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Article

Ecological Recreation Across the Jinma Mountain Region: A Comprehensive Evaluation of Suburban Mountain Greenway Networks

by
Wen Wei
1,
Ao Yang
1,
Lanxi Jiang
1,
Gillian Lawson
2,* and
Wen Lei
1
1
Faculty of Architecture and City Planning, Kunming University of Science and Technology, Kunming 650500, China
2
School of Landscape Architecture, Lincoln University, Lincoln 7647, New Zealand
*
Author to whom correspondence should be addressed.
Land 2025, 14(8), 1532; https://doi.org/10.3390/land14081532
Submission received: 20 June 2025 / Revised: 18 July 2025 / Accepted: 18 July 2025 / Published: 25 July 2025
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Abstract

Investigating the construction of greenway network systems in mountainous suburban areas from an integrated “ecology–recreation” perspective is crucial for promoting the coordinated development of regional multifunctionality. Taking Jinma Mountain in Kunming as a specific case study, this research comprehensively adopts a multivalue, multidimensional perception evaluation method to construct an assessment framework for suburban mountainous greenway networks that couples ecological and recreational functions. The results show that the Jinma Mountain greenway network exhibits a unique “multiple rings intertwined and dense network” pattern, with an optimized density of 0.79 km/km2, achieving efficient utilization. Compared to single-function greenways, the network’s ring index (α), connectivity index (β), and cohesion index (γ) have improved by 12.88%, 20%, and 4.19%, respectively, demonstrating a high degree of coupling and coordination. These improvements demonstrate the rationality and scientific rigor of the designed evaluation system, offering significant advantages over traditional single-function greenways. This comprehensive evaluation system not only supplements existing research on greenway networks but also provides a theoretical reference for integrated “ecology–recreation” and sustainable development in mountainous suburban areas.

1. Introduction

Greenway networks constitute a multi-scale, multifunctional green infrastructure that integrates ecological, social, and cultural values [1]. The theoretical exploration of greenways originated in the United States [2], where their development has progressed from fragmented linear green spaces to interconnected linear corridors, ultimately culminating in the contemporary greenway network model. Currently, research on regional greenway networks primarily focuses on both urban [3] and rural [4] contexts. In addition to considering human usage [5], it places greater emphasis on conserving species diversity [6,7]. Meanwhile, studies investigating the relationship between greenways and social phenomena encompass a broad spectrum of topics, including blue-green space networks and mental health [8], greenways and crime rates [9], railway greenways and housing prices [10], among others. Although research in China began later, it has become increasingly diverse and comprehensive. Research has progressed from traditional city and provincial scales to a national scale [11,12]. For instance, Lin et al. pioneered a graph theory-based construction of China’s nationwide forest network, providing a novel analytical framework for integrated ecological space planning at the national level [13]. Methodologically, the integration of multi-model chains has emerged as a trend. Xiong et al. developed a diagnostic framework for “Production–Living–Ecological Space” (PLES) conflicts, enabling synergistic territorial optimization [14]. Regarding study areas, most studies concentrate on densely populated megacities [15,16], leaving suburban mountain areas—where ecological sensitivity intersects with high recreational demand—severely understudied [17]. Functionally, greenway planning has shifted from single purposes, such as ecology [18] and commuting [19], to multifunctional integration [20]; however, the synergy between ecological preservation and recreational development remains poorly quantified. Technologically, multi-source data, such as shared-bike trajectories [21] and social media [22], coupled with AI algorithms, exemplified by ant colony optimization [23], have been applied to urban greenway routing, yet there is little post-construction validation of simulated networks. Consequently, current evaluation systems exhibit limitations, particularly in quantifying interactions within composite systems, which hinders the scientific siting of suburban mountainous greenway networks. Thus, planning “suburban greenways”, constructing “multifunctional greenway networks”, and refining “comprehensive evaluation frameworks” constitute the primary focus of this study.
To scientifically evaluate the composite systemic characteristics of greenway networks, this study introduces the coupling coordination theory (CCT) [24], a systems science framework for quantifying interaction states and developmental stages in complex systems. It can be successfully applied to suburban mountain greenway networks, as greenways inherently function as socio-ecological integrated systems [25,26], particularly in peri-urban areas where sensitive mountainous terrain intersects with high recreational demand. Coupling coordination theory effectively diagnoses whether ecological and recreational functions exhibit synergistic cooperation or imbalanced conflict, quantifies overall network efficacy, and identifies developmental bottlenecks. This approach constitutes a significant advancement beyond existing evaluation frameworks in assessing multi-system interactions [27].
With the progress of urbanization, suburban areas have emerged as regions with substantial development potential [28]. The construction of greenway networks serves as an important means to integrate tourism activities with the ecological environment [29]. By reorganizing fragmented ecological spaces in the suburbs, it is not only feasible to maintain regional ecological security but also to meet residents’ demand for nearby leisure activities, as emphasized in the “China Leisure Development Report (2023–2024)”. The distinctive characteristics of “suburban” and “mountainous” regions set these greenway systems apart from others. Suburbs within the urban sphere of influence benefit from urban development and offer good accessibility [30,31], presenting significant advantages for greenway construction. Mountainous regions, which are widely distributed throughout China, offer a more diverse landscape and sensory experiences compared to plains [32]. These regions have specific requirements for protection, utilization, and development, rendering evaluation methods designed for plains unsuitable.
This study adopts a macroscopic “ecology–recreation” perspective for the entire region and explores the establishment of an evaluation system that integrates greenway network simulation path evaluation and post-construction assessment through the integration of resource elements. The objective is to utilize all-for-one tourism as a framework [33], integrate the dual requirements of ecology and recreation, and create a spatial carrier centered on greenway networks to facilitate the coordinated development of urban and rural industrial clusters [34]. This study selects the planning of the Jinma Mountain greenway network in Kunming, Yunnan Province, as an empirical case to verify the scientific rigor and practical value of the method. It is anticipated that this will provide a reference for planning and managing mountain greenways in suburban areas, promoting orderly development and sustainable growth in the region.

2. Selection of Indicators for Evaluation

This study is grounded in three major theories: landscape ecology, recreation science, and coupling coordination theory. These theories collectively form the foundation for constructing an evaluation system for greenway networks. With crucial support from a greenway construction model that emphasizes multivalue and multidimensional perception, this study simulates pathways for ecological sub-greenways, recreational sub-greenways, and integrated ecological–recreational greenway networks. Utilizing algorithms such as the ant colony algorithm and measures of coupling coordination, the research achieves the optimization and multifaceted evaluation of greenway networks [35]. Ultimately, a comprehensive greenway network planning scheme is proposed.

