Spatiotemporal Heterogeneity and Zoning Strategies for Urban Ecological Resilience in Yichang, China

Spatiotemporal Heterogeneity and Zoning Strategies for Urban Ecological Resilience in Yichang, China | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL Land Degradation & Development This is a preprint and has not been peer reviewed. Data may be preliminary. 17 April 2025 V1 Latest version Share on Spatiotemporal Heterogeneity and Zoning Strategies for Urban Ecological Resilience in Yichang, China Authors : Xiaotang Xia , Fan Zhou , and Jian Chen 0009-0008-6893-5685 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.174487765.50863734/v1 Published Land Degradation & Development Version of record Peer review timeline 362 views 216 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Urban Ecological Resilience (UER) is a critical capability for addressing ecological imbalances resulting from the pressures of rapid urbanization. This study developed a comprehensive UER assessment model encompassing three dimensions: adaptability, resistance, and recovery. Utilizing geographic detectors and the Patch-generating Land Use Simulation (PLUS) model, the study analyzed the spatiotemporal evolution and influencing mechanisms of Yichang’s UER from 2003 to 2023. Furthermore, it simulated multiple development scenarios for 2035 and proposed zoning optimization strategies, providing scientific evidence for sustainable ecological development. The results indicated that Yichang’s UER followed a “decline-then-rise” temporal trend. Spatially, higher resilience was observed in the northwest and lower resilience in the southeast, reflecting a heterogeneous spatial distribution. At the regional level, UER was influenced by factors such as ecological protection efforts, agricultural development, and water resource conditions. At the zonal level, ecological protection areas were primarily influenced by industrial development, whereas ecological conservation areas were shaped by the interaction between terrain conditions and ecological protection measures. Resilience-enhancing areas were mainly driven by the availability and management of water resources. Multi-scenario simulations indicated that, under a development model oriented toward water ecology protection, Yichang’s UER significantly outperformed other scenarios. The study proposed a three-tier spatial regulation framework: ”rigid constraints for protection areas, threshold control for conservation areas, and dynamic balance for resilience-enhancing areas.” This framework facilitated the coordinated improvement of UER across the region and offered a reference model for differentiated ecological governance in cities along the middle reaches of the Yangtze River. Spatiotemporal Heterogeneity and Zoning Strategies for Urban Ecological Resilience in Yichang, China Xiaotang Xia a ，Fan Zhou a ，Jian Chen b,* a School of Urban Construction Engineering, Wuhan University of Science and Technology, Wuhan, 430065, China b College of Urban and Environmental Sciences, Hunan University of Technology, Zhuzhou, 412007, China Corresponding author, E-mail address: [email protected] Running Title: Urban Ecological Resilience and Zoning in Yichang Abstract: Urban Ecological Resilience (UER) is a critical capability for addressing ecological imbalances resulting from the pressures of rapid urbanization. This study developed a comprehensive UER assessment model encompassing three dimensions: adaptability, resistance, and recovery. Utilizing geographic detectors and the Patch-generating Land Use Simulation (PLUS) model, the study analyzed the spatiotemporal evolution and influencing mechanisms of Yichang’s UER from 2003 to 2023. Furthermore, it simulated multiple development scenarios for 2035 and proposed zoning optimization strategies, providing scientific evidence for sustainable ecological development. The results indicated that Yichang’s UER followed a “decline-then-rise” temporal trend. Spatially, higher resilience was observed in the northwest and lower resilience in the southeast, reflecting a heterogeneous spatial distribution. At the regional level, UER was influenced by factors such as ecological protection efforts, agricultural development, and water resource conditions. At the zonal level, ecological protection areas were primarily influenced by industrial development, whereas ecological conservation areas were shaped by the interaction between terrain conditions and ecological protection measures. Resilience-enhancing areas were mainly driven by the availability and management of water resources. Multi-scenario simulations indicated that, under a development model oriented toward water ecology protection, Yichang’s UER significantly outperformed other scenarios. The study proposed a three-tier spatial regulation framework: ”rigid constraints for protection areas, threshold control for conservation areas, and dynamic balance for resilience-enhancing areas.” This framework facilitated the coordinated improvement of UER across the region and offered a reference model for differentiated ecological governance in cities along the middle reaches of the Yangtze River. Keywords: Urban ecological resilience – Spatiotemporal