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Direct snow depth measurements are sparse, especially in remote areas. In this study, we assess the potential of ICESat laser altimetry for providing snow depths for its operational period 2003–2009 on the example of the Scandinavian Mountains in southern Norway. Snow cover during ICESat campaigns typically results in positive elevation differences (dh) between ICESat GLAH14 elevations and reference elevations from Digital Elevation Models (DEMs). Three DEMs are used: the Norwegian national DEM for the entire study area, and the SRTM DEM and a high-resolution airborne lidar DEM for a spatial subset on the Hardangervidda mountain plateau. To account for uncertainty in elevation data, ICESat samples are grouped into spatial subsets, elevation bands, and over time (e.g. all winter campaigns together). We find that ICESat has the potential to provide regional-scale snow depths for the years 2003–2009 for its winter (March) and late spring (June) campaigns. ICESat-derived snow depth time series for different elevation bands agree well with measured (RMSE 0.47 m) and modelled (RMSE 0.61 m) snow depths in the study area. Annual differences in snow amounts and the increase of snow depths with elevation and coastal proximity over the study area are correctly reproduced. Uncertainties in reference elevations exceed ICESat elevation uncertainty and good control over errors and biases in reference DEMs turn out essential. Spatially varying vertical offsets between ICESat and the reference DEMs make it necessary to bias-correct March/June snow depths with autumn dh per spatial unit or elevation band. Best results are achieved when samples are summarised per season over the entire observation period. After correction of local DEM biases, the spatial pattern of ICESat 2003–2009 March dh matches spatially distributed modelled snow depths in southern Norway with decimeter-scale accuracy. In the western part of Hardangervidda, ICESat-based March snow depths agree better with measurements (RMSE ≤0.15 m for all DEMs) than modelled snow depths do (RMSE 0.61 m). In eastern Hardangervidda, the coarse resolution SRTM DEM (RMSE 0.41 m) performs better than the 10 m Norwegian DEM (RMSE 0.64 m) which is based on a less consistent mosaic of elevation data. Using the high-resolution lidar DEM, even single footprints show good agreement (R 2 0.59, RMSE 0.94 m) with measured snow depths from the same year. Snow depth estimates could be further improved by using full waveform ICESat data or elevation measurements from ICESat-2 once this satellite is operational. Good quality reference DEMs may still be acquired in the future even in areas where no such data exists today. [ABSTRACT FROM AUTHOR]

Satellite-based monitoring strategies for permafrost remain under development and are not yet operational. Remote sensing allows indirect observation of permafrost, a subsurface phenomenon, by mapping surface features or measuring physical parameters that can be used for permafrost modeling. We have explored high temporal resolution time series of TerraSAR-X backscatter intensity and interferometric coherence for the period between August 2012 and September 2013 to assess their potential for detecting major seasonal changes to the land surface in a variety of tundra environments within the Lena River Delta, Siberia. The TerraSAR-X signal is believed to be strongly affected by the vegetation layer, and its viability for the retrieval of soil moisture, for example, is therefore limited. In our study individual events, such as rain and snow showers, that occurred at the time of TerraSAR-X acquisition, or a refrozen crust on the snowpack during the spring melt were detected based on backscatter intensity signatures. The interferometric coherence showed marked variability; the snow cover onset and snow melt periods were identified by significant reduction in coherence. Principal component analysis provided a good spatial overview of the essential information contained in backscatter and coherence time series and revealed latent relationships between both time series and the surface temperature. The results of these investigations suggest that although X-band SAR has limitations with respect to monitoring seasonal land surface changes in permafrost areas, high-resolution time series of TerraSAR-X backscatter and coherence can provide new insights into environmental conditions. [ABSTRACT FROM AUTHOR]

