Search Results (61)

Accurate delineation of glacier extent is crucial for monitoring glacier degradation in the context of global warming. Satellite remote sensing with high spatial and temporal resolution offers an effective approach for large-scale glacier mapping. However, persistent cloud cover limits its application on the Tibetan Plateau, leading to substantial omissions in glacier identification. Therefore, this study proposed a novel sub-cloudy glacier extraction model (SCGEM) designed to extract glacier boundaries from cloud-affected satellite images. First, the cloud-insensitive characteristics of topo-graphic (Topo.), synthetic aperture radar (SAR), and temporal (Tempo.) features were investigated for extracting glaciers under cloud conditions. Then, a transformer-based generative adversarial network (GAN) was proposed, which incorporates an image reconstruction and an adversarial branch to improve glacier extraction accuracy under cloud cover. Experimental results demonstrated that the proposed SCGEM achieved significant improvements with an IoU of 0.7700 and an F1 score of 0.8700. The Topo., SAR, and Tempo. features all contributed to glacier extraction in cloudy areas, with the Tempo. features contributing the most. Ablation studies further confirmed that both the adversarial training mechanism and the multi-task architecture contributed notably to improving the extraction accuracy. The proposed architecture serves both to data clean and enhance the extraction of glacier texture features. Full article

Since the 1990s, glaciers in the Ányêmaqên Mountains of the Qinghai–Tibet Plateau have exhibited anomalous retreat and thinning. This persistent deglaciation has triggered secondary disasters including glacial debris flows, ice collapses, and glacial lake outburst floods, posing significant threats to regional ecological security and sustainable socioeconomic development. To address this issue, we conducted a comprehensive analysis of glacial morphological characteristics using multi-source time-series high-resolution remote sensing imagery spanning 2013–2024. Glacier boundaries were extracted through integrated methodologies combining manual visual interpretation, band ratio thresholding, three-dimensional geomorphic analysis, and an optimized DeepLabV3+ convolutional neural network with adaptive activation thresholds. Extraction accuracy was rigorously validated using quantitative metrics (Accuracy, Precision, Recall, Loss, and F1-score). Key findings reveal the following: dominant glacier types include ice caps, valley glaciers, and hanging glaciers distributed at mean elevations of 5200–5600 m; total glacial area decreased from 102.71 km² to 81.10 km², yielding an average annual decrease rate of −1.93%; glacier count increased from 74 to 86, corresponding to a mean relative change rate of 1.18% per annum; and thirty-eight geohazard sites were identified predominantly on upper slopes (30–50°) of north-facing terrain, with elevations ranging from 4500–5400 m (base) to 5120–6050 m (crest). These results provide critical data support for enhancing ecological resilience, strengthening disaster mitigation capabilities, and safeguarding public safety and infrastructure against climate change impacts in the region. Full article

Glaciers and glacierets of the Kuznetsky Alatau Mountains are distributed at altitudes of 1200–1500 m above sea level, which is not typical for continental areas. The main factor contributing to the persistence of glaciation here is abundant winter precipitation. According to ground surface temperature measurements, the negative annual values are typical for upper glacier boundaries only. Since intensive study during the compilation of the USSR Glacier Inventory (1965–1980), the glaciation of the region has undergone notable changes. To assess the current state of glaciation, Sentinel-2 satellite images were used; contours of the glaciers were traced on the basis of images from 2021 to 2023. In total, 78 glaciers and 57 glacierets were identified. UAV imagery and field inspection were used for validation. The total glaciated area has reduced from 8.5 to 3.1 km², which is 50–75% for selected river basins, with slope morphological types decreasing the most. According to our opinion, the morphological classification requires clarification due to absence of hanging glaciers, described previously. Full article