analysis – Geographic detectors – PLUS model – Zoning Strategies 1 | Introduction Urban ecosystems are complex systems in which natural, social, and economic components are closely interconnected. Their stability and resilience are directly tied to the sustainable development of cities. However, the rapid pace of urbanization has intensified challenges such as land resource overuse, environmental pollution, and the degradation of ecological services, all of which undermine urban ecological resilience (UER). Consequently, enhancing UER and promoting coordinated development between humans and nature have become central concerns in global urban governance. The concept of resilience has evolved significantly since its introduction to ecology by Holling (1973), shifting from an initial focus on a system’s ability to withstand disturbances—referred to as ”engineering resilience”—to a broader emphasis on adaptation and recovery capacity, known as ”ER” (Ahern, 2011). Building on this foundation, Meerow et al. (2016) systematically defined urban resilience as the resistance, adaptation, and recovery capacity of urban ecosystems in response to disturbances. More recently, the introduction of the Social-Ecological-Technological Systems (SETS) resilience framework (Sharifi, 2023) has underscored the importance of multi-system collaboration in enhancing urban resilience. Significant progress has been made in the assessment and optimization of UER, with research primarily focusing on three dimensions: resistance, adaptation, and recovery. These efforts employ multiple indicators encompassing natural, social, and economic factors. For instance, Li and Wang (2023) assessed the ER of 281 cities in China using the entropy method, revealing notable regional differences. Wang et al. (2024) constructed a resilience index for the Guangdong-Hong Kong-Macao Greater Bay Area from social, economic, institutional, ecological, and engineering perspectives and analyzed its dynamic evolution. Ge et al. (2024) developed the Urban Resilience Index (URI) system based on four dimensions—economy, society, ecology, and infrastructure—integrating the three core capacities of the resilience process to assess Qingdao City. Xu et al. (2024) determined indicator weights using the entropy method and established an UER assessment system based on the DPSIR model. These studies provided a multi-dimensional framework for assessing urban resilience and offered both theoretical and empirical support for resilience optimization. In terms of influencing factors, numerous scholars have examined the combined effects of natural conditions (e.g., topography and vegetation), human activities (e.g., urbanization and land use), and socio-economic factors (e.g., population density and industrial policy). Topography and vegetation cover were identified as fundamental determinants of ER (Zhang et al., 2023). The multi-dimensional aspects of urbanization had a complex impact on ecosystem stability (Wang et al., 2023). Research by Chen et al. (2024) on Jiangsu showed that the urbanization rate and variations in the composition of human landscapes were primary factors affecting ecological vulnerability, which, in turn, influenced ER. Collectively, these studies elucidated the diverse factors shaping ER and provided a foundation for targeted resilience optimization. These studies emphasized the importance of accounting for the synergistic effects of natural factors, socio-economic conditions, and human activities in efforts to enhance ecosystem stability. Optimization simulation studies have explored pathways for improving resilience through system dynamics (SD) models, PLUS models, and multi-scenario analyses. Nathwani et al. (2019) employed system dynamics models to simulate the effects of coastal city management strategies on ER, providing a theoretical foundation for sustainable development. Li et al. (2020) used an SD model to identify the three-stage characteristics of resilience enhancement in Beijing, highlighting the sensitivity of policy adjustments to subsystem dynamics. Wang et al. (2022) analyzed the green transformation of resource-based cities in China and proposed that ER could be improved through policy optimization and effective resource management. Hong et al. (2022) emphasized the enhancement of network resilience by protecting key nodes within ecological networks and implementing both positive and negative list controls. Liang et al. (2023) proposed a collaborative optimization strategy for balancing ecological and economic benefits under complex land use dynamics based on spatial segmentation. Mohammadyari et al. (2023) optimized land use in a watershed in Iran using multi-scenario simulations to promote sustainable landscape planning. Luo et al. (2024) integrated land use data and machine learning to develop a multi-scale carbon emission prediction model, which was used to assess the impact of emission reduction strategies on ER. Zhang et al. (2025) applied genetic algorithms to optimize the distribution of landscape infrastructure in Chongqing, dynamically simulating disturbance scenarios and proposing innovative methods for enhancing resilience. Collectively, these studies