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Observation of glacier surface characteristics through remotely sensed time-series data is essential for understanding glacier seasonality, mass balance, and long-term trends. Yet, the reliability of these observations depends significantly on the quality of the time-series data. This study presents a meticulous preprocessing scheme to improve the quality of Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Snow Index (NDSI) time-series data for glacier monitoring. We propose a three-step algorithm specifically crafted to overcome the challenges associated with cloud contamination reduction, outlier removal and data gap handling. This innovative approach iteratively compares the median values of automatically adjusted asymmetrical moving windows to achieve convergence, removing outliers using minimal window size to keep the temporal resolution as high as possible. The methodology’s effectiveness is demonstrated through its application to two glaciers from the United States Geological Survey (USGS) Benchmark Project, showcasing significant improvements in the quality of smoothed MODIS NDSI time series. These results affirm the efficacy of the proposed scheme in rendering a more reliable evaluation of glacier surface condition and seasonal fluctuations. Consequently, this study contributes significant methodological advancements to the fields of remote sensing and glaciology, enhancing the accuracy of glacier monitoring techniques.

Glacier monitoring plays a crucial role in understanding the impacts of climate change on these dynamic natural systems. One or more time-lapse cameras are often employed to acquire short-term observations of glacier flow dynamics. However, the lack of multi-camera photogrammetric software packages for multi-temporal 3D scene reconstruction, especially in case of wide camera baselines, hinders the application of Structure-from-Motion techniques to these scenarios. To address this, we present ICEpy4D, a novel Python toolkit designed for 4D monitoring of alpine glaciers using low-cost time-lapse cameras and state-of-the-art computer vision techniques. ICEpy4D leverages deep-learning-based matching algorithms to solve 3D reconstruction with wide camera baselines, making it well-suited for challenging scenarios encountered in mountainous regions. The toolkit offers comprehensive functionalities for multi-epoch monitoring, enabling short-term glacier 3D reconstruction and extraction of relevant information from time-series point clouds, such as volume variations and glacier retreat. In a pilot study on the Belvedere Glacier northern snout (Italian Alps), ICEpy4D estimated glacier volume loss of 63 × 103 m3 of ice and ∼17.5m of retreat. Results showcased the toolkit’s potential for analyzing a glacier ice cliff, with prospects for application to other glaciers with varying characteristics. ICEpy4D is actively being developed as an open-source project at github.com/labmgf-polimi/icepy4d/, promoting ease of extension and customization.

Close-range sensors are employed to observe glaciological processes that operate over short timescales (e.g. iceberg calving, glacial lake outburst floods, diurnal surface melting). However, under poor weather conditions optical instruments fail while the operation of radar systems below 17 GHz do not have sufficient angular resolution to map glacier surfaces in detail. This letter reviews the potential of millimetre-wave radar at 94 GHz to obtain high-resolution 3-D measurements of glaciers under most weather conditions. We discuss the theory of 94 GHz radar for glaciology studies, demonstrate its potential to map a glacier calving front and summarise future research priorities.

Image-based monitoring has emerged as a prevalent technique for sensing mountain environments. Monoscopic time-lapse cameras, which rely on digital image correlation to quantify glacier motion, have limitations due to the need for a Digital Elevation Model for deriving 3D flow velocity fields. Multi-camera systems overcome this limitation, as they allow for a 3D reconstruction of the scene. This paper presents a replicable low-cost stereoscopic system designed for 4D glacier monitoring. The system consists of independent and autonomous units, built from off-the-shelves components, such as a DSLR camera, an Arduino microcontroller, and a Raspberry Pi Zero, reducing costs compared to pre-built time-lapse cameras. The units are energetically self-sufficient and resistant to harsh alpine conditions. The system was successfully tested for more than a year to monitor the northwest terminus of the Belvedere Glacier (Italian Alps). Daily stereo-pairs acquired were processed with Structure-from-Motion to derive 3D point clouds of the glacier terminus and estimate glacier retreat and ice volume loss. By combining the information about ice volume loss with ablation estimates and ice flow velocity information, e.g., derived from monoscopic-camera time series, a multi-camera system enables a comprehensive understanding of sub-seasonal glacier dynamics.