Alpine scree, a distinctive plateau ecosystem, serves as habitat for numerous rare and endangered species. However, current research does not differentiate it from desert in terms of spatial boundary, hindering biodiversity conservation and ecological monitoring efforts. Using the Tibetan Plateau as a case study, we defined the spatial boundary of alpine scree based on its surface formation process and examined its distribution and long-term evolution. The results show that in 2020, alpine scree on the Tibetan Plateau covered 73,735.34 km², 1.5 times the area of glaciers. Alpine scree is mostly distributed at elevations between 4000 and 6000 m, with a slope of approximately 30–40 degrees. Characterized by low temperature and sparse rainfall, the regions are located in the humid zone. From 1975 to 2020, the area of alpine scree initially increased before declining, with an overall decrease of 560.68 km². Climate warming was the primary driver of these changes, leading to an increase in scree from 1975 to 1995 and a decrease in scree from 1995 to 2020. Additionally, between 1975 and 2020, the Tibetan Plateau’s grasslands shifted upward by 16.47 km². This study enhances our understanding of the spatial distribution and dynamics of this unique ecosystem, alpine scree, offering new insights into climate change impacts on alpine ecosystems. Full article

The Alpine Periglacial Weathering Zone (APWZ) is a critical transitional belt between alpine vegetation and glaciers, and a highly sensitive region to climate change. Its dynamic variations profoundly reflect the surface environment’s response to climatic shifts. Taking Gongga Mountain as the study area, this study utilizes summer Landsat imagery from 1986 to 2024 and constructs a remote sensing method based on NDVI and NDSI indices using the Otsu thresholding algorithm on the Google Earth Engine platform to automatically extract the positions of the upper limit of vegetation and the snowline. Results show that over the past four decades, the APWZ in Gongga Mountain has exhibited a continuous upward shift, with the mean elevation rising from 4101 m to 4575 m. The upper limit of vegetation advanced at an average rate of 17.43 m/a, significantly faster than the snowline shift (3.9 m/a). The APWZ also experienced substantial areal shrinkage, with an average annual reduction of approximately 13.84 km², highlighting the differential responses of various surface cover types to warming. Spatially, the most pronounced changes occurred in high-elevation zones (4200–4700 m), moderate slopes (25–33°), and sun-facing aspects (east, southeast, and south slopes), reflecting a typical climate–topography coupled driving mechanism. In the upper APWZ, glacier retreat has intensified weathering and increased debris accumulation, while the newly formed vegetation zone in the lower APWZ remains structurally fragile and unstable. Under extreme climatic disturbances, this setting is prone to triggering chain-type hazards such as landslides and debris flows. These findings enhance our capacity to monitor alpine ecological boundary changes and identify associated disaster risks, providing scientific support for managing climate-sensitive mountainous regions. Full article

Glacier classification offers a structured framework for assessing glacier characteristics and understanding their responses to climate change. In this study, we apply the Shi–Xie glacier classification system, proposed by Chinese glaciologists Shi and Xie, to evaluate the transformation of extremely continental, subcontinental, and maritime glaciers across China over the past four decades. Our results show a widespread rise in equilibrium line altitudes (ELAs), alongside complex changes in climatic and glaciological parameters. Notably, despite ongoing warming trends, nearly half of the glaciers experienced cooling at the ELA, and over two-thirds showed a decline in summer mean temperatures. This apparent contradiction is explained by elevation-induced cooling; as ELAs rise to higher altitudes, the corresponding summer air temperatures decline due to the lapse rate effect. Near-surface ice temperatures (20 m depth) were strongly consistent with changes in annual air temperature. Precipitation trends were spatially heterogeneous, yet around 70% of glaciers experienced stable or slightly increasing annual precipitation. In contrast, maritime glaciers, particularly those in the southeastern glacierized regions, exhibited marked decreases. Glacier surface velocities generally declined, with 90% of glaciers flowing at speeds below 50 m a⁻¹. Threshold-based analysis reveals that glaciers in transitional zones frequently exhibit multi-indicator deviations. Extremely continental glaciers near classification boundaries showed a shift toward warmer, wetter subcontinental conditions, while maritime glaciers tended toward drier, colder subcontinental characteristics. These findings offer new insights into the differentiated responses and ongoing transformation of glacier types in China under climate change. Full article