2.1. Selection of Indicators for Ecological Sensitivity

Landscape ecology is a discipline that studies the interactions among landscape structure, function, and dynamic changes, with a core focus on understanding how the spatial configuration of ecosystems and landscape patterns influence ecological processes and their functioning [36]. The selection of indicators for greenway path simulation primarily focuses on source areas and resistance surfaces. In previous studies, the most typical method for identifying ecological greenway source areas has been the combination of Morphological Spatial Pattern Analysis (MSPA) and the Conefor–Minimum Cumulative Resistance (MCR) model. Given that vegetation cover and ecological conditions in suburban mountainous areas are generally better than those in urban built-up areas, comprehensive indicators are necessary to accurately reflect regional ecological quality. Additionally, connectivity analysis is often limited to static assessments of a single time slice, whereas the Remote Sensing-based Ecological Index (RSEI) can capture dynamic changes in ecological environment quality within suburban mountainous areas [37]. Therefore, combining these two indicators allows for the identification of high-ecological-value patches as ecological source areas.
Ecological sensitivity refers to the degree to which the ecological environment is disturbed and impacted by human activities [38]. Mountainous suburban areas are profoundly impacted by urban expansion, development, and construction activities. Therefore, land-use type and distance from major urban roads are selected as indicators of human disturbance [39]. In terms of ecological attributes, these areas are primarily composed of ecological land uses, such as forest land, which form open spaces on the periphery of cities and help prevent urban sprawl. Consequently, their land-use attributes can be classified as ecological isolation zones, playing a crucial role in conserving soil and water. Vegetation cover and elevation pose resistance to greenway construction [19], while slope influences the feasibility and cost of greenway development [40]. To protect the intact ecological base, the scope of greenway network construction is strictly constrained by ecological protection red lines and permanent basic farmland red lines. Ultimately, single and comprehensive ecological sensitivity factors are weighted and superimposed (Table 1) to generate the ecological resistance raster data for the greenway network.

2.2. Leisure Theory and Selection of Indicators for Recreational Sub-Greenways

The construction of greenway networks not only needs to ensure the convenience of daily travel and provide rich recreational experiences for residents and tourists but also needs to deeply integrate cultural elements, achieving functional diversification and value pluralism. For tourists, big data from social media are leveraged to extract relevant evaluation texts pertaining to regional recreational landscape nodes. Key landscape nodes are then identified through text semantic network analysis. For residents, the quality assessments and spatial distributions of scenic spots are determined through comprehensive planning and on-site investigations. By integrating the needs of both user groups, nodes with high perceived attractiveness are selected. Furthermore, through field research, cultural landscape information points are identified, and spatial hotspots and importance assessments are conducted to pinpoint nodes with significant cultural appeal. The organic connections between these nodes form tourist flows. Recreational paths are spatial carriers of tourist flows, not only reflecting potential migration patterns among recreational crowds between nodes but also serving as a critical foundation for simulating greenway paths [42]. Building upon this foundation, a structured questionnaire was distributed to 14 experts in landscape architecture and urban–rural planning, each with more than 8 years of professional experience in this field. The Analytic Hierarchy Process (AHP) was employed to quantitatively standardize each evaluation factor, followed by a group decision analysis using Yaahp (V10) software to determine the weight coefficients of the factors (Table 2). The consistency ratio (CR = CI/RI = 0.038 < 0.1) confirmed that the judgment matrix met the required consistency threshold.
The construction of mountain recreational greenways in city suburbs should prioritize suitability. According to the Greenway Planning and Design Guidelines (2016) [43], appropriate slopes for trails vary by greenway type, and it is emphasized that “the routing of country-style greenways should fully utilize existing mountain trails, hiking paths, forest fire prevention roads, and other pathways to minimize ecological and landscape disruption.” Moreover, given that mountainous suburban areas, which are situated between urban and rural regions, have relatively underdeveloped infrastructure, a dense spatial distribution of service and landscape facilities near residential settlements can significantly enhance recreational convenience and greenway utilization.
Furthermore, recreational visual quality constitutes the most immediate and direct element of human perception. A landscape visual evaluation model is developed by integrating four key metrics: Patch Richness Density (PRD), Shannon’s Diversity Index (SHDI), Simpson’s Evenness Index (SIEI), and Contagion Index (CONT) [44]. Researchers believe that evaluating landscape visual suitability and construction suitability are equally important, so the weights are assigned a value of 0.5 each. The two types of suitability surface are combined to obtain the recreational resistance raster data (Table 3).

2.3. Selection of Indicators for Coupling Coordination Theory and Multifunctional Greenway Network

Positive mutual promotion among system elements leads to beneficial coupling of indicators or factors, thereby fostering development; conversely, negative interactions hinder development. This study employed the coupling coordination degree formula to select indicators that are significantly influenced by single-function greenways [45]. These indicators were used to evaluate the coupling coordination level of the “ecology–recreation” functions in simulated greenway network pathways. The selection criteria included quantitative indicators and factors with higher weights from previous research, which have demonstrated significant positive impacts on the ecological or recreational benefits of the greenway network.

2.4. Construction of a Comprehensive Evaluation System

This study established a comprehensive evaluation system for suburban mountainous greenway networks under an “ecology–recreation” framework (Figure 1). Initially, data collection was conducted to form a robust dataset. Next, ecological and recreational sub-greenway pathways were modeled separately. Following this, the greenway network simulation results were generated by buffering individual function-specific greenways and overlaying source areas, with overall network optimization achieved through pathway adjustment and hierarchical classification. Ultimately, the comprehensive benefits of the simulated greenway network were assessed from three perspectives—structural integrity, implementation feasibility, and functional effectiveness—to finalize the greenway network planning scheme.

3. Case Study and Methods

3.1. Study Site

Kunming, a plateau mountainous city, has the prominent Jinma Mountain on its southern outskirts. Jinma Mountain spans the Guandu and Panlong districts of Kunming, encompassing over ten villages, including Tanglipo, Hamazhe Village in Shuanglong Township, and Maichong Village. According to the “Panlong District Government Document No. Pan Zheng Fa (2022) No. 4; Planning for Panlong District Ecological—Civilization Construction Demonstration Area (2021–2030). Panlong District People’s Government: Kunming, China, 2022.”, specific areas of Jinma Mountain have been designated as ecological tourism and ecological function zones, emphasizing the need to “coordinate the construction of suburban greenways to promote the development of ecological tourism.” Given its rich agricultural tourism and ecological resources, the overall development quality of Jinma Mountain can be significantly enhanced by deepening and enhancing its integrated “ecology–recreation” functions.
This study adopted the “Jinma Mountain” urban ecological isolation zone and scenic area green space, as defined in the land-use planning map of the central urban area from the “Guo Han (2016) No. 153; Kunming City Master Plan (2011–2020). The State Council of the People’s Republic of China: Beijing, China, 2016”, as a case study for the regional evaluation of greenways (Figure 2). The area encompasses approximately 92.3 square kilometers.

3.2. Research Methods

3.2.1. Data Sources

This study utilized the following four data sources:
(1)
Remote sensing imagery and elevation data: The 2021 Landsat 8 OLI_TIRS imagery of Kunming and DEM data were obtained from the Geospatial Data Cloud (https://www.gscloud.cn/), with less than 5% cloud cover.
(2)
Official datasets: The 2020 land use and road network data for Kunming were sourced from the Resource and Environmental Sciences and Data Center of the Chinese Academy of Sciences (https://www.resdc.cn/). Additionally, the National Science and Technology Infrastructure, National Earth System Science Data Center (http://www.geodata.cn/), provided China’s annual precipitation data (2001–2020) at a 1 km resolution, the 2018 Yunnan Province soil erodibility factor dataset at a 30 m resolution, and the 2010 China annual slope length and steepness factor dataset at a 1 km resolution. The “three lines” data for territorial spatial planning was provided by the Kunming Natural Resources and Planning Bureau and finalized in late 2022.
(3)
Collected data: Hiking trail data from “2 BuLu” was imported into ArcGIS 10.8 for visualization, with coordinates standardized to WGS_1984_UTM_Zone_48N.
(4)
Attraction review data from social platforms, including Ctrip, Weibo, and Xiaohongshu, underwent data cleaning and were analyzed using ROST_CM6. POI data were gathered from 91 Weitu locations and reclassified into categories such as public services, cultural facilities, and transportation services. Historical and cultural information was derived from national, provincial, and municipal cultural relics protection units, supplemented by field investigations to assess the importance of attractions and ensure precise spatial locations.
After completing data collection, cleaning, and calculation, we established evaluation indicators for the greenway system (Table 1 and Table 2) and obtained the simulated results of the greenway network through path model analysis.

