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The dual challenges of urbanization and ecological protection: a new path for optimizing ecological patterns in semi-humid areas
Abstract
In semi-humid regions, urban expansion and uneven water distribution cause ecological stress, especially under water scarcity. Establishing ecological security patterns aids ecosystem restoration. Unlike hierarchical approaches, this study normalized data to balance unit/scale differences and incorporated human factors to design ecological corridors. Focusing on Luoyang—a key ecological barrier in Central China—results show: (1) MSPA analysis revealed core landscapes (65.19% area) with high fragmentation and human impact; ten critical patches were identified using dPC/dIIC. (2) Standardized resistance values ranged 0.08–0.72, with higher resistance in south-central zones. (3) MCR and gravity models identified 45 potential corridors (16 primary). (4) Adding 5 nodes and 17 corridors enhanced connectivity. These findings guide urban ecosystem restoration in semi-humid areas.
Keywords:
Luoyang city
Ecological Security Pattern (ESP)
MSPA
MCR Model
urban ecological security
1. Introduction
The ecological environmental issues facing the world have become increasingly severe with rapid economic development. Over the past few decades, as China has undergone swift industrialization and urbanization, it has faced serious ecological challenges, including air pollution, water quality deterioration, and declines in biodiversity(Bian et al., 2024; Lal et al., 2022; Lewis, 2018). Urban centers are areas where populations are highly concentrated, and their ecological security concerns not only the flora and fauna within the ecosystem but also closely relates to the lives of the human population. Parks are vital green patches in urban centers that play crucial roles in purifying the air and improving urban environmental functions(Ge et al., 2024). Worldwide, an array of legal frameworks, planning strategies, and scientific research initiatives have been implemented to safeguard and rehabilitate urban ecological environments(T. Liu et al., 2023; W. Zhang, Zhou, Chen, Fan, & Health, 2022; Zhou, Yi, Su, & Sun, 2023). For example, experts and scholars have conducted numerous studies at the microscopic and mesoscopic levels, such as on accessibility, and building a framework for improving the relationship between people and green space provision, aiming to optimize the urban ecological environment(Liang, Yan, Yan, & Zhang, 2024; Ma, Brindley, Wang, Lange, & Practice, 2025; Ma, Brindley, Lange, & Planning, 2024). Due to the vulnerability of urban ecosystems and the scarcity of water resources, Ecological security has become an important issue in achieving sustainable social development.
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As landscape ecology has evolved, the emphasis in ecological conservation and restoration research has transitioned from primarily conducting ecological risk assessments to designing and implementing more comprehensive ecological networks. This shift has cultivated a wider emphasis on the systematic integrated management of ecological conservation. Moreover, it encourages the assessment of ecosystem integrity and connectivity(HAN et al., 2019; Yang et al., 2021). In this context, the Ecological Security Pattern (ESP) framework, comprising 'ecological sources—resistance surfaces—ecological corridors', has been progressively formulated(Q. Li, Zhou, & Yi, 2022). Within this framework, ecological sources are defined as areas capable of providing biodiversity and ecological services. Resistance surfaces are areas that may pose barriers to species migration or affect the normal functioning of ecosystem services. Ecological corridors are the pathways that link ecological sources(C. Chen, Shi, Lu, Yang, & Liu, 2020). The ESP framework promotes the circulation of ecological resources within urban contexts, also plays a pivotal role in guiding urban planning and fostering regional sustainable development(Bian et al., 2024). Attention is focused on how to effectively identify ecological source areas, construct scientific resistance surfaces, and extract functional ecological corridors(F. Zhang, Jia, Liu, Li, & Gao, 2024). These steps not only help planners identify key biodiversity areas but also aid in predicting and mitigating the potential impacts of urban development. Consequently, the significance of the ESP in the construction of ecological environments is increasingly highlighted. The Morphological Spatial Pattern Analysis (MSPA) model and the Minimum Cumulative Resistance (MCR) model have become two core tools for constructing ESP. The MSPA model identifies ecological core areas, edge areas, and corridors through morphological analysis of landscape structures, providing a spatial basis for the construction of ecological networks. Meanwhile, the MCR model enhances the connectivity and stability of ecosystems by optimizing connectivity within the ecological network and identifying potential ecological corridors(Dong et al., 2020; C. Hu, Wang, Wang, Sun, & Zhang, 2022; Ye, Yang, & Xu, 2020). A substantial body of research demonstrates that the MSPA model can effectively reveals the landscape fragmentation and the spatial relationship between landscape elements(J. Liu et al., 2023; Luo, Zhu, & Fu, 2024; Qin, Dai, Li, Zhang, & Peng, 2024; Wei, Halike, Yao, Chen, & Balati, 2022). Consequently, these models are gradually being applied to the study of ecological networks in cities and watersheds. For instance, studies in Harbin City, Mudanjiang City, and the Dawen River Basin have not only optimizes the method of extracting sources and corridors but also enhanced the practicality and scientific validity of ecological network designs by integrating ecological functions and dynamic variables of human interventions(P.-X. Liu et al., 2024; Liu, Lu, Xu, Zhou, & Zhang, 2024; F. Zhang et al., 2024).
