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Article

How to Coordinate Urban Ecological Networks and Street Green Space Construction? Insights from a Multi-Scale Perspective

by
Shujun Hou
1,
Ying Yu
2,
Taeyeol Jung
1,* and
Xin Han
3,*
1
Department of Landscape Architecture, Kyungpook National University, Daegu 41566, Republic of Korea
2
Department of Landscape Architecture, Shandong Agricultural University, Tai’an 271018, China
3
College of Landscape Architecture, Zhejiang A&F University, Hangzhou 311300, China
*
Authors to whom correspondence should be addressed.
Land 2025, 14(1), 26; https://doi.org/10.3390/land14010026
Submission received: 19 November 2024 / Revised: 15 December 2024 / Accepted: 24 December 2024 / Published: 26 December 2024
(This article belongs to the Topic Biophilic Cities and Communities: Human-Environment Interaction and Sustainable Governance)
Browse Figures
Figure 1
<p>Location and land use of the study area.</p> "> Figure 2
<p>Research framework and technical route.</p> "> Figure 3
<p>Spatial characteristics of each resistance surface. (<b>a</b>) habitat quality; (<b>b</b>) NDVI; (<b>c</b>) distance from rivers; (<b>d</b>) land use type; (<b>e</b>) distance from railroads; (<b>f</b>) distance from roads.</p> "> Figure 4
<p>The process of collecting and calculating the GVI of streets. (<b>a</b>) The process of collecting street view images; (<b>b</b>) Image segmentation process of street view images.</p> "> Figure 5
<p>Landscape classification of MSPA. (<b>a</b>–<b>c</b>) Examples from the northern, western, and southeastern regions.</p> "> Figure 6
<p>Selection of ecological source areas. Numbers indicate extracted ecological source areas.</p> "> Figure 7
<p>Integrated ecological resistance surfaces.</p> "> Figure 8
<p>Ecological corridors and important levels. Numbers indicate extracted ecological source areas.</p> "> Figure 9
<p>Pinch point identification. Numbers indicate extracted ecological source areas.</p> "> Figure 10
<p>Barrier point identification. Numbers indicate extracted ecological source areas.</p> "> Figure 11
<p>Spatial distribution of pinch points and Barrier points. Numbers indicate extracted ecological source areas. (<b>a</b>–<b>d</b>) Typical examples of ecological pinch points, mainly located in forested areas along urban rivers; (<b>e</b>–<b>h</b>) Typical examples of ecological barrier points, predominantly found in densely built-up areas and road intersections.</p> "> Figure 12
<p>GVI distribution of Street Sites.</p> "> Figure 13
<p>GVI distribution at the subdistrict level.</p> "> Figure 14
<p>GVI hot and cold spots analysis. (<b>a</b>,<b>b</b>) Examples of hot spot areas near parks and universities; (<b>c</b>,<b>d</b>) Examples of cold spot areas near commercial streets and transport hubs.</p> "> Figure 15
<p>Spatial correlation analysis of GVI and ecological resistance values: (<b>a</b>) Global Moran’s I for GVI; (<b>b</b>) Global Moran’s I for ecological resistance; (<b>c</b>) Global Moran’s I for ecological resistance and GVI; (<b>d</b>) Local autocorrelation analysis of GVI; (<b>e</b>) Local autocorrelation analysis of ecological resistance; (<b>f</b>) Local autocorrelation analysis of GVI and ecological resistance; (<b>g</b>) Identification of priority restoration and conservation areas.</p> "> Figure A1
<p>Comparison of edge width analysis results at 30 m, 60 m, and 90 m in MSPA analysis.</p> ">
Versions Notes

