Rapid urban expansion has seriously threatened ecological security and the natural environment on a global scale, thus, the simulation of dynamic urban expansion is a hot topic in current research. Existing urban expansion simulation models focus on the mining of spatial neighborhood features among driving factors, however, they ignore the over-fitting, gradient explosion, and vanishing problems caused by the long-term dependence of time series data, which results in limited model accuracy. In this study, we proposed a new dynamic urban expansion simulation model. Considering the long-time dependence issue, long short term memory (LSTM) was employed to automatically extract the transformation rules through memory units and provide the optimal attribute features for cellular automata (CA). This study selected Lanzhou, which is a semi-arid region in Northwest China, as an example to confirm the validity of the model performance using data from 2000 to 2020. The results revealed that the overall accuracy of the model was 91.01%, which was higher than that of the traditional artificial neural network (ANN)-CA and recurrent neural network (RNN)-CA models. The LSTM-CA framework resolved existing problems with the traditional algorithm, while it significantly reduced complexity and improved simulation accuracy. In addition, we predicted urban expansion to 2030 based on natural expansion (NE) and ecological constraint (EC) scenarios, and found that EC was an effective control strategy. This study provides a certain theoretical basis and reference value toward the realization of new urbanization and ecologically sound civil construction, in the context of territorial spatial planning and healthy/sustainable urban development.

Detailed urban land use information is the prerequisite and foundation for implementing urban land policies and urban land development, and is of great importance for solving urban problems, assisting scientific and rational urban planning. The existing results of urban land use mapping have shortcomings in terms of accuracy or recognition scale, and it is difficult to meet the needs of fine urban management and smart city construction. This study aims to explore approaches that mapping urban land use based on multi-source data, to meet the needs of obtaining detailed land use information and, taking Lanzhou as an example, based on the previous study, we proposed a process of urban land use classification based on multi-source data. A combination road network dataset of Gaode and OpenStreetMap (OSM) was synthetically applied to divide urban parcels, while multi-source features using Sentinel-2A images, Sentinel-1A polarization data, night light data, point of interest (POI) data and other data. Simultaneously, a set of comparative experiments were designed to evaluate the contribution and impact of different features. The results showed that: (1) the combination utilization of Gaode and OSM road network could improve the classification results effectively. Specifically, the overall accuracy and kappa coefficient are 83.75% and 0.77 separately for level I and the accuracy of each type reaches more than 70% for level II; (2) the synthetic application of multi-source features is conducive to the improvement of urban land use classification; (3) Internet data, such as point of interest (POI) information and multi-time population information, contribute the most to urban land use mapping. Compared with single-moment population information, the multi-time population distribution makes more contributions to urban land use. The framework developed herein and the results derived therefrom may assist other cities in the detailed mapping and refined management of urban land use.

The adoption of the 2030 Agenda and its 17 Sustainable Development Goals (SDGs) contribute to addressing the multiple and complex challenges faced by humankind. In particular, the increasing impact of climate change and globalization represent a great challenge for the sustainable development of Arctic countries and efforts are needed to measure, assess, and compare the progress toward SDGs and the trends in this region. This study used 69 indicators closely related to Arctic countries and a composite indicator approach to assess their sustainability performance. SDG interlinkages were also assessed through Spearman's rank order correlation. The results showed that the sustainable development of Arctic countries gradually improved from 2000 to 2020, with increasing scores recorded for 82% of the goals and 73% of the indicators. Overall, significant progress was observed in the SDG 10 (reduction of inequality), SDG 3 (health improvement), and SDG 4 (quality of education). The highest-ranking scores were obtained for poverty reduction (SDG 1), SDG 3, and climate actions (SDG 13) in 2020. Over the 21-year period, Sweden reached the highest overall score for sustainable development, and Russia showed the greatest improvement. Synergies dominated over trade-offs among and within SDGs. SDGs 1, 3, 9, 10, and 11 presented a relatively higher proportion of synergies, while higher proportions of trade-offs were observed for the 8-9, 8-11, 3-12, and 10-12 SDG pairs. The associations of industry development with clean energy use and environmental conservation were strengthened during the study period. However, the performance varied greatly for different indicators, goals, and their correlations among Arctic countries. The results indicate that the main challenges for these countries in relation to SDGs consist in promoting an inclusive economic model as well as sustainable consumption and production patterns.

In recent decades, the oases in the Hexi Corridor have gone through a tremendous transformation, which has caused a series of social and environmental problems. We aim to explore quantitatively the characteristics of the oasis expansion and their dynamic mechanism(s) in the Hexi Corridor, and their implications and impact on current and future policies. The spatial distribution pattern and dynamic changes experienced by the oases are examined using Landsat imagery. Their spatio-temporal changes are analyzed using the grid-transformed model and the dynamic-degree model. The model drivers are analyzed based on data from statistics yearbooks and field surveys. The total area of oases in the Hexi Corridor has expanded tremendously during the last 30 years from 10,709 km² to 14,950 km², almost 40% of the original value. Oasis evolution patterns of ‘unchanged’, ‘expanding’, ‘shrinking’, and ‘oscillating’ are observed at different periods in the three basins. In terms of area, almost half of the oases experienced some change, where most of the changes took place in the ecotone between oases and deserts, and the interior of oases due to the reclamation of abandoned land. Oasis expansion is mainly determined by the human instincts for survival and well-being, which are generally governed by population growth, agricultural policies and economic development. These changes reflect the need to find a balance in the relationship between ecological protection and increasing the well-being of local residents, because unreasonable or excessive development and utilization will cause damage to the local ecological environment.