Ice thickness is a key parameter for glacier mass estimations and glacier dynamics simulations. Multiple physical models have been developed by glaciologists to estimate glacier ice thickness. However, obtaining internal and basal glacier parameters required by physical models is challenging, often leading to simplified models that struggle to capture the nonlinear characteristics of ice flow and resulting in significant uncertainties. To address this, this study proposes a convolutional neural network (CNN)-based deep learning model for glacier ice thickness estimation, named the Coordinate-Attentive Dense Glacier Ice Thickness Estimate Model (CADGITE). Based on in situ ice thickness measurements in the Swiss Alps, a CNN is designed to estimate glacier ice thickness by incorporating a new architecture that includes a Residual Coordinate Attention Block together with a Dense Connected Block, using the distance to glacier boundaries as a complement to inputs that include surface velocity, slope, and hypsometry. Taking ground-penetrating radar (GPR) measurements as a reference, the proposed model achieves a mean absolute deviation (MAD) of 24.28 m and a root mean square error (RMSE) of 37.95 m in Switzerland, outperforming mainstream physical models. When applied to 14 glaciers in High Mountain Asia, the model achieves an MAD of 20.91 m and an RMSE of 27.26 m compared to reference measurements, also exhibiting better performance than mainstream physical models. These comparisons demonstrate the good accuracy and cross-regional transferability of our approach, highlighting the potential of using deep learning-based methods for larger-scale glacier ice thickness estimation. Full article

Glaciers are one of the most important water resources in the arid regions of Xinjiang, making it crucial to accurately monitor glacier changes for the region’s sustainable development. However, due to their typical distribution in remote, high-altitude areas, large-scale and long-term field observations are often constrained by the high costs of manpower, resources, and finances. Globally, fewer than 40 glaciers have been monitored for more than 20 years, and, in China, only Glacier No. 1 at the headwaters of the Urumqi River has monitoring records exceeding 50 years. To address these challenges, this study analyzed glacier changes in the Tomur Peak region of the Tianshan Mountains over the past 35 years using Landsat satellite imagery. Through experiments with deep learning models, the results show that the 3-4-5 band combination performed best for glacier boundary extraction. The DeepLabV3+ model, with MobileNetV2 as the backbone, achieved an overall accuracy of 90.44%, a recall rate of 82.75%, and a mean Intersection over Union (IoU) that was 1.6 to 5.94 percentage points higher than other models. Based on these findings, the study further analyzed glacier changes in the Tomur Peak region, revealing an average annual glacier reduction rate of 0.18% and a retreat rate of 6.97 km²·a⁻¹ over the past 35 years. This research provides a more precise and comprehensive scientific reference for understanding glacier changes in arid regions, with significant implications for enhancing our understanding of the impacts of climate change on glaciers, optimizing water resource management, and promoting regional sustainable development. Full article

Accurately delineating the range of the Xiang-E submarine uplifts is the key to the exploration and development of Silurian shale gas in the Western Hunan–Hubei region. Based on the graptolite stratigraphic division of Well JD1 in Jianshi area, Hubei Province, and combined with the GBDB online database (Geobiodiversity Database), the study compared the shale graptolite sequences of the Wufeng Formation and Longmaxi Formation from 23 profile points and 11 wells which cross the Ordovician–Silurian boundary. The range of the Xiang-E submarine uplift was delineated, and its evolution model and formation mechanism at the Ordovician–Silurian transition were discussed. The graptolite stratigraphic correlation results of drillings and profiles confirmed the development of submarine uplifts in the Western Hunan–Hubei region at the Ordovician–Silurian transition–Xiang-E submarine uplift. Under the joint control of the Guangxi movement and the global sea-level variation caused by the condensation and melting of polar glaciers, the overall evolution of the Xiang-E submarine uplift is characterized by continuous uplift from the Katian Age to the early Rhuddanian Age, with the influence gradually expanding, and then gradually shrinking back in the middle and late Rhuddanian Age. The initial form of the Xiang-E submarine uplift may have originated from the Guangxi movement, and the global sea-level variation caused by polar glacier condensation and melting is the main controlling factor for the changes in its influence range. Within the submarine uplifts range, the Wufeng–Longmaxi Formations generally lack at least two graptolite zone organic-rich shales in the WF2-LM4, and the shale gas reservoir has a poor hydrocarbon generation material foundation, posing a high risk for shale gas exploration. The Silurian in Xianfeng, Lichuan, Yichang of Hubei and Wushan of Chongqing has good potential for shale gas exploration and development. Full article

