Intangible cultural heritage (ICH) represents the outstanding crystallization of human civilization and it has received extensive attention from scholars in various countries. Studying the spatial distribution and influencing factors of ICH in the Three Gorges Reservoir Area can help to improve the protection and utilization of ICH. Using quantitative statistical analysis methods, GIS spatial analysis methods, and Geodetector, we analyzed the level structure (provincial and national levels), category structure (ten categories), and spatial distribution of 509 national and provincial ICH items in the Three Gorges Reservoir Area and then explored their influencing factors. We concluded that: (1) The structural characteristics of ICH vary significantly, and the level structure is dominated by provincial ICH items; the category structure is complete and mainly includes traditional skill and traditional music. (2) The spatial distribution of ICH in the Three Gorges Reservoir Area is dense in the west and sparse in the east, with a pattern of “one main core, three major cores, and two minor cores”. There are large differences in the degree of concentration of ICH at the county level; different categories of ICH have different distribution densities and concentration areas. Yuzhong District, Shizhu County, and Wanzhou District are dense areas of distribution for different categories of ICH. (3) The influences of different factors on the spatial distribution of ICH in the Three Gorges Reservoir Area vary greatly. Socioeconomic and historical–cultural factors are more influential than natural geographic factors, among which economic development, culture, and ethnicity are the most influential, but the interaction between the two dimensions of natural geography and socioeconomic and historical culture has a more significant influence on the spatial distribution of ICH than single-dimensional factors. (4) Proposals for optimizing the spatial layout, protection, and development of ICH in the Three Gorges Reservoir Area are provided from the perspectives of culture and tourism integration and sustainable development.

Understanding the spatial pattern of soil chemical properties (SCPs) together with topological factors and soil management practices is essential for land management. This study examines the spatial changes in soil chemical properties and their impact on China’s subtropical mountainous areas. In 2007 and 2017, 290 and 200 soil samples, respectively, were collected in Hefeng County, a mountainous county in central China. We used descriptive statistics and geostatistical methods, including ANOVA, semivariance, Moran’s I, and fractal dimensions, to analyze the characteristics and spatial autocorrelation changes in soil organic matter (OM), available phosphorus (AP), available potassium (AK), and pH value from 2007 to 2017. We explored the relationship between each SCP and the relationship between SCPs with topographic parameters, soil texture, and cropping systems. The results show that the mean value of soil OM, AP, AK, and pH in Hefeng increased from 2007 to 2017. The spatial variation and spatial dependency of each SCP in 2007, excluding AP and AK in 2007, were higher than in 2017. The soil in areas with high topographic relief, profile curvature, and planform curvature had less AP, AK, and pH. Soil at higher elevation had lower OM (r = −0.197, p < 0.01; r = −0.334, p < 0.01) and AP (r = −0.043, p < 0.05; r = −0.121, p < 0.05) and higher AK (r = −0.305, p < 0.01; r = 0.408, p < 0.01) in 2007 and 2017. Soil OM and AK in 2007 were significantly (p < 0.05) correlated with soil texture (p < 0.05). In contrast, oil AP and soil pH in 2007 and all SCPs in 2017 were poorly correlated with soil texture. The cropping systems played an important role in affecting all SCPs in 2007 (p < 0.01), while they only significantly affected AK in 2017 (p < 0.05). Our findings demonstrate that both topological factors, that is, the changes in cropping management and the changes in acid rain, impact soil chemical properties. The local government should place more focus on reducing soil acid amounts, soil AP content, and soil erosion by improving water conservancy facilities.

Constructing a scientific and quantitative quality-assessment model for farmland is important for understanding farmland quality, and can provide a theoretical basis and technical support for formulating rational and effective management policies and realizing the sustainable use of farmland resources. To more accurately reflect the systematic, complex, and differential characteristics of farmland quality, this study aimed to explore an intelligent farmland quality-assessment method that avoids the subjectivity of determining indicator weights while improving assessment accuracy. Taking Xiangzhou in Hubei Province, China, as the study area, 14 indicators were selected from four dimensions—terrain, soil conditions, socioeconomics, and ecological environment—to build a comprehensive assessment index system for farmland quality applicable to the region. A total of 1590 representative samples in Xiangzhou were selected, of which 1110 were used as training samples, 320 as test samples, and 160 as validation samples. Three models of entropy weight (EW), backpropagation neural network (BPNN), and random forest (RF) were selected for training, and the assessment results of farmland quality were output through simulations to compare their assessment accuracy and analyze the distribution pattern of farmland quality grades in Xiangzhou in 2018. The results showed the following: (1) The RF model for farmland quality assessment required fewer parameters, and could simulate the complex relationships between indicators more accurately and analyze each indicator’s contribution to farmland quality scientifically. (2) In terms of the average quality index of farmland, RF > BPNN > EW. The spatial patterns of the quality index from RF and BPNN were similar, and both were significantly different from EW. (3) In terms of the assessment results and precision characterization indicators, the assessment results of RF were more in line with realities of natural and socioeconomic development, with higher applicability and reliability. (4) Compared to BPNN and EW, RF had a higher data mining ability and training accuracy, and its assessment result was the best. The coefficient of determination (R²) was 0.8145, the mean absolute error (MAE) was 0.009, and the mean squared error (MSE) was 0.012. (5) The overall quality of farmland in Xiangzhou was higher, with a larger area of second- and third-grade farmland, accounting for 54.63%, and the grade basically conformed to the trend of positive distribution, showing an obvious pattern of geographical distribution, with overall high performance in the north-central part and low in the south. The distribution of farmland quality grades also varied widely among regions. This showed that RF was more suitable for the quality assessment of farmland with complex nonlinear characteristics. This study enriches and improves the index system and methodological research of farmland quality assessment at the county scale, and provides a basis for achieving a threefold production pattern of farmland quantity, quality, and ecology in Xiangzhou, while also serving as a reference for similar regions and countries.

