high-resolution glacier inventory (area and surface elevation) was compiled for the years 2017 and 2018 to quantify glacier changes for the Austrian Silvretta region in full. Glacier outlines were mapped manually, based on orthophotos and elevation models and patterns of volume change of 1 to 0.5 m spatial resolution. The vertical accuracy of the DEMs generated from 6 to 8 LiDAR points per m2 is in the order of centimetres. calculated in relation to the previous inventories dating from 2004/2006 (LiDAR), 2002, 1969 (photogrammetry) and to the Little Ice Age maximum extent (moraines). Between 2004/06 and 2017/2018, the 46 glaciers of the Austrian Silvretta lost −29 ± 4 % of their area and now cover 13.1 ± 0.4 km2. This is only 32 ± 2 % of their LIA extent of 40.9 ± 4.1 km2. The area change rate increased from −0.6 %/year (1969–2002) to −2.4 %/year (2004/06–2017/18). The annual geodetic mass balance showed a loss increasing from −0.2 ± 0.1 m w.e./year (1969–2002) to –0.8 m ±0.1 w.e./year (2004/06–2017/18) with an interim peak in 2002–2004/06 at −1.5 ± 0.7 m w.e./year. Identifying the glacier outlines offers a wide range of possible interpretations of former glaciers that have evolved into small and now totally debris-covered cryogenic geomorphological structures. Only the patterns and amounts of volume changes allow us to estimate the area of the buried glacier remnants. To keep track of the buried ice and its fate, and to distinguish increasing debris cover from ice loss, we recommend inventory repeat frequencies of three to five years and surface elevation data with a spatial resolution of one metre.

scholarly journals Tracing glacier changes in Austria from the Little Ice Age to the present using a lidar-based high-resolution glacier inventory in Austria

2015 ◽  
Vol 9 (2) ◽  
pp. 753-766 ◽  
Author(s):  
A. Fischer ◽  
B. Seiser ◽  
M. Stocker Waldhuber ◽  
C. Mitterer ◽  
J. Abermann
Keyword(s):  
High Resolution ◽  
Little Ice Age ◽  
Ice Age ◽  
Relative Area ◽  
Glacier Area ◽  
Regional Variability ◽  
Glacier Inventory ◽  
Digital Elevation ◽  
Glacier Changes ◽  
The Mean

Abstract. Glacier inventories provide the basis for further studies on mass balance and volume change, relevant for local hydrological issues as well as for global calculation of sea level rise. In this study, a new Austrian glacier inventory has been compiled, updating data from 1969 (GI 1) and 1998 (GI 2) based on high-resolution lidar digital elevation models (DEMs) and orthophotos dating from 2004 to 2012 (GI 3). To expand the time series of digital glacier inventories in the past, the glacier outlines of the Little Ice Age maximum state (LIA) have been digitalized based on the lidar DEM and orthophotos. The resulting glacier area for GI 3 of 415.11 ± 11.18 km2 is 44% of the LIA area. The annual relative area losses are 0.3% yr−1 for the ~119-year period GI LIA to GI 1 with one period with major glacier advances in the 1920s. From GI 1 to GI 2 (29 years, one advance period of variable length in the 1980s) glacier area decreased by 0.6% yr−1 and from GI 2 to GI 3 (10 years, no advance period) by 1.2% yr−1. Regional variability of the annual relative area loss is highest in the latest period, ranging from 0.3 to 6.19% yr−1. The mean glacier size decreased from 0.69 km2 (GI 1) to 0.46 km2 (GI 3), with 47% of the glaciers being smaller than 0.1 km2 in GI 3 (22%).

scholarly journals Assessing Optimal Digital Elevation Model Selection for Active River Area Delineation Across Broad Regions

2021 ◽  
Author(s):  
Shizhou Ma ◽  
Karen Beazley ◽  
Patrick Nussey ◽  
Chris Greene
Keyword(s):  
High Resolution ◽  
Digital Elevation Model ◽  
Smoothing Algorithm ◽  
Processing Efficiency ◽  
Spatial Approach ◽  
Digital Elevation ◽  
Elevation Data ◽  
Computer Based ◽  
Lidar Dem ◽  
Elevation Model

Abstract The Active River Area (ARA) is a spatial approach for identifying the extent of functional riparian area. Given known limitations in terms of input elevation data quality and methodology, ARA studies to date have not achieved effective computer-based ARA-component delineation, limiting the efficacy of the ARA framework in terms of informing riparian conservation and management. To achieve framework refinement and determine the optimal input elevation data for future ARA studies, this study tested a novel Digital Elevation Model (DEM) smoothing algorithm and assessed ARA outputs derived from a range of DEMs for accuracy and efficiency. It was found that the tested DEM smoothing algorithm allows the ARA framework to take advantage of high-resolution LiDAR DEM and considerably improves the accuracy of high-resolution LiDAR DEM derived ARA results; smoothed LiDAR DEM in 5-meter spatial resolution best balanced ARA accuracy and data processing efficiency and is ultimately recommended for future ARA delineations across large regions.

scholarly journals A new glacier inventory on southern Baffin Island, Canada, from ASTER data: I. Applied methods, challenges and solutions