3.2.2. Data Calculations

(1)
Extraction of Source Points Utilizing the RSEI.
The RSEI integrates four indicators: the Normalized Difference Vegetation Index (NDVI), wetness (WET), Land Surface Temperature (LST), and the average value of the Bare Soil Index and the Built-up Index (NDSBI) as a dryness index. The RSEI results were obtained through principal component analysis (PCA) coupling. To assess variations in ecological environment quality within the study area, ecological quality is classified into five categories—excellent, good, moderate, poor, and very poor—based on the grading standards for the Ecological Index (EI) outlined in the “Technical Specifications for Ecological Environment Assessment” [46]. Areas with “excellent” ecological quality are identified, and ecological source areas are selected according to the spatial distribution of these patches, which exhibit high connectivity values.
(2)
Extraction of Source Points Based on Semantic Network Analysis.
(a)
Data Collection: social media evaluations were systematically mined from major Chinese platforms (Ctrip, Weibo, Xiaohongshu) using Octopus Data Collector. The primary search keywords included “Jindian Back Mountain” and related location-specific terms. Data spanned posts published during the study’s focal period (2020–2023).
(b)
Semantic Analysis Workflow: term extraction was carried out first, processing the cleaned corpus using ROST CM6’s word segmentation module. Then a domain-specific lexicon was compiled, including local toponyms, followed by network construction, in which co-occurrence matrices with a default window size (ROST CM6 standard) were generated and undirected semantic networks were built based on term associations. Finally, core node identification was carried out with the selection of high-frequency terms significantly associated with recreational preferences and the prioritization of nodes exhibiting both lexical centrality and landscape relevance.
(3)
Ant Colony Algorithm for Path Classification and Optimization.
The ant colony algorithm emulates the pheromone-based communication and cooperative behavior of ants to identify optimal paths within the greenway network [47]. Drawing on the relevant literature [48,49], we set the parameters as follows: number of ants, m = 50; α = 2; β = 5; ρ = 0.25; Q = 100. By conducting 30 iterations using MATLAB R2023b, the optimal connectivity relationships within the greenway network were derived.
(4)
Functional Evaluation of Paths Based on Coupling Coordination Degree Simulation.
Regarding the radiation range of greenways, a 300-m buffer zone on either side of each composite greenway is selected as the analysis unit [50]. Utilizing the coupling coordination degree formula, the coupling coordination degree (D value) of the composite greenway network under the synergistic influence of “ecological–recreational” functions is determined. Following the classification criteria for varying coordination levels, an overall assessment of the coupling coordination level of the simulated greenway network is conducted.

4. Simulation of Jinma Mountain Greenway Network Paths

4.1. Simulation of Ecological Greenways

Based on the analysis of (a) landscape component number (NC), (b) landscape connection number (NL), (c) equivalent integral index of connectivity (EC (IIC)), and (d) equivalent potential index of connectivity (EC (PC)), it is evident that landscape stability is relatively strong when the distance threshold is between 400 and 800 m (Figure 3 and Figure 4). Within this range, a smaller difference between the patch importance indices dPC and dIIC indicates a more accurate distance threshold setting [51]. Consequently, the optimal distance threshold for ecological patches in Jinma Mountain was determined to be 400 m. High-connectivity ecological sources were identified based on the distribution of patch connectivity values. Furthermore, the four key indicators of ecological quality were normalized and subjected to PCA to synthesize the RSEI, with the first principal component accounting for over 90% of the total variance. After evaluating the ecological quality of Jinma Mountain, the top five largest patches in the “excellent” category were selected as high-importance ecological sources. These sources were then merged, and any duplicate patches within 1 km of each other were eliminated.
The resistance values of various ecological resistance factors, as detailed in Table 4, were calculated using the entropy weight method. Soil erosion emerged as the most significant factor, highlighting its critical influence on the construction of the ecological sub-greenway in Jinma Mountain. Furthermore, resistance values were assigned to the vector data of both the ecological protection red line and the permanent basic farmland protection red line in Jinma Mountain. By overlaying these datasets, an ecological resistance surface was generated (Figure 5). The highest resistance values are concentrated within the “Ecological Protection Red Line” and “Permanent Basic Farmland Red Line” areas, while elevated values are observed along the main streets of villages. Conversely, low-value areas are predominantly characterized by woodlands with high vegetation cover. Overall, the ecological resistance in Jinma Mountain is predominantly in the medium to low range.
Based on the MCR model, potential ecological sub-greenways in Jinma Mountain were identified. These greenways demonstrate strong connectivity and a relatively intact landscape structure. The western side exhibits a notably higher density of greenways compared to the eastern side, where the greenways are more dispersed and elongated. According to the results of the gravity model (GM), the simulated paths for the ecological sub-greenways were selected (Figure 6). Overall, the greenways form a loop, effectively covering the entire site.

4.2. Simulation Results of Recreational Greenways

“Imagery” is a term within the realm of psychology that refers to the mental representation of previously experienced objects by the cognitive subject [52]. “The Back Mountain of the Golden Temple” represents the landscape imagery of Jinma Mountain as perceived by residents. Using “The Back Mountain of the Golden Temple” as a keyword and Octoparse 8 software, we trawled landscape-related reviews from Xiaohongshu, Weibo, and Ctrip. Semantic network analysis was conducted in ROST CM6 to extract high-frequency keywords and construct a co-occurrence matrix, generating a semantic network diagram (Figure 7). The results indicate that the Golden Temple, World Expo Garden, Hamazhe Street, Wildlife Park, and general parks are key landscape attractions. Further sentiment analysis was performed on these attractions. The findings reveal that the Golden Temple, World Expo Garden, Hamazhe Street, and Wildlife Park received greater positive sentiment, indicating they are preferred landscape nodes among visitors (Figure 8). Additionally, based on upper-level planning references and interviews with residents, three community landscape nodes—Maichong Village, Dongda Forest Park, and Yuntai Creative Farm—were selected for further study.
Ten cultural landscape points in Jinma Mountain were selected for spatial autocorrelation and hot spot analysis. The results indicate that Moran’s I index = 0.090536 > 0, Z = 4.420964 > 1.96, and p = 0.000010 < 0.05; this suggests a strong positive spatial auto-correlation among the cultural landscape nodes within the study area. Based on an evaluation of their resource significance, the Taihegong Golden Temple has been designated as a national-level cultural relic protection unit. Hamazhe Village and Wulong Village are recognized for their distinctive cultural characteristics. Wolong Temple and Liangmiansi Temple are classified as ancient monasteries. The Yongle Bronze Bell holds municipal-level cultural relic protection status, while the Tanglipo Architectural Complex embodies local historical and cultural heritage.
The highest weight value among the resistance factors in the suitability analysis is attributed to the distribution of service facilities (Table 5), indicating that a denser distribution of service facilities enhances the suitability for greenway construction. By performing a weighted overlay of the construction suitability and landscape visual suitability results, a comprehensive resistance surface for recreational sub-greenways was generated (Figure 9).
The spatial distribution of recreational greenway routes, derived from simulations using the MCR model and the GM, is notably uniform. The resulting network structure is comprehensive and well-defined, providing extensive coverage across the study area (Figure 10).

