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In order to control the development of urban space, it is important to explore scientific methods to provide a reference for regional territorial space planning. On the basis of the minimum cumulative resistance (MCR) model and the cellular automaton (CA)-Markov model, we constructed a new technical method for delineating urban development boundaries, exploring the temporal and spatial distribution characteristic of land use in Wuhan from 2010 to 2020 through nighttime and remote sensing images, and simulating the urban development boundaries of Wuhan from 2025 to 2035. The results show that: (1) the scales of Wuhan City’s built-up areas in 2010, 2015, and 2020 were 500 km2, 566.13 km2, and 885.11 km2, respectively, and the trends of expansion run to the east and southeast, and (2) on the basis of the MCR model, the urban development boundary scale of Wuhan City in 2025, 2030, and 2035 from the perspective of actual supply will be 903.52 km2, 937.48 km2, and 1021.44 km2, respectively, and based on the CA-Markov model, the urban development boundary scales of Wuhan City in 2025, 2030, and 2035 from the perspective of ideal land demand will be 912.75 km2, 946.40 km2, and 1041.91 km2, respectively. By combining the results of the two methods, we determined areas of 901.62 km2, 944.39 km2, and 1015.36 km2 as the urban development boundaries of Wuhan City in 2025, 2030, and 2035, respectively. According to the principle of supply–demand balance, the urban development boundary delineated by the integration of the MCR model and CA-Markov model, which is in line with the spatial expansion trend of growing cities, could optimize the urban development pattern; solve the contradiction between urban development, farmland protection, and ecological protection; and provide a methodological reference and decision-making basis for planning practice.

Recent urbanization and growing food consumption have had a severely detrimental effect on the ecological environment of the Jianghan Plain. The ecological fragility of the Jianghan Plain must be continually monitored for environmental conservation and sustainable development. This study utilized principal component analysis (PCA) to quantitatively assess the ecological vulnerability of the Jianghan Plain based on the remote sensing ecological index (RSEI) and analyzed the space-time changes and drivers in the Jianghan Plain from 2000 to 2020 using the Google Earth Engine Platform (GEE). The findings of this research indicated that the ecological vulnerability of the Jianghan Plain from 2000 to 2020 was predominantly Moderate or Strong level. But still, the EVI displayed a changing decreasing trend, revealing a small development towards a healthier ecological environment. The most significant ecological vulnerability deterioration occurred between 2005 and 2010, accounting for roughly 44.90 percent, whereas the highest improvement occurred between 2000 and 2005, occupying approximately 37.52% of the area. Moran’s I of EVI was greater than 0 in Jianghan Plain and displayed a growing and subsequently a falling trend, representing that the spatial distribution of regional ecological vulnerability was strongly correlated and aggregated and that the degree of aggregation has declined. The effects of heat, greenness, wetness, and dryness on the ecological vulnerability of Jianghan Plain were all significant, with greenness and wetness being the primary determinants of the change in Jianghan Plain’s ecological vulnerability. The results of this study can offer a theoretical and scientific foundation for ecological protection and restoration in the Jianghan Plain. Meanwhile, this study also provides a practical and rapid method for monitoring regional ecological vulnerability using RSEI, GEE, and PCA, which can be applied elsewhere for ecological vulnerability evaluation.

Accelerated land use and land cover changes affect regional landscape patterns and change the ecological environment, including soil conservation capabilities. This is not conducive to the sustainable development of human society. In this research, we explored the land use change pattern and landscape change pattern in western Hubei from 2000 to 2020. Using the Chinese soil loss equation and stepwise regression, we measure how landscape patterns affect soil erosion under land use and cover changes in western Hubei Province. The results show that average soil erosion in the mountainous areas of western Hubei tended to increase from 2000 to 2010 and decrease from 2010 to 2020; soil erosion was higher in the western than in the eastern part of the study area. The land in areas with high-intensity and low-intensity soil erosion was mainly waterfront/grassland and cropland/forestland, respectively, and the area of moderate to severe soil erosion was greatest when the slope was 10–20°. When the slope exceeded 20°, the soil erosion area of each grade tended to decrease; thus, 20° is the critical slope for soil erosion in the study area. The landscape pattern in mountainous areas changed dramatically from 2000 to 2020. At the landscape level, landscape fragmentation increased and connectivity decreased, but the area of landscape diversity was stable. Soil erosion in western Hubei was positively correlated with the contiguity index, aggregation index and largest patch index but negatively correlated with the Shannon evenness index. The higher the landscape fragmentation and the greater the accumulation of single land-use types, the more severe the soil erosion is, while the higher the landscape connectivity and the richer the landscape diversity, the less severe the soil erosion is. The results can inform regional landscape management and soil conservation research.

Controlling soil erosion is beneficial to the conservation of soil resources and ecological restoration. Understanding the spatial distribution characteristics of soil erosion helps find the key areas for soil control projects and optimal scale for investing in a soil and water conservation project at the lowest cost. This study aims to answer the question of how the spatial distribution of soil erosion in Hubei Province changed between 2000 and 2020. Moreover, how do the effects of natural factors and human activities on soil erosion vary over the years? What are the differences in landscape pattern characteristics and the spatial cluster of soil erosion at multiple administrative scales? We simulated the spatial distribution of soil erosion in Hubei province from 2000 to 2020 by the Chinese Soil Loss Equation model at three administrative scales. We investigated the relationship between soil erosion and driving factors by Geodector. We explored the landscape pattern and hotspots of land at different levels of soil erosion by Fragstat and hotspot analysis. The results show that: (1) The average soil erosion rate decreased from 2000 to 2020. Soil erosion is severe in the mountainous areas of western Hubei province, while it is less severe in the central plains. (2) Land-cover type, precipitation, and normalized difference vegetation index are the most influencing factors of soil erosion in 2000–2010, 2015, and 2020, respectively. (3) The aggregation index values at the town scale are higher than those at the city and county scales, while the fractal dimension index values at the town scale are lower, which indicates that soil erosion projects are most efficient when the project unit is ‘town’. (4) At the town scale, if the hotspot area (6.84% of the total area) is treated as the protection target, it can reduce 50.42% of the total soil erosion of Hubei province. Hotspots of soil erosion overlap with high erosion zones, mainly in the northwestern, northeastern, and southwestern parts of Hubei province in 2000, while the hotspots in northwestern Hubei disappear in 2020. In conclusion, land managers in Hubei should optimize the land-use structure, soil and water conservation in slope land, and eco-engineering controls at the town scale.