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.

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.

The 17 Sustainable Development Goals of the United Nations propose to “ensure healthy lives and promote well-being for all at all ages”, and to achieve this goal requires that countries strengthen their capacity to manage health risks. As a concept to describe urban construction, advocated by the World Health Organization, healthy cities can effectively solve the contradictions existing along the current urban development path at a macro level. A healthy city is a sustainable city that interacts with its environment, economy, population, services, and space, and realizes the well-being of its population from all perspectives. The construction of a healthy city is an important part of the transformation of Chinese urbanization. This article refers to the index systems of domestic and foreign government agencies, along with a literature research, to construct a healthy city evaluation index that takes into account the five aspects of environment, economy, population, service, and space, and selects Beijing (a policy-oriented city), Shanghai (an economy-oriented city), Nanchang (an industry-oriented city), Guiyang (a tourism-oriented city), Datong (resource-oriented city) as five cities according to type of urban development, using the entire-array-polygon method to analyze the construction level of these cities in terms of environment, economy, population, service, space and overall state of health from 2014 to 2018 based on statistical and land use data. The results of the study found that, in general, the construction of healthy cities in China currently experiences large year-to-year fluctuations and significant differences between cities. The construction and development of healthy cities are also closely related to factors such as urban economic strength, social welfare, and policy support.

Assessing land use-based changes in ecosystem service values (ESVs) is a beneficial approach for land resource planning and ecologically sustainable development. Located in the south of China, Guilin is one of the first Sustainable Development Goals (SDGs) Innovation Demonstration Zones set up by China. It is a typical ecotourism city with an important ecological and economic status. In recent years, the time series, model fit, and spatial scale of ESV assessment in Guilin have needed to be improved in the context of rapid urbanization and natural change. In this study, an improved ESV assessment methodology was utilized to incorporate the effects of biomass, soil conservation, and precipitation and to adjust the equivalence factors based on the ratio of geographic and environmental parameters to the national average to make them heterogeneous in time and space in improving the practical fit of the assessment results. The study analyzed the evolution of land use and its contribution to ESVs in Guilin from 2000 to 2020. County and 3 km × 3 km grid scales were combined to reveal both broad and detailed spatial and temporal characteristics of ESVs in Guilin. The results show that the expansion of building land in Guilin is notable, and the amount of land use transfer continues to increase. ESVs fluctuated in a lateral S-shape, with significant differences in ESV effectiveness between counties, consistently high ESVs near waterbodies and ecological reserves, and low ESVs near commercial and industrial land and cultivated land. Despite the recovery trend in ESVs in the later years, there is still a gap between 2020 and 2000. To a certain extent, it helps Guilin optimize land allocation from different perspectives and promote ecological improvement and resource planning optimization.