Abstract

:
Rapid socio-economic development and imbalanced ecosystem conservation have heightened the risk of species extinction, reduced urban climate adaptability, and threatened human health and well-being. Constructing ecological green space networks is an effective strategy for maintaining urban ecological security. However, most studies have primarily addressed biodiversity needs, with limited focus on coordinating street spaces in human settlement planning. This study examines the area within Chengdu’s Third Ring Road, employing the following methodologies: (1) constructing the regional ecological network using Morphological Spatial Pattern Analysis (MSPA), the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, and circuit theory; (2) analyzing the street green view index (GVI) through machine learning semantic segmentation techniques; and (3) identifying key areas for the coordinated development of urban ecological networks and street green spaces using bivariate spatial correlation analysis. The results showed that (1) Chengdu’s Third Ring Road exhibits high ecological landscape fragmentation, with 41 key ecological sources and 94 corridors identified. Ecological pinch points were located near urban rivers and surrounding woodlands, while ecological barrier points were concentrated in areas with dense buildings and complex transportation networks. (2) Higher street GVI values were observed around university campuses, urban parks, and river-adjacent streets, while lower GVI values were found near commercial areas and transportation hubs. (3) To coordinate the construction of ecological networks and street green spaces, the central area of the First Ring Road and the northwestern region of the Second and Third Ring Roads were identified as priority restoration areas, while the northern, western, and southeastern areas of the Second and Third Ring Roads were designated as priority protection areas. This study adopts a multi-scale spatial perspective to identify priority areas for protection and restoration, aiming to coordinate the construction of urban ecological networks and street green spaces and provide new insights for advancing ecological civilization in high-density urban areas.

1. Introduction

Rapid urban expansion, accompanied by a significant increase in high-density buildings and impermeable surfaces, has led to habitat fragmentation, obstructed migration paths for species, and a sharp decline in regional biodiversity [1,2]. At the same time, under the influence of the climate crisis, urban heat island effects and flooding disasters have become more frequent, posing challenges to human health and the sustainable development of urban living environments [3,4,5]. Therefore, how to integrate nature into the urban environment and guarantee regional ecological security by coordinating the construction of ecological green space networks in multi-scale spaces has become a key issue in global green development strategies and high-quality urban renewal [6,7,8].
In previous studies, the methods and techniques for constructing ecological networks have become relatively well-established. The prevalent research framework primarily involves “identifying ecological sources—constructing resistance surfaces—extracting ecological corridors” [9,10]. Early studies on ecological source identification prioritized the direct selection of large habitat patches, including nature reserves and forest parks [3]. Subsequently, the approach evolved to optimize source selection through quantitative assessments of the connectivity and importance of ecological patches [11,12,13]. In constructing resistance surfaces, most studies employed indicator weighting methods based on land use, natural environmental features, and anthropogenic characteristics [14,15,16]. Additionally, some studies incorporated the InVEST model to evaluate the integration of habitat quality assessment with resistance values of ecological resistance surfaces, while considering the spatial variability of habitat quality across the study area based on land use [17,18]. Ecological corridors are primarily analyzed using approaches such as graph theory [19,20], ant colony model [21,22], least-cost path models [10,14,15], and circuit theory [17,23,24]. The results of related research show that circuit theory can not only identify the optimal paths of ecological corridors, but also identify all possible paths for species to move between habitats, and can identify the critical locations of ecological corridors based on the intensity of current [25,26]. While the aforementioned methods have yielded significant progress in macro-scale ecological network research, they encounter several limitations in high-density urban areas: (1) Traditional administrative unit divisions often struggle to capture the integrity of habitats within the study area and the representativeness of regional socio-economic characteristics [1,16] (2) Ecological network construction predominantly focuses on species survival needs, often neglecting the critical role of improving human habitats [27]; (3) Multi-scale analysis tends to prioritize the superimposition of large-scale areas, while lacking detailed studies on the fine-scale coordination of urban green spaces, such as those at the street level.
Furthermore, given the above limitations and the constrained land resources in high-density urban areas, expanding green space on a large scale presents significant challenges in the future [28]. Urban streets, as small-scale spaces, are vital components of urban green space networks [29,30,31]. Related studies suggest that the establishment of street green space networks not only covers a wide area but also provides ecological services and enhances human quality of life [32,33,34]. GVI refers to the proportion of green vegetation visible within a person’s field of view [35]. Studies have shown that GVI is a key indicator for assessing street green space quality, and its applicability in urban center areas surpasses that of the traditional Normalized Difference Vegetation Index (NDVI) [36,37]. At present, research on GVI at the urban street level primarily focuses on evaluating urban greening levels, its impacts on human health, and analyses of social equity in the urban environment [36,38,39]. In studies related to ecological networks, some researchers have proposed incorporating GVI as an indicator of the human environment when constructing integrated ecological resistance surfaces, reflecting the greening levels of streets in high-density urban areas [1]. Moreover, with the rapid development of remote sensing and street-view image technologies, obtaining GVI has become more efficient and precise [40,41,42]. Using artificial intelligence and deep learning technologies, large-scale street-view images can be processed quickly, enabling quantitative assessment of urban street GVI [43,44,45,46]. However, how to introduce street greening into the ecological network building framework and identify key areas where the two can be synergized still needs to be further explored.
Against this backdrop, considering that Chengdu is one of China’s pioneering demonstration areas for “Park City” construction, how to better achieve sustainable development of the urban ecological environment and human habitats remains a critical question for further exploration. Therefore, this study focuses on the high-density urban area within Chengdu’s Third Ring Road and proposes a method to coordinate the construction of urban ecological networks and street green space networks from a multi-scale perspective. The specific research objectives are: (1) to identify potential and core ecological sources within the region, as well as ecological corridors and their associated ecological pinch points and barrier points through the construction of ecological networks; (2) to identify areas with higher and lower greening levels within the street green space network, based on human visual perception; (3) to explore the correlation between the ecological network and street green spaces and identify key areas for their coordinated development, aiming to provide scientific reference for prioritizing protection and restoration schemes in the ecological civilization construction of high-density urban areas.

