The understanding of glacier and ice shelf front changes is of paramount importance in analyzing their material balance and their significant contributions to global sea-level variations. Recently, the increasing availability of remote sensing images has provided ample data support for calving front detection. By combining massive amounts of data, deep learning techniques have demonstrated great potential in front extraction, as they can automatically generate task-specific features. In this study, in contrast to the traditional framework based on a pure CNN architecture, we propose a novel calving front detection network, GLA-STDeepLab, by incorporating the Swin transformer module into DeepLabv3+ to enhance the modeling capability of long-range contextual dependencies. We also introduce Global-local attention mechanisms into the module, striving to foster interactions between the corresponding coarse and fine-grained feature maps. Through extensive experimentation on a diverse and challenging SAR database, CaFFe, our network is shown to surpass the current state-of-the-art methods, achieving results with an IoU of 0.94 and a mean distance error (MDE) of 473±34 m. More meaningfully, we extracted the time-series front information of the Amery Ice Shelf from 2015 to 2023 and precisely captured the calving events of the D-28 and D-32 icebergs. The findings demonstrate that our method enables precise monitoring of the dynamic information of glacier and ice shelf fronts, as well as their far-reaching implications for ice sheet mass balance and global climate change. The code used in this study is provided at https://github.com/Tangyu35/Calving-front-detection.

Rapid urbanization has produced a huge amount of construction waste. The operations and consequences of construction and demolition can lead to windblown dust problems, profoundly affecting the living environment of residents. Fortunately, dust-proof nets have been used in construction sites to reduce and prevent pollution by fine particles such as dust, so it is important to monitor and evaluate their efficacy. In this study, Earth observation techniques were used for the extraction and monitoring of solid waste and dust-proof nets. In order to fully perceive the validity and necessity of dust-proof nets for urban air health, we conducted a case study in Zhengzhou, China. We explored the potential of multispectral remote sensing available for monitoring urban construction waste and proposed a multi-layer classification method to identify construction waste and dust-proof nets based on Landsat-8 OLI and Sentinel-2 MSI data, with an average identification accuracy and Kappa coefficient of 96.27% and 0.93 for construction waste in the study area from 2015 to 2020, respectively. In addition, our study revealed the driving factors and impact of temporal variations in regional construction waste areas and dust-proof nets coverage. The results indicate the classification can track municipal solid waste management and changes in air pollutant concentrations and is useful for achieving SDG 11.6 (reduce the adverse per capita environmental impact of cities, including by paying special attention to air quality and municipal and other waste management). This study has the potential to monitor construction waste and dust-proof nets, paving the way for better urban environmental governance and surveillance actions in the future, especially involving big data.

Rich datasets related to the earth have been obtained because of the rapid development of earth observation technologies. A broad range of prior research has investigated how to obtain the correlation relationships of relevant features from a large number of data containing spatio-temporal information, which is also the technical basis for big data analysis. Based on the dynamic time warping (DTW) algorithm, this research proposes a correlation analysis method of time series data, and applies it to the correlation analysis between the time series features of the surface temperature and melting area of the Antarctic ice sheet. The results show that our method based on the DTW algorithm can effectively distinguish the change details of the non-linear time series. The correlation coefficients between these two highly-correlated factors computed by our method are higher than Pearson’s correlation coefficients by more than 0.2 almost in all study areas where data are available. In summary, the method proposed in this study provides a new feasible way for the correlation study of time-series data. It outperforms than traditional correlation coefficients such as Pearson’s correlation coefficient in some fields, specifically when complex nonlinear time series data with certain periodicity are used.

Ice shelves play an essential role in the dynamics of the Antarctic ice sheet. The surface meltwater is important, as it can irreversibly weaken ice shelves by exerting additional hydrostatic pressure. Therefore, high-resolution snowmelt products are urgently needed to accurately analyze melting patterns of ice shelves and further estimate mass loss. In this study, a new high-resolution (40 m) snowmelt dataset over all of the Antarctic Peninsula ice shelves larger than 100 km ${}^2$ was developed based on the modified snowmelt detection framework by using a co-orbit normalization method. The dataset provides detailed snowmelt information on each ice shelf, including the image coverage, melting area and melting ratio every 5 days. The melting patterns of three typical ice shelves (George VI, Wilkins and Larsen C Ice Shelves) and the spatio-temporal melting distribution of the Antarctic Peninsula (AP) were further analyzed. The snowmelt information indicates that both the extent and duration of snowmelt have been increasing in the Antarctic Peninsula from 2015 to 2021, and we found that the snowmelt on the Antarctic Peninsula showed a spatial pattern of significantly intense snowmelt on the western side. We believe this study will provide essential data for ice shelf investigation to support other fields of polar research.

Landslides are one of the extremely high-incidence and serious-loss geological disasters in the world, and the early monitoring and warning of landslides are of great importance. The Cheyiping landslide, located in western Yunnan Province, China, added many cracks and dislocations to the surface of the slope due to the severe seasonal rainfall and rise of the water level, which seriously threaten the safety of residents and roads located on the body and foot of the slope. To investigate the movement of the landslide, this paper used Sentinel-1A SAR data processed by time-series interferometric synthetic aperture radar (InSAR) technology to monitor the long-time surface deformation. The landslide boundary was defined, then the spatial distribution of landslide surface deformation from 5 January 2018 to 27 December 2021 was obtained. According to the monthly rainfall data and the temporal deformation results, the movement of the landslide was highly correlated with seasonal rainfall, and the Cheyiping landslide underwent seasonal sectional accelerated deformation. Moreover, the water level change of the Lancang River caused by the water storage of the hydropower station and seasonal rainfall accelerates the deformation of the landslide. This case study contributes to the interpretation of the slow deformation mechanism of the Cheyiping landslide and early hazard warning.

High Mountain Asia (HMA), with its high altitude, widely distributed snow and frozen soil, influences the climate of the northern hemisphere and even the world through thermal balance and the water vapor cycle and is also an indicator of global climate change. The influence of HMA snow cover on its surrounding areas has always been a research hotspot. Taking the Yangtze River Basin (YRB) of China as an example, this paper analyzes the relationship between winter snow depth in HMA and drought and flood in spring and summer in the YRB in the recent 40 years by using Singular Value Decomposition (SVD). The results show that the influence of snow cover on drought and flood in spring is inversely different between eastern and western parts of HMA, while the effect in summer is consistent. When the snow depth is larger (smaller) in the east and smaller (larger) in the west in winter, the YRB is drier (wetter) in spring. When the overall snow depth in HMA is larger (smaller) in winter, the northern part of the middle and lower reaches of the YRB is drier (wetter) in summer. The results provide support for understanding the impact of HMA snow cover on the surrounding climate and some important indicators for drought and flood prediction in the YRB.