demonstrate that optimization simulation approaches hold substantial theoretical and practical significance for strengthening urban and ecosystem resilience. Yichang, situated as an ecological barrier in the middle and upper reaches of the Yangtze River and serving as a key node within the Yangtze River Economic Belt, exemplifies the tension between its complex ”mountain-water-forest-field” ecological foundation and the rapid pace of urbanization (Zhao et al., 2022; Yang et al., 2024). The total water area of Yichang accounted for 6.3% of the city’s land area, significantly exceeding the national average of 2.8%. The main stream of the Yangtze River flowed through the city for 234 kilometers and was joined by 34 first-level tributaries, including the Qingjiang River and the Xiangxi River, forming a dendritic river system. In combination with artificial water bodies such as the Three Gorges Reservoir and the Gezhouba Reservoir, these water systems constituted a composite ”river-lake-reservoir” ecosystem. The high-density, multi-form distribution of these water bodies substantially enhanced the hydrological connectivity of Yichang’s ecosystem. Studying the spatiotemporal evolution patterns of ER and optimizing zoning pathways not only addresses existing theoretical gaps but also offers practical insights for ecological system planning in similar urban contexts. Against this backdrop, the present study employed Yichang as a case study, integrating adaptive capacity, resistance, and recovery to construct an ER assessment model. It analyzed the spatiotemporal differentiation of resilience from 2003 to 2023, utilizing geographic detectors to reveal the multi-scale interactive effects of natural factors, human activities, and socio-economic variables. Additionally, the study incorporated the PLUS model to simulate four development scenarios for 2035—natural evolution, cultivated land protection, urban expansion, and water ecological protection. These simulations supported the identification of an optimal development pathway and informed the proposal of zoning optimization strategies. The study aims to answer the following questions: (1) How does the spatiotemporal pattern of UER respond to multidimensional driving factors? (2) What are the regional variations in dominant influencing factors and their interactions? (3) Which factors predominantly influence urban development scenarios, and what pathways can optimize ER? The innovation of this study lies in integrating the detection of key regional factors with multi-scenario simulations, thereby overcoming the limitations of traditional single-scale analyses. It introduces a systematic framework of ”assessment-attribution-simulation-optimization,” which serves as a methodological complement to UER research. By accounting for regional heterogeneity, the study develops differentiated management strategies, facilitating the transition from a ”one-size-fits-all” approach to ”precise governance” in resilience building. This approach contributes to enhancing ecological system security and promoting high-quality, sustainable development in typical cities along the middle reaches of the Yangtze River. 2 | Materials and Methods 2.1 | Overview of the Study Area Yichang is located in the southwestern part of Hubei Province, at the junction of the middle and upper reaches of the Yangtze River, serving as a vital node city within the Yangtze River Economic Belt. The city’s terrain is predominantly mountainous and hilly, with higher elevations in the northwest and lower elevations in the southeast. Its climate is subtropical monsoon humid, characterized by distinct seasons, and the forest coverage rate exceeds 59%. The water system in Yichang is well-developed, with the Yangtze River running through the city for 232 kilometers, and tributaries such as the Qingjiang River intertwining with it (Jiang et al., 2020). The primary land use types in the city are forest land and cultivated land, with forest land covering approximately 59.64% and cultivated land covering about 12.61 (Figure 1). 2.2 | Data Source This study focuses on the UER of Yichang, extensively collecting multi-source data (Table 1). 2.3 | Methods In Figure 2 we present a schematic of the study design and methodology. 2.3.1 | Assessment of ER This study constructed an innovative three-dimensional ER assessment framework grounded in fundamental ecological principles and complex systems theory. It integrated three interrelated yet relatively independent dimensions: ”Adaptability, Resistance, and Recovery” (Yin et al., 2024). Adaptability assessed the ecosystem’s capacity to respond to sustained environmental pressures through behavioral regulation. Resistance measured the structural stability in withstanding sudden disturbances. Recovery quantified the dynamic process of returning to a steady state following system disruption. These three dimensions functioned synergistically to provide a comprehensive analysis of ER formation mechanisms. This framework not only addressed the limitations of traditional single-indicator assessments but also effectively identified ecosystem vulnerabilities, enabling the detection of critical weak points and sources of