Research on urban ecological security patterns employing the MSPA-MCR model has progressed both domestically and internationally. However, most studies primarily focus on variables such as ecological safety, landscape connectivity, and the assessment of ecological functions. These studies commonly select patches based on patch size and attributes. Additionally, the methodology for constructing ecological resistance surfaces typically involves resistance allocation or the development of corresponding assessment systems(Yan et al., 2019). The identification of ecological corridors necessitates the utilization of constructed resistance surfaces, which are generally modified based on elements like slope and elevation(Wei et al., 2022). Consequently, the potential ecological corridors that are identified can function as optimal routes for the circulation of resources(Santos et al., 2018). Nevertheless, earlier studies on urban Ecological Security Patterns (ESP) frequently encountered a limitation:They fail to explain well the impact of socio-demographic factors on regional ecology(Wei et al., 2022). Moreover, during the construction of resistance surfaces, most current studies adopt a method of constructing a resistance factor assessment framework. Although this method is widely used, straightforward, and easy to interpret and manage, it often results in the loss of much data detail and is significantly influenced by human factors(W. Chen, Liu, & Wang, 2024; Guan, Hu, & Li, 2024; Xu, Liu, Sun, & Qi, 2024). Furthermore, current research on the application of ESP in semi-humid areas is relatively scarce. Balancing ecological protection with urban expansion remains an unresolved issue. This study introduces an innovative approach by merging the MSPA and MCR models to formulate and refine the ESP for cities located in semi-humid regions. It specifically incorporates human activity factors into the ecological resistance surface, creating a 'Natural-Human Activity Composite Resistance Factor' and employs a method that combines hierarchical processing with normalization. This approach aims to standardize the process uniformly, overcoming the limitations of traditional hierarchical processing in data handling. By eliminating the differences in dimensions and metrics among various data sources, the method enhances the accuracy and applicability of the model. It not only addresses the insufficient consideration of human disturbances in existing research but also provides more realistic strategies for optimizing ecological security. Focusing on typical cities like Luoyang, the study offering suggestions for addressing specific issues in constructing ESP under the backdrop of rapid urbanization in semi-humid areas, and provides new insights for ecological environment restoration.
As a prominent city in the Central Plains region and a quintessential example of China's semi-humid areas, Luoyang boasts a unique geographical and cultural background. Known as the "Ancient Capital of Thirteen Dynasties," Luoyang is not only an important cradle of Chinese culture but also a key area for national ecological protection and construction(Ge et al., 2024). The ecological security of Luoyang, a major city in the Central Plains region, is intrinsically connected to the preservation of its cultural heritage and the city's sustainable development. Consequently, this research is centered on Luoyang City, employing a combined use of the MSPA and MCR models to establish an ESP that reflects the unique local attributes. This approach is intended to provide a model for other and semi-humid regions facing similar ecological challenges. Our objectives are: (1) to identify core areas using MSPA and landscape connectivity indices; (2) to conduct a thorough analysis of resistance variables derived from natural and human factors, and to analyze the directionality and spatial clustering of the resistance surface; (3)to identify different levels of importance corridors, thereby creating an ecological network. And analyze the deficiencies in the established ecological network and optimize it;(4)to combine the human resources and cultural resources of Luoyang City, a solution to the urban ecological problem is given.
2. Materials and Methods
This study constructed the ESP for the central urban area of Luoyang. The framework of this methodology is illustrated in Fig. 1.
Fig. 1
Research Framework Diagram.
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2.1 Research area overview