4.3. Simulation of “Ecology–Recreation” Greenway Network Routes

By integrating ecological and recreational sources as primary source points for the composite greenway network and utilizing the intersections of these two types of greenways as secondary source points, we controlled the distance between the intersections and the primary source points. Buffer zones of <30 m, 30–80 m, 80–120 m, 120–150 m, and >150 m were assigned weights of 1, 2, 3, 4, and 5, respectively. A weighted overlay was then applied to generate the landscape resistance surface (Figure 11).
The MCR model was applied to generate the simulation results for the composite greenway network. Additionally, the GM was employed to screen and identify significant greenway networks (Figure 12).

5. Optimization and Evaluation of the Jinma Mountain Greenway Network Post-Construction

5.1. Optimization of the Greenway Network

Per the “Greenway Planning and Design Guidelines,” greenway segments traversing residential areas are realigned to integrate with appropriate rural road networks, such as township roads and village pathways, particularly near scenic spots and towns adjacent to the greenways. This adjustment enhances resource connectivity and accessibility. At intersections with high-speed transportation routes, bridges are employed to ensure seamless integration between the greenway network and the surrounding environment. Disconnected greenway segments between source points 10 and 14 have been added, and pathways in the simulated greenway network that cross ecological protection zones and permanent basic farmland boundaries have been largely removed. Where minor crossings of these protected areas are unavoidable, adjustments are made to minimize human impact on the ecological environment. Additionally, the development model of isolated and fragmented greenways is avoided, ensuring the greenway system connects with the “One Ring, Two Axes, Two Belts, and Multiple Lines” framework outlined in the “Special Planning for the Greenway System in Panlong District, Kunming City.” This approach facilitates supplementary connectivity and promotes systematic and spatial development of the greenway network (Figure 13).

5.2. Hierarchical Optimization of the Greenway Network

Given the rich tourist resources in Jinma Mountain and the substantial human modifications, recreational source points are identified as optimal nodes for route planning to construct the most efficient greenway routes. Since the Yunnan Wildlife Park is a fee-charging scenic area, it is excluded from the scope of optimal route planning analysis. This study utilized the ant colony optimization (ACO) algorithm to optimize path selection within the simulated greenway network, thereby achieving optimal connectivity between major scenic spots (Figure 14). Based on the established greenway network framework and these optimized connections, the optimal greenway for Jinma Mountain was constructed, ensuring a scientifically hierarchical structure within the greenway network.

5.3. Evaluation of the Greenway Network Structure

A comprehensive comparison of the structural indices of ecological sub-greenways, recreational sub-greenways, and composite greenway networks is presented in Table 6. The composite greenway network exhibits the highest value for the network cycle index (α), indicating superior efficiency in information and energy flow within this type of greenway. The network connection indices (β) for all three types of greenways are relatively similar, with each node having approximately three connection points. Notably, the composite greenway network demonstrates the highest level of network integrity. Additionally, it also has the highest network connectivity index (γ), indicating that the connections between nodes are stronger and the degree of connectivity is higher. Overall, the structure of the composite greenway network surpasses single-function greenways in multiple aspects, leading to enhanced overall service efficiency.

5.4. Feasibility Evaluation of the Greenway Network

The composite greenway network in Jinma Mountain effectively integrates the natural and built environments, seamlessly connecting with the greenway system of Panlong District to establish a robust ecological service flow. The “Kunming Territorial Spatial Master Plan (2021–2035)” highlights an integrated green space system characterized by “mountains encircling the city, rivers, and lakes interwoven into the urban fabric, green wedges interspersed, spots and corridors interconnected, and harmony between the city and its scenery.” This plan designates Jinma Mountain as a comprehensive ecological and recreational mountain cluster (Figure 15), which aligns with the objective of this study to develop the Jinma Mountain greenway network with a focus on regional “ecology–recreation” integration.

5.5. Functional Evaluation of the Greenway Network

This study revealed that the evaluation of single-function greenways is significantly influenced by soil erosion and land-use types in the construction of ecological sub-greenways. Given that land use data are qualitative indicators, they were quantified using the ecosystem service value equivalent method along with modified equivalent factors [53]. For assessing recreational functions, four key indicators were chosen: facility convenience, recreational accessibility, landscape visual quality, and the density of cultural recreational spots. Since this study focused on the construction of a greenway network integrating ‘ecological–recreational’ functions, both ecological and recreational sub-greenways were considered equally important, and each was assigned a weight of 0.5. The weights of individual indicators were determined using the expert scoring method, with questionnaires collected from 30 experts and scholars specializing in landscape architecture and urban–rural planning, each with more than 8 years of professional experience in this field. The weights of these indicators were calculated using the AHP. The consistency ratios (CR) for the ecological sub-greenway (CR = CI/RI = 0.07 < 0.1) and the recreational sub-greenway (CR = CI/RI = 0.084 < 0.1) both passed the consistency test. The analysis revealed that ecological environment quality makes the greatest contribution to the ecological function of the composite greenway, while landscape visual quality contributes the most to recreational function, with the highest weight (Table 7).
A 30 m × 30 m fishnet model was created, generating a total of 44,708 grid cells. A 300 m buffer zone was established on both sides of each of the 34 greenways in the Jinma Mountain greenway network. Based on the coupling coordination degree formula, the ecological–recreational functional coupling coordination degrees of these greenways within the buffer zones were evaluated. The coupling coordination degree (D) ranges from 0 to 1, with 10 classification levels assigned according to D values. Higher D values indicate better functional matching, while lower values suggest poorer matching. The average coupling coordination index of all grid cells within each greenway’s buffer zone was used to represent the overall coupling coordination degree of that greenway [54]. The results showed that all 34 greenways achieved ecological and recreational functional coordination. Specifically, 26 greenways (76.5% of the total) exhibited moderate coordination. Six greenways demonstrated primary coordination and two greenways achieved good coordination. These findings indicate that the Jinma Mountain greenway network constructed in this study demonstrates excellent functional matching and coordination, validating the scientific rigor and practical value of the proposed greenway construction methodology.

5.6. Planning Scheme for the Jinma Mountain Greenway Network

Based on comprehensive research, it is evident that the Jinma Mountain Greenway Network, constructed using an integrated evaluation system, exhibits a high degree of functional compatibility and coordination. Consequently, a greenway network planning scheme characterized by “multiple interconnected rings and a dense network” was developed (Figure 16). The composite greenway network comprises a total of 34 greenways, with a density of 0.79 km/km2, which falls within the appropriate range for country-type greenway densities [43]. Specifically, 11 optimal greenways, totaling 36 km in length, were identified; the remaining 23 secondary greenways cover approximately 46 km. The planning outcomes of the Jinma Mountain Greenway Network underscore the scientific rigor and guiding significance of the evaluation system.

