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Comprehending the dynamic change characteristics of land use/cover and the driving factors causing the change are prerequisites for protecting land resources. This paper analyzes changes in cultivated land, the driving factors that cause them, and their tremendous impact on landscape pattern changes in the Dongting Lake Basin. For this purpose, we used mathematical statistics, buffer analysis, trend analysis, landscape pattern index, and logistic regression model to analyze the land use data of the study area from 1980 to 2018. The results show that the cultivated land showed a decreasing trend, with the total area decreased by 4.76% (or 716.13 km2) from 1980 to 2018, and the activity of mutual transformation with other land use types decreased. The spatial distribution pattern of cultivated land and landscape shows the change characteristics gradually from Dongting Lake to the surroundings. Among the driving factors of cultivated land changes, the influence of human activities was gradually increasing, while the natural factors were decreasing. The cultivated land landscape pattern index and the overall landscape pattern index have a significant positive correlation, showing relatively consistent change trend and spatial distribution characteristics. We believe that the decrease of cultivated land area has a certain relationship with the increase of landscape fragmentation in the Dongting Lake Basin. Our research is expected to provide a reference for strengthening regional cultivated land management and rational development and utilization of regional land resources.

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The karst area in northwestern Guangxi is poor, underdeveloped, and ecologically fragile. It is experiencing rocky desertification, which creates challenges that are more severe than those of other regional ecological environments. In this paper, the ecological footprint (EF) model is used to analyze the ecological carrying capacity (EC) in northwestern Guangxi from 1995 to 2015, and the differences in karst counties with different poverty levels are discussed. The results show that (1) since 1995, the EC of northwestern Guangxi has continued to decrease, the EF has continued to increase, the ecological deficit (ED) has been expanding, and the status of the region has been unsustainable for a long time. (2) The evolutionary patterns, EF and EC of karst counties with different poverty levels are different. The county with the lowest poverty rate has the fastest growth rate of the per capita EF. The county with the largest proportion of karst area has the lowest EC. (3) It is recommended that different types of counties take different measures, including strengthening ecological environment protection, carrying out rocky desertification control and ecological resettlement projects, and reducing energy consumption. This study can provide information for the sustainable development of the karst region and provide decision support for regional poverty alleviation.

Shape similarity measurement model is often used to solve shape-matching problems in geospatial data matching. It is widely used in geospatial data integration, conflation, updating and quality assessment. Many shape similarity measurements apply only to simple polygons. However, areal entities can be represented either by simple polygons, holed polygons or multipolygons in geospatial data. This paper proposes a new shape similarity measurement model that can be used for all kinds of polygons. In this method, convex hulls of polygons are used to extract boundary features of entities and local moment invariants are calculated to extract overall shape features of entities. Combined with convex hull and local moment invariants, polygons can be represented by convex hull moment invariant curves. Then, a shape descriptor is obtained by applying fast Fourier transform to convex hull moment invariant curves, and shape similarity between areal entities is measured by the shape descriptor. Through similarity measurement experiments of different lakes in multiple representations and matching experiments between two urban area datasets, results showed that the method could distinguish areal entities even if they are represented by different kinds of polygons.