Traditional villages are the spatial carriers of the excellent traditional culture of the Chinese nation, the concentrated expression of thousands of years of farming civilization, and a nonrenewable precious cultural heritage. However, in recent decades, with the rapid development of urbanization and modernization, traditional villages have rapidly declined or even disappeared. Therefore, exploring the spatial distribution characteristics and influencing factors of traditional villages and clarifying their formation and development laws, can provide a new perspective for the continuity protection of traditional villages on a cross-regional scale. This study takes six batches of 462 traditional villages in the Wuling Mountains, Hunan, China as the research object. On the basis of the analysis of the spatial distribution characteristics of overall and different ethnic minority traditional villages, this research proposes the idea of simulating the spatial distribution mechanism of traditional villages by using the multiscale geographical weighted regression (MGWR) model. Results indicate that (1) the spatial distribution of the traditional villages of the overall and different ethnic minorities shows significant agglomeration characteristics (among them, traditional Miao and Tujia villages have formed the main and sub cores of the space gathering of traditional villages in the Wuling Mountains in Hunan); and (2) compared with the least squares (OLS) method and the classic geographical (GWR) weighted regression model, the MGWR model can reflect the impact scales of different factors, and the simulation effect is better. From the perspective of influence factors, natural factors are the basis of affecting the distribution of traditional villages in the Wuling Mountain area and are generally positively correlated. Socioeconomic factors have a dual influence on the spatial distribution of traditional villages and generally have a negative correlation. Cultural factors are the key to the spatial distribution of traditional villages and are generally positively correlated. Spatial factors have no significant impact on the spatial distribution of traditional villages. On the basis of these conclusions, this study discusses the relevant strategies for the protection and development of traditional villages.

As a policy, protected green space in the rapidly developing the Chang-Zhu-Tan Urban Agglomeration is of great practical significance to study the vegetation changes and influencing factors in the Green Heart area. In this paper, data processing, grading and area statistics were carried out for the maximum value of normalized differential vegetation index (NDVI) from 2000 to 2020. Combined with Theil–Sen median trend analysis and Mann–Kendall, the change trend of long-time series NDVI was studied, and investigation of NDVI influencing factors, processes and mechanisms using geographical detectors. The results showed that: (1) The spatial distribution characteristics of NDVI in the study area were high in the middle and inlaid transition between adjacent grades. Except for the low grades, the distribution of NDVI in other grades was relatively scattered, and the overall trend of NDVI change was rising. (2) Population density was the main factor affecting NDVI changes, with an explanatory power of up to 40%, followed by elevation, precipitation and minimum temperature. (3) The influence of influencing factors on the change of NDVI was not the result of independent action of a single factor, but the result of the interaction between human factors and natural factors, and the factor combinations with greater interaction had significant differences in the spatial distribution of NDVI.

Harmonious interactions among multiple functions form the foundation of sustainable rural development. This study proposes an innovative network-based framework to analyze the relationship between the Concentrated-Contiguous Conservation and Utilization (CCU) of traditional villages and their socio-ecological functions. An empirical analysis of 432 traditional villages in the Wuling Mountain Area of Hunan Province, China, reveals that: (1) The Concentrated-Contiguous Conservation and Utilization Network (CCUN) exhibits low density, local clustering, and limited long-distance connections, with its spatial distribution closely corresponding to forested land. (2) The socio-economic functional system is characterized by unobstructed cultural exchange, a clustered spatial pattern, transportation-dependent development, and a scattered distribution of villages with high attribute values, while the central region serves a significant ecological conservation role. (3) Correlation analysis reveals an overall low level of synergy between the CCUN and the socio-economic system. Through a role matching analysis of core villages across various networks, diverse multifunctional combinations were identified and categorized into four primary types: Coordinated Development, Conflict, Restricted, and Potential. The proposed methodological framework provides a practical tool to guide local governments in steering CCU practices toward multifunctional coordination and sustainability.

