Land use/cover change (LUCC) is becoming one of the most important and interesting problems in the study of global environmental change. Identifying the spatiotemporal behavior and associated driving forces behind changes in land use is crucial for the regional sustainable utilization of land resources. In this study, we consider the four municipalities of China (Beijing, Tianjin, Shanghai, and Chongqing) and compare their spatiotemporal changes in land use from 1990 to 2015 by employing intensity analysis and barycenter migration models. We then discuss their driving forces. The results show that the largest reduction and increase variations were mainly concentrated in arable and construction land, respectively. The decrement and increment were the largest in Shanghai, followed by Beijing and Tianjin, and the least in Chongqing. Furthermore, the results of the barycenter migration model indicate that in addition to Beijing, the migration distances of construction land were longer than those of arable land in three other cities. Moreover, the application of intensity analysis revealed that the rate of land use change was also the greatest in Shanghai and the slowest in Chongqing during the whole study period, with all of their arable land being mainly transformed into construction land. The driving force analysis results suggest that the spatial and temporal patterns of land use change were the results of the socio-economic development, national policies, and major events. In other words, where there was a high rate of economic and population growth, the intensity of land use change was relatively large.

In this paper, we selected Xilingol League in Inner Mongolia, China, as the research area, based on the land-use data of five Landsat remote sensing images taken between 1980 and 2015. Then, we calculated the complex network eigenvalues, such as the average shortest path, betweenness centrality, and degree, to identify the key land-use types, stability, and ecological environment change regularity from the perspective of the land-use system. Finally, we used the Canonical Correspondence Analysis (CCA) method to explore the main driving forces behind changes in the land-use system, to provide scientific support for the study of changing trends in land-use, and regional grassland ecological management. The findings are shown below. First, in the last 35 years, grasslands have always been the major type of change in land-use transfer matrices. Grasslands play a controlling role in the whole land-use system. Second, grassland and cultivated land are the major “transfer out” type of land in the Xilingol area, while construction land and water area belong to the major “transfer in” type of land. Third, the average shortest path values of four transition matrix networks were all less than 1.5, indicating that the land-use system becomes less stable, but the average shortest path values of these four networks present an increasing trend, leading to a more stable development of the land-use system. However, on the whole, it shows an upward trend, and the land-use system is moving in a stable direction. Fourth, the average annual rainfall, population, topographic factors, GDP, and distance from settlements play a prominent role in determining the spatial distribution of change in land-use in Xilingol.

The quantitative evaluation of the intensity of regional human activity can reflect changes in the relationship between people and the environment and provide a scientific basis for the formulation of land-use policy and for the construction of the ecological environment. This paper adopted the ratio of the area of construction land to the total area to calculate human activity intensity in Inner Mongolia at the league/city, banner/county, and pixel levels, respectively. On this basis, the overall change in human activity intensity and its spatiotemporal differentiation, geographical unit change, and spatial autocorrelation were analyzed. The results are as follows: (1) The intensity of human activity increased from 2000 to 2020. The intensity of human activity in Inner Mongolia was 8.71% in 2000, and it increased to 8.73% in 2010 and to 9.71% in 2020. The growth rate has accelerated since 2010. (2) The areas with high and medium intensity of human activities are mainly concentrated in the area south of the Hinggan–Yinshan–Helan Mountains. The intensity in the Hulunbeier grassland area and Alxa desert area is relatively low, and the diffusion effect gradually becomes prominent in the proximal local area. (3) The results for the detection of influencing factors indicate that the livestock density, multi-year average precipitation conditions, and population density have significant driving effects on the changes in intensity of human activity in Inner Mongolia.