A number of acquisition constellations for airborne or spaceborne optical images involve small time-lags and produce near-simultaneous images, a type of data which has thus far been little exploited to detect or quantify target motion at the Earth’s surface. These time-lag constellations were for the most part not even meant to exhibit motion tracking capabilities, or these capabilities were considered a drawback. In this contribution, we give the first systematic overview of the methods and issues involved in exploiting near-simultaneous airborne and satellite acquisitions. We first cover the category of the near-simultaneous acquisitions produced by individual stereo sensors, typically designed for topographic mapping, with a time-lag on the order of a minute. Over this time period, we demonstrate that the movement of river ice debris, sea ice floes or suspended sediments can be tracked, and we estimate the corresponding water surface velocity fields. Similarly, we assess cloud motion vector fields and vehicle trajectories. A second category of near-simultaneous acquisitions, with much smaller time-lags of at most a few seconds, is associated with along-track offsets of detector lines in the focal plane of pushbroom systems. These constellations are demonstrated here to be suitable to detect motion of fast vehicles, such as cars and airplanes, or, for instance, ocean waves. Acquisition delays are, third, also produced by other constellations such as ‘trains’ of satellites following each other and leading to time-lags of minutes to tens of minutes, which are in this contribution used to track icebergs and features of floating ice crystals on the sea, and an algae bloom. For all acquisition categories, the higher the spatial resolution of the data and the longer the time-lag, the smaller the minimum speed that can be detected. [ABSTRACT FROM AUTHOR]

Abstract: This study evaluates the performance of two fundamentally different approaches to achieve sub-pixel precision of normalised cross-correlation when measuring surface displacements on mass movements from repeat optical images. In the first approach, image intensities are interpolated to a desired sub-pixel resolution using a bi-cubic interpolation scheme prior to the actual displacement matching. In the second approach, the image pairs are correlated at the original image resolution and the peaks of the correlation coefficient surface are then located at the desired sub-pixel resolution using three techniques, namely bi-cubic interpolation, parabola fitting and Gaussian fitting. Both principal approaches are applied to three typical mass movement types: rockglacier creep, glacier flow and land sliding. In addition, the influence of pixel resolution on the accuracies of displacement measurement using image matching is evaluated using repeat images resampled to different spatial resolutions. Our results show that bi-cubic interpolation of image intensity performs best followed by bi-cubic interpolation of the correlation surface. Both Gaussian and parabolic peak locating turn out less accurate. By increasing the spatial resolution (i.e. reducing the ground pixel size) of the matched images by 2 to 16 times using intensity interpolation, 40% to 80% reduction in mean error in reference to the same resolution original image could be achieved. The study also quantifies how the mean error, the random error, the proportion of mismatches and the proportion of undetected movements increase with increasing pixel size (i.e. decreasing spatial resolution) for all of the three mass movement examples investigated. [ABSTRACT FROM AUTHOR]

Abstract: Global Land Ice Measurements from Space (GLIMS) is an international consortium established to acquire satellite images of the world''s glaciers, analyze them for glacier extent and changes, and to assess these change data in terms of forcings. The consortium is organized into a system of Regional Centers, each of which is responsible for glaciers in their region of expertise. Specialized needs for mapping glaciers in a distributed analysis environment require considerable work developing software tools: terrain classification emphasizing snow, ice, water, and admixtures of ice with rock debris; change detection and analysis; visualization of images and derived data; interpretation and archival of derived data; and analysis to ensure consistency of results from different Regional Centers. A global glacier database has been designed and implemented at the National Snow and Ice Data Center (Boulder, CO); parameters have been expanded from those of the World Glacier Inventory (WGI), and the database has been structured to be compatible with (and to incorporate) WGI data. The project as a whole was originated, and has been coordinated by, the US Geological Survey (Flagstaff, AZ), which has also led the development of an interactive tool for automated analysis and manual editing of glacier images and derived data (GLIMSView). This article addresses remote sensing and Geographic Information Science techniques developed within the framework of GLIMS in order to fulfill the goals of this distributed project. Sample applications illustrating the developed techniques are also shown. [Copyright &y& Elsevier]