resilience. Consequently, it provided a scientific basis for developing targeted conservation strategies. As the calculated values of adaptability, resistance, and recovery varied in units and dimensions, data normalization was necessary to standardize each indicator within the [0,1] range. This normalization facilitated the comprehensive evaluation of regional ER. The calculation formula is as follows: \begin{equation} ER=\sqrt[3]{A\times P\times R}\nonumber \\ \end{equation} Ecological adaptability (A) refers to an ecosystem’s capacity to adjust, self-repair, adapt, and maintain sustainable development following external shocks. In this study, adaptability was characterized using ecosystem service types, including carbon storage, habitat quality, food production, and water source conservation (Ling et al., 2019). Ecological resistance (P) represents the structural stability of an ecosystem. A set of landscape indices—landscape heterogeneity (LH), landscape connectivity (LC), and landscape shape (LS)—was used to calculate resistance through a weighted coefficient model (Shen et al., 2023). Ecological recovery (R) primarily reflects the ecosystem’s ability to return to its original state after experiencing substantial damage. As the region with the highest water network density in the middle reaches of the Yangtze River, Yichang exhibits a unique hydrological and geographical pattern and functions as an ecological barrier for the Three Gorges Reservoir Area. To more accurately measure recovery capacity, this study optimized the resilience coefficient for water bodies when determining the ER coefficients for six land use types (Table 2). Based on this framework, the calculation methods for adaptability, resistance, and recovery were established (Table 3). 2.3.2 | Geographical Detector This study employed geographic detectors to quantify the explanatory power of various influencing factors on ER (Zhang et al., 2023). The calculation formula was as follows: \begin{equation} \begin{matrix}\\ Q=1-\frac{\sum_{h=1}^{L}N_{h}{\sigma_{h}}^{2}}{N\sigma^{2}}\\ \end{matrix}\nonumber \\ \end{equation} In this formula, \(Q\) indicated the explanatory power of the influencing factors on ER, with values ranging from 0 to 1. L denoted the number of strata for each factor. \(N_{h}\) referred to the number of units in the h-th stratum, \({\sigma_{h}}^{2}\) was the variance within the h-th stratum, \(N\) was the total number of units in the study, and \(\sigma^{2}\) was the overall variance of the dependent variable. This study systematically reviewed previous research findings and incorporated Yichang’s regional strategic positioning—as both an ecological barrier for the Three Gorges Reservoir and a key node in the Yangtze River Economic Belt. Based on three dimensions—natural factors (Zhao et al.,2024,Yang et al.,2024), human activities (Wang et al.,2023, Tang et al.,2023), and socio-economic conditions (Karimian et al.,2022, Yanget al.,2022)—ten key influencing factors were selected to construct an indicator system for the ER factors of Yichang (Table 4). This analytical framework comprehensively captured the complexity of Yichang’s ecosystem and provided a scientific foundation and innovative perspective for ER assessment. 2.3.3 | PLUS Model The PLUS model was a novel cellular automaton (CA) framework developed based on the future land use simulation (FLUS) model. It incorporated an innovative land expansion analysis strategy (LEAS) and a CA model based on multiple random patch seeds (CARS). This model not only facilitated a more accurate analysis of the driving forces behind various land use changes but also effectively simulated dynamic changes at the scale of multiple land use patches (Liang et al., 2021). The computational formulas for LEAS and CARS were provided in the appendix. For the parameter setup of multi-scenario simulation predictions, this study extracted land use expansion data from 2003 to 2013. Based on the driving factors of 2013, land use simulation for 2023 was conducted using the PLUS model and compared with the actual 2023 land use data. The simulation accuracy was validated at 91.94%. Subsequently, land use simulation for 2035 was carried out using both the PLUS model and IDRISI. The simulation parameters were determined based on the findings of Liu et al. (2024). 3 | Results 3.1 | Analysis of ecological adaptability, resistance, and recovery This study employed a multi-dimensional ER assessment framework and applied the range normalization method to analyze the spatiotemporal characteristics of ecological adaptability, resistance, and recovery in Yichang City for the years 2003, 2013, and 2023 (Figure 3). The evaluation results for the three components of resilience were classified into five significance levels using the natural breaks classification method (Figure 4), providing a systematic representation of the spatial differentiation patterns and dynamic evolution of UER. From a temporal perspective, Yichang’s overall ecological adaptability exhibited an upward trend. Between 2003 and 2023, the area of low-value zones remained stable, while medium-high value zones decreased and high-value zones expanded significantly. Resistance capacity