2. Material and Methods

2.1. Study Area

Chengdu is an important national central city in Western China, located between 102°54′ E and 104°53′ E longitude and 30°05′ N and 31°26′ N latitude, with a permanent population of 21.19 million. Situated in the western part of the Sichuan Basin, Chengdu lies in a subtropical humid region with a mild climate, abundant rainfall, and rich biological resources. In 2018, Chinese President Xi Jinping first proposed the concept of “park city” in Chengdu. Subsequently, the Chengdu Municipal Government carried out a series of planning measures to promote the construction of park city demonstration zones, with an emphasis on the development of urban ecological civilization and the integration of urban landscape with park forms.
The “Chengdu Park City Green Space System Planning (2019–2035)” and the “Chengdu Park City Street Integration Design Guidelines” have been published, providing relevant planning guidelines. The area within Chengdu’s Third Ring Road encompasses the central regions of five districts, with a total area of approximately 190 km2 [47]. This region is the most economically developed area but faces challenges such as limited green space land resources, complex transportation networks, and difficulties in coordinating ecological and green space networks. Figure 1 shows the location and land use of the study area.

2.2. Data Source

This study utilizes multiple data sources to ensure the accuracy and reliability of the analysis. Detailed information about the data is as follows: (1) The 2020 land cover data were obtained from ESA WorldCover (https://esa-worldcover.org/en, accessed on 20 June 2024) with a spatial resolution of 10 m × 10 m. ESA WorldCover provides globally consistent and highly detailed land cover classification data, which are crucial for identifying ecological sources and assessing habitat quality in high-density urban areas. (2) The 2020 NDVI was calculated using the Sentinel-2 dataset on the Google Earth Engine remote sensing cloud platform (https://earthengine.google.com, accessed on 20 June 2024), with a spatial resolution of 10 m × 10 m. (3) Vector data for roads, railways, and rivers were obtained from the OpenStreetMap website (https://www.openstreetmap.org/en, accessed on 23 May 2024). OpenStreetMap provides comprehensive and detailed vector data on infrastructure elements such as roads, railways, and rivers, which are critical for constructing ecological resistance surfaces and understanding ecological barriers in the study area. (4) Street-view imagery was obtained from the Baidu Maps platform (https://map.baidu.com, accessed on 23 May 2024). The street-view imagery was used to calculate the GVI, providing a detailed perspective on street greening levels. Baidu Maps provides extensive high-resolution street-view imagery with substantial coverage in Chinese cities, making it well-suited for studying the urban environment in Chengdu.