High-intensity urban development and economic exploitation have led to the fragmentation and isolation of regional habitat patches, and biodiversity is under serious threat. Scientific identification and effective optimization of ecological networks are essential for maintaining and restoring regional ecosystem connectivity and guiding sustainable socio-economic development. Taking the mountainous areas of southwest Hubei Province (MASHP) in central China as an example, this study first developed a new integrated approach to identify ecological sources based on a quantitative assessment of ecosystem services and the morphological spatial pattern analysis (MSPA) method; it then used the Linkage Mapper tool to extract ecological corridors, applied the principle of hydrological analysis to identify ecological nodes, evaluated each ecological element to quantify its importance, and finally constructed the ecological network and further proposed some optimization countermeasures. The results show that the ecological network in the MASHP is dominated by ecological resources composed of forestland. Connectivity in the central region is significantly better than in other regions, including 49 ecological sources with an area of 3837.92 km², 125 ecological corridors with a total length of 2014.61 km, and 46 ecological nodes. According to the spatial distribution of crucial ecological landscape elements, a complete and systematic ecological framework of “two verticals, three belts, three groups, and multiple nodes” was proposed. The internal optimization of the ecological network in mountainous areas should focus on improving ecological flow, and strategies such as enhancing the internal connectivity of ecosystems, unblocking ecological corridors, and dividing ecological functional zones can be adopted. Based on the above analyses, this study also made recommendations for ecological protection and development and construction planning in mountainous areas. This study can provide realistic paths and scientific guidelines for ecological security and high-quality development in the MASHP, and it can also have implications for the construction of ecological networks and comprehensive ecological management in other mountainous areas.

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.

Land use/cover change (LUCC) accompanied by climate change and human activities will have unpredictable impacts on watershed ecosystems. However, the extent to which these land use changes affect the spatial and temporal distribution of ecosystem services (ESs) in different regions remains unclear. The impact of LUCC on ESs in the Qingjiang Watershed (QJW), an ecologically sensitive area, and LUCC’s role in future ESs under different land use scenarios are crucial to promoting ecological conservation and land use management. This paper assessed water yield (WY), soil conservation (SC), carbon storage (CS) and habitat quality (HQ) using the InVEST model, and their responses to LUCC in the QJW from 1990 to 2018 using the geodetector and multiscale geographically weighted regression. We predicted land use patterns using the Logistic–CA–Markov model and their effects on ESs in 2034 under business as usual (BAU), ecological land protection (ELP), arable land protection (ALP) and ecological economic construction (EEC) scenarios. From 1990 to 2018, the area of cropland and woodland decreased by 28.3 and 138.17 km², respectively, while the built-up land increased by 96.65 km². The WY increased by 18.92%, while the SC, CS and HQ decreased by 26.94%, 1.05% and 0.4%, respectively. The increase in the arable land area led to a increase in WY, and the decrease in forest land and the increase in construction land led to a decrease in SC, CS and HQ. In addition to being influenced by land use patterns, WY and SC were influenced mainly by meteorological and topographical factors, respectively. In 2034, there was an obvious spatial growth conflict between cropland and construction land, especially in the area centered on Lichuan, Enshi and Yidu counties. Under four scenarios, WY and SC were ranked ALP > BAU > EEC > ELP, while CS and HQ were ranked ELP > EEC > BAU > ALP. Considering the sustainable eco-socio-economic development of the QJW, the EEC scenario can be chosen as a future development plan. These results can indicate how to rationally improve the supply of watershed ESs through land resource allocation, promoting sustainable regional development in mountainous watershed areas.