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Article

Multi-Scale Remote Sensing Analysis of Terrain–Resilience Coupling in Mountainous Traditional Villages: A Case Study of the Qinba Mountains, China

1
School of Human Settlements and Civil Engineering, Xi’an Jiaotong University, Xi’an 710049, China
2
Department of Architecture and Urban Studies (DASTU), Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
3
Department of Architecture, Built Environment and Construction Engineering (ABC), Politecnico di Milano, 20133 Milan, Italy
*
Author to whom correspondence should be addressed.
Land 2025, 14(12), 2299; https://doi.org/10.3390/land14122299
Submission received: 20 October 2025 / Revised: 11 November 2025 / Accepted: 18 November 2025 / Published: 21 November 2025

Abstract

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.
Keywords: mountainous traditional villages; remote sensing; spatial resilience; terrain–resilience coupling; multi-scale spatial analysis; Qinba Mountains mountainous traditional villages; remote sensing; spatial resilience; terrain–resilience coupling; multi-scale spatial analysis; Qinba Mountains

Share and Cite

MDPI and ACS Style

Li, Y.; Wang, P.; Zhai, B.; Villa, D.; Luigi, S.; Xiao, C.; Huang, C.; Xu, Y.; Angelo, L. Multi-Scale Remote Sensing Analysis of Terrain–Resilience Coupling in Mountainous Traditional Villages: A Case Study of the Qinba Mountains, China. Land 2025, 14, 2299. https://doi.org/10.3390/land14122299

AMA Style

Li Y, Wang P, Zhai B, Villa D, Luigi S, Xiao C, Huang C, Xu Y, Angelo L. Multi-Scale Remote Sensing Analysis of Terrain–Resilience Coupling in Mountainous Traditional Villages: A Case Study of the Qinba Mountains, China. Land. 2025; 14(12):2299. https://doi.org/10.3390/land14122299

Chicago/Turabian Style

Li, Yiqi, Peiyao Wang, Binqing Zhai, Daniele Villa, Spinelli Luigi, Chufan Xiao, Chuhan Huang, Yishan Xu, and Lorenzi Angelo. 2025. "Multi-Scale Remote Sensing Analysis of Terrain–Resilience Coupling in Mountainous Traditional Villages: A Case Study of the Qinba Mountains, China" Land 14, no. 12: 2299. https://doi.org/10.3390/land14122299

APA Style

Li, Y., Wang, P., Zhai, B., Villa, D., Luigi, S., Xiao, C., Huang, C., Xu, Y., & Angelo, L. (2025). Multi-Scale Remote Sensing Analysis of Terrain–Resilience Coupling in Mountainous Traditional Villages: A Case Study of the Qinba Mountains, China. Land, 14(12), 2299. https://doi.org/10.3390/land14122299

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