2.3. Research Framework

The methodological framework of this study consists of three stages, as shown in Figure 2. The specific research process is as follows: First, (1) Extract the core area through MSPA, and identify the important ecological source areas in combination with landscape connectivity index analysis. (2) Combine the ecological resistance factors to construct an integrated ecological resistance surface. (3) The ecological corridors are extracted based on circuit theory, and the importance of ecological corridors, ecological pinch points, and barrier points are recognized using Centrality Mapper, Pinch Point Mapper, and Barrier Mapper. In the second stage, (1) Evaluate street GVI using street view image acquisition and semantic analysis techniques. (2) Analyze the hot and cold spot areas of GVI in the street green space network by Getis-Ord Gi* statistics implemented in ArcGIS 10.8. In the third stage, based on the previous two stages, bivariate spatial autocorrelation analyses of ecological resistance surfaces and street GVI were conducted to identify priority protection and restoration areas for coordinating the development of the regional ecological network and the street green space network.

2.4. Ecological Source Areas Identification

2.4.1. MSPA

MSPA is a classification process based on mathematical morphology that classifies raster images into seven landscape categories, Core, Islet, Loop, Bridge, Perforation, Edge and Branch, and Background [24,48]. (1) Using ArcGIS 10.8, the land cover maps of areas within Chengdu’s Third Ring Road were reclassified. Tree cover, shrubland, grassland, and water bodies with ecological functions were extracted as the foreground for MSPA analysis and assigned a value of 2; other land types were designated as background and assigned a value of 1, thus obtaining the binary raster image.
In MSPA analysis, the parameter settings for different grain sizes and edge widths affect the spatial patterns of different ecological patches. In this study, based on the size of the study area, by comparing the analysis results of edge widths of 1 (30 m), 2 (60 m), and 3 (90 m), we found that an edge width of 1 allowed for more accurate identification of rivers, water systems, and fine patches in the study area (Appendix A.1). Therefore, we set the edge width parameter of the raster data to 1 and used the eight-neighborhood analysis method in Guidos Toolbox 3.0 to obtain seven types of landscape elements.

2.4.2. Landscape Connectivity Evaluation

Landscape connectivity refers to the degree of spatial connection and mutual influence between two patches of the same type, which is essential for constructing landscape ecological security patterns, protecting biodiversity, and maintaining ecosystem stability [14,49,50]. This study calculated the Integral Index of Connectivity (IIC), the Probability of Connectivity (PC), the Importance Index (dI), and the Relative Importance Index ( d I ′ )of landscape patches [31].
I I C = ∑ i = 1 n ∑ j = 1 n a i ⋅ a j 1 + n I i j A L 2
P C = ∑ i = 1 n ∑ j = 1 n a i × a j × P i j A L 2
d I = I − I r e m o v e I × 100 %
d I ′ = 0.5 × d I I C + 0.5 × d P C
where n represents the total number of patches in the landscape, a i and a j represent the area of patches i, j, respectively, n I i j represents the number of connections between patches i, j, P i j represents the maximum likelihood of species migration and dispersal between patches i, j, and A L 2 is the total area of landscape patches in the study area. I is the connectivity index value for IIC and PC, I r e m o v e is the landscape connectivity index after removing patch i; d I ′ is the relative importance value of d I .

