Background: Associated with the significant decrease in water resources, natural vegetation degradation has also led to many widespread environmental problems in the Aral Sea Basin. However, few studies have examined long-term vegetation dynamics in the Aral Sea Basin or distinguished between natural vegetation and cultivated land when calculating the fractional vegetation cover. Methods: Based on the multi-temporal Moderate Resolution Imaging Spectroradiometer, this study examined the natural vegetation coverage by introducing the Linear Spectral Mixture Model to the Google Earth Engine platform, which greatly reduces the experimental time. Further, trend line analysis, Sen trend analysis, and Mann–Kendall trend test methods were employed to explore the characteristics of natural vegetation cover change in the Aral Sea Basin from 2000 to 2018. Results: Analyses of the results suggest three major conclusions. First, the development of irrigated agriculture in the desert area is the main reason for the decrease in downstream water. Second, with the reduction of water, the natural vegetation coverage in the Aral Sea Basin showed an upward trend of 17.77% from 2000 to 2018. Finally, the main driving factor of vegetation cover changes in the Aral Sea Basin is the migration of cultivated land to the desert.

As resources are depleted, resource-based cities face unique challenges in the process of socio-economic development. We constructed a multidimensional socio-economic development level model by adopting Entropy Value Method, Analytical Hierarchy Process, time series weighting method, and Game Theory approach for the data of 10 indicators in 4 dimensions of 115 resource-based cities in China from 2004 to 2019 to explore the spatial and temporal divergence characteristics of multidimensional socio-economic development level and the driving mechanism of its pattern of evolution. The results show that: (1) the overall socio-economic development level of resource-based cities has improved from 2004 to 2019, but the overall level is low. Large differences exist in the spatial distribution of socio-economic development levels between cities with more significant regional spatial aggregation characteristics. (2) Secondary industry, tertiary industry, retail trade goods sales, urban construction land area, and total freight transport have a significant positive impact on socio-economic development; the correlation coefficient between the number of schools and the socio-economic development level index is negative. (3) Retail trade merchandise sales contribute the most to the Gini coefficient, where the percentage of secondary industry and urban construction land area have a higher cumulative contribution to growing cities (55.02%), the percentage of secondary industry has the lowest contribution to regenerating cities (10.94%), and the percentage of tertiary industry has an increasing contribution to declining cities year by year. Based on the above findings, some specific suggestions are provided to provide reference for resource-based city development planning.

Understanding the causes of poverty and identifying the transformation characteristics of poverty is the basis for achieving poverty eradication. In order to clarify the availability of construction land for poverty assessment, this paper explores the spatio-temporal synergy between urban built-up areas and poverty transformation in Tibet. The following conclusions are drawn: (1) the built-up areas in Tibetan counties have been growing from 2013 to 2019; (2) the proportion of counties with very low and low levels of relative poverty have decreased significantly, and the overall spatial characteristics of poverty are “high in the center and low in the surroundings”; (3) the overall coupling-coordination level between the built-up areas and the relative poverty level is gradually improving from the initial antagonism, and the relative-poverty index shows a significant negative correlation with coupling coordination (correlation coefficient of −0.63); and (4) the built-up area has a strong explanatory power for the spatial distribution of regional relative-poverty transfer compared to temperature, precipitation, elevation, and slope. The results of the study prove that the built-up area cannot be directly used as an indicator factor when constructing the multidimensional relative-poverty model and, instead, should use urban built-up areas by region to participate in poverty-estimation models based on regional economic development.