Glaciers are subject to drastic mass loss in the last decades and their importance as an environmental and hydrological resource requires regular monitoring. Geomatics techniques are the ideal tool to survey these complex and remote areas, with different remote sensing platforms and sensors to choose from depending on the scale and accuracy to be monitored.In this case study, the Broulè glacier (Valpelline, Aosta Valley, Italy) was monitored for several years using a DJI Phantom 4 RTK UAV, an airborne PhaseOne camera, and a very high-resolution images Pléiades satellite stereo pair to estimate the glacier's melting and to investigate the possibility of carrying out monitoring campaigns limiting in-situ surveys. A comparison of the 3D models and cartographic products obtained from the different sensors made it possible to estimate the accuracies achievable and check them on rocky areas around the glacier considered to be invariant over time. Although the orography of the area and the poor accessibility did not allow a balanced distribution of Ground Control Points (GCPs) and Check Points (CPs), the RMSE in the photogrammetric models was a few centimetres. An altimetric comparison between the photogrammetric DSMs allowed us to estimate the error at -1.6 m, which is strongly influenced by the altimetry of the terrain.Therefore, although it is impossible to rely solely on satellite imagery for a centimetric survey, it seems possible to continue the multi-temporal monitoring over the years using remote sensing (aerial and satellite) with a previous good cartographic base supported by good field campaigns.

The measurement of geometric changes in Alpine glaciers is an essential aspect to assess their reaction to climate change effects. Archive aerial images may integrate valuable information to this purpose at times when other types of remotely sensed data are not available. The application of photogrammetric techniques such as Structure-from-Motion (SfM) and Multi-View-Stereo matching allows the extraction of dense point clouds to model the glacier environment. The comparison of multiple datasets requires to setup a stable reference system, a task that in archive photos is generally carried out by means of ground control points (GCPs). This paper would like to propose and assess some techniques to cope with the lack of ground control. Multitemporal SfM (MSfM) is presented and tested on a dataset including six different aerial blocks collected by means of analogue (PAN/RGB) and digital airborne cameras from 1967 to 2006. These images have been downloaded from the IGNF online repository and cover the area of the Val Veny (Brenva and Miage glaciers) in the Mount Blanc massif, at the border between Italy and France. Coupled with other solutions (i.e., extraction of GCPs from maps and DTMs and ICP co-registration of point clouds), MSfM has revealed as a suitable technique for coregistration of multiple photogrammetric blocks of aerial photos with minimum ground control. Some tests carried out in the case study area demonstrated that the integration of MSfM and ICP coregistration for refinement may significantly improve the comparison between multiple point clouds, which is a fundamental pre-requisite for the analysis of glacier changes over time.

In this article, we present a method for combining image-based displacements to build time series. This method takes advantage of the redundancy of these displacements, which comes from multiple possible combinations between images. The proposed method combines common master series with a different master date to construct a single time series of relative displacements. We were able to test this method on displacements computed using images from a stereo time-lapse device recording images of the Argentière glacier during the summer and fall of 2019. Our method was compared to two other displacement aggregation strategies: a simple common master approach and the classical inversion method. In order to perform this comparison, displacement data from four permanent GPS are used. The results show that our method provides a more accurate time series of relative displacements than those obtained with the other methods. The higher accuracy of the proposed method makes the detection of seasonal variations in glacier velocity possible.

Continuous monitoring of glaciers is of key importance to understand their morphological evolution over time and monitor the impact of climate change. Recently, Unmanned Aerial Vehicles (UAVs) have proven to be ideal candidates for glacier monitoring thanks to their flexibility and ease of processing with software packages. Traditionally, for high-accurate and geodetically relevant results, Ground Control Points (GCPs) need to be homogeneously distributed over the area of interest and manually identified in the imagery to guarantee accurate reconstructions. However, the GCP setup is always time consuming and, in many cases, a difficult operation due to logistic constraints. Nowadays, many UAVs offer GNSS Real Time Kinematic (RTK) capabilities that usually highly improve 3D reconstructions. However, there are circumstances in which an RTK solution cannot be directly achieved in the field. This is particularly frequent in challenging mountain environments such as glaciers. In such cases, post-processing UAV GNSS kinematic tracks could represent a powerful approach for improving the quality of 3D models. The goal of this work is to investigate the potential of UAV track post-processing combined with direct georeferencing for accurate 3D reconstructions without the need for GCPs in a complex environment of an Alpine glacier. The study area is Forni Glacier in the Rhaetian Alps, Italy. The data were acquired during two campaigns performed in August 2020 and August 2021 and include UAV images captured using a DJI Phantom 4 RTK and target positions measured with Leica GS18 I receivers. The data were processed using a pipeline entirely implemented in the Leica Infinity software that combines GNSS post-processing, a standard photogrammetric pipeline and a new tool to post-process GNSS kinematic tracks of UAVs. The approach based on UAV track post-processing and direct georeferencing was assessed using the acquired targets as Check Points (CPs) and compared to a standard photogrammetric approach in terms of glacier height loss computation. The results show Root Mean Square Errors (RMSEs) of the CPs below 4 cm for both the 2020 and 2021 campaigns. As for glacier height loss computation, the DPCs generated from the two surveys using a standard photogrammetric approach and a workflow based on UAV track post-processing and direct georeferencing were differentiated to compute the height differences of the glacier surfaces over one year. The two investigated approaches show similar results with an average height loss of 5 metres measured on the glacier tongue and demonstrate that UAV track post-processing can compensate for the RTK signal loss allowing accurate 3D reconstruction and eliminating the need for GCPs, especially if pre-calibration is performed.