gradually improved, reflected in an increased proportion of areas within the medium-high and high-value zones, accompanied by a corresponding decrease in medium-low and low-value zones. In contrast, recovery capacity showed a downward trend: although high-value zones remained dominant, their extent continued to decline. Spatially, ecological adaptability was generally well balanced across the study area. The northwest mountainous areas, benefiting from abundant forest resources, predominantly fell into medium-high or high-value categories, whereas the southeastern plains, where characterized by concentrated cultivated land and built-up areas, primarily exhibited medium-low values. Resistance capacity showed significant spatial differentiation. The northwest mountainous regions (e.g., Xingshan County and Wufeng Tujia Autonomous County), with high landscape heterogeneity, demonstrated stronger resistance. In contrast, the central urban area, marked by a contiguous distribution of built-up land, exhibited weaker resistance. The spatial distribution of recovery capacity was notably influenced by land use and water resources. Densely cultivated areas in the Zhijiang Plain generally fell into medium-low levels, while forested and water-rich mountainous areas, such as Changyang Tujia Autonomous County, reached medium-high or high levels. 3.2 | Spatial-Temporal Evolution Characteristics of Urban Ecological Resilience To address the scale transmission disconnect in traditional planning, this study analyzed the spatial-temporal evolution characteristics of ER from both city-wide and zonal perspectives. By examining common patterns at the city level and spatial heterogeneity at the zonal level, the study provided scientific support for proposing optimized resilience zoning strategies. 3.2.1 | Urban overall Characteristics The UER of Yichang exhibited significant spatiotemporal variation (Figure 5). The natural breaks method was used to categorize ER levels for 2003, 2013, and 2023 into five classes: low (0-0.3), low-middle (0.3-0.4), middle (0.4-0.5), middle-high (0.5-0.6), and high (0.6-0.8). Temporal analysis revealed that between 2003 and 2013, the proportion of low-level zones increased, while high-level zones decreased, indicating a general decline in ER. However, from 2013 to 2023, low-level zones stabilized, and high-level zones expanded, reflecting a notable recovery in resilience. In terms of spatial patterns, the proportion of middle-high and high-level zones increased from 33.9% in 2003 to 39.8% in 2023, while middle-low and low-level zones declined from 37.3% to 31.4%. The northwestern mountainous regions (e.g., Xingshan County and Wufeng Tujia Autonomous County) exhibited high ER due to dense forests and abundant water resources, which supported stable and diverse ecosystems. In contrast, the central urban areas and the southeastern Zhi River Plain, characterized by extensive cultivated and built-up land, exhibited less ecological diversity and lower resilience. Overall, the spatial distribution of ER across the city was imbalanced, with higher resilience in the northwest and lower resilience in the southeast. 3.2.2 | Urban Sub-Division Characteristics Based on the ER assessment results, administrative divisions, and current land use, this study divided Yichang into three zones: the Resilience Enhancement Zone, the Ecological Conservation Zone, and the Ecological Protection Zone (Figure 6). The Resilience Enhancement Zone encompassed areas with the lowest ER in Yichang, including Dangyang City, Zhijiang City, and Xiling District. From 2003 to 2023, the proportion of low-resilience areas in this zone decreased by 14.3%, while the proportion of medium-high and high-resilience areas increased by 18.6%, indicating a gradual improvement in ecological resilience. The Ecological Conservation Zone exhibited a moderate level of ER and included counties such as Xingshan and Yuan’an. By 2023, areas with medium-high and high resilience accounted for 55% of this zone, an increase of 21.5% compared to 2003. In contrast, the proportion of medium-low resilience areas declined by 32.8%. The Ecological Protection Zone comprised areas such as Zhiwei County and Dianjun District, which demonstrated the highest levels of ER. In 2023, 75% of this zone consisted of high and medium-high resilience areas, while medium-low and low-resilience areas decreased to 21% and 4%, respectively. 3.3 | Analysis of Influencing Factors at the Urban Level 3.3.1 | Single factor detection Factor detection was conducted on variables X1 through X10 from the ER influencing factor indicator system developed for Yichang, revealing differentiated effects of each factor on the city’s ER (Table 5). Based on the explanatory power (Q value) analysis, the factors influencing Yichang’s ER were ranked as follows: X5 > X7 > X3 > X6 > X1 > X10 = X8 > X9 > X2 > X4. The Q values for X5, X7, and X3 were 0.53, 0.49, and 0.48, respectively, with X5 identified as the most influential factor. In contrast, X10 and X8 both had Q values of 0.27, and X9 had a Q value of 0.26, indicating a relatively low impact on ER. The remaining variables all had Q values below 0.2, suggesting weak influences on ecological resilience in Yichang. 