Search Results (14)

The glaciers in the High Mountain Asia (HMA) region are highly vulnerable to global warming, posing significant threats to downstream populations and infrastructure through glacier lake outburst floods (GLOFs). The monitoring and early warnings of these events are challenging due to sparse observations in these remote regions. To explore reproducing the evolution of GLOFs with sparse observations in situ, this study focuses on the outburst event and corresponding GLOFs in August 2018 caused by the Kyagar Glacier lake, a typical glacier lake of the HMA in the Karakoram, which is known for its frequent outburst events, using a combination of multi-satellite remote sensing data (Sentinel-1 and Sentinel-2) and the HEC-RAS hydrodynamic model. The water depth of the glacier lake and downstream was extracted from satellite data adapted by the Floodwater Depth Elevation Tool (FwDET) as a baseline to compare them with simulations. The elevation-water volume curve was obtained by extrapolation and was applied to calculate the water surface elevation (WSE). The inundation of the downstream of the lake outburst was obtained through flood modeling by incorporating a load elevation-water volume curve and the Digital Elevation Model (DEM) into the hydrodynamic model HEC-RAS. The results showed that the Kyagar glacial lake outburst was rapid and destructive, accompanied by strong currents at the end of each downstream storage ladder. A series of meteorological evaluation indicators showed that HEC-RAS reproduced the medium and low streamflow rates well. This study demonstrated the value of integrating remote sensing and hydrodynamic modeling into GLOF assessments in data-scarce regions, providing insights for disaster risk management and mitigation. Full article

Continuously monitoring and mapping glacial lake variation is of great importance for determining changes in water resources and potential hazards in alpine cryospheric regions. The semi-automated glacial lake mapping methods used currently are hampered by inherent subjectivity and inefficiency. This study used improved YOLOv5 strategies to extract glacial lake boundaries from Sentinel-2 imagery. These strategies include using the space-to-depth technique to identify small glacial lakes, and adopting the coordinate attention and the convolution block attention modules to improve mapping performance and adaptability. In terms of glacial lake extraction, the improved YOLOv5-seg network achieved values of 0.95, 0.93, 0.96, and 0.94 for precision (P), recall (R), mAP_0.5, and the F1 score, respectively, indicating an overall improvement in performance of 12% compared to that of the newest YOLOv8 networks. In High Mountain Asia (HMA), 23,108 glacial lakes with a total area of 1847.5 km² were identified in imagery from 2022 using the proposed method. Compared with the use of manual interpretation for lake boundary extraction in test sites of HMA, the proposed method achieved values of 0.89, 0.87, and 0.86 for P, R, and the F1 score, respectively. Our proposed deep learning method has improved accuracy in glacial lake extraction because it can address the challenge represented by frozen or high-turbidity glacial lakes in HMA. Full article

The glacial lakes in the Southeastern Qinghai–Tibet Plateau (SEQTP) have undergone dramatic expansion in the context of global warming, leading to several glacial lake outburst floods (GLOFs) disasters. However, there is a gap and incompleteness in glacial lake inventories across this area due to the heavy cloud cover. In this study, an updated and comprehensive glacial lake inventory was produced by object-based image analysis (OBIA) and manual vectorization based on the Sentinel-1 SAR and Sentinel-2 MSI images acquired in 2022. Detailed steps regarding the OBIA were provided, and the feature set of Sentinel-1 SAR images suitable for extracting glacial lakes was also determined in this paper. We found that the mean combination of ascending-orbit and descending-orbit images is appropriate for mapping glacial lakes. VV-polarized backscattering coefficients from ascending-orbit achieved a better performance for delineating glacial lakes within the study area. Moreover, the distribution of glacial lakes was characterized in terms of four aspects: size, type, elevation, and space. There were 3731 glacial lakes with a total area of 1664.22 ± 0.06 km² in the study area; most of them were less than 0.07 km². Ice-contacted lakes were primarily located in the Palongzangbo basin (13.24 ± 0.08 km²). Nyang Qu basin had the most abundant glacial lake resources (2456 and 93.32 ± 0.18 km²). A comparison with previously published glacial lake datasets demonstrated that our dataset is more complete. This inventory is useful for evaluating water resources, studying glacier–glacial lake interactions, and assessing GLOFs’ susceptibility in the SEQTP. Full article