2.5. Analysis of Ecological Resistance Surfaces

2.5.1. Habitat Quality Assessment

Habitat quality refers to the suitability of an ecosystem to provide a viable living environment for species within a specific area [51,52]. This study employed the habitat quality assessment module of the InVEST model to quantify habitat quality in the study area based on land use data and threat factor attributes. The specific analysis process included the following steps: (1) Identifying land use types and their habitat suitability, while recognizing major threat factors (e.g., cropland, built-up, and bare land) and defining their maximum impact distance, weights, and decay types. (2) Calculating habitat degradation by considering the spatial influence, sensitivity, and weights of threat factors on each grid cell to generate a habitat degradation map. (3) Integrating habitat suitability to calculate the habitat quality index and generate a habitat quality map, providing a visual representation of the ecological health of the study area. Detailed parameter settings are provided in Appendix A.2.

2.5.2. Ecological Resistance Factors

This study, considering the characteristics of high-density urban areas, selected six resistance factors for constructing the ecological resistance surface: habitat quality, NDVI, distance from rivers, land use type, and distances from railways and roads, accounting for the combined influence of natural and human environmental factors [1,3,10,17,18]. Referring to previous studies and expert consultations, a 1–9 scale was used to score each resistance factor, with 9 representing the highest resistance value and 1 the lowest [53]. As shown in Figure 3, habitat quality was selected as a resistance factor for the natural environment. Higher habitat quality corresponded to lower resistance values (1 and 3), while lower quality was associated with higher resistance values (7 and 9), with 5 as the intermediate value. For the human environment, the ecological resistance surface was significantly influenced by urban transportation networks [3,4,54]. The closer to railways or roads, the higher the resistance value, and the farther away, the lower the resistance value. Subsequently, the weights of each resistance factor were determined using the Analytic Hierarchy Process (AHP) via Yaahp software (version 10.1), followed by a consistency check (Appendix A.3).

2.6. Analysis of Ecological Corridors Based on Circuit Theory

Circuit theory’s assessment of ecological network connectivity is based on stochastic wandering theory, which combines physics-based circuit theory with behavioral ecology. This allows for effective modeling of the paths of random migration and the probability of population dispersal for biological species [25,26,55]. In this study, ecological corridors within the study area were identified using the Linkage Mapper plugin in ArcGIS to construct ecological networks. The connectivity of the ecological network was analyzed in conjunction with Circuitscape software (version 4.0.5), which included a total of three models [23,56,57]. (1) Calculate the centrality of ecological corridors using Centrality Mapper. The centrality was calculated by simulating the electrical flow during the migration of biological species to assess the importance of nodes and the path of least resistance to the connectivity of the entire ecological network. The importance level of ecological corridors can be judged based on the magnitude of the centrality value. (2) Calculate ecological corridors’ pinch point zones using Pinch point Mapper. Ecological pinch points are locations where currents are most concentrated in the migratory pathways of modeled biological species, which can be used to assess key locations in ecological networks that affect corridor connectivity [1]. (3) Calculate barrier point areas of ecological corridors using Barrier Mapper. Barrier point areas are regions where biological species are hindered in the migration process. Identifying barrier points in ecological corridors plays an important role in restoring the connectivity of ecological networks [58].

2.7. Construction of Street Green Space Network Based on GVI Measurement

2.7.1. Street View Data Preprocessing

In order to measure the condition of the street green space network in the study area, this study collected street images from the Baidu map platform for the area within the Third Ring Road in Chengdu. ArcGIS was used to set up collection points for the road network data at 50 m intervals. Based on human visual perception, parameters for street-view images at various angles were configured using Python3.8.5 and the Baidu Maps API (http://api.map.baidu.com/panorama/v2, accessed on 23 May 2024). High-resolution street-view images (900 × 600 pixels) were retrieved via HTTP URLs, resulting in a total of 341,196 images. After merging images and removing those that were blurred or heavily occluded—85,299 images were retained. The workflow for street-view image acquisition is illustrated in Figure 4a.