Cropland fragmentation affects regional ecosystem functioning and biodiversity conservation, making the spatio-temporal coupling between cropland fragmentation (CLF) and habitat quality (HQ) a key basis for land management and ecological protection. However, the reported CLF–HQ coupling differs in sign between regions, and the mechanism underlying this divergence remains unclear. This study evaluated the spatio-temporal dynamics of CLF and HQ and their interrelationship across Hunan Province, China, at the township scale (∼2450 units) from 1994 to 2024. CLF was measured by a four-dimensional index consisting of Scale (SPI), Natural Endowment (NPI), Aggregation (API), and Convenience (CPI), with weights determined by a combined AHP–Entropy method; HQ was modelled with InVEST. The relationship between the two was analysed through bivariate spatial autocorrelation, Mantel tests, Random Forest, redundancy analysis (RDA), and XGBoost–SHAP. CLF peaked in urban fringes and was lowest in the western mountains; mean HQ declined modestly, mainly before 2014. Spatially, CLF and HQ were negatively associated (bivariate Moran’s I between −0.38 and −0.51, $p<0.001$), opposite in sign to the positive coupling found in arid Northwest China. Decomposition of the composite index showed that the negative association was carried largely by the natural-endowment dimension, in which slope and elevation were the dominant indicators. Structural fragmentation dimensions uniquely explained only 1.5–5.4% of HQ variance, and controlling for terrain reduced the CLF–HQ correlation by 67–96%. The sign also reversed under an alternative directional standardisation, so it is not robust to a defensible analytical choice. In these data, the observed negative coupling is largely consistent with a shared topographic gradient rather than a direct fragmentation effect. For management, this correlational evidence suggests that reducing fragmentation alone may do little to improve habitat quality; conservation is better guided by the underlying terrain and land-use gradients. More broadly, CLF–HQ assessments become more reliable when cropland structure is separated from natural endowment and terrain confounding is accounted for.

Ecological changes in mining areas are characterized by long-term dynamics, temporal stages, and spatial heterogeneity. Existing monitoring methods often face challenges in balancing multidimensional ecological condition characterization with spatially consistent analysis and have limited capability to identify complex disturbance–recovery trajectories. To address these issues, an Object-based Ecological Index-driven Framework (OEI-GTCD) is proposed to monitor ecological disturbance and recovery trajectories in mining areas. The framework uses the Mining-area Ecological Index (MEI) to characterize multidimensional ecological conditions and conducts analysis based on spatially consistent object units. On this basis, a Globally Optimized Time-series Change Detection method (GTCD) is proposed to improve the identification of complex ecological disturbance–recovery trajectories by integrating trend-change detection, abrupt-change detection, and segmentation optimization. Landsat imagery from 2000 to 2024 was used to characterize the spatiotemporal patterns of ecological disturbance and recovery across the Daye Mining Area, China. The findings suggest that MEI captures multiple dimensions of ecological conditions, while object-scale analysis characterizes ecological changes through overall object-level trajectories, supporting their spatial interpretation. OEI-GTCD achieved disturbance and recovery detection accuracies of 88.4% and 84.8%, respectively, representing improvements of 6.0 and 5.2 percentage points over OEI-LandTrendr. Ablation analyses further highlighted the contributions of abrupt-change detection and the improved segmentation criterion to event identification, as well as the role of local breakpoint refinement in temporal localization. From 2000 to 2024, stable and continuously improving ecological states together accounted for 82.07% of the area represented by the non-water objects used in the ecological analysis, while disturbance and recovery processes exhibited pronounced spatiotemporal heterogeneity. Overall, OEI-GTCD provides an integrated approach for monitoring ecological disturbance and recovery and characterizing their spatiotemporal patterns in mining areas.

Long-term mining activities can cause complex ecological and environmental problems, including mineral surface exposure, dust-related disturbance, and vegetation degradation, while conventional ecological indices may not fully characterize the distinctive ecological conditions of mining areas. This study developed a mining-area Modified Remote Sensing Ecological Index (MRSEI) by incorporating the Lithological Mineral Index (LMI), Normalized Dust Difference Index (NDDI), and Vegetation Health Index (VHI) into the Remote Sensing Ecological Index (RSEI) framework. Object-based analytical units were constructed using Landsat time-series data from 2000 to 2024 and G-means clustering, followed by the development of an Object-Based XGBoost-SHAP (OB-XGBoost-SHAP) framework to quantify the relative model contributions and nonlinear associations of explanatory factors. The results showed that, in the Daye mining area (DMA), MRSEI provided clearer differentiation of ecological quality among land-use types than RSEI and Mining-Specific Eco-Environment Index (MSEEI), with an overall gradient of forest land > cultivated land > construction land > mining land. Annual Principal Component Analysis (Annual-PCA) and Global Principal Component Analysis (Global-PCA) results showed high consistency (Pearson r = 0.985). Across the seven evaluation years, the mean MRSEI of the DMA increased from 0.5493 in 2000 to 0.6035 in 2024, representing an overall change of 9.87%. The object-based model consistently outperformed the regular-grid model under random splitting, while model performance decreased under spatially separated validation but retained moderate predictive capability. Particulate Matter (PM10), Digital Elevation Model (DEM), Temperature (TEM), and Land Use Intensity (LU) showed relatively high model contributions, with their relative importance varying across different periods. This study integrates mining-specific ecological characterization, object-based spatial analysis, and explanatory-factor contribution analysis, providing a methodological reference for ecological quality assessment and long-term change monitoring in mining areas.