Establishing an effective real-time monitoring and early warning system for glacier lake outburst floods (GLOFs) requires a full understanding of their occurrence mechanism. However, the harsh conditions and hard-to-reach locations of these glacial lakes limit detailed fieldwork, making satellite imagery a critical tool for monitoring. Lake Mercbacher, an ice-dammed lake in the central Tian Shan mountain range, poses a significant threat downstream due to its relatively high frequency of outbursts. In this study, we first monitored the daily changes in the lake area before the 2022 Lake Mercbacher outburst. Additionally, based on historical satellite images from 2014 to 2021, we calculated the maximum lake area (MLA) and its changes before the outburst. Furthermore, we extracted the proportion of floating ice and water area during the period. The results show that the lake area of Lake Mercbacher would first increase at a relatively low speed (0.01 km²/day) for about one month, followed by a relatively high-speed increase (0.04 km²/day) until reaching the maximum, which would last for about twenty days. Then, the lake area would decrease slowly until the outburst, which would last five days and is significant for early warning. Moreover, the floating ice and water proportion provides more information about the outburst signals. In 2022, we found that the floating ice area increased rapidly during the early warning stage, especially one day before the outburst, accounting for about 50% of the total lake area. Historical evidence indicates that the MLA shows a decreasing trend, and combining it with the outburst date and climate data, we found that the outburst date shows an obvious advance trend (6 days per decade) since 1902, caused by climate warming. Earlier melting results in an earlier outburst. This study provides essential references for monitoring Lake Mercbacher GLOFs and building an effective early warning system. Full article

Against the background of the ongoing atmospheric warming, the glacial lakes that are nourished and expanded in High Mountain Asia pose growing risks of glacial lake outburst floods (GLOFs) hazards and increasing threats to the downstream areas. Effectively extracting the area and consistently monitoring the dynamics of these lakes are of great significance in predicting and preventing GLOF events. To automatically extract the lake areas, many deep learning (DL) methods capable of capturing the multi-level features of lakes have been proposed in segmentation and classification tasks. However, the portability of these supervised DL methods need to be improved in order to be directly applied to different data sources, as they require laborious effort to collect the labeled lake masks. In this work, we proposed a simple glacial lake extraction model (SimGL) via weakly-supervised contrastive learning to extend and improve the extraction performances in cases that lack the labeled lake masks. In SimGL, a Siamese network was employed to learn similar objects by maximizing the similarity between the input image and its augmentations. Then, a simple Normalized Difference Water Index (NDWI) map was provided as the location cue instead of the labeled lake masks to constrain the model to capture the representations related to the glacial lakes and the segmentations to coincide with the true lake areas. Finally, the experimental results of the glacial lake extraction on the 1540 Landsat-8 image patches showed that our approach, SimGL, offers a competitive effort with some supervised methods (such as Random Forest) and outperforms other unsupervised image segmentation methods in cases that lack true image labels. Full article

Alpine lakes, which include glacial and nonglacial lakes, are widely distributed in high mountain areas and are sensitive to climate and environmental changes. Remote sensing is an effective tool for identifying alpine lakes over large regions, but in the case of small lakes, the complex terrain and extreme weather make their accurate identification extremely challenging. This paper presents an automated method for alpine lake identification developed by leveraging deep learning algorithms and multi-source high-resolution satellite data. The method is able to detect the outlines and types of alpine lakes from high-resolution optical and Synthetic Aperture Radar (SAR) satellite data. In this study, a total of 4584 alpine lakes (including 2795 glacial lakes) were identified in the Eastern Himalayas from Sentinel-1 and Sentinel-2 data acquired during 2016–2020. The average area of the lakes was 0.038 km², and the average elevation was 4974 m. High accuracy was reported for the dataset for both segmentation (mean Intersection Over Union (MIoU) > 72%) and classification (Overall Accuracy, User’s and Producer’s Accuracies, and F1-Score are all higher than 85%). A higher accuracy was found for the combination of optical and SAR data than relying on single-sourced data, for which the MIoU increased by at least 12%, suggesting that the combination of optical and SAR data is critical for improving the identification of alpine lakes. The deep learning-based method demonstrated a significant improvement over traditional spectral extraction methods. Full article