2.7.2. Calculation of GVI of Streets

This study employed the SegNet algorithm for semantic segmentation of street-view images. SegNet, based on an encoder-decoder architecture, extracts high-dimensional features via the encoder and reconstructs feature maps using the decoder, producing pixel-wise segmentation results [59,60], as shown in Figure 4b. Referring to related studies [61], the SegNet model was pre-trained on the ADE20K dataset, which contains 150 urban environmental elements, providing diverse support for the model’s adaptability to various environments [44]. The model achieved a pixel accuracy of 81.44% on the training set and 66.83% on the test set, demonstrating robust segmentation performance and reliability. Related research highlights that street-view images are a valuable data source for analyzing urban GVI and have broad applicability [36,41,43]. By applying high-precision semantic segmentation algorithms, street-view images can be processed to accurately identify green vegetation, facilitating the precise calculation of GVI, which reflects street greening levels.
In this study, the percentage of green plant elements (trees, grass) in the image is obtained through street view image segmentation using the following formula:
G r e e n   V i e w   i n d e x = ∑ i = 1 4 G r e e n   s p a c e   p i x e l s i ∑ i = 1 4 T o t a l   p i x e l s i
Here, GVI represents the proportion of green plant pixels in the total pixel area of the image acquired from four different directions, and i denotes the number of images.

2.7.3. Getis-Ord Gi* Statistics

Getis-Ord Gi* statistical analysis, as a method for testing local spatial autocorrelation in geospatial data, effectively identifies the spatial distribution characteristics of high-value and low-value aggregations in the sample data [62,63]. This study employs Getis-Ord Gi* analysis to identify statistically significant high-value clusters (hot spots) and low-value clusters (cold spots) in the spatial distribution of street GVI.
G i * = ∑ j = 1 n ω i , j x j − X ¯ ∑ j = 1 n ω i , j n ∑ j = 1 n ω i , j 2 − ∑ j = 1 n ω i , j 2 S n − 1
where x j represents the amount of change in the green visibility of spatial unit j, ω i , j represents the binary spatial weight matrix, n represents the number of spatial units, and the higher the z-score, the higher the   G i * index, and the more tightly clustered the hot spot areas; conversely, the lower the score, the more densely clustered the cold spot areas.

2.8. Bivariate Spatial Autocorrelation Analysis

Bivariate spatial autocorrelation analysis is a method used to identify the correlation between two variables in terms of geographic location [64]. In this study, ecological resistance values were selected to evaluate the urban ecological network, while GVI was chosen as the variable to assess the street green space network. The analysis was conducted in two steps: (1) Moran’s I was used to assess global spatial autocorrelation, with values ranging from −1 to 1, where negative values indicate negative spatial correlation, positive values indicate positive spatial correlation, and values near zero suggest weak or no correlation; (2) local spatial autocorrelation was analyzed to identify spatial relationships at finer scales, classifying regions into four patterns: high–high, low–low, high–low, and low–high.
This method provides a comprehensive understanding of the spatial relationship between urban ecological and street green space networks, offering macroscopic insights into overall spatial correlations and microscopic identification of key areas for ecological planning and green space optimization.