The West Kunlun Mountains (WKL) gather lots of large-scale glaciers, which play an important role in the climate and freshwater resource for central Asia. Despite extensive studies on glaciers in this region, a comprehensive understanding of inter-annual variations in glacier area, flow velocity, and terminus remains lacking. This study used a deep learning model to derive time-series glacier boundaries and the sub-pixel cross-correlation method to calculate inter-annual surface flow velocity in this region from 71 Sentinel-2 images acquired between 2016 and 2024. We analyzed the spatial-temporal variations of glacier area, velocity, and terminus. The results indicate that, as follows: (1) The glacier area in the WKL remained relatively stable, with three glaciers expanding by more than 0.5 km² and five glaciers shrinking by over 0.5 km² from 2016 to 2024. (2) Five glaciers exhibited surging behavior during the study period. (3) Six glaciers, with velocities exceeding 50 m/y, have the potential to surge. (4) There were eight obvious advancing glaciers and nine obvious retreating glaciers during the study period. Our study demonstrates the potential of Sentinel-2 for comprehensively monitoring inter-annual changes in mountain glacier area, velocity, and terminus, as well as identifying glacier surging events in regions beyond the study area. Full article

In this study, we utilized Random Forest machine learning classification to assess the current state of glaciers in the western United States using Sentinel-2A satellite imagery. By analyzing Sentinel-2A imagery from September 2020 and comparing it to the RGI inventory, the study determined the current conditions of the glaciers. Our findings unveiled a significant reduction in both glacier area and volume in the western United States since the mid-20th century. Currently, the region hosts 2878 glaciers and perennial snowfield spanning eight states, covering a total area of 428.32 ± 7.8 km² with a corresponding volume of 9.00 ± 0.9 km³. During the study period, a loss of 244.31 km² in glacier area was observed, representing a 36.32% decrease when contrasted with the RGI boundaries. The volume lost during this period amounted to 4.96 km³, roughly equivalent to 4.7 gigatons of water. Among the states, Washington experienced the most significant glacier area reduction, with a loss of 133.16 km². Notably, glaciers in the North Cascade Range of Washington, such as those in Mt. Baker and Mt. Shuksan, now cover, on average, only 85% of their original glacier boundaries with ice and snow at the conclusion of the 2020 hydrological year. Major glaciers, including the White River Glacier, West Nooksack Glacier, and White Chuck Glacier, have lost more than 50 percent of their original area. Full article

Glaciers have experienced a global trend of recession within the past century. Quantification of glacier variations using satellite imagery has been of great interest due to the importance of glaciers as freshwater resources and as indicators of climate change. Spatiotemporal glacier dynamics must be monitored to quantify glacier variations. The potential methods to quantify spatiotemporal glacier dynamics with increasing complexity levels include detecting the terminus location, measuring the length of the glacier from the accumulation zone to the terminus, quantifying the glacier surface area, and measuring glacier volume. Although some deep learning methods designed purposefully for glacier boundary segmentation have achieved acceptable results, these models are often localized to the region where their training data were acquired and further rely on the training sets that were often curated manually to highlight glacial regions. Due to the very large number of glaciers, it is practically impossible to perform a worldwide study of glacier dynamics using manual methods. As a result, an automated or semi-automated method is highly desirable. The current study has built upon our previous works moving towards identification methods of the 2D glacier profile for glacier area segmentation. In this study, a deep learning method is proposed for segmentation of temporal Landsat images to quantify the glacial region within the Mount Cook/Aoraki massif located in the Southern Alps/Kā Tiritiri o te Moana of New Zealand/Aotearoa. Segmented glacial regions can be further utilized to determine the relationship of their variations due to climate change. This model has demonstrated promising performance while trained on a relatively small dataset. The permanent ice and snow class was accurately segmented at a 92% rate by the proposed model. Full article