Glacial lakes are important freshwater resources in southern Tibet. However, glacial lake outburst floods have significantly jeopardized the safety of local residents. To better understand the changes in glacial lakes in response to climate change, it is necessary to conduct a long-term evaluation on the areal dynamics of glacial lakes, assisted with local observations. Here, we propose an innovative method of classification and stacking extraction to accurately delineate glacial lakes in southwestern Tibet from 1990 to 2020. Based on Landsat images and meteorological data, we used geographic detectors to detect correlation factors. Multiple regression models were used to analyze the driving factors of the changes in glacier lake area. We combined bathymetric data of the glacial lakes with the changes in climatic variables and utilized HEC-RAS to determine critical circumstances for glacial lake outbursts. The results show that the area of glacial lakes in Nyalam County increased from 27.95 km² in 1990 to 52.85 km² in 2020, and eight more glacial lakes were observed in the study area. The glacial lake area expanded by 89.09%, where we found significant growth from 2015 to 2020. The correlation analysis between the glacial lake area and climate change throughout the period shows that temperature and precipitation dominate the expansion of these lakes from 1990 to 2020. We also discover that the progressive increase in water volume of glacial lakes can be attributed to the constant rise in temperature and freeze–thaw of surrounding glaciers. Finally, the critical conditions for the glacial lake’s outburst were predicted by using HEC-RAS combined with the changes in the water volume and climatic factors. It is concluded that GangxiCo endures a maximum water flow of 4.3 × 10⁸ m³, and the glacial lake is in a stable changing stage. This conclusion is consistent with the field investigation and can inform the prediction of glacial lake outbursts in southwestern Tibet in the future. Full article

We assessed the nature and spatial and temporal patterns of deformation over the Miami Park bluffs on the eastern margin of Lake Michigan and investigated the factors controlling its observed deformation. Our approach involved the following steps: (1) extracting bluff deformation rates (velocities along the line of sight of the satellite) using a stack of Sentinel-1A radar imagery in ascending acquisition geometry acquired between 2017 and 2021 and applying the Intermittent Small Baseline Subset (ISBAS) InSAR time series analysis method; (2) generating high-resolution (5 cm) elevation models and orthophotos from temporal unmanned aerial vehicle (UAV) surveys acquired in 2017, 2019, and 2021; and (3) comparing the temporal variations in mass wasting events to other relevant datasets including the ISBAS-based bluff deformation time series, lake level (LL) variations, and local glacial stratigraphy. We identified areas witnessing high line-of-sight (LOS) deformation rates (up to −21 mm/year) along the bluff from the ISBAS analysis and seasonal deformation patterns associated with freeze-thaw cycles, suggesting a causal effect. The acceleration of slope failures detected from field and UAV acquisitions correlated with high LLs and intensified onshore wave energy in 2020. The adopted methodology successfully predicts landslides caused by freezes and thaws of the slope face by identifying prolonged slow deformation preceding slope failures, but it does not predict the catastrophic landslides preceded by short-lived LOS deformation related to LL rise. Full article

Accurately assessing the dynamic changes of glaciers under the background of climate warming is of great significance for taking scientific countermeasures to cope with climate change. Aiming at the difficulties of glacier identification, such as mountain and cloud shadow, cloud cover and seasonal snow cover in high altitude areas, this paper proposes a reflectivity difference index for identifying glaciers in shadow and glacial lakes and a multi-temporal minimum band ratio index for reducing the influence of snow cover. It establishes a new large-scale glacier extraction method (so-called Double RF) based on the random forest algorithm of Google Earth Engine (GEE) and applies it to the Tibetan Plateau. The verification results based on 30% sample points show that overall accuracies of the first and second classification of 96.04% and 90.75%, respectively, and Kappa coefficients of 0.92 and 0.83, respectively. Compared with the real glacier dataset, the percentage of correctly extracted glacier area of the total area of glacier dataset (PGD) was 84.07%, and the percentage of correctly extracted glacier area of the total area of extracted glacier (PGE) was 89.06%; the harmonic mean (HM) of the two was 86.49%. The extraction results were superior to the commonly used glacier extraction methods: the band ratio method based on median composite image (Median_Band) (HM = 79.47%), the band ratio method based on minimum composite image (Min_Band) (HM = 81.19%), the normalized difference snow cover index method based on median composite image (Median_NDSI) (HM = 83.48%), the normalized difference snow cover index method based on minimum composite image (Min_NDSI) (HM = 84.08%), the random forest method based on median composite image (Median_RF) (HM = 83.87%) and the random forest method based on minimum composite image (Min_RF) (HM = 85.36%). The new glacier extraction method constructed in this study could significantly improve the identification accuracy of glaciers under the influences of shadow, snow cover, cloud cover and debris. This study provides technical support for obtaining long-term glacier distribution data on the Tibetan Plateau and revealing the impact of climate warming on glaciers on the Tibetan Plateau. Full article