Glaciers and their changes through time are increasingly obtained from a wide range of satellite sensors. Due to the often remote location of glaciers in inaccessible and high-mountain terrain, satellite observations frequently provide the only available measurements. Furthermore, satellite data provide observations of glacier characteristics that are difficult to monitor using ground-based measurements, thus complementing the latter. In the Glaciers_cci project of the European Space Agency (ESA), three of these characteristics are investigated in detail: glacier area, elevation change and surface velocity. We use (a) data from optical sensors to derive glacier outlines, (b) digital elevation models from at least two points in time, (c) repeat altimetry for determining elevation changes, and (d) data from repeat optical and microwave sensors for calculating surface velocity. For the latter, the two sensor types provide complementary information in terms of spatio-temporal coverage. While (c) and (d) can be generated mostly automatically, (a) and (b) require the intervention of an analyst. Largely based on the results of various round robin experiments (multi-analyst benchmark studies) for each of the products, we suggest and describe the most suitable algorithms for product creation and provide recommendations concerning their practical implementation and the required post-processing. For some of the products (area, velocity) post-processing can influence product quality more than the main-processing algorithm. [ABSTRACT FROM AUTHOR]

Abstract: Rockglaciers — permafrost creep features on mountain slopes — are common landforms in high mountain areas. The present contribution reports about the exploration of a dynamic modelling approach using cellular automata to assess their regional distribution patterns. The designed prototype model allows the numerical simulation of the spatial and temporal occurrence of talus-derived rockglaciers in the Upper Engadine (eastern Swiss Alps) during the Holocene. The dynamic model considers processes in the spatial and temporal domain and accounts for both external and internal processes, implemented by means of six modules (A to G). The external processes are: (A) rock-debris accumulation, (B) hydrology, (C) climate, (D) glacier extent. The internal processes are: (E) creep initiation, (F) advance rate, (G) creep termination. Comparison between field evidence and modelling results shows that the dynamic model enables the simulation of spatio-temporal creep processes on a regional scale, but that the model is highly dependent on the accurate modelling of the relevant (input) parameters. These deficiencies have been recognized and analyzed, and it is planned that future research activities will address these issues. [Copyright &y& Elsevier]

Automated glacier mapping from satellite multispectral image data is hampered by debris cover on glacier surfaces. Supraglacial debris exhibits the same spectral properties as lateral and terminal moraines, fluvioglacial deposits, and bedrock outside the glacier margin, and is thus not detectable by means of multispectral classification alone. Based on the observation of low slope angles for debris-covered glacier tongues, we developed a multisource method for mapping supraglacial debris. The method combines the advantages of automated multispectral classification for clean glacier ice and vegetation with slope information derived from a digital elevation model (DEM). Neighbourhood analysis and change detection is applied for further improvement of the resulting glacier/debris map. A significant percentage of the processing can be done automatically. In order to test the sensitivity of our method against different DEM qualities, it was also applied to a DEM obtained from ASTER stereo data. Additionally, we compared our multisource approach to an artificial neural network (ANN) classification of debris, using only multispectral data. While the combination with an ASTER-derived DEM revealed promising results, the ANN classification without DEM data does not. [Copyright &y& Elsevier]

Satellite data provide a large range of information on glacier dynamics and changes. Results are often reported, provided and used without consideration of measurement accuracy (difference to a true value) and precision (variability of independent assessments). Whereas accuracy might be difficult to determine due to the limited availability of appropriate reference data and the complimentary nature of satellite measurements, precision can be obtained from a large range of measures with a variable effort for determination. This study provides a systematic overview on the factors influencing accuracy and precision of glacier area, elevation change (from altimetry and DEM differencing), and velocity products derived from satellite data, along with measures for calculating them. A tiered list of recommendations is provided (sorted for effort from Level 0 to 3) as a guide for analysts to apply what is possible given the datasets used and available to them. The more simple measures to describe product quality (Levels 0 and 1) can often easily be applied and should thus always be reported. Medium efforts (Level 2) require additional work but provide a more realistic assessment of product precision. Real accuracy assessment (Level 3) requires independent and coincidently acquired reference data with high accuracy. However, these are rarely available and their transformation into an unbiased source of information is challenging. This overview is based on the experiences and lessons learned in the ESA project Glaciers_cci rather than a review of the literature. [ABSTRACT FROM AUTHOR]