3.3.2 | Multi-factor interaction detection Due to the interdependence among influencing factors, none of which affected UER independently, the results of the multifactor interaction detection showed that these interactions amplified the influence of individual factors on UER (Figure 7). Among all interactions, those involving X5 had the most substantial impact, with Q values exceeding 0.55. The interaction between X5 and X7 demonstrated the highest explanatory power, with a Q value of 0.58. 3.4 | Analysis of Influencing Factors at the Urban Sub-Division Level 3.4.1 | Single factor detection This study divided Yichang into three zones, Resilience Enhancement Zone, Ecological Conservation Zone, and Ecological Protection Zone, and conducted a single-factor analysis for each to identify differences in ER influencing factors (Tables 6-8). In the Resilience Enhancement Zone, variable X6 exhibited the strongest explanatory power for ER, with a Q value of 0.54. Variables X8 and X10 had identical Q values of 0.12, indicating relatively minor effects on ER in this zone. In the Ecological Conservation Zone, X5 demonstrated the highest explanatory power. In the Ecological Protection Zone, X3 accounted for the most influence on ER, with a Q value of 0.58. 3.4.2 | Multi-factor interaction detection The testing results indicated that, across different regions of Yichang, pairwise interactions between influencing factors generally exerted a stronger impact on UER than individual factors (Figure 8). In the Resilience Enhancement Zone, the interaction between X7 and other variables had the greatest influence on UER, with the highest explanatory power (Q value) of 0.73 observed when paired with X6. In contrast, the interaction between X4 and X3 exhibited the lowest explanatory power, with a Q value of only 0.05. In the Ecological Conservation Zone, interactions involving X5 and other variables significantly affected ER. However, the interaction between X6 and X2 showed the lowest explanatory power in this zone, with a Q value of just 0.08. In the Ecological Protection Zone, the combined interaction of X2, X3, and X7 demonstrated the highest explanatory power, with Q values reaching 0.75, indicating a substantial influence on ER. 3.5 | Multi-Scenario Land Use Simulation Analysis This study was based on the ER zoning characteristics and influencing factors of Yichang City (Figure 9). The PLUS model was employed to simulate changes in six major land use categories under four development scenarios projected for the year 2035: ”natural evolution,” ”cropland protection,” ”urban expansion ” and ”water ecology protection.” The objective was to provide a scientific foundation and decision-making support for ecological protection and sustainable development in Yichang City. The natural evolution scenario simulated development without human intervention, revealing the baseline trends in ER and serving as a reference for comparison with other scenarios. The cropland protection scenario restricted the conversion of arable land to construction land, enabling assessment of its impact on ER and the potential for coordinated agricultural and ecological development. The urban expansion scenario simulated the pressure exerted by construction land growth on ER, offering insights to inform rational urban planning. Lastly, the water ecology protection scenario modeled the effects of water resource preservation and ecological restoration on ER, exploring opportunities to enhance resilience under an ecological protection–first strategy and providing a scientific basis for policy formulation. 3.6 | Multi-Scenario Urban Ecological Resilience Measurement Based on the analysis of ER influencing factors in Yichang City for the years 2003, 2013, and 2023, along with land use change projections under multiple scenarios for 2035, this study calculated the ER levels of Yichang City for each scenario (Figure 10). The results indicated that, compared to the natural evolution scenario, the cropland protection scenario had a relatively favorable effect on ER, with approximately 57.26% of areas exhibiting higher or high resilience, and only 13.37% showing low resilience. In contrast, the urban expansion scenario led to a substantial decline in ER, with just 13.68% of areas classified as highly resilient. The water ecology protection scenario, which involved converting arable and construction land into forests and water bodies, resulted in a more balanced resilience distribution. In this scenario, low-resilience areas accounted for only 7.76%, while high-resilience areas represented the largest proportion across all scenarios, comprising approximately 22.56%. Scenario analysis revealed that the expansion of both construction and arable land significantly undermined UER. In contrast, the water ecology protection scenario resulted in the highest ER levels, highlighting the crucial role of water ecology protection in enhancing UER in Yichang City. Therefore, when developing strategies to improve the urban ecological environment, it is essential to consider the city’s unique ecological characteristics and practical needs. 