Mountainous traditional villages represent unique socio-ecological systems that have evolved through centuries of adaptation to complex topographies and multi-hazard environments. Understanding their terrain–resilience coupling mechanisms is essential for risk-sensitive planning and heritage preservation in mountainous regions. This study integrates multi-source remote sensing data and GIS spatial analysis to investigate 57 national-level traditional villages in the southern Qinba Mountains, China. Using kernel density estimation (KDE), nearest neighbor index (NNI), and Geodetector modeling, we identify the spatial distribution characteristics and topographic driving forces that shape settlement patterns across macro-meso-micro scales. Results reveal that 83% of the villages are clustered in low-mountain and hilly zones (550–1200 m elevation), preferring slopes below 15° and south-facing aspects. Elevation exerts the strongest influence (q = 0.46), followed by slope (q = 0.32) and aspect (q = 0.29), forming a multi-level adaptation framework of “macro-elevation differentiation, meso-slope constraint, and micro-aspect optimization.” Morphological Spatial Pattern Analysis (MSPA) further indicates that traditional villages achieve ecological balance and disaster avoidance through adaptive spatial strategies such as terrace-based flood prevention, convex-bank stabilization, and platform-based hazard avoidance. These strategies are not merely topographic preferences but natural adaptation mechanisms formed by long-term responses to multi-hazard environments—dynamic adaptation processes that reduce disaster exposure and optimize resource use efficiency through active adjustment of site selection and spatial transformation (the disaster density in the 100m core zone buffer is 0.077 events/km², significantly lower than 0.290 events/km² in peripheral areas). These findings demonstrate that remote sensing techniques can effectively reveal the terrain–resilience coupling of traditional villages, providing quantitative evidence for integrating spatial resilience into cultural landscape conservation, ecological security assessment, and rural revitalization planning. The proposed multi-scale analytical framework offers a transferable approach for evaluating settlement adaptability and resilience in other mountainous cultural heritage regions worldwide.

Mountainous traditional villages are rural heritage settlements shaped by complex environmental and socioeconomic interactions. Rapid urbanization has driven resource outflow and spatial restructuring, intensifying the conservation–development conflict. Existing conservation models relying on standardized criteria neglect village heterogeneity, and current studies insufficiently capture cluster-specific driving mechanisms across spatial scales. Using 153 traditional villages in the Qin–Ba Mountains of southern Shaanxi, this study shifts the analysis from overall spatial pattern identification to the comparison of cluster-specific driving mechanisms. The results indicate (1) traditional villages display an agglomeration pattern of “one primary core, one secondary core, and multiple peripheries,” forming valley corridor and hilly barrier clusters; (2) socioeconomic factors show greater explanatory power than natural factors at the global scale; (3) influencing factors exhibit dual heterogeneity, with intensity and direction varying across space and clusters displaying distinct dominant-factor combinations; and (4) socioeconomic factors may provide enabling conditions for traditional village conservation and adaptive reuse under appropriate governance contexts. Accordingly, valley corridor clusters can follow “conservation through development,” whereas hilly barrier clusters should adopt “development through conservation.” These findings offer empirical support for spatially differentiated governance of mountainous traditional villages.

Current research on disaster-adaptive resilience predominantly focuses on urban systems, with insufficient attention paid to the unique scale of traditional settlements and their formation mechanisms and pathways to systemic realization remain significantly understudied. There is also a lack of multi-dimensional coupling analysis and innovative methods tailored to the specific contexts of rural areas. To address this, this study innovatively introduces ant colony bionic intelligence, drawing on its characteristics of swarm intelligence, positive feedback, path optimization, and dynamic adaptation to reframe emergency decision-making logic in human societies. An evaluation model for disaster-adaptive resilience is constructed based on these four dimensions as the criterion layer. The weights of dimensions and indicators are determined using a combined AHP–entropy weight method, enabling a comprehensive assessment of settlement resilience. Taking Fenghuang Ancient Town as an empirical case, the research utilizes methods such as field surveys, questionnaire surveys, and GIS data analysis. The results indicate that (1) the overall resilience evaluation score of Fenghuang Ancient Town is 3.408 (based on a 5-point scale); (2) the path optimization dimension contributes the most to the overall resilience, with road redundancy design (C21) being the core driving factor; within the positive feedback mechanism dimension, soil and water conservation projects (C15) provide the fundamental guarantee for village safety; (3) based on these findings, hierarchical planning strategies encompassing infrastructure reinforcement, community capacity enhancement, and ecological risk management are proposed. This study verifies the applicability of the evaluation model based on ant colony bionic intelligence in assessing the disaster resilience of traditional settlements, revealing a new paradigm of “bio-intelligence-driven” resilience planning. It successfully translates ant colony behavioral principles into actionable planning and design guidelines and governance tools, providing a replicable method for resilience evaluation and enhancement for traditional settlements in ecological barrier areas such as the Qinling Mountains.

Landscape character change in mountain traditional villages is difficult to assess from a single perspective because roof-material replacement, material–color deviation, and visual exposure vary under complex terrain and settlement configurations. This study develops a multi-source UAV framework for reproducible, spatially explicit assessment of courtyard-scale visual–material integrity (CI), a spatially observable component of landscape character integrity. Using 749 courtyards in nine nationally designated traditional villages in Shangluo, China, the framework integrates UAV orthophotos, 3D mesh models, and point-cloud data to derive material penetration rate (PR), material–color conflict (CC), and standardized visual exposure (VC). PR and CC represent baseline material–color loss, whereas VC is incorporated as an exposure-amplification condition. Formula-structure sensitivity analysis against blinded ratings of 50 sampled courtyards showed that the proposed formulation had the highest rank consistency with expert judgments (Spearman’s ρ = 0.949). The frozen framework also showed a strong association with blinded professional ratings in geographically independent Longnan villages (ρ = 0.912, p < 0.001). XGBoost–SHAP identified courtyard impervious-surface ratio, primary material, and roof form as the leading model-associated predictors of CI variation. The framework supports courtyard-scale CI assessment, priority screening, and repeat-survey reassessment under comparable acquisition conditions.

This study analyzed the spatiotemporal dynamics of the vegetation ecological quality under climate change. Focusing on the vegetation conditions, a vegetation ecological quality index was constructed, expressing as the product of vegetation fraction cover (VFC), net primary productivity (NPP), and geographic coverage area. The results of trend and significance analysis showed that from 2000 to 2023, the VEQI in the Qinling Mountains exhibited a significant improvement, with an average slope of 4.91 gC·a⁻¹ and 96.2% of the area showing high stable improvement. Partial correlation analysis revealed that precipitation had a stronger positive influence on VEQI than temperature, with over 98% of the area showing a positive correlation with precipitation, while temperature was positively correlated in 95.0% of the area but negatively correlated in high-altitude mountain zones. Therefore, four climate-driven patterns were identified: precipitation-driven (31.2%), temperature-driven (2.3%), co-driven (54.2%), and climate-stable (12.3%), suggesting that vegetation ecological quality in most regions is co-driven by both temperature and precipitation. Based on the results of trend and significance analysis and climate-driven patterns, the Qinling Mountains were divided into three ecological risk zones: low-risk (36.1%), middle-risk (56.9%), and high-risk (7.0%), with corresponding differentiated control measures proposed.