Figure 2 illustrates that the urban area of Luoyang City encompasses seven districts situated along the Luo River in the Central Plains. This region is known for its agreeable weather conditions. The annual average temperature in Luoyang typically varies between 14°C and 15°C, with average yearly precipitation around 610 mm. The central urban zone, bolstered by its abundant historical and cultural assets alongside its advantageous natural surroundings, acts as the political, economic, and cultural hub of the city. Therefore, Luoyang has rich historical heritage, including the ruins of Luoyang City from the Han and Wei Dynasties and the Mangshan Tombs. On the other hand, since the early 21st century, Luoyang City has experienced rapid urbanization. Population and resources are concentrated in cities in a short period of time. While the swift urbanization of Luoyang has facilitated the city's modernization, it has concurrently given rise to numerous ecological and environmental problems. Specifically, in Luoyang's central urban district, the escalating exploitation of land resources has progressively infringed upon traditional ecological patches, diminished green spaces, and led to a significant decrease in biodiversity.
Fig. 2
Map of the Study Area.
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2.2 Data Sources and Processing
This study utilized data on land use types, Normalized Difference Vegetation Index (NDVI), slope, elevation, population density, nighttime lights, and distance to roads. The 10-meter resolution land use data for 2023 was obtained from a dataset jointly produced by Esri, Impact Observatory, and Microsoft based on Sentinel-2 satellite imagery with a 10-meter resolution. NDVI data was sourced from the National Ecosystem Science Data Center (https://www.nesdc.org.cn/) under the National Science and Technology Basic Conditions Platform, derived from Landsat 8 imagery on the GEE platform, with a resolution of 30 meters. Elevation and slope data were acquired from the Geospatial Data Cloud (http://www.gscloud.cn/) and calculated from 30-meter resolution DEM data. Population density data was obtained from the WorldPop website (https://www.worldpop.org/) with a resolution of 100 meters. Nighttime light data was sourced from the National Earth System Science Data Center under the National Science and Technology Basic Conditions Platform (http://www.geodata.cn/). All data were accessed on September 7, 2023. all produced raster maps were transformed into a standardized spatial coordinate system (WGS1984, UTM Zone 49N). Additionally, the Raster Calculator tool was employed to segment the data into grid units measuring 30m by 30m.
2.3 Research Methods
All formulas and illustrations in the following text can be found in Appendix Table S1.
2.3.1 Analysis of Landscape Patterns Employing the MSPA Technique
Compared to traditional source identification methods, the MSPA analysis method has the advantage of accurately defining various landscape elements(Hua, ZHENG, HUANG, Feiping, & Technology, 2024). Specifically, raster images were first reclassified into foreground and background images. Natural ecological resources including water bodies, forests, grasslands, and submerged vegetation, characterized at a raster resolution of 30 meters, were designated as the foreground. Conversely, land types like farmland and built-up areas were categorized as the background, with missing values assigned a value of 0. Subsequently, the Guidos Toolbox 3.2 software was used for analysis. Based on different image processing methods, seven types of landscape functions were identified (Table S2). The results of the landscape classification, representing different ecological significances, underwent statistical analysis and enhancement. Ultimately, the connectivity of the identified core areas was analyzed to facilitate the identification of key ecological pollution sources.
2.3.2 Identification of Ecological Source Areas and Landscape Connectivity Evaluation
Key ecological patches (such as source areas and corridors) can more accurately classify the spatial patterns of raster images within the functional structure, thereby enhancing the scientific validity of ecological source areas and the selection of ecological corridors. Landscape connectivity indicates how landscapes facilitate or hinder the dispersal of species between ecological patches(Forman, Collinge, & planning, 1997; Taylor, Fahrig, Henein, & Merriam, 1993). From a macroscopic and quantitative perspective, it effectively assesses the connectivity between ecological patches(Clergeau, Burel, & planning, 1997). Therefore, the MSPA classification of landscape types was used to identify core areas that play a significant role in maintaining regional landscape connectivity, serving as the basis for selecting ecological source patches. We used Confer 2.6 software to set the connectivity distance and connectivity probability thresholds of ecological patches at 2500 and 0.5, respectively, and calculated the Probability of Connectivity Index (PC) and the Integral Index of Connectivity (IIC) for each patch in the core area. As a measure of landscape connectivity between different patches, the dIIC and dPC values of each patch were computed in the model. By assigning equal weights to dIIC and dPC for each patch, the importance of each patch within the source areas was determined. Finally, the ecological sources in the study area were identified based on the dPC and dIIC values.
2.3.3 Construction of the Minimum Cumulative Resistance (MCR) Model