Remote sensing techniques are frequently applied for the surveying of remote areas, where the use of conventional surveying techniques remains difficult and impracticable. In this paper, we focus on one of the remote glacier areas, namely the Tyndall Glacier area in the Southern Patagonian Icefield in Chile. Based on optical remote sensing data in the form of multi-spectral Sentinel-2 imagery, we analyze the extent of different snow and ice classes on the surface of the glacier by means of pixel-wise classification. Our study comprises three main steps: (1) Labeled Sentinel-2 compliant data are obtained from theoretical spectral reflectance curves, as there are no training data available for the investigated area; (2) Four different classification approaches are used and compared in their ability to identify the defined five snow and ice types, thereof two unsupervised approaches (k-means clustering and rule-based classification via snow and ice indices) and two supervised approaches (Linear Discriminant Analysis and Random Forest classifier); (3) We first focus on the pixel-wise classification of Sentinel-2 imagery, and we then use the best-performing approach for a multi-temporal analysis of the Tyndall Glacier area. While the achieved classification results reveal that all of the used classification approaches are suitable for detecting different snow and ice classes on the glacier surface, the multi-temporal analysis clearly reveals the seasonal development of the glacier. The change of snow and ice types on the glacier surface is evident, especially between the end of ablation season (April) and the end of accumulation season (September) in Southern Chile.

Glacier observation data from major mountain regions of the world are key to improving our understanding of glacier changes: they deliver fundamental baseline information for climatological, hydrological, and hazard assessments. In many mountain ecosystems, as well as in the adjacent lowlands, glaciers play a crucial role in freshwater provision and regulation. This article first presents the state of the art on glacier monitoring and related strategies within the framework of the Global Terrestrial Network for Glaciers (GTN-G). Both in situ measurements of changes in glacier mass, volume, and length as well as remotely sensed data on glacier extents and changes over entire mountain ranges provide clear indications of climate change. Based on experiences from capacity-building activities undertaken in the Tropical Andes and Central Asia over the past years, we also review the state of the art on institutional capacity in these regions and make further recommendations for sustainable mountain development. The examples from Peru, Ecuador, Colombia, and Kyrgyzstan demonstrate that a sound understanding of measurement techniques and of the purpose of measurements is necessary for successful glacier monitoring. In addition, establishing durable institutions, capacity-building programs, and related funding is necessary to ensure that glacier monitoring is sustainable and maintained in the long term. Therefore, strengthening regional cooperation, collaborating with local scientists and institutions, and enhancing knowledge sharing and dialogue are envisaged within the GTN-G. Finally, glacier monitoring enhances the resilience of the populations that depend on water resources from glacierized mountains or that are affected by hazards related to glacier changes. We therefore suggest that glacier monitoring be included in the development of sustainable adaptation strategies in regions with glaciated mountains.