4 | Discussion 4.1 | Characteristics of Influencing Factors on Ecological Resilience of Urban Zones This study demonstrated that both natural evolution and human activities were key factors influencing the ER of Yichang City, with ER exhibiting significant spatial heterogeneity. Ecological resilience was notably high in the city’s ecological protection areas, which benefited from favorable natural conditions. These areas—such as Zigui County, Dianjun District, Yidu City, and Xiaoting District—were located within critical ecological function zones. The water resources in these zones played a crucial role in maintaining ecological flow through strategies such as water source conservation and runoff regulation, thereby enhancing ecosystem stability. In contrast, resilience enhancement areas exhibited relatively low ER, primarily due to the expansion of arable land, which reduced ecological land, fragmented habitats, and introduced pollution from agricultural activities, all of which significantly undermined ER. The ecological conservation areas, situated in transitional zones, showed relatively stable levels of ER. Through ecological restoration initiatives such as vegetation recovery, soil improvement, ecological corridor construction, soil and water conservation, and land reclamation, these areas effectively expanded urban green spaces, enhanced ecosystems’ resistance to external disturbances, and improved overall ecosystem stability and adaptability. Furthermore, the synergistic interaction between ecological protection and industrial development played a critical role in enhancing regional resilience, creating favorable conditions for the growth of emerging sectors such as green industries and ecological tourism. However, in resilience enhancement areas, the interaction between climate conditions and urbanization remained weak, indicating that ER in these regions was less influenced by natural factors. With ongoing urbanization, these areas increasingly adapted to prevailing climate conditions. 4.2 | Ecological Resilience Zoning Optimization Path for the Optimal Development Scenario Based on the simulation results of the water ecology protection scenario, as well as the spatiotemporal differentiation characteristics of ER in Yichang City and the spatial heterogeneity of its dominant influencing factors, the following zoning optimization strategies were proposed. These strategies aimed to promote the coordinated enhancement of ER across the entire region through differentiated governance approaches. 1. Ecological Protection Areas are high-resilience zones located in the mountainous northwest region. Based on watershed ecological security, a strict regulatory mechanism should be established for core water source conservation zones. In addition, the transformation of ecological resources into valuable assets should be promoted by developing a system for valuing natural capital and fostering the integrated growth of green industries. 2. Ecological Conservation Areas are transitional zones situated in the central hilly region. Efforts should focus on reinforcing the resilience threshold control of the agricultural-ecological complex system and establishing a synergistic mechanism for land use transformation and ecological restoration. This approach would support the development of a spatial adaptation model that efficiently utilizes water and soil resources while increasing the added value of characteristic industries. 3. Resilience Enhancement Areas are low-resilience zones located in the southeastern plains. The primary objective is to establish a dynamic balance between urbanization and the ecosystem. To achieve this, a negative feedback control mechanism should be developed to optimize the blue-green space structure and regulate the expansion of construction land. Additionally, an innovative decision-making response system should be established to integrate resilience assessment with spatial planning. 4.3 | Limitations and Future Work Although this study systematically elucidated the evolution patterns and optimization pathways of ER in Yichang City, several limitations remain. The current model requires further refinement to better capture complex interactions, such as dynamic feedback from climate change. Future research should delve deeper into the mechanisms underlying ER evolution within the interconnected ”climate-ecology-economy” system. These findings not only offer methodological support and practical frameworks for optimizing ecological security patterns in cities along the Yangtze River Basin, but also serve as valuable scientific references for ecological degradation restoration and resilience enhancement in similar river basin node cities globally. 