When species migrate between ecological patches or transmit information, they often face obstacles and disturbances from various factors. Therefore, constructing a comprehensive resistance surface that integrates the resistance values of different factors is a critical basis for extracting ecological corridors. In this study, natural factors such as land use data, elevation, slope, and Normalized Difference Vegetation Index (NDVI) data were used to determine the weight of each natural factor. Additionally, human factors, including population density index, nighttime light index, and distance to roads, were incorporated to adjust the comprehensive ecological resistance surface for the central urban area of Luoyang(Wei et al., 2022). The weights of each resistance factor were calculated using the Analytic Hierarchy Process (AHP). Finally, a normalized resistance factor assignment system was constructed (Table 1), and the directionality and spatial distribution characteristics of the resistance surface were analyzed. Given that most factors consist of continuous numerical data with varying dimensions and units, we adopted a combination of hierarchical processing and dimensionless normalization(P.-X. Liu et al., 2024). Compared to conventional hierarchical assignment methods, this approach retains the true value of each pixel, resulting in a more refined and scientifically accurate construction of the ecological resistance surface. For example, for land use data, forest land, farmland, other land, water bodies, and built-up areas were first assigned values of 1, 2, 3, 4, and 5, respectively, followed by normalization(Z. Chen, Kuang, Wei, Zhang, & Geomatics, 2017; X. Zhang, Cui, & Liang, 2024). In ArcGIS 10.7, the Raster Calculator was used to process each factor using either positive normalization or negative normalization. For positive indicators (e.g., elevation), Min-Max normalization was directly applied. For negative indicators (e.g., NDVI), a negative transformation was first performed (where smaller values, which are less desirable, were converted to larger values, which are more desirable) before normalization.
Table 1
Resistance Factor Construction System.
Data Type
Weight
Processing Method
Land Use Type
0.2443
First classify, then normalize positively
Elevation
0.0861
Normalize positively
Slope
0.0572
Normalize positively
Distance from Roads
0.0328
Normalize negatively
Nighttime Lights
0.0520
Normalize positively
NDVI
0.3624
Normalize negatively
Population Density
0.1652
Normalize positively
2.3.4 Construction and Evaluation of Ecological Networks
Identifying potential ecological corridors is a key step in constructing ecological networks, as the extraction of ecological corridors strengthens interactions between species and enhances ecological functions. The MCR model calculates the cost incurred by species when overcoming landscape resistance to move from the initial ecological patch to the target patch. In this study, 45 potential ecological corridors were generated using the Cost Path tool in ArcGIS 10.7.
The gravity model can extract important ecological corridors by calculating the gravitational force between patches. In this study, the gravity model was applied to select 16 paths with gravity thresholds greater than 12,000 as primary ecological corridors, while the remaining 29 paths were classified as secondary ecological corridors.
Network analysis is a primary method for reflecting ecological benefits and exploring the structure of ecological networks. This study selected the network closure index (α), line-to-node ratio (β), and network connectivity (γ) to quantitatively evaluate the connectivity and connectivity rates of the ecological network constructed within Luoyang City. The feasibility of optimizing the ecological network direction was assessed by comparing the numerical values before and after the construction of the ecological network(X. P. Chen & Chen, 2016).
3. Results
3.1 Analysis and extract ecological sources
3.1.1 Landscape Element Analysis Based on the MSPA Method
Figure 3 and Table 2 illustrate that Luoyang encompasses 50 core landscape regions, covering a total of 21,000.76 hectares. This constitutes 65.19% of the overall foreground area. Most of these regions are situated within the wooded zones of the northern and southern areas, as well as the central regions surrounding the Luo River, Yi River, and adjacent green spaces. Overall, the spatial integrity of the core landscapes has been disrupted, with patch distributions in the central area being fragmented and sparse. This indicates weak connectivity between patches, which may limit species migration and ecological functions.Edge areas and perforation areas account for 14.23% and 1.28%, respectively. As key components of core areas, edge areas have high expansibility but are susceptible to disturbances, while perforation areas have a lower proportion and less internal fragmentation. Isolated areas account for 4.43%, existing as isolated patches and serving as priority regions for future ecological corridors or stepping stone construction. Branch areas and loop areas account for 2.96% and 6.23%, respectively. Although their areas are small, they have an undeniable impact on the overall landscape structure.
Fig. 3
Landscape Element Classification of Luoyang City.
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Fig. 4
Ecological Source Point Map Extracted.
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Table 2
Area Statistics of Landscape Elements.
Landscape type
Area/hm2
Percentage of forestland, grassland, and water bodies
Percentage of total area
Core
21000.76
65.19
9.21
Bridge
1830.44
5.68
0.8
Islet
1426.34
4.43
0.63
Edge
4584.19
14.23
2.01
Perforation
413.48
1.28
0.18
Branch
954.18
2.96
0.42
Loop
2008.21
6.23
0.88
Table 3
Selection of Ecological Source Areas.
Node
Area/km2
dIIC
dPC
 
