Spatial and Temporal Evolution Assessment of Landscape Ecological Resilience Based on Adaptive Cycling in Changsha–Zhuzhou–Xiangtan Urban Agglomeration, China

Huaizhen Peng, Huachao Lou, Yifan Liu, Qingying He, Maomao Zhang () and Ying Yang ()
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Huaizhen Peng: College of Landscape Architecture & Hunan Big Data Engineering Research Center of Natural Protected Areas Landscape Resources & Institute of Urban and Rural Landscape Ecology, Central South University of Forestry and Technology, Yuelushan Laboratory Carbon Sinks Forests Variety Innovation Center, Changsha 410004, China
Huachao Lou: Management Committee of Hangzhou Campus of Zhejiang Normal University, Jinhua 321004, China
Yifan Liu: College of Landscape Architecture & Hunan Big Data Engineering Research Center of Natural Protected Areas Landscape Resources & Institute of Urban and Rural Landscape Ecology, Central South University of Forestry and Technology, Yuelushan Laboratory Carbon Sinks Forests Variety Innovation Center, Changsha 410004, China
Qingying He: College of Landscape Architecture & Hunan Big Data Engineering Research Center of Natural Protected Areas Landscape Resources & Institute of Urban and Rural Landscape Ecology, Central South University of Forestry and Technology, Yuelushan Laboratory Carbon Sinks Forests Variety Innovation Center, Changsha 410004, China
Maomao Zhang: College of Public Administration, Huazhong University of Science and Technology, Wuhan 430079, China
Ying Yang: College of Landscape Architecture & Hunan Big Data Engineering Research Center of Natural Protected Areas Landscape Resources & Institute of Urban and Rural Landscape Ecology, Central South University of Forestry and Technology, Yuelushan Laboratory Carbon Sinks Forests Variety Innovation Center, Changsha 410004, China

Land, 2025, vol. 14, issue 4, 1-29

Abstract: Urban agglomeration ecosystems are impacted by human activities and natural disasters, so analyzing the spatial and temporal evolution of landscape ecological resilience from the perspective of adaptive cycling is crucial. Using the Changsha–Zhuzhou–Xiangtan urban agglomeration in China as a case study, this research constructs a “Risk-Potential-Connectivity” framework to evaluate ecological resilience. This framework applies exploratory spatial data analysis methods to examine the spatiotemporal evolution and associated patterns of resilience and the Geodetector model to measure the driving factors of spatial variation. This study constructs an adaptive cycle model based on ecological resilience analysis, integrating potential and connectivity indices to classify the development stages of urban agglomeration regions dynamically. The results showed that the overall spatial distribution pattern of ecological risk decreased from the center outward, whereas ecological potential and connectivity increased. The average resilience index from 2000 to 2020 was 0.31, with a declining trend and shifting center of gravity from northwest to southeast. The spatial and temporal distribution of toughness exhibited high and low aggregation, with an overall Moran index greater than 0.75. Land-use intensity had the strongest explanatory power (q = 0.3662) for the spatial differentiation of landscape ecological resilience drivers and the joint effects of factor interaction had a higher explanatory power than single factors. Adaptive cycle analysis revealed that Furong District is in the protection stage, Xiangtan County in the development stage, and Liling City in the reorganization stage, with no region yet in the release stage. The findings offer a better understanding of the interactive adaptation characteristics and evolutionary patterns of social-ecological systems over extended periods, providing scientific support for the formulation of protection strategies to respond to dynamic changes in urban agglomeration ecosystems.

Keywords: adaptive cycling; ecological connectivity; ecological risks; landscape ecological resilience; spatial and temporal evolution; urban agglomeration (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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