Supra-glacial debris cover is important for the control of surface ice melt and glacier retreat in mountain regions. Despite the progress in techniques based on various satellite imagery, the mapping of debris-covered glacier boundaries over large regions remains a challenging task. Previous studies of the debris-covered glaciers in the Greater Caucasus have only focused on limited areas. In this study, using the Sentinel 1–2 imagery (2020), DebCovG-carto toolbox, and existing glacier inventory (2020), we produced the first detailed assessment of supra-glacial debris cover for individual glaciers in the entire Greater Caucasus. Our study shows that in 2020, 10.3 ± 5.6% of the glacier surface in this mountain region was covered by debris. A comparison of sub-regions such as the Elbrus Massif and other individual glaciers from the central Greater Caucasus shows an increasing trend of supra-glacial debris cover from 2014 to 2020. The total area of supra-glacial debris cover expanded from ~4.6% to ~5.8% for Elbrus and from ~9.5% to ~13.9% for the glaciers of the central Greater Caucasus during the same period. Supra-glacial debris cover also expanded upward on these glaciers between 2014 and 2020. A recent increase in rock-ice avalanche activity in combination with increased air temperature and decreased precipitation in the Greater Caucasus may be responsible for this upward migration and expanded area of supra-glacial debris cover. This study provides valuable insights into the spatial distribution, temporal evolution, and factors influencing supra-glacial debris cover in the Greater Caucasus. The findings contribute to our understanding of glacier dynamics and highlight the importance of continuous monitoring and assessment of supra-glacial debris cover in the context of climate change and glacier retreat. We recommend using the DebCovG-carto toolbox for regional assessment of supra-glacial debris coverage in other mountain regions as well. Full article

Glacier inventories are fundamental in understanding glacier dynamics and glacier-related environmental processes. High-resolution mapping of glacier outlines is lacking, although high-resolution satellite images have become available in recent decades. Challenges in development of glacier inventories have always included accurate delineation of boundaries of debris-covered glaciers, which is particularly true for high-resolution satellite images due to their limited spectral bands. To address this issue, we introduced an automated, high-precision method in this study for mapping debris-covered glaciers based on 1 m resolution Gaofen-2 (GF-2) imagery. By integrating GF-2 reflectance, topographic features, and land surface temperature (LST), we used an attention mechanism to improve the performance of several deep learning network models (the U-Net network, a fully convolutional neural network (FCNN), and DeepLabV3+). The trained models were then applied to map the outlines of debris-covered glaciers, at 1 m resolution, in the central Karakoram regions. The results indicated that the U-Net model enhanced with the Convolutional Block Attention Module (CBAM) outperforms other deep learning models (e.g., FCNN, DeepLabV3+, and U-Net model without CBAM) in terms of precision for supraglacial debris identification. On the testing dataset, the CBAM-enhanced U-Net model achieved notable performance metrics, with its accuracy, F1 score, mean intersection over union (MIoU), and kappa coefficient reaching 0.93, 0.74, 0.79, and 0.88. When applied at the regional scale, the model even exhibits heightened precision (accuracies = 0.94, F1 = 0.94, MIoU = 0.86, kappa = 0.91) in mapping debris-covered glaciers. The experimental glacier outlines were accurately extracted, enabling the distinction of supraglacial debris, clean ice, and other features on glaciers in central Karakoram using this trained model. The results for our method revealed differences of 0.14% for bare ice and 10.36% against the manually interpreted glacier boundary for supraglacial debris. Comparison with previous glacier inventories revealed raised precisions of 8.74% and 4.78% in extracting clean ice and with supraglacial debris, respectively. Additionally, our model demonstrates exceptionally high exclusion for bare rock outside glaciers and could reduce the influence of non-glacial snow on glacier delineation, showing substantial promise in mapping debris-covered glaciers. Full article

Small intermountain river basins are most suitable for developing new methods to estimate water balance due to their well-defined catchment boundaries, relatively rapid runoff processes, and accessible landscapes for study. In general terms, dissecting the hydrograph of a small mountain river requires calibration of the flow model against multi-year data sets, including (a) glacier mass balance and snow water content, (b) radiation balance calculation, (c) estimation of the groundwater contribution, and (d) water discharge measurements. The minimum primary data set is limited to the precipitation and temperature distributions at the catchment. This approach postulates that the conditions for the formation of all components of river flow are known in advance. It is reduced to calculating the dynamic balance between precipitation (input part) and runoff, ablation, and evaporation (output part). In practice, accurately accounting for the inflow and outflow components of the balance, as well as the impact of regulating reservoirs, can be a challenging task that requires significant effort and expense, even for the extensively researched catchments. Our studies indicate the potential benefits of an approach based on one-time, but detailed, observations of stable isotope composition, temperature, and water chemistry, in addition to standard datasets. This paper presents the results of the 2022–2023 work conducted in the basin of the small mountain river Ala-Archa, located on the northern slope of the Kyrgyz Range in Tien-Shan, which was chosen as an example due to its well-studied nature. Our approach could identify previously unknown factors of flow formation and assess the time and effectiveness of work in similar conditions. Full article