3. Results

3.1. Construction of Regional Ecological Networks

3.1.1. Ecological Source Areas

The results of the MSPA analysis are shown in Table 1 and Figure 5. The land area of ecological landscape elements within the Third Ring Road of Chengdu City is 28.77 km2, accounting for only 14.86% of the total area of the Third Ring Road. The core area constitutes the largest proportion, covering 10.33 km2 or 35.89%. The core area is mainly distributed in the north and west, with relatively concentrated woodlands and waters in the southeast, fewer in the center and northeast, and more scattered, smaller areas in the southwest. As a buffer zone connecting the core area with the outside, the edge area is the second largest ecological landscape element, accounting for 32.35%. The other five types of landscape elements are characterized by a highly fragmented distribution. It can be seen that the distribution of core areas within the overall area is uneven and highly fragmented, with poor connectivity between ecological patches.
Considering the serious fragmentation of ecological habitats in high-density urban areas, two principles for the selection of important ecological source areas were established through a literature review and expert assessment: (1) Prioritize areas with large size and high landscape connectivity; (2) Use existing urban park green spaces as benchmarks, as they can provide recreational areas where humans can connect with nature while also serving as habitats for small wildlife such as birds, squirrels, and frogs. Therefore, in this study, 51 core areas with an area greater than 0.03 km2 were selected as potential ecological source areas within the Third Ring Road of Chengdu City (Figure 6).
On this basis, 41 potential ecological source areas with d I ′   ≥ 1 were selected as important ecological source areas by calculating the landscape connectivity between potential ecological source areas, and the important ecological source areas were ranked according to the d I ′ value. The d I ′ value of ecological patch No. 1 was the largest at 21.50, and the d I ′ value of ecological patch No. 41 was the smallest at 1.09. Statistically, the important ecological source areas in Chengdu City within the Third Ring Road mainly include urban rivers and the surrounding green space areas, urban parks, scenic spots, street greenspaces, and green spaces of university campuses, etc. (Appendix A.4).

3.1.2. Building Ecological Networks and Identifying the Importance of Corridors

In this study, ecological corridors were extracted by calculating the least-cost paths that integrate ecological source areas with ecological resistance surfaces, thereby constructing an urban ecological network. As shown in Figure 7, the value of the integrated ecological resistance surface is 2.14–9 by calculating the ecological resistance factors of natural and artificial environments. Areas with high ecological resistance values are concentrated in buildings and areas with dense transportation networks such as railroads and road loops.
Ecological corridors serve as vital channels for biological migration, connecting relatively isolated ecological source areas within the city, which is crucial for the establishment of urban ecological networks [1]. In this study, a total of 94 ecological corridors were identified, with an average length of 1.85 km and a cumulative length of 173.62 km, utilizing the Linkage Pathways Tool within the Linkage Mapper plugin. The shortest ecological corridor measured only 0.27 km, while the longest extended to 7.83 km, indicating significant variability in the lengths of ecological corridors across the region. Furthermore, despite the proximity of certain ecological source areas, some exhibited a lack of connectivity in terms of ecological functionality.
To flexibly adapt to the complexity of urban environments, this study classifies ecological corridors into three levels based on their centrality calculations. Previous studies have indicated that ecological corridors with higher centrality values are more crucial for the overall connectivity of the ecological network [17,23,24]. As shown in Figure 8, there are 21 first-level ecological corridors, with corridor lengths ranging from 0.03 to 5.47 km, with an average length of 2.02 km, and a total length of 42.37 km, accounting for 24.4% of the total ecological corridor length. These corridors are predominantly located in areas where ecological patches, such as urban green spaces and rivers, are concentrated, playing a vital role in facilitating biological migration and maintaining biodiversity. There are 43 s-level ecological corridors, with lengths ranging from 0.04 to 7.83 km, an average length of 2.08 km, and a total length of 89.31 km, representing 51.43% of the total ecological corridor length. Secondary corridors are longer and more numerous than primary corridors, providing auxiliary and supportive functions. There are 30 third-level ecological corridors, with corridor lengths ranging from 0.03 to 6.62 km, with an average length of 1.38 km, and a total length of 41.92 km, accounting for 24.14% of the total ecological corridor length. Tertiary ecological corridors exhibit a lower current density compared to primary and secondary corridors, indicating weaker connectivity between ecological patches; however, they still play an important role in the overall connectivity of the ecological network’s structure and function.