Node
Area/km2
dIIC
dPC
1
63.39
0.25
0.66
 
6
657.23
0.31
0.48
2
3664.84
17.16
21.36
 
7
75.88
0.39
0.71
3
360.61
1.43
1.75
 
8
313.03
2.70
3.82
4
2363.35
11.74
17.32
 
9
1492.05
11.48
15.92
5
272.81
1.04
1.98
 
10
9739.22
76.03
71.76
3.1.2 Selection of Ecological Source Areas Based on Landscape Connectivity Analysis
Ecological sources with dIIC values exceeding 0.2 and dPC values above 0.4 were selected, with the top 10 patches ranked by priority considered as important ecological source areas in this study, while the rest were classified as general ecological sources. As shown in Fig. 4 and Table 3, Node 10 has the largest area (9739.22 km²) and the highest connectivity (dIIC = 76.03,dPC = 71.76), making it the most critical core ecological source area in the region. Node 2 (3664.84 km²) and Node 4 (2363.35 km²) are key ecological patches that contribute significantly to the connectivity network, while Node 9 (1492.05 km²) plays a supplementary role with relatively high connectivity (dIIC = 11.48, dPC = 15.92).
Nodes 3 (360.61 km²) and 8 (313.03 km²) serve as local bridges with limited functionality, and Nodes 1, 5, 7, and 6 are smaller in size or have lower connectivity, making them more vulnerable to disturbances and in need of optimized layouts to enhance the overall ecological network. The central part of the urban core has only one patch with significant connectivity, resulting in poor north-south ecological source connectivity and uneven spatial distribution. Additionally, significant variations in dPC values among different ecological sources highlight the urgent need to establish an ecological network to enhance the suitability and landscape connectivity of the central urban area of Luoyang.
3.2 Construction and Analysis of the Comprehensive Resistance Surface
3.2.1 Results of the Comprehensive Resistance Surface Construction
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Utilizing the ecological resistance factor allocation guidelines provided by the "Analytic Hierarchy Process," a detailed ecological resistance surface was constructed for the study area, depicted in Fig. 5. In Fig. 5a, land use categories have been reclassified, while Fig. 5b presents the reclassified elevation. Figure 5c displays the reclassified slope aspect, and Fig. 5d shows the reclassified distance to roads. Figure 5e details the reclassified nighttime light data, indicating that social activities in Luoyang City mainly occur in the Luolong, Xigong, Old City, and Jianxi districts. Figure 5f outlines the reclassified NDVI, Fig. 5g illustrates the reclassified population density, and Fig. 5h demonstrates the comprehensive ecological resistance surface, adjusted for both natural and human factors. The ecological resistance values range from 0.08 to 0.70, with higher resistance observed in the central region compared to the northern and southern regions.
3.2.2 Spatial Autocorrelation and Directional Analysis
The Moran's I index has been calculated at 0.233928, accompanied by a z-value of 0.000029 and a statistically significant p-value of 0.000. These results indicate that ecological resistance exhibits significant positive spatial autocorrelation. Figure 6a reveals that zones of concentrated human activity along the Yi and Luo Rivers are associated with significant high-high (HH) spatial clustering, covering the administrative districts of Luolong, Xigong, Old City, Jianxi, and Chanhe Hui. In contrast, Mengjin and Yanshi districts primarily show low clustering (LL) characteristics, although a few small areas within these districts also demonstrate high clustering (HH).
The ecological resistance in Luoyang exhibits a clear directional pattern, primarily distributed along the northeast-southwest axis in Fig. 6b. In terms of distribution concentration, the X-axis (east-west direction) demonstrates a broader spread (indicated by a larger standard deviation), while the Y-axis (north-south direction) exhibits a narrower distribution. The data distribution in the study area is likely influenced more significantly by geographical or environmental factors in the east-west direction. Conversely, the limited expansion in the north-south direction may be associated with terrain, administrative boundaries, or other physical barriers.
a
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b
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c
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d
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h
e
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f
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g
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Figure 5༎Integrated Resistance Surface: (a) Land Use Classification, (b) Elevation, (c) Slope, (d) Distance from Roads, (e) Nighttime Light, (f) NDVI, (g) Population Density, (h) Integrated Resistance Surface.
Fig. 6
Resistance surface analysis : (a) Local autocorrelation Lisa diagram (b) standard deviation ellipse analysis.
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3.3 Construction and Analysis of Ecological Corridors
In accordance with the MCR model, 45 potential ecological corridors were set up (refer to Fig. 7), and the interaction strengths among the 10 ecological source points were ascertained by means of the gravity model (see Table 4). The greater the strength, the higher the level of connectivity between the patches. Sixteen ecological corridors having a gravitational threshold exceeding 12,000 are recognized as crucial ecological corridors, whereas the remaining 29 are categorized as ordinary ecological corridors. As shown in Table 4, ecological sources 1 and 3 exhibit the highest degree of interaction, indicating a close connection between the sources, which allows species migration and exchange to occur with minimal disruption. Patches 4 and 8, patches 1 and 2, patches 8 and 10, as well as patches 2 and 3, all exhibit high levels of interaction. These interactions facilitate species migration across various terrains, including from mountain to mountain, from the southern to the northern parts of the city, and from forested areas to wetlands. By establishing ecological channels for species energy exchange, a robust ecological protection barrier is constructed. The interaction between patch 8 and patches 1, 2, and 3 is relatively weak, mainly due to significant differences in distance from roads, elevation factors, etc., which obstruct species migration in that direction.
Fig. 7
The Establishment of Ecological Corridors in Luoyang City.
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Table 4
Statistics of Interaction Forces Between Key Ecological Sources.
Patch Number
Ecological source
1
2
3
4
5
6
7
8
9
10
1
0
102063
2377937
12403
4592
3718
2043
1709
3752
3826
2
  