6. Discussion and Conclusions

6.1. Discussion

6.1.1. Methodological Contributions

The innovation of this study lies in its systematic approach to coordinating ecological conservation and recreational development in suburban mountainous greenway planning, providing a replicable methodological framework for similar regions.
First, an enhanced ecological source identification approach integrates MSPA-Conefor connectivity analysis and RSEI-based dynamic monitoring [55] to improve objectivity over single-method approaches. The incorporation of mandatory policy barriers such as “Ecological Protection Red Lines” into resistance modeling, addresses regulatory gaps in prior studies [56]. Second, dual-perspective recreational routing is implemented with a route selection model that combines subjective appeal, such as visual and cultural hotspots, with objective suitability, like slope and construction feasibility [57]. Recreational sources are identified through multi-method integration including social media analysis, field surveys, and expert scoring, although platform demographics (e.g., Xiaohongshu’s youth bias) may result in an underrepresentation of the needs of elderly individuals [58]. Future models will embed PPGIS for inclusivity [59]. Third, coupled network optimization involves directly coupling ecological and recreational sources with dual resistance surfaces, resolving resource inefficiencies in single-function greenways, and “Ecological Protection Red Lines” are embedded into resistance modeling, eliminating functional conflicts due to spatial overlays. This systematic approach enables synergistic eco-tourism development in peri-urban mountains.

6.1.2. Comparative Positioning with International Studies

Unlike urban greenway models that prioritize compact land-use integration [60], this study establishes a peri-urban mountain-specific framework by systematically embedding terrain resistance, visual sensitivity, and policy-mandated barriers like China’s “Ecological Protection Red Lines”. While single-objective paradigms dominate international research, with ecological networks focusing on biodiversity corridors [61] and trail studies isolating recreational impacts [62], our CCT model quantifies ecological-recreational interactions, yielding a measurable synergy index to resolve land-use trade-offs. Algorithmically, we advance beyond species connectivity optimization in forest corridors [63]: our ant colony algorithm integrates dual flows (ecological and recreational), elevating network connectivity while minimizing on-ground conflicts via adaptive rerouting. The framework is adaptable to regions with policy-driven conservation mandates and peri-urban mountain interfaces using slope-resistance models.

6.1.3. Limitations

This study has the following limitations: (1) stakeholder exclusion in validation. Despite robust technical modeling, the absence of structured input from local residents, tourism operators, and protected-area managers in route validation limits social feasibility. Future work must integrate public participatory GIS (PPGIS) workshops to co-define priorities, particularly in socially complex zones like restricted scenic areas. (2) Policy implementation gap. While China’s “Ecological Protection Red Lines” were embedded as resistance factors, on-ground enforcement mechanisms were not assessed. Quantitative diagnostics of regulatory conflicts should supplement spatial simulations. (3) Temporal data mismatches. While this study successfully constructed the Jinma Mountain greenway network by integrating multiple datasets, discrepancies in temporal resolution due to data acquisition constraints may have introduced some analytical errors, which should be addressed in future research.

6.2. Conclusions

This study aimed to develop a comprehensive evaluation framework for the construction and optimization of urban–suburban mountainous greenway networks, integrating multiple values and multidimensional perspectives from an overarching “ecological-recreational” viewpoint.
Empirical analysis revealed that the constructed composite greenway network exhibits a spatial configuration characterized by “multiple interconnected rings and a dense web,” with 34 greenways achieving a density of 0.79 km/km2. The network’s ring index (α), connectivity index (β), and coherence index (γ) are 0.62, 3.06, and 0.76, respectively, representing average improvements of 12.88%, 20%, and 4.19% over single-function greenways. The Jinma Mountain composite greenway network aligns with the development objectives and spatial orientation of higher-level planning, ensuring harmonious integration of ecological and recreational functions. The greenway network established using this evaluation system demonstrates significant advantages in spatial coverage, functional diversity, and structural stability compared to single-function greenways.
In summary, this framework offers a replicable blueprint for mountainous peri-urban regions in which policy rigidity and terrain complexity necessitate integrated solutions.

Author Contributions

Methodology, W.W.; Software, A.Y. and L.J.; Validation, A.Y.; Formal analysis, L.J. and W.L.; Data curation, A.Y.; Writing—Original Draft: W.W. and A.Y. Writing—review & editing, W.W. and G.L.; Supervision, G.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by National Natural Science Foundation of China, grant number 52468010.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Acknowledgments