3.1.3. Analysis of Pinch Points and Barrier Points in Ecological Networks

In this study, ecological pinch points and barrier points were extracted in ArcGIS according to the five-level classification of the natural breakpoint method, selecting the class with the highest current density value as areas prioritized for protection and restoration. The overall current density value of ecological pinch points in the area within the Third Ring Road of Chengdu City was 0–0.23, and the cumulative current recovery value of ecological barrier points was 0–15.6 (Figure 9 and Figure 10). Among these, ecological pinch points are relatively clustered within the first-level ecological corridor between ecological source areas No. 32 (Chengdu Youth Activity Center) and No. 36 (Chenghua Park), as well as in the first-level ecological corridor between ecological source areas No. 36 and No. 16 (green area surrounding the confluence of the Foo River and the South River). The distribution of ecological barrier points is more decentralized, primarily concentrated in secondary and tertiary ecological corridors. An analysis combining land use type ratios with satellite imagery indicates that ecological pinch points are predominantly located in forested areas, especially along urban rivers such as the South River, Foo River, and Shah River. In contrast, ecological barriers are mainly found in built-up areas, particularly in densely constructed zones such as residential districts and at intersections of primary roads (Figure 11 and Table 2).

3.2. Construction of Urban Street Green Space Network

3.2.1. Measurement of GVI on Streets

The values of GVI sample points within the Third Ring Road of Chengdu City range from 0.0% to 89.62%, with a mean value of 22.55% and a standard deviation of 16.50%, derived from the collection and calculation of street view images (Figure 12). The GVI values were categorized into five levels according to the evaluation criteria proposed by Japanese scholar Orihara. When GVI < 5% the street greening perceivable by human sight is evaluated as very poor; 5~15% greening is evaluated as poor; 15~25% greening is evaluated as ordinary; 25~35% greening is evaluated as good, and >35% greening can be perceived as high level, which gives a more comfortable feeling and is evaluated as very good [65]. According to statistics, the proportion of GVI evaluation levels from low to high within Chengdu’s Third Ring Road are as follows: 15.05%, 24.04%, 21.42%, 17.30%, and 22.18%. Among these, areas rated as “poor” constitute the largest proportion, while those rated as “very good” rank second. This indicates that the spatial distribution of GVI within the study area exhibits significant variation, and the overall greening level still requires improvement.

3.2.2. Analysis of Cold and Hot Spots in the Street Green Space Network

In this study, to better assess the connectivity of the urban street green space network, the values of GVI collection points were linked to the urban road network using ArcGIS. The average GVI values for each street in the city were calculated and analyzed through spatial autocorrelation (Figure 13). The results indicate that the global Moran’s I = 0.381 (p = 0.001), suggesting a positive spatial correlation at the street level within the Third Ring Road of Chengdu City, where high GVI values cluster together and low GVI values also cluster together.
Subsequently, an analysis of the hot and cold spots within the street green space network was conducted. As shown in Figure 14, the hot spots of the street green space network within Chengdu’s Third Ring Road are predominantly located in the west, south, northwest, and southwest areas of the city. This phenomenon can be attributed to the fact that many of these hot spot areas are situated near university campuses and urban parks, close to rivers, characterized by favorable natural environments and better green spaces, resulting in relatively high GVI values. Conversely, the cold spots are primarily found in the center, northeast, and southeast of the city, mostly near commercial streets and urban transportation hubs. These areas are densely populated with commercial facilities, have high building densities, and exhibit complicated traffic conditions, leading to relatively low GVI values.

3.3. Assessment of Spatial Relevance of Regional Ecological and Street Green Space Network

To enhance the multi-scale spatial overlay analysis of urban ecological and street green space networks, ecological resistance and GVI data were processed into a 250 m × 250 m grid format within ArcGIS. This grid size was selected based on the spatial distribution characteristics of ecological patches ensuring that the analysis accurately reflects the ecological and green space network conditions at an appropriate spatial scale [66].
Global Moran’s I analysis indicated positive spatial autocorrelation for both street GVI (0.351, p = 0.001) and ecological resistance (0.403, p = 0.001) (Figure 15a,b). Local spatial autocorrelation (LISA) further identified high–high and low–low clusