Atmospheric warming is intensifying glacier melting and glacial-lake development in High Mountain Asia (HMA), and this could increase glacial-lake outburst flood (GLOF) hazards and impact water resources and hydroelectric-power management. There is therefore a pressing need to obtain comprehensive knowledge of the distribution and area of glacial lakes and also to quantify the variability in their sizes and types at high resolution in HMA. In this work, we developed an HMA glacial-lake inventory (Hi-MAG) database to characterize the annual coverage of glacial lakes from 2008 to 2017 at 30 m resolution using Landsat satellite imagery. Our data show that glacial lakes exhibited a total area increase of 90.14 km² in the period 2008–2017, a +6.90 % change relative to 2008 (1305.59±213.99 km²). The annual increases in the number and area of lakes were 306 and 12 km², respectively, and the greatest increase in the number of lakes occurred at 5400 m elevation, which increased by 249. Proglacial-lake-dominated areas, such as the Nyainqêntanglha and central Himalaya, where more than half of the glacial-lake area (summed over a 1^∘ × 1^∘ grid) consisted of proglacial lakes, showed obvious lake-area expansion. Conversely, some regions of eastern Tibetan mountains and Hengduan Shan, where unconnected glacial lakes occupied over half of the total lake area in each grid, exhibited stability or a slight reduction in lake area. Our results demonstrate that proglacial lakes are a main contributor to recent lake evolution in HMA, accounting for 62.87 % (56.67 km²) of the total area increase. Proglacial lakes in the Himalaya ranges alone accounted for 36.27 % (32.70 km²) of the total area increase. Regional geographic variability in debris cover, together with trends in warming and precipitation over the past few decades, largely explains the current distribution of supraglacial- and proglacial-lake area across HMA. The Hi-MAG database is available at https://doi.org/10.5281/zenodo.4275164 (Chen et al., 2020), and it can be used for studies of the complex interactions between glaciers, climate and glacial lakes, studies of GLOFs, and water resources.

Floods are some of the most serious and devastating natural hazards on earth, bringing huge threats to lives, properties, and living environments. Rapid delineation of the spatial extent of flooding is of great importance for the dynamic monitoring of flood evolution and corresponding emergency strategies. Some of the current flood mapping methods mainly process single date images characterized by simple flood situations and homogenous backgrounds. Although other methods show good performance for images with harsh conditions for floods, they must be trained—many times based on pre-classified samples—or undergo complicated parameter tuning processes, which require computation efforts. The widely used change detection methods utilize multi-temporal Synthetic Aperture Radar (SAR) images for the detection of flood area, but the results are largely influenced by the quality of defined reference images. Furthermore, these methods were mostly applied for some river basin floods, which are not effective for the large scale, semi-arid regions with complex flood conditions, and various land cover types. All of these extremely limited the use of these methods for the timely and accurate extraction of the spatial distribution pattern of floods in other typical and broad areas. Based on the above considerations, this paper presents a new method for rapidly determining the extent of flooding in large, semi-arid areas with challenging environmental conditions, based on multi-temporal Sentinel-1 Synthetic Aperture Radar (SAR) data. First, a preprocessing scheme is applied to perform geometric correction and to estimate the intensity of the imagery. Second, an automatic thresholding procedure is used to generate an initial land and water classification through the integration of the probability density distribution. A fuzzy logic-based approach, combining SAR backscattering information and other auxiliary data, is then used to refine the initial classified image. The fuzzy logic-based refinement removes areas that look similar to water in the SAR images, significantly enhancing the flood mapping accuracy. Finally, a post-processing step consisting of morphological operations and extraction improves the homogeneity of the extracted flood segments, discards isolated pixels, and gives the final flood map. This method can automatically detect the extent of floods at little computational cost. As Sentinel-1 data are publicly available and have a fast repeat cycle, the procedure can provide near real time results for rapid emergency response following flash floods. The accuracy of the proposed method is assessed at three test sites in Pakistan, which covered diverse landscapes and suffered large scale serious flooding after a long and severe drought in 2015. In comparison with the more recent studies from Ohki et al., 2020, and Shahabi et al., 2020, our results indicate the best spatial agreement with GF-2 panchromatic multi-spectral (PMS) water classification, with an encouraging overall accuracy ranging from 91.1% to 96.6%, and Kappa coefficients ranging from 0.893 to 0.954. Especially for the areas with fragmented floods, heterogeneous backgrounds, and the areas where samples are highly unbalanced in the SAR images, our method combines the global statistics and local relationships of backscattering properties, terrain, and other auxiliary information, enabling to effectively preserve the detailed structures and also remove the noise.

The current glacial lake datasets in the High Mountain Asia (HMA) region still need to be improved because their boundary divisions in the land–water transition zone are not precisely delineate, and also some very small glacial lakes have been lost due to their mixed reflectance with backgrounds. In addition, most studies have only focused on the changes in the area of a glacial lake as a whole, but do not involve the actual changes of per pixel on its boundary and the potential controlling factors. In this research, we produced more accurate and complete maps of glacial lake extent in the HMA in 2008, 2012, and 2016 with consistent time intervals using Landsat satellite images and the Google Earth Engine (GEE) cloud computing platform, and further studied the formation, distribution, and dynamics of the glacial lakes. In total, 17,016 and 21,249 glacial lakes were detected in 2008 and 2016, respectively, covering an area of 1420.15 ± 232.76 km² and 1577.38 ± 288.82 km²; the lakes were mainly located at altitudes between 4400 m and 5600 m. The annual areal expansion rate was approximately 1.38% from 2008 to 2016. To explore the cause of the rapid expansion of individual glacial lakes, we investigated their long-term expansion rates by measuring changes in shoreline positions. The results show that glacial lakes are expanding rapidly in areas close to glaciers and had a high expansion rate of larger than 20 m/yr from 2008 to 2016. Glacial lakes in the Himalayas showed the highest expansion rate of more than 2 m/yr, followed by the Karakoram Mountains (1.61 m/yr) and the Tianshan Mountains (1.52 m/yr). The accelerating rate of glacier ice and snow melting caused by global warming is the primary contributor to glacial lake growth. These results may provide information that will help in the understanding of detailed lake dynamics and the mechanism, and also facilitate the scientific recognition of the potential hazards associated with glacial lakes in this region.

Glacial lakes are highly sensitive indicators of global climate change. The accurate extraction of glacial lakes from image data is of great importance to the evaluation of the hydrological environment in high mountain regions. This paper demonstrates a systematic approach-TSCV (threshold and simplified C-V)-that integrates the advantages of the threshold segmentation method and the active contour model to improve the effect of glacial lake extraction, by overcoming the problems involved in the efficient extraction of small, overlooked glacial lakes and in the removal of some mountain shadows. Three typical areas of glacial lake development in High Asia were selected for glacial lake extraction from Landsat-8 imagery and the results were then compared with those obtained using the threshold segmentation method and the fuzzy clustering method (FCM). The values of the kappa coefficient (KC) and average extraction accuracy (AEA) shows that the TSCV (KC = 0.895, AEA = 0.739) performed superior than other two methods, especially for the small glacial lakes, but also that the threshold segmentation method (KC = 0.869, AEA = 0.631) requires the least time and that the FCM (KC = 871, AEA = 0.663) has the lowest commission error. Our findings lead to a new approach to improve the accuracy of glacial lake extraction in situation where a large proportion of the lakes are small and with a potential for automated glacial lake mapping in rugged mountain areas at a large scale.

Visual question answering (VQA) aims to build an interactive system that infers the answer according to the input image and text-based question. Recently, VQA for remote sensing has attracted considerable attention since it is essential and expedient for monitoring global resources and querying objective attributes. In reality, question-related semantic information is helpful for the reasoning and understanding capabilities, which is always contained in remote sensing images or complex questions. To capture the valuable information and extend the applications of remote-sensing VQA, we propose an end-to-end multiple-step question-driven VQA (MQVQA) system for remote sensing. In MQVQA, we employ a multiple-step attention mechanism to interactively reason and concomitantly mark the region that is most related to the question. To understand the semantic information in complex questions, we build a question-driven module that classifies the question types and keywords, which will further guide the combination of image feature maps in different scales. To benchmark this model, we construct a new complex remote sensing VQA dataset (CRSVQA), wherein the question is asked in complex forms and involves various remote sensing scenes. The evaluation results on CRSVQA, RSVQA and RSIVQA datasets indicate that the proposed MQVQA model surpasses other remote sensing VQA models. The visualization results demonstrate that MQVQA has a robust ability in reasoning and understanding the content from images and complex questions. Our code and dataset are publicly available at: https://github.com/MeimeiZhang-data/MQVQA.

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.