The Sentinel-1 mission has now reached its maturity, and is acquiring high-quality images with a high revisit time, allowing for effective continuous monitoring of our rapidly changing planet. The purpose of this work is to assess the performance of the different synthetic aperture radar products made available by the European Space Agency through the Sentinels Data Hub against glacier displacement monitoring with offset tracking methodology. In particular, four classes of products have been tested: the medium resolution ground range detected, the high-resolution ground range detected, acquired in both interferometric wide and extra-wide swath, and the single look complex. The first are detected pre-processed images with about 40, 25, and 10-m pixel spacing, respectively. The last category, the most commonly adopted for the application at issue, represents the standard coherent synthetic aperture radar product, delivered in unprocessed focused complex format with pixel spacing ranging from 14 to 20 m in azimuth and from approximately 2 to 6 m in range, depending on the acquisition area and mode. Tests have been performed on data acquired over four glaciers, i.e., the Petermann Glacier, the Nioghalvfjerdsfjorden, the Jackobshavn Isbræ and the Thwaites Glacier. They revealed that the displacements estimated using interferometric wide swath single look complex and high-resolution ground range detected products are fully comparable, even at computational level. As a result, considering the differences in memory consumption and pre-processing requirements presented by these two kinds of product, detected formats should be preferred for facing the application.

Rock glaciers are creep phenomena of mountain permafrost. Typically, these landforms look like lava flows from a bird’s eye view. Active rock glaciers move downslope with flow velocities in the range of few centimeters to several meters per year. Thus, large masses of rock and ice can be gradually transported down-valley. In this paper we present a comparative study analyzing surface change for Tschadinhorn rock glacier, a relatively fast moving rock glacier located in the Hohe Tauern Range of the Austrian Alps. Aerial photographs (1954–2017) of both metric (conventional) and non-metric (UAV-based) aerial surveys were compared to derive multi-annual to annual flow vector fields and surface height change. For each time interval given we computed a single representative value for flow velocity and, if applicable, also for area-wide surface height change, i.e. volume change. The velocity graph obtained represents the temporal evolution of the kinematics of the rock glacier with good discrimination. Volume change was difficult to quantify since temporal changes were rather small and close to insignificance. The precision and accuracy of the results obtained were numerically quantified. Our study showed that for the Tschadinhorn rock glacier UAV-based aerial surveys can substitute conventional aerial surveys as carried out by national mapping agencies, such as the Austrian Federal Office of Metrology and Surveying (BEV). Thus, UAV-based aerial surveys can help to bridge the data gap between regular aerial surveys. The high accuracy of the UAV-derived results would even allow intra-annual change detection of flow velocity.

It is widely accepted that glaciers are retreating throughout the world and that their decline causes serious impacts on many societies. Knowledge of glacier distribution and quantification of glacier changes is crucial to assessing the impact of glacier shrinkage on the transboundary hydrological cycle and related issues, such as irrigation, energy production, and natural hazard prevention. Therefore, glacier monitoring is vital to the development of sustainable adaptation strategies in regions with glaciated mountains. Baseline documentation is needed to assess the current status of glacier monitoring. The aim of this study is to assess the status of national implementations of the international monitoring strategy developed by the Global Terrestrial Network for Glaciers (GTN-G) to make the data easily accessible to a broader audience, to identify gaps in the monitoring setup, and to guide countries in improving their monitoring schemes. We developed a standardized procedure to evaluate existing glacier data from international data repositories; these freely accessible data on glacier distribution and changes (as of 2015) for all glacierized countries and regions form the basis of this study. The resulting country profiles are analyzed in relation to the existing GTN-G monitoring strategy. Gaps between the current implementation of glacier monitoring and implementation targets are compiled in a solid gap analysis, which allows countries to be categorized as having poorly developed monitoring, needing improvement, or having well-developed monitoring. Three pilot cases (Kyrgyzstan, Bolivia, and Switzerland) are presented in detailed country profiles.

Glacier mass loss is among the clearest indicators of atmospheric warming. The observation of these changes is one of the major objectives of the international climate monitoring strategy developed by the Global Climate Observing System (GCOS). Long-term glacier mass balance measurements are furthermore the basis for calibrating and validating models simulating future runoff of glacierised catchments. This is essential for Central Asia, which is one of the driest continental regions of the Northern Hemisphere. In the highly populated regions, water shortage due to decreased glacierisation potentially leads to pronounced political instability, drastic ecological changes and endangered food security. As a consequence of the collapse of the former Soviet Union, however, many valuable glacier monitoring sites in the Tien Shan and Pamir Mountains were abandoned. In recent years, multinational actors have re-established a set of important in situ measuring sites to continue the invaluable long-term data series. This paper introduces the applied monitoring strategy for selected glaciers in the Kyrgyz and Uzbek Tien Shan and Pamir, highlights the existing and the new measurements on these glaciers, and presents an example for how the old and new data can be combined to establish multi-decadal mass balance time series. This is crucial for understanding the impact of climate change on glaciers in this region.