96707
14910
4570
3022
1799
1496
3433
3434
3
   
13610
5094
4355
2342
1958
4342
4391
4
    
26752
5977
4284
34656
9079
8960
5
     
4812
6313
4973
9564
12208
6
      
3428
2744
14723
7745
7
       
895361
14388
65431
8
        
15555
85278
9
         
80796
10
         
0
3.4 Ecological Network Construction and Optimization
3.4.1 Protection and Addition of Core Patches
In the southern central region of Luoyang City, where connectivity is low, five core patches with high connectivity levels have been selected. These are located at Shouyang Mountain in Yanshi District, the northern section of the Luo River, and the entire stretch of the Yi River. These are designated as new ecological source sites, and 17 new ecological corridors have been established (see Fig. 8). Luoyang City is rich in rare resources and has superior habitats, thus it is imperative to protect the natural scenic areas along the Luo and Yi Rivers to the greatest extent. For fragmented patches like Longmen Mountain, it is essential to rehabilitate the ecological environment in the fragmented zones, enhance the vegetation cover, and develop extensive contiguous ecological source sites. These measures will facilitate the expansion of habitats for wildlife.
3.4.2 Addition of Ecological Stepping Stones
The greater the distance between ecological source sites, the more resistance factors need to be overcome, and the mortality rate of wildlife during migration tends to increase. Ecological stepping stones refer to a series of small patches set up between large ecological patches, serving as temporary refuges and migration pathways for wildlife. Because the intersection of corridors generally has a higher species richness, better site conditions, or habitat adaptability than other parts of the corridors, they often act as relay points. This research, relying on the distribution of ecological networks and bridging areas, added a total of 13 ecological stepping stones at crucial corridor junctions (see Fig. 8).
Fig. 8
Ecological Network Construction.
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3.5 Feasibility Analysis of the Ecological Network
Employing network analysis techniques, the initial network closure index (α) for Luoyang City was determined to be 0.47, the line-to-point ratio (β) was recorded at 1.6, and the network connectivity (γ) was measured at 0.67 (refer to Table 5). Following the optimization process, the updated values for α, β, and γ improved to 0.76, 2.2, and 0.85, respectively. A comparison before and after optimization shows that the complexity of the ecological network has increased, the connectivity level of ecological sources has improved, and the capacity for species migration and information exchange has been enhanced. This suggests that the optimized network is highly effective and has enhanced its role in conserving biodiversity.
Table 5
Analysis of the Ecological Network in the Central Urban Area of Luoyang City.
Ecological Network
Number of Source Points
Number of Corridors
Network Closure Index (α)
Line-to-Point Ratio (β)
Network Connectivity (γ)
Before Construction
10
16
0.47
1.6
0.67
After Construction
15
33
0.76
2.2
0.85
4. Discussion
4.1 Construction and Optimization Suggestions for the Ecological Network
In this study, we employed land use data and the MSPA method to delineate habitat patches critical for connecting landscapes at the pixel level(C. Hu et al., 2022). Utilizing the landscape connectivity indices dPC and dIIC, we identified patches that serve as sources for the growth and movement of biological species, pinpointing 10 key ecological sources. The analysis revealed that well-connected core patches predominantly occupy the northern and southern zones of the study area, while connectivity is notably deficient in the central region, which exhibits a pronounced division between the north and south. Over the past two decades, the central urban area of Luoyang City has experienced a marked reduction in arable land and a significant expansion in built-up areas, which has adversely affected the quality of the ecological environment(H. Li, Jing, Yan, Guo, & Luan, 2023). Consequently, it is imperative to enhance the protection and restoration efforts in the central region. Establishing ecological patches that support species survival, promoting an ecological network that facilitates the flow of materials and energy between the northern and southern regions, and nurturing the health and sustainability of the ecosystem are crucial steps forward.
In the creation of the resistance surface, it is recognized that alongside objective factors, human subjective activities significantly influence changes in ecological environment quality. Research highlights the pivotal role of governmental interventions in mitigating environmental degradation and socio-economic instability, thereby advancing urban sustainable development(Rana & Sustainability, 2011). Factors such as land use type, slope, elevation, vegetation cover index, nighttime lights, population density, and distance from roads were selected as influencing factors for the resistance surface, aligning with the reality of the study area. Although there is no universally recognized standard for selecting ecological resistance surfaces internationally, this research has special reference significance for future studies of urban central district ecological resistance surfaces(F. Li, Ye, Song, & Wang, 2015; Teng et al., 2011). In the central region, the Luo River and Yi River ecological tourism areas, as the most important water bodies, have only the northern section of the Luo River exhibiting good landscape connectivity. Therefore, it is necessary to further protect and restore the Luo and Yi River ecosystems, develop riverside green spaces, create urban forests in the central urban area of Luoyang, and optimize water resource allocation(Yu et al., 2022). Since the water system area in Luoyang occupies a relatively small proportion, it poses significant interference resistance to the migration and exchange of aquatic life. It is essential to consider the protection and restoration of the surrounding water systems comprehensively and to conduct reasonable afforestation projects, providing green spaces for terrestrial wildlife activities without disturbing wetland biological activities.
