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

Yiqi Li, Peiyao Wang, Binqing Zhai (), Daniele Villa, Spinelli Luigi, Chufan Xiao, Chuhan Huang, Yishan Xu and Lorenzi Angelo
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Yiqi Li: School of Human Settlements and Civil Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Peiyao Wang: School of Human Settlements and Civil Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Binqing Zhai: School of Human Settlements and Civil Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Daniele Villa: Department of Architecture and Urban Studies (DASTU), Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
Spinelli Luigi: Department of Architecture and Urban Studies (DASTU), Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
Chufan Xiao: School of Human Settlements and Civil Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Chuhan Huang: School of Human Settlements and Civil Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Yishan Xu: School of Human Settlements and Civil Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Lorenzi Angelo: Department of Architecture, Built Environment and Construction Engineering (ABC), Politecnico di Milano, 20133 Milan, Italy

Land, 2025, vol. 14, issue 12, 1-25

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/km 2 , significantly lower than 0.290 events/km 2 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 (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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