2009 ◽  
Vol 50 (53) ◽  
pp. 11-21 ◽  
Author(s):  
Felix Svoboda ◽  
Frank Paul
Keyword(s):  
Climate Change Impacts ◽  
Ice Age ◽  
Baffin Island ◽  
Glacier Inventory ◽  
Ice Caps ◽  
Digital Elevation ◽  
Glacier Length ◽  
Length And Area ◽  
Shortwave Infrared ◽  
Glacier Mapping

AbstractThe quantitative assessment of glacier changes as well as improved modeling of climate-change impacts on glaciers requires digital vector outlines of individual glacier entities. Unfortunately, such a glacier inventory is still lacking in many remote but extensively glacierized gions such as the Canadian Arctic. Multispectral satellite data in combination with digital elevation models (DEMs) a particularly useful for creating detailed glacier inventory data including topographic information for each entity. In this study, we extracted glacier outlines and a DEM using two adjacent Terra ASTER scenes acquired in August 2000 for a remote region on southern Baffin Island, Canada. Additionally, Little Ice Age (LIA) extents we digitized from trimlines and moraines visible on the ASTER scenes, and Landsat MSS and TM scenes from the years 1975 and 1990 we used to assess changes in glacier length and area. Because automated delineation of glaciers is based on a band in the shortwave infrared, we have developed a new semi-automated glacier-mapping approach for the MSS sensor. Wrongly classified debris-coved glaciers, water bodies and attached snowfields we corrected manually for both ASTER and MSS. Glacier drainage divides we manually digitized by combining visual interptation with DEM information. In this first paper, we describe the applied methods for glacier mapping and the glaciological challenges encounted (e.g. data voids, snow cover, ice caps, tributaries), while the second paper ports the data analyses and the derived changes.

scholarly journals Accelerating retreat and high-elevation thinning of glaciers in central Spitsbergen

2016 ◽  
Vol 10 (3) ◽  
pp. 1317-1329 ◽  
Author(s):  
Jakub Małecki
Keyword(s):  
Little Ice Age ◽  
High Elevation ◽  
Comprehensive Analysis ◽  
Ice Age ◽  
The Arctic ◽  
Digital Elevation ◽  
Glacier Changes ◽  
The Mean ◽  
Geodetic Mass Balance ◽  
The 1960S

Abstract. Svalbard is a heavily glacier-covered archipelago in the Arctic. Dickson Land (DL), in the central part of the largest island, Spitsbergen, is relatively arid and, as a result, glaciers there are relatively small and restricted mostly to valleys and cirques. This study presents a comprehensive analysis of glacier changes in DL based on inventories compiled from topographic maps and digital elevation models for the Little Ice Age (LIA) maximum, the 1960s, 1990, and 2009/2011. Total glacier area has decreased by  ∼ 38 % since the LIA maximum, and front retreat increased over the study period. Recently, most of the local glaciers have been consistently thinning in all elevation bands, in contrast to larger Svalbard ice masses which remain closer to balance. The mean 1990–2009/2011 geodetic mass balance of glaciers in DL is among the most negative from the Svalbard regional means known from the literature.

scholarly journals Surface elevation and mass changes of all Swiss glaciers 1980–2010

2015 ◽  
Vol 9 (2) ◽  
pp. 525-540 ◽  
Author(s):  
M. Fischer ◽  
M. Huss ◽  
M. Hoelzle
Keyword(s):  
Mass Balance ◽  
Accuracy Assessment ◽  
Swiss Alps ◽  
Surface Elevation ◽  
European Alps ◽  
Volume Changes ◽  
Glacier Inventory ◽  
Digital Elevation ◽  
Source Data ◽  
Geodetic Mass Balance

Abstract. Since the mid-1980s, glaciers in the European Alps have shown widespread and accelerating mass losses. This article presents glacier-specific changes in surface elevation, volume and mass balance for all glaciers in the Swiss Alps from 1980 to 2010. Together with glacier outlines from the 1973 inventory, the DHM25 Level 1 digital elevation models (DEMs) for which the source data over glacierized areas were acquired from 1961 to 1991 are compared to the swissALTI3D DEMs from 2008 to 2011 combined with the new Swiss Glacier Inventory SGI2010. Due to the significant differences in acquisition dates of the source data used, mass changes are temporally homogenized to directly compare individual glaciers or glacierized catchments. Along with an in-depth accuracy assessment, results are validated against volume changes from independent photogrammetrically derived DEMs of single glaciers. Observed volume changes are largest between 2700 and 2800 m a.s.l. and remarkable even above 3500 m a.s.l. The mean geodetic mass balance is −0.62 ± 0.07 m w.e. yr−1 for the entire Swiss Alps over the reference period 1980–2010. For the main hydrological catchments, it ranges from −0.52 to −1.07 m w.e. yr−1. The overall volume loss calculated from the DEM differencing is −22.51 ± 1.76 km3.

scholarly journals Mountain glaciers of southeast Siberia: current state and changes since the Little Ice Age