The observed increase in summer temperatures and the related glacier downwasting has led to a noticeable decrease of frozen water resources in Central Asia, with possible future impacts on the economy of all downstream countries in the region. Glaciers in the Ak-Shyirak massif, located in the Inner Tien Shan, are not only affected by climate change, but also impacted by the open pit gold mining of the Kumtor Gold Company. In this study, glacier inventories referring to the years 2003 and 2013 were created for the Ak-Shyirak massif based on satellite imagery. The 193 glaciers had a total area of 351.2 ± 5.6 km 2 in 2013. Compared to 2003, the total glacier area decreased by 5.9 ± 3.4%. During 2003–2013, the shrinkage rate of Ak-Shyirak glaciers was twice than that in 1977–2003 and similar to shrinkage rates in Tien Shan frontier ranges. We assessed glacier volume in 2013 using volume–area (VA) scaling and GlabTop modelling approaches. Resulting values for the whole massif differ strongly, the VA scaling derived volume is 30.0–26.4 km 3 whereas the GlabTop derived volume accounts for 18.8–13.2 km 3 . Ice losses obtained from both approaches were compared to geodetically-derived volume change. VA scaling underestimates ice losses between 1943 and 2003 whereas GlabTop reveals a good match for eight glaciers for the period 2003–2012. In comparison to radio-echo soundings from three glaciers, the GlabTop model reveals a systematic underestimation of glacier thickness with a mean deviation of 16%. GlabTop tends to significantly underestimate ice thickness in accumulation areas, but tends to overestimate ice thickness in the lowermost parts of glacier snouts. Direct technogenic impact is responsible for about 7% of area and 5% of mass loss for glaciers in the Ak-Shyirak massif during 2003–2013. Therefore the increase of summer temperature seems to be the main driver of accelerated glacier shrinkage in the area. [ABSTRACT FROM AUTHOR]

Abstract: Global Land Ice Measurements from Space (GLIMS) is an international consortium established to acquire satellite images of the world''s glaciers, analyse them for glacier extent and changes, and assess change data for causes and implications for people and the environment. Although GLIMS is making use of multiple remote-sensing systems, ASTER (Advanced Spaceborne Thermal Emission and reflection Radiometer) is optimized for many needed observations, including mapping of glacier boundaries and material facies, and tracking of surface dynamics, such as flow vector fields and supraglacial lake development. Software development by GLIMS is geared toward mapping clean-ice and debris-covered glaciers; terrain classification emphasizing snow, ice, water, and admixtures of ice with rock debris; multitemporal change analysis; visualization of images and derived data; and interpretation and archiving of derived data. A global glacier database has been designed at the National Snow and Ice Data Center (NSIDC, Boulder, Colorado); parameters are compatible with and expanded from those of the World Glacier Inventory (WGI). These technology efforts are summarized here, but will be presented in detail elsewhere. Our presentation here pertains to one broad question: How can ASTER and other satellite multispectral data be used to map, monitor, and characterize the state and dynamics of glaciers and to understand their responses to 20th and 21st century climate change? Our sampled results are not yet glaciologically or climatically representative. Our early results, while indicating complexity, are generally consistent with the glaciology community''s conclusion that climate change is spurring glacier responses around the world (mainly retreat). Whether individual glaciers are advancing or retreating, the aggregate average of glacier change must be climatic in origin, as nonclimatic variations average out. We have discerned regional spatial patterns in glaciological response behavior; these patterns are best attributed to climate-change variability and to regional differences in glacier size and response times. In many cases, glacier length changes under-represent the magnitude of glacier ablation, because thinning (sometimes without immediate length changes) is also important. An expanded systematic, uniform analysis of many more glaciers is needed to isolate the glacier response components due to climatic and nonclimatic perturbations, to produce quantitative measures of regional variation in glacier changes, and to predict future regional glacier trends relevant to water resources, glaciological hazards, and global sea level. This comprehensive assessment (to be completed in stages) is expected to lend a critically needed filter to identify successful climate models that explain recent glacier changes and change patterns (and hence, are apt to describe future changes) and to eliminate unsuccessful models. [Copyright &y& Elsevier]