Glacial lake extraction is essential for studying the response of glacial lakes to climate change and assessing the risks of glacial lake outburst floods. Most methods for glacial lake extraction are based on either optical images or synthetic aperture radar (SAR) images. Although deep learning methods can extract features of optical and SAR images well, efficiently fusing two modality features for glacial lake extraction with high accuracy is challenging. In this study, to make full use of the spectral characteristics of optical images and the geometric characteristics of SAR images, we propose an atrous convolution fusion network (ACFNet) to extract glacial lakes based on Landsat 8 optical images and Sentinel-1 SAR images. ACFNet adequately fuses high-level features of optical and SAR data in different receptive fields using atrous convolution. Compared with four fusion models in which data fusion occurs at the input, encoder, decoder, and output stages, two classical semantic segmentation models (SegNet and DeepLabV3+), and a recently proposed model based on U-Net, our model achieves the best results with an intersection-over-union of 0.8278. The experiments show that fully extracting the characteristics of optical and SAR data and appropriately fusing them are vital steps in a network’s performance of glacial lake extraction. Full article

Glaciers and numerous glacial lakes that are produced by glacier melting are key indicators of climate change. Often overlooked, supra-glacial lakes develop in the melting area in the low-lying part of a glacier and appear to be highly variable in their size, shape, and location. The lifespan of these lakes is thought to be quite transient, since the lakes may be completely filled by water and burst out within several weeks. Changes in supra-glacial lake outlines and other surface features such as supra-glacial rivers and crevasses on the glaciers are useful indicators for the direct monitoring of glacier changes. Synthetic aperture radar (SAR) is not affected by weather and climate, and is an effective tool for study of glaciated areas. The development of the Chinese GaoFen-3 (GF-3) SAR, which has high spatial and temporal resolution and high-precision observation performance, has made it possible to obtain dynamic information about glaciers in more detail. In this paper, the classical Canny operator, the variational B-spline level-set method, and U-Net-based deep-learning model were applied and compared to extract glacial lake outlines and other surface features using different modes and Chinese GF-3 SAR imagery in the Mount Everest Region of the Himalayas. Particularly, the U-Net-based deep-learning method, which was independent of auxiliary data and had a high degree of automation, was used for the first time in this context. The experimental results showed that the U-Net-based deep-learning model worked best in the segmentation of supra-glacial lakes in terms of accuracy (Precision = 98.45% and Recall = 95.82%) and segmentation efficiency, and was good at detecting small, elongated, and ice-covered supra-glacial lakes. We also found that it was useful for accurately identifying the location of supra-glacial streams and ice crevasses on glaciers, and quantifying their width. Finally, based on the time series of the mapping results, the spatial characteristics and temporal evolution of these features over the glaciers were comprehensively analyzed. Overall, this study presents a novel approach to improve the detection accuracy of glacier elements that could be leveraged for dynamic monitoring in future research. Full article

Glacial lakes (GLs), a vital link between the hydrosphere and the cryosphere, participate in the local hydrological process, and their interannual dynamic evolution is an objective reflection and an indicator of regional climate change. The complex terrain and climatic conditions in mountainous areas where GLs are located make it difficult to employ conventional remote sensing observation means to obtain stable, accurate, and comprehensive observation data. In view of this situation, this study presents an algorithm with a high generalization ability established by optimizing and improving a deep learning (DL) semantic segmentation network model for extracting GL contours from combined synthetic-aperture radar (SAR) amplitude and multispectral imagery data. The aim is to use the high penetrability and all-weather advantages of SAR to reduce the effects of cloud cover as well as to integrate the multiscale and detail-oriented advantages of multispectral data to facilitate accurate, quantitative extraction of GL contours. The accuracy and reliability of the model and algorithm were examined by employing them to extract the contours of GLs in a large region of south-eastern Tibet from Landsat 8 optical remote sensing images and Sentinel-1A amplitude images. In this study, the contours of a total 8262 GLs in south-eastern Tibet were extracted. These GLs were distributed predominantly at altitudes of 4000–5500 m. Only 17.4% of these GLs were greater than 0.1 km² in size, while a large number of small GLs made up the majority. Through analysis and validation, the proposed method was found highly capable of distinguishing rivers and lakes and able to effectively reduce the misidentification and extraction of rivers. With the DL model based on combined optical and SAR images, the intersection-over-union (IoU) score increased by 0.0212 (to 0.6207) on the validation set and by 0.038 (to 0.6397) on the prediction set. These validation data sufficiently demonstrate the efficacy of the model and algorithm. The technical means employed in this study as well as the results and data obtained can provide a reference for research and application expansion in related fields. Full article

Glacial lakes mapping using satellite remote sensing data are important for studying the effects of climate change as well as for the mitigation and risk assessment of a Glacial Lake Outburst Flood (GLOF). The 3U cubesat constellation of Planet Labs offers the capability of imaging the whole Earth landmass everyday at 3–4 m spatial resolution. The higher spatial, as well as temporal resolution of PlanetScope imagery in comparison with Landsat-8 and Sentinel-2, makes it a valuable data source for monitoring the glacial lakes. Therefore, this paper explores the potential of the PlanetScope imagery for glacial lakes mapping with a focus on the Hindu Kush, Karakoram and Himalaya (HKKH) region. Though the revisit time of the PlanetScope imagery is short, courtesy of 130+ small satellites, this imagery contains only four bands and the imaging sensors in these small satellites exhibit varying spectral responses as well as lower dynamic range. Furthermore, the presence of cast shadows in the mountainous regions and varying spectral signature of the water pixels due to differences in composition, turbidity and depth makes it challenging to automatically and reliably extract surface water in PlanetScope imagery. Keeping in view these challenges, this work uses state of the art deep learning models for pixel-wise classification of PlanetScope imagery into the water and background pixels and compares the results with Random Forest and Support Vector Machine classifiers. The deep learning model is based on the popular U-Net architecture. We evaluate U-Net architecture similar to the original U-Net as well as a U-Net with a pre-trained EfficientNet backbone. In order to train the deep neural network, ground truth data are generated by manual digitization of the surface water in PlanetScope imagery with the aid of Very High Resolution Satellite (VHRS) imagery. The created dataset consists of more than 5000 water bodies having an area of approx. $71{\mathrm{km}}^2$ in eight different sites in the HKKH region. The evaluation of the test data show that the U-Net with EfficientNet backbone achieved the highest F1 Score of 0.936. A visual comparison with the existing glacial lake inventories is then performed over the Baltoro glacier in the Karakoram range. The results show that the deep learning model detected significantly more lakes than the existing inventories, which have been derived from Landsat OLI imagery. The trained model is further evaluated on the time series PlanetScope imagery of two glacial lakes, which have resulted in an outburst flood. The output of the U-Net is also compared with the GLakeMap data. The results show that the higher spatial and temporal resolution of PlanetScope imagery is a significant advantage in the context of glacial lakes mapping and monitoring. Full article

Glacial lakes are an important component of the cryosphere in the Tibetan Plateau. In response to climate warming, they threaten the downstream lives, ecological environment, and public infrastructures through outburst floods within a short time. Although most of the efforts have been made toward extracting glacial lake outlines and detect their changes with remotely sensed images, the temporal frequency and spatial resolution of glacial lake datasets are generally not fine enough to reflect the detailed processes of glacial lake dynamics, especially for potentially dangerous glacial lakes with high-frequency variability. By using full time-series Sentinel-1A/1B imagery over a year, this study presents a new systematic method to extract the glacial lake outlines that have a fast variability in the southeastern Tibetan Plateau with a time interval of six days. Our approach was based on a level-set segmentation, combined with a median pixel composition of synthetic aperture radar (SAR) backscattering coefficients stacked as a regularization term, to robustly estimate the lake extent across the observed time range. The mapping results were validated against manually digitized lake outlines derived from Gaofen-2 panchromatic multi-spectral (GF-2 PMS) imagery, with an overall accuracy and kappa coefficient of 96.54% and 0.95, respectively. In comparison with results from classical supervised support vector machine (SVM) and unsupervised Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA) methods, the proposed method proved to be much more robust and effective at detecting glacial lakes with irregular boundaries that have similar backscattering as the surroundings. This study also demonstrated the feasibility of time-series Sentinel-1A/1B SAR data in the continuous monitoring of glacial lake outline dynamics. Full article