5 | Conclusion Amid rapid urbanization and escalating ecological pressures, the conflict between urban expansion and ecological preservation has become increasingly pronounced. Yichang City, serving as a critical ecological barrier and strategic node in the Yangtze River Economic Belt, faces substantial challenges related to sustainability and resilience. This study developed a systematic ”Assessment-Attribution-Simulation-Optimization” analytical framework, addressing the limitations of traditional single-scale approaches. It revealed the interactive mechanisms shaping ER across spatiotemporal dimensions and proposed differentiated governance strategies tailored to regional heterogeneity. The key findings are as follows: From 2003 to 2023, the ER of Yichang City followed a temporal pattern of initial decline followed by recovery. Spatially, ER exhibited significant heterogeneity, with high resilience concentrated in the northwest and low resilience in the southeast. Ecological protection, agricultural development, and water resource status were identified as the primary drivers of ER. The interaction between ecological protection and agricultural development significantly enhanced resilience. Influencing factors varied across regions, reflecting notable spatial heterogeneity: industrial development was the dominant driver in ecological protection areas; terrain and ecological protection jointly shaped ER in ecological conservation areas; and water resource management was crucial for resilience enhancement areas. (3) In the water ecology protection priority scenario simulation, the expansion of forests and water bodies was projected to increase the proportion of high-resilience areas in Yichang City to 22% by 2035, while reducing low-resilience areas to 7%, significantly outperforming other development models. The ”Zoning Strategy” framework proposed in this study—based on three core mechanisms: resilience evolution assessment, spatial heterogeneity diagnosis, and policy scenario projection—generated targeted ecological protection and restoration strategies according to the dominant influencing factors and current resilience levels in different regions. This approach enabled the coordinated enhancement of ER across the entire region. The framework provides a scientific paradigm for precise ecological governance in cities located at the intersection of mountainous and aquatic systems within the Yangtze River Economic Belt. Acknowledgements This work is financially supported by the National Natural Science Foundation of China [grant number 42401568]. Conflict of interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Appendix-A.LEAS LEAS not only avoids complex calculations during multi-land use analysis, but also analyzes land use change mechanisms within a certain time period. It transforms the extraction of land use conversion rules into a binary classification problem, using random forest classification algorithms to explore the relationship between the expansion of different land use types and multiple driving factors, obtain the development probabilities of each land type, and assess the contribution of driving factors to land use expansion during the period. Land use change is the result of the combined effect of multiple factors. This paper selects 10 influencing factors from the natural, human activity, and socio-economic dimensions. Factors such as GDP, population, annual average temperature, and annual precipitation are calculated and input as their average values during the period. Policy factors play an important role in land use change in China, but because these factors are difficult to obtain and quantify, they are considered in this study in the form of conversion costs. Appendix-B.CARS The CARS simulation of the evolution of multiple land use patches adopts a multi-type random patch seed mechanism based on threshold decay. The transfer cost matrix mainly relies on historical land use data and expert knowledge. Based on previous research, this paper sets the calculation formula as follows, according to the total area (TA) change of each land use patch during the study period: \begin{equation} X_{i}=\frac{\mathrm{\Delta}TA_{i}}{\mathrm{\Delta}TA_{\text{sum}}}\nonumber \\ \end{equation} In the formula: \(X_{i}\) is the neighborhood weight parameter of a certain land use type i; \(\mathrm{\Delta}TA_{i}\) is the change in TA of this land use type during the study period;\(\mathrm{\Delta}TA_{\text{sum}}\) is the total change in TA during the study period. The neighborhood weight parameter represents the expansion intensity of different land use types, reflecting the expansion capacity of each land use type under the influence of spatial driving factors. 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Supplementary Material File (figure legends.docx) Download 11.60 KB File (tables.docx) Download 26.16 KB Information & Authors Information Version history V1 Version 1 17 April 2025 Peer review timeline Published Land Degradation & Development Version of Record 29 Jul 2025 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Collection Land Degradation & Development Keywords geographic detectors plus model spatiotemporal analysis urban ecological resilience zoning strategies Authors Affiliations Xiaotang Xia Wuhan University of Science and Technology View all articles by this author Fan Zhou Wuhan University of Science and Technology View all articles by this author Jian Chen 0009-0008-6893-5685 [email protected] Hunan University of Technology View all articles by this author Metrics & Citations Metrics Article Usage 362 views 216 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Xiaotang Xia, Fan Zhou, Jian Chen. 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