This study employed the gravity model to construct an ecological network and conducted an evaluation of the network's performance before and after optimization. The evaluation revealed that subsequent to optimization, the parameters α, β, and γ exhibited increases of 0.29, 0.6, and 0.18, respectively. Additionally, the primary locations for the newly incorporated source sites were identified in the central urban area of the Yiluo River Basin. Existing studies predict that from 2020 to 2030, ecological risks around the Luo River, Yi River, and Yellow River basins in Luoyang City will significantly increase(Honghao, Hongbin, Ranhao, & Development, 2023). This may be due to urbanization in later stages encroaching on surrounding farmlands, especially those near water sources, resulting in increased landscape fragmentation and separation, thereby increasing regional risk losses(Shi, Wang, Zhao, & Health, 2023). This corroborates our research findings. Combined with the 'high-quality development' ecological topics advocated in the 'Luoyang City Land and Space Master Plan (2021–2035)', constructing a suburban ecological green ring formed by Mangshan, Zhoushan, Wanan Mountain, and along the Yi River and Luo River, and setting up urban open space corridors to connect peripheral mountains, strengthening river ecological restoration and management, and constructing a park system composed of comprehensive parks, specialized parks, and community conventions with a complete structure, reasonable grading, and rational distribution. This further reflects the viewpoints of this study, providing important reference values for future planning.
4.2 Solving the contradiction between urbanization and ecological protection in semi-humid areas : Luoyang's experience and ideas
Luoyang, as a typical historical and cultural city, its rapid expansion has posed a huge challenge to the ecological environment. Especially in semi-humid areas, the uneven distribution of water resources and seasonal precipitation fluctuations make the ecological environment more fragile. Based on Luoyang's unique historical, cultural and ecological resources, this study proposes to build an ecological and cultural composite urban green heart based on the Mangshan Mausoleum Group, Luoyang City of the Han and Wei Dynasties, and the Yi and Luo Rivers Beach Area(Tang, Liu, Feng, Xiao, & Ogbodo, 2023). This composite green heart area can not only effectively restore the ecological environment, but also highlight the key carrier of Luoyang's historical and cultural characteristics. In the process of green heart construction, the organic combination of ecological restoration and cultural heritage protection fundamentally coordinates the contradiction between urban development and ecological protection(L. Li, Feng, Xi, & Health, 2021), so that economic growth, historical protection and ecological restoration can achieve coordinated progress.
This study also emphasizes that ecological protection cannot be limited to the city, and should be planned from the perspective of the overall regional layout(Figure 9). In sub-humid areas, water scarcity and limited space require more sophisticated ecological planning. By forming a "double-city-surround" residential layout between the main urban area and surrounding areas such as Mengjin and Yanshi, the encroachment of urban construction on ecologically sensitive areas and historical and cultural protection areas is reduced, and optimize the regional ecological space layout and improve the efficiency of water resource utilization(Z. Hu et al., 2023). In addition, by continuing the traditional axis of the city and building ecological corridors, regional ecological connectivity is promoted, and the coordination and unity of urban development and ecological environment are guaranteed. At the same time, high-quality ecological and cultural spaces are laid out at important urban functional nodes. By improving ecological facilities and optimizing residents' activity areas, the quality of life of residents is not only improved, but also the deep integration of historical relics and modern life is strengthened(Yingying & Roadkasamsri).