The authors would like to express their gratitude to the National Science and Technology Infrastructure Platform—National Earth System Science Data Center (http://www.geodata.cn) for providing technical support.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Jongman, R.H.G.; Külvik, M.; Kristiansen, I. European ecological networks and greenways. Landsc. Urban Plan. 2004, 68, 305–319. [Google Scholar] [CrossRef]
  2. Fabos, J.G. Greenway planning in the United States: Its origins and recent case studies. Landsc. Urban Plan. 2004, 68, 321–342. [Google Scholar] [CrossRef]
  3. Horte, O.S.; Eisenman, T.S. Urban Greenways: A Systematic Review and Typology. Land 2020, 9, 40. [Google Scholar] [CrossRef]
  4. Carlier, J.; Moran, J. Hedgerow typology and condition analysis to inform greenway design in rural landscapes. J. Environ. Manag. 2019, 247, 790–803. [Google Scholar] [CrossRef] [PubMed]
  5. Anderson, C.E.; Zimmerman, A.; Lewis, S.; Marmion, J.; Gustat, J. Patterns of Cyclist and Pedestrian Street Crossing Behavior and Safety on an Urban Greenway. Int. J. Environ. Res. Public Health 2019, 16, 201. [Google Scholar] [CrossRef]
  6. Lopes, I.J.; Biondi, D.; Corte, A.P.; Reis, A.R.; Oliveira, T.G. A methodological framework to create an urban greenway network promoting avian connectivity: A case study of Curitiba City. Urban For. Urban Green. 2023, 87, 128050. [Google Scholar] [CrossRef]
  7. Balbi, M.; Croci, S.; Petit, E.J.; Butet, A.; Georges, R.; Madec, L.; Caudal, J.P.; Ernoult, A. Least-cost path analysis for urban greenways planning: A test with moths and birds across two habitats and two cities. J. Appl. Ecol. 2021, 58, 632–643. [Google Scholar] [CrossRef]
  8. Wang, R.; Browning, M.H.; Kee, F.; Hunter, R.F. Exploring mechanistic pathways linking urban green and blue space to mental wellbeing before and after urban regeneration of a greenway: Evidence from the Connswater Community Greenway, Belfast, UK. Landsc. Urban Plan. 2023, 235, 104739. [Google Scholar] [CrossRef]
  9. Harris, B.; Larson, L.; Ogletree, S. Different Views from the 606: Examining the Impacts of an Urban Greenway on Crime in Chicago. Environ. Behav. 2018, 50, 56–85. [Google Scholar] [CrossRef]
  10. Noh, Y. Does converting abandoned railways to greenways impact neighboring housing prices? Landsc. Urban Plan. 2019, 183, 157–166. [Google Scholar] [CrossRef]
  11. Yu, K.; Li, D.; Li, N. The Evolution of Greenways in China. Landsc. Urban Plan. 2006, 76, 223–239. [Google Scholar] [CrossRef]
  12. Liu, Z.; Lin, Y.; De Meulder, B.; Wang, S. Can greenways perform as a new planning strategy in the Pearl River Delta, China? Landsc. Urban Plan. 2019, 187, 81–95. [Google Scholar] [CrossRef]
  13. Lin, J.; Wang, Y.; Lin, Z.; Li, S. National-scale connectivity analysis and construction of forest networks based on graph theory: A case study of China. Ecol. Eng. 2025, 216, 107639. [Google Scholar] [CrossRef]
  14. Xiong, S.; Yang, F.; Zhang, J.; Tang, Y. An integrated model chain for diagnosing and predicting conflicts between production-living-ecological space in lake network regions: A case of the Dongting Lake region, China. Ecol. Indic. 2024, 166, 112237. [Google Scholar] [CrossRef]
  15. Li, L.; Gao, X.; Li, J.; Meng, L.; Wang, Z.; Yang, L. Difference of Usage Behavior between Urban Greenway and Suburban Greenway: A Case Study in Beijing, China. Land 2022, 11, 1245. [Google Scholar] [CrossRef]
  16. Xu, B.; Shi, Q.; Zhang, Y. Evaluation of the Health Promotion Capabilities of Greenway Trails: A Case Study in Hangzhou, China. Land 2022, 11, 547. [Google Scholar] [CrossRef]
  17. Callau, A.À.; Pérez-Albert, Y.; Farías-Torbidoni, E.I.; Mas-Alòs, S.; González, J.A.; Peris, M.G. Enhancing outdoor recreational experience: A methodology for assessing peri-urban greenway suitability based on hiker physical activity levels and motivations. J. Outdoor Recreat. Tour. 2025, 50, 100893. [Google Scholar] [CrossRef]
  18. Keith, S.J.; Larson, L.R.; Shafer, C.S.; Hallo, J.C.; Fernandez, M. Greenway use and preferences in diverse urban communities: Implications for trail design and management. Landsc. Urban Plan. 2018, 172, 47–59. [Google Scholar] [CrossRef]
  19. Liu, X.R.; Yin, H.W.; Xu, J.G.; Kong, F.H.; Hou, Y.J. Developing Multi-functional and Composite Urban Greenway Network in Luoyang Based on LCP. Mod. Urban Res. 2018, 1, 126–131. [Google Scholar]
  20. Tang, Z.Y.; Ye, Y.; Jiang, Z.D.; Fu, C.W.; Huang, R.; Yao, D. A data-informed analytical approach to human-scale greenway planning: Integrating multi-sourced urban data with machine learning algorithms. Urban For. Urban Green 2020, 56, 126871. [Google Scholar] [CrossRef]
  21. Yuan, S.; Dai, W.; Zhang, Y.; Yang, J. Cycling Greenway Planning towards Sustainable Leisure and Recreation: Assessing Network Potential in the Built Environment of Chengdu. Sustainability 2024, 16, 6185. [Google Scholar] [CrossRef]
  22. Bai, Y.; Bai, Y.; Wang, R.; Yang, T.; Song, X.; Bai, B. Exploring associations between the built environment and cycling behaviour around urban greenways from a human-scale perspective. Land 2023, 12, 619. [Google Scholar] [CrossRef]
  23. Jiang, J.L.; Gao, Z.; Xu, Y.Y.; Chen, J. Research on Planning of Intelligent Route Selection for Greenway in Central Urban Area: A Case Study of Suqian. Landsc. Archit. 2023, 30, 103–110. [Google Scholar]
  24. Cheng, X.; Long, R.; Chen, H.; Li, Q. Coupling coordination degree and spatial dynamic evolution of a regional green competitiveness system—A case study from China. Ecol. Indic. 2019, 104, 489–500. [Google Scholar] [CrossRef]
  25. Mu, W.; Li, C. Optimization of urban greenway route using a coverage maximization model for lines. Comput. Environ. Urban Syst. 2024, 112, 102155. [Google Scholar] [CrossRef]
  26. Kurdoğlu, B.Ç.; Kurt, S.S. Determination of greenway routes using network analysis in Amasya, Turkey. J. Urban Plan. Dev. 2017, 143, 05016013. [Google Scholar] [CrossRef]
  27. Luo, D.; Liang, L.; Wang, Z.; Chen, L.; Zhang, F. Exploration of coupling effects in the Economy–Society–Environment system in urban areas: Case study of the Yangtze River Delta Urban Agglomeration. Ecol. Indic. 2021, 128, 107858. [Google Scholar]
  28. Yu, L.; Wang, Y.; Li, M. The emergence of counter-urbanisation in China: Can it be a pathway for rural revitalisation? Habitat Int. 2024, 144, 102998. [Google Scholar] [CrossRef]
  29. Tang, H.; Hong, Q.; Xu, B. Landscape performance assessment of phase I of greenway around Qingshan Lake National Forest Park, Zhejiang Province. J. Zhejiang AF Univ. 2020, 37, 1177–1185. [Google Scholar]
  30. Trojanowska, M. The Importance of Resilient, Health-Promoting, and Accessible Cultural Landscapes Using the Example of One Suburb of Gdańsk, Poland. Sustainability 2024, 16, 3652. [Google Scholar] [CrossRef]
  31. Ying, B.; Ke, L.; Zhu, F.; He, N. Towards differentiated development policies for mountainous areas: A spatial analysis of mountain distribution and characteristics in a geographic region. Land Degrad. Dev. 2023, 34, 5071–5082. [Google Scholar] [CrossRef]
  32. Li, F.; Wang, R.; Paulussen, J.; Liu, X. Comprehensive concept planning of urban greening based on ecological principles: A case study in Beijing, China. Landsc. Urban Plan. 2005, 72, 325–336. [Google Scholar] [CrossRef]
  33. Conine, A.; Xiang, W.N.; Young, J.; Whitley, D. Planning for multi-purpose greenways in Concord, North Carolina. Landsc. Urban Plan. 2004, 68, 271–287. [Google Scholar] [CrossRef]
  34. Zhao, N.; Liu, Z.; Lin, Y.; De Meulder, B. User, public, and professional perceptions of the greenways in the Pearl River Delta, China. Sustainability 2019, 11, 7211. [Google Scholar] [CrossRef]
  35. Vizzari, M. Spatial modelling of potential landscape quality. Appl. Geogr. 2011, 31, 108–118. [Google Scholar] [CrossRef]
  36. Liu, S.L.; Hou, X.Y.; Yin, Y.J.; Cheng, F.Y.; Zhang, Y.Q.; Dong, S. Research progress on landscape ecological networks. Acta Ecol. Sin. 2017, 37, 3947–3956. [Google Scholar] [CrossRef]