2014 ◽  
Vol 55 (66) ◽  
pp. 167-176 ◽  
Author(s):  
E.Yu. Osipov ◽  
O.P. Osipova
Keyword(s):  
Little Ice Age ◽  
Snow Accumulation ◽  
Ice Age ◽  
High Mountain ◽  
Late 20Th Century ◽  
Glacier Inventory ◽  
Mountain Glaciers ◽  
Current State ◽  
Field Investigations ◽  
Glacier Changes

AbstractContemporary glaciers of southeast Siberia are located on three high-mountain ridges (east Sayan, Baikalsky and Kodar). In this study, we present an updated glacier inventory based on high- to middle-resolution satellite imagery and field investigations. The inventory includes 51 glaciers with a total area of - 15 km2. Areas of individual glaciers vary from 0.06 to 1.33 km2, lengths from 130 to 2010 m and elevations from 1796 to 3490 m. The recent ice maximum extents (Little Ice Age) have been delineated from terminal moraines. On average, debris-free surface area shrunk by 59% between 1850 and 2006/11 (0.37% a–1), by 44% between 1850 and 2001/02 (0.29% a–1) and by 27% between 2001/02 and 2006/11 (3.39% a–1). The Kodar glaciers have experienced the largest area shrinkage, while the area loss on Baikalsky ridge was more moderate. Glacier changes are mainly related to regional summer temperature increase (by 1.7-2.6C from 1970 to 2010). There are some differences in glacier response due to different spatial patterns of snow accumulation, local topography (e.g. glacier elevation, slope) and geological activity. The studied glaciers (especially of Kodar ridge) are the most sensitive in Siberia to climate change since the late 20th century.

scholarly journals Improved estimation of flood parameters by combining space based SAR data with very high resolution digital elevation data

2009 ◽  
Vol 13 (5) ◽  
pp. 567-576 ◽  
Author(s):  
H. Zwenzner ◽  
S. Voigt
Keyword(s):  
High Resolution ◽  
Flood Mapping ◽  
Scaling Effects ◽  
Data Set ◽  
Digital Elevation ◽  
Geometric Precision ◽  
Elevation Data ◽  
Lidar Dem ◽  
Sar Data ◽  
Very High

Abstract. Severe flood events turned out to be the most devastating catastrophes for Europe's population, economy and environment during the past decades. The total loss caused by the August 2002 flood is estimated to be 10 billion Euros for Germany alone. Due to their capability to present a synoptic view of the spatial extent of floods, remote sensing technology, and especially synthetic aperture radar (SAR) systems, have been successfully applied for flood mapping and monitoring applications. However, the quality and accuracy of the flood masks and derived flood parameters always depends on the scale and the geometric precision of the original data as well as on the classification accuracy of the derived data products. The incorporation of auxiliary information such as elevation data can help to improve the plausibility and reliability of the derived flood masks as well as higher level products. This paper presents methods to improve the matching of flood masks with very high resolution digital elevation models as derived from LiDAR measurements for example. In the following, a cross section approach is presented that allows the dynamic fitting of the position of flood mask profiles according to the underlying terrain information from the DEM. This approach is tested in two study areas, using different input data sets. The first test area is part of the Elbe River (Germany) where flood masks derived from Radarsat-1 and IKONOS during the 2002 flood are used in combination with a LiDAR DEM of 1 m spatial resolution. The other test data set is located on the River Severn (UK) and flood masks derived from the TerraSAR-X satellite and aerial photos acquired during the 2007 flood are used in combination with a LiDAR DEM of 2 m pixel spacing. By means of these two examples the performance of the matching technique and the scaling effects are analysed and discussed. Furthermore, the systematic flood mapping capability of the different imaging systems are examined. It could be shown that the combination of high resolution SAR data and LiDAR DEM allows the derivation of higher level flood parameters such as flood depth estimates, as presented for the Severn area. Finally, the potential and the constraints of the approach are evaluated and discussed.

scholarly journals DEMNAS: MODEL DIGITAL KETINGGIAN NASIONAL UNTUK APLIKASI KEPESISIRAN

OSEANA ◽  
2018 ◽  
Vol 43 (4) ◽  
Author(s):  
Marindah Yulia Iswari ◽  
Kasih Anggraini
Keyword(s):  
Remote Sensing ◽  
Digital Elevation Model ◽  
Spatial Resolution ◽  
Digital Data ◽  
Coastal Hazards ◽  
Remote Sensing Images ◽  
Geospatial Information ◽  
Digital Elevation ◽  
Elevation Data ◽  
Elevation Model

DEMNAS : NATIONAL DIGITAL ELEVATION MODEL FOR COASTAL APPLICATION. DEM is a digital data which contain information about elevation. In Indonesia, DEM can be generated from elevation points or contours in RBI (Rupabumi Indonesia). DEM can be performed to research of coastal application i.e. inundation or tsunami. DEM can help to analyze vulnerability or evacuation zone for coastal hazards. DEMNAS is one product of BIG (Geospatial Information Agency) which consist of elevation data from remote sensing images. DEMNAS data has not been widely used and is still being developed but DEMNAS has an advantage of spatial resolution. DEMNAS has spatial resolution 0.27 arc-second, which is bigger than the spatial resolution of global DEM.

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