Based on the research results, this study puts forward the following ecological planning suggestions: First, build an ecological network system to connect the internal ecological space of the city with the surrounding natural ecological landscape and cultural heritage to promote the overall improvement of the regional ecological environment; second, focus on repairing and protecting the ecological corridors in the Luohe and Yihe River basins, optimize regional water resources allocation, and enhance ecological resilience; third, optimize the layout of existing ecological corridors, reasonably add ecological stepping stones, strengthen the connectivity between ecological patches, ensure the smooth migration of species, and improve the overall function of the ecosystem. Finally, through the close combination of ecological restoration and historical culture, a sustainable development path is provided for urban development in areas, which helps Luoyang move towards a new era of eco-friendly cities while maintaining its historical and cultural charm.
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Fig. 9
Solving the contradiction between urbanization and ecological protection.
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4.3 Limitations of the Study
Primarily, Resistance factor selection and weight assignment play a critical role in shaping Ecological Spatial Planning (ESP) outcomes, yet no internationally standardized guidelines for factor selection and evaluation systems exist; this often necessitates reliance on findings from previous studies, introducing a degree of subjectivity. It is anticipated that future research will develop more scientifically rigorous methodologies for constructing these models. Additionally, the resistance surface constructed lacks effective indicators for evaluating specific target ecological functions, which poses certain constraints(Boitani, Falcucci, Maiorano, & Rondinini, 2007). In the future, more comprehensive analyses could be conducted using multi-source data. Lastly, this research established the dPC threshold by adopting parameters from prior studies, specifically setting a connectivity distance threshold at 2000 meters and a connection probability at 0.5. The value of the PC index varies in accordance with the determined threshold. When the distance between two ecological sources surpasses this threshold, connectivity is considered absent, thereby illustrating the dispersal range of wildlife(Saura, Pascual-Hortal, & planning, 2007). Consequently, future research will implement a scientifically derived threshold, tailored to specific study conditions. This approach will enable more precise investigations, for instance, through the utilization of target species methods that address the migration and dispersal needs of local wetland fauna(Amici & Battisti, 2009).
5. Conclusions
Urban ecosystems in semi-humid regions face unique challenges due to rapid urbanization and ecological fragility. This study advances the integration of ecological security pattern (ESP) frameworks by coupling the MSPA and MCR model with innovative resistance factors, offering a replicable methodology for balancing ecological conservation with urban development in vulnerable environments. The findings underscore the critical role of harmonizing natural and anthropogenic factors in constructing resilient ecological networks, particularly in regions where human activities intensely intersect with fragile ecosystems.
The research demonstrates that a holistic approach to ESP construction—incorporating landscape connectivity, resistance surface optimization, and strategic corridor design—can mitigate fragmentation and enhance biodiversity conservation. By prioritizing core ecological sources and corridors while integrating human activity dynamics, the proposed model provides a scalable framework for urban planners to address connectivity gaps and spatial inequities in ecological resource distribution. This approach not only strengthens regional ecological resilience but also aligns with broader goals of sustainable urbanization, emphasizing the need for adaptive management in rapidly developing areas.
Furthermore, the study highlights the importance of interdisciplinary methodologies in addressing complex ecological challenges. The MSPA-MCR integration offers a nuanced understanding of landscape structure-function relationships, enabling targeted interventions that transcend traditional sectoral planning. Such frameworks are pivotal for guiding policy decisions that reconcile economic growth with ecological integrity, particularly in culturally and historically significant regions like Luoyang.
In a global context, this work contributes to the evolving discourse on urban ecological security by demonstrating how localized interventions can inform universal strategies for climate-resilient cities. It advocates for a paradigm shift toward proactive ecological network design, emphasizing connectivity as a cornerstone of urban sustainability. Future applications of this model could empower cities worldwide to navigate the dual imperatives of development and conservation, fostering ecosystems that thrive alongside human progress.
Declarations
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Funding:
252102320294
Ethics statements
not applicable
Clinical Trial Number
not applicable
Electronic Supplementary Material
Below is the link to the electronic supplementary material
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Author Contribution
Conceptualization: Y.G.1, Y.M., S.C.Methodology: Y.G.1, Y.M., S.C.Formal analysis: Y.G.1, S.C.Resources: Y.G.1, S.C.Software: Y.M., S.C., Q.G.Project administration: Y.M.Funding acquisition: Y.M.Writing – original draft: S.C.Writing – review & editing: Y.G.1, S.C., Q.G., Y.G.2, Y.W.Visualization: Y.G.1
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Data Availability
The data are not publicly available due to privacy restrictions.
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