  37. Su, X.; Wu, J.; Li, P.; Li, R.; Cheng, P. RSEI-Based Modeling of Ecological Security and Its Spatial Impacts on Soil Quality: A Case Study of Dayu, China. Sustainability 2022, 14, 4428. [Google Scholar] [CrossRef]
  38. Luo, Q.; Bao, Y.; Wang, Z.; Chen, X.; Wei, W.; Fang, Z. Vulnerability assessment of urban remnant mountain ecosystems based on ecological sensitivity and ecosystem services. Ecol. Indic. 2023, 151, 110314. [Google Scholar] [CrossRef]
  39. Zhao, Y.; Kasimu, A.; Liang, H.; Reheman, R. Construction and Restoration of Landscape Ecological Network in Urumqi City Based on Landscape Ecological Risk Assessment. Sustainability 2022, 14, 8154. [Google Scholar] [CrossRef]
  40. Floress, K.; Baumgart-Getz, A.; Prokopy, L.S.; Janota, J. The quality of greenways planning in northwest Indiana: A focus on sustainability principles. J. Environ. Plan. Manag. 2009, 52, 61–78. [Google Scholar] [CrossRef]
  41. SL 773-2018; Guidelines for Measurement and Estimation of Soil Erosion in Production and Construction Projects. China Water & Power Press: Beijing, China, 2018.
  42. Mou, N.; Zheng, Y.; Makkonen, T.; Yang, T.; Tang, J.J.; Song, Y. Tourists’ digital footprint: The spatial patterns of tourist flows in Qingdao, China. Tour. Manag. 2020, 81, 104151. [Google Scholar] [CrossRef]
  43. Ministry of Housing and Urban-Rural Development of the People’s Republic of China. Guidelines for Greenway Planning and Design. Available online: https://www.mohurd.gov.cn/gongkai/zc/wjk/art/2016/art_17339_229168.html (accessed on 14 October 2016).
  44. Zhang, Y.; Li, M.; Chen, D. Grain effect and appropriate grain choosing of land-use spatial pattern. In Proceedings of the Geoinformatics 2007: Geospatial Information Science, Nanjing, China, 25–27 May 2007; Volume 6753, pp. 216–226. [Google Scholar]
  45. Cui, D.; Chen, X.; Xue, Y.; Li, R.; Zeng, W. An integrated approach to investigate the relationship of coupling coordination between social economy and water environment on urban scale—A case study of Kunming. J. Environ. Manag. 2019, 234, 189–199. [Google Scholar] [CrossRef]
  46. Ministry of Environmental Protection of the People’s Republic of China. Technical Specification for Evaluation of Ecological Environment Status (HJ 192-2015); China Science Press: Beijing, China, 2015. [Google Scholar]
  47. Peng, J.; Zhao, S.; Dong, J.; Liu, Y.; Meersmans, J.; Li, H.; Wu, J. Applying ant colony algorithm to identify ecological security patterns in megacities. Environ. Model. Softw. 2019, 117, 214–222. [Google Scholar] [CrossRef]
  48. Pingjiang, Z.; Guofeng, D. Construction of ecological security pattern of Tao River Basin based on MCR model and ant colony algorithm. Ecol. Environ. 2023, 32, 481. [Google Scholar]
  49. Qiu, X.; He, W.; Zheng, S. Research on ecological network identification in rare animal habitats based on the MaxEnt model and ant colony algorithm: A case study of Giant Panda National Park, China. Biodivers. Conserv. 2024, 33, 3207–3229. [Google Scholar] [CrossRef]
  50. Tan, K.W. A greenway network for Singapore. Landsc. Urban Plan. 2006, 76, 45–66. [Google Scholar] [CrossRef]
  51. Zhang, Z.; Meerow, S.; Newell, J.P.; Lindquist, M. Enhancing landscape connectivity through multifunctional green infrastructure corridor modeling and design. Urban For. Urban Green. 2019, 38, 305–317. [Google Scholar] [CrossRef]
  52. Liu, Y.F.; Liang, J.Y.; Huang, Z.W.; Nan, J.N. Study on the Translations and Changes of the Image of Mountains and Waters in Zhuge Village in Zhejiang Province. Chin. Landsc. Archit. 2020, 36, 63–67. [Google Scholar]
  53. Zhou, G.; Huan, Y.; Wang, L.; Lan, Y.; Liang, T.; Shi, B.; Zhang, Q. Linking ecosystem services and circuit theory to identify priority conservation and restoration areas from an ecological network perspective. Sci. Total Environ. 2023, 873, 162261. [Google Scholar] [CrossRef]
  54. Li, J.; Sun, W.; Li, M.; Meng, L. Coupling coordination degree of production, living and ecological spaces and its influencing factors in the Yellow River Basin. J. Clean. Prod. 2021, 298, 126803. [Google Scholar] [CrossRef]
  55. Jieling, L.; Ling, Z.; Hui, F. Construction of wetland ecological network based on MSPA-Conefor-MCR: A case study of Haikou City. Ecol. Indic. 2024, 166, 112329. [Google Scholar]
  56. Lu, J.; Wu, X. Research on Urban Greenway Alignment Selection Based on Multisource Data. Sustainability 2022, 14, 12382. [Google Scholar] [CrossRef]
  57. Teng, M.; Wu, C.; Zhou, Z.; Lord, E.; Zheng, Z. Multipurpose greenway planning for changing cities: A framework integrating priorities and a least-cost path model. Landsc. Urban Plan. 2011, 103, 1–14. [Google Scholar] [CrossRef]
  58. Wang, Z.; Huang, W.-J.; Liu-Lastres, B. Impact of user-generated travel posts on travel decisions: A comparative study on Weibo and Xiaohongshu. Ann. Tour. Res. Empir. Insights 2022, 3, 2. [Google Scholar] [CrossRef]
  59. Brown, G.; Kyttä, M. Key issues and research priorities for public participation GIS (PPGIS): A synthesis based on empirical research. Appl. Geogr. 2014, 46, 122–136. [Google Scholar] [CrossRef]
  60. Jang, M.; Kang, C.-D. Urban greenway and compact land use development: A multilevel assessment in Seoul, South Korea. Landsc. Urban Plan. 2015, 143, 160–172. [Google Scholar] [CrossRef]
  61. Hoctor, T.S.; Bohnett, E.; O’Brien, M.; Thompson, E.; Noss, R.; Lockhart, S.; Volk, M.; Carr, P.; Smith, D.J.; Guthrie, J.M.; et al. Refining the Florida ecological greenways network for improved landscape planning and conservation prioritization. Landsc. Ecol 2025, 40, 36. [Google Scholar] [CrossRef]
  62. Effrey, L. Marion, Trail sustainability: A state-of-knowledge review of trail impacts, influential factors, sustainability ratings, and planning and management guidance. J. Environ. Manag. 2023, 340, 117868. [Google Scholar]
  63. Beltrão, M.G.; Gonçalves, C.F.; Brancalion, P.H.; Carmignotto, A.P.; Silveira, L.F.; Galetti Jr, P.M.; Galetti, M. Priority areas and implementation of ecological corridor through forest restoration to safeguard biodiversity. Sci. Rep. 2024, 14, 30837. [Google Scholar] [CrossRef] [PubMed]
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Figure 1. The technology roadmap.
Figure 1. The technology roadmap.
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Figure 2. Location and images of the Jinma Mountain area.
Figure 2. Location and images of the Jinma Mountain area.
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Figure 3. Relationship between NC, NL, and distance thresholds.
Figure 3. Relationship between NC, NL, and distance thresholds.
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Figure 4. Relationship between (EC (IIC)), (EC (PC)), and distance thresholds.
Figure 4. Relationship between (EC (IIC)), (EC (PC)), and distance thresholds.
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Figure 5. Eco-friendly greenways with integrated resistance surfaces.
Figure 5. Eco-friendly greenways with integrated resistance surfaces.
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Figure 6. Eco-based sub-greenway simulation path.
Figure 6. Eco-based sub-greenway simulation path.
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Figure 7. Semantic network analysis results.
Figure 7. Semantic network analysis results.
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Figure 8. Visitor sentiment analysis of important landscape nodes.
Figure 8. Visitor sentiment analysis of important landscape nodes.
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Figure 9. Recreational greenway integrated resistance surface.
Figure 9. Recreational greenway integrated resistance surface.
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Figure 10. Recreational sub-greenway simulation path.
Figure 10. Recreational sub-greenway simulation path.
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Figure 11. Composite greenway network integrated resistance surface.
Figure 11. Composite greenway network integrated resistance surface.
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Figure 12. Composite greenway network simulation path.
Figure 12. Composite greenway network simulation path.
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Figure 13. Composite greenway network.
Figure 13. Composite greenway network.
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Figure 14. Ant colony algorithm results.
Figure 14. Ant colony algorithm results.
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Figure 15. Kunming territorial spatial planning and Jinma Mountain.
Figure 15. Kunming territorial spatial planning and Jinma Mountain.
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Figure 16. Master plan of Golden Horse Mountain greenway network.
Figure 16. Master plan of Golden Horse Mountain greenway network.
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Table 1. Evaluation indicators of ecological sensitivity for ecotype sub-greenways.
Table 1. Evaluation indicators of ecological sensitivity for ecotype sub-greenways.
Criteria Layer (Weights)Index LayerIndicator Calculation
Single factor
(0.5)		Land use Resistance values for various land-use types, as determined by previous studies.
Distance from the main roads in the cityObtain the official urban road vector data and calculate the Euclidean distance from the road to the entire space.
Soil and water loss The Universal Soil Loss Equation (USLE) was used to evaluate the amount of soil erosion, where the rainfall erosion force was calculated according to the Guidelines for Calculation of Soil Loss in Production and Construction Projects (SL773-2018 [41]).
SlopeThe Digital Elevation Model (DEM) data are processed using Geographic Information System (GIS) technology to obtain the slope data of the study area, which is then classified according to the road construction standard.
Elevation DEM data were classified into five height classes using equal interval average classification in GIS.
Fractional Vegetation CoverAfter calculating Fractional Vegetation Cover (FVC) values with Environment for Visualizing Images (ENVI), the ranks were divided using the equal interval mean classification in GIS.
Composite factor (0.5)Ecological Protection Red Lines and Permanent Basic Farmland Protection Red Lines Since relevant studies rarely involve the protection of red line buffer zones, the “within two lines” and “outside two lines” are assigned.
Table 2. Cultural attractiveness evaluation form.
Table 2. Cultural attractiveness evaluation form.
Criteria Layer (Weights)Indicator LayerScore Value
Resource level
(0.45)		National protection, famous historical towns and villages, and traditional villages.5
Provincial protection, temples, and characteristic cultural villages.3
City protection, historical and cultural relics, and general villages.1
Degree of protection
(0.19)		Complete body shape, comprehensive information, or cultural protection is particularly good.5
Complete integrity, with a lack of data or good cultural protection.3
Incomplete body, with a lack of data or general cultural protection.1
Cultural value
(0.29)		Built a long time ago or there are distinctive folk customs.5
Built a long time ago and the characteristic folk customs are widely spread.3
The construction age is not too long or there is a lack of characteristic folk customs.1
Accessibility
(0.07)		Proximity to arterial roads, scenic roads, or village roads with good accessibility.5
Proximity to the village branch road or the village road accessibility is general.3
Poor transportation.1
Table 3. Recreational sub-greenway suitability evaluation indicators.
Table 3. Recreational sub-greenway suitability evaluation indicators.
Criterion Layer (Weight)Index LayerIndicator Calculation
Construction suitability (0.5)SlopeThe suitability is divided according to the “Guidelines for Greenway Planning and Design” and previous studies.
Distance from the roadObtain the existing road data for the hiking path, hiking path, and forest fire path, and calculate the Euclidean distance from the road to the entire space.
Distribution of service facilitiesNuclear density analysis and natural breakpoint methods are used to divide the construction feasibility class of service facilities.
Distance from the residential According to the walking circle radius of 5 min, 10 min, and 5 min, the buffer distances are defined as 300 m, 500 m, 1000 m, and 1500 m, respectively.
Landscape visual suitability (0.5)Patch Richness Density(PRD)The richness density is calculated using the moving window function in Fragstats 4.2.
Shannon’s Diversity Index (SHDI)Shannon’s Diversity Index is calculated by using the moving window function in Fragstats.
Simpson’s Evenness Index (SIEI)The Simpson uniformity index is calculated using the moving window function in Fragstats.
Contagion Index (CONT)The Contagion Index is calculated using the moving window function in Fragstats.
Table 4. Ecotype resistance indicators.
Table 4. Ecotype resistance indicators.
Resistance FactorWeightResistance Level Division and Value Assignment
12345
Land use0.105forestrymeadowwater areacultivated landUrban and rural areas, industrial and residential
Distance from the main road0.024≥1500900~1500600~900300~600<300
Soil and water loss 0.707<0.980.98~3.653.65~8.558.55~16.6916.69~35.76
Slope0.0570~2°2°~10°10°~20°20°~25°≥25°
Elevation 0.0321753~18641864~19761976~20872087~21992199~2311
Vegetation coverage0.075≥0.80.6~0.80.4~0.60.2~0.4<0.2
Table 5. Recreational resistance indicators.
Table 5. Recreational resistance indicators.
IndexResistance FactorWeight Suitable Grade
12345
Construction suitability evaluationFalling gradient/%0.011 >8%5%~8%3%~5%2.5%~3%<2.5%
Distance from the road0.025 ≥160120~16080~12040~80<40
Distribution of service facilities0.933 <1.521.52~5.055.05~9.629.62~14.76>14.76
Distance from the residential point0.031 >15001000~1500500~1000300~500<300
Evaluation of landscape visual suitabilityCONTAG0.197<0.10.1~0.40.4~0.70.7~0.9≥0.9
PRD0.247<0.20.2~0.40.4~0.60.6~0.8≥0.8
SHDI0.278<0.20.2~0.30.3~0.4, ≥0.90.4~0.6, 0.7~0.90.6~0.7
SIEI0.278<0.20.2~0.40.4~0.70.6~0.8≥0.8
Table 6. Results of the structural index of the three types of greenway networks.
Table 6. Results of the structural index of the three types of greenway networks.
Greenway Typeα Indexβ Indexγ Index
Ecotype sub-greenway0.57 3.00 0.75
Recreational sub-greenway0.53 3.00 0.71
Composite greenway network0.62 3.06 0.76
Table 7. Indicators and weightings of ecological and recreational functions of the greenway network.
Table 7. Indicators and weightings of ecological and recreational functions of the greenway network.
Criterion Layer (Weight)Index LayerIndicator AcquisitionWeight
Ecology function
(0.5)		Ecological environment qualityRSEI 0.545
Soil and water loss USLE 0.296
Climate regulation Ecosystem service 0.091
BiodiversityFunction value equivalent0.068
Recreation function
(0.5)		Landscape visual qualityLandscape visual evaluation0.490
Convenience of service Nuclear density of service facilities0.309
Recreation facilities accessibilityDistance from the residential point0.137
Number of cultural recreation resourcesNumber of cultural recreation points0.064
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Wei, W.; Yang, A.; Jiang, L.; Lawson, G.; Lei, W. Ecological Recreation Across the Jinma Mountain Region: A Comprehensive Evaluation of Suburban Mountain Greenway Networks. Land 2025, 14, 1532. https://doi.org/10.3390/land14081532

AMA Style

Wei W, Yang A, Jiang L, Lawson G, Lei W. Ecological Recreation Across the Jinma Mountain Region: A Comprehensive Evaluation of Suburban Mountain Greenway Networks. Land. 2025; 14(8):1532. https://doi.org/10.3390/land14081532

Chicago/Turabian Style

Wei, Wen, Ao Yang, Lanxi Jiang, Gillian Lawson, and Wen Lei. 2025. "Ecological Recreation Across the Jinma Mountain Region: A Comprehensive Evaluation of Suburban Mountain Greenway Networks" Land 14, no. 8: 1532. https://doi.org/10.3390/land14081532

APA Style

Wei, W., Yang, A., Jiang, L., Lawson, G., & Lei, W. (2025). Ecological Recreation Across the Jinma Mountain Region: A Comprehensive Evaluation of Suburban Mountain Greenway Networks. Land, 14(8), 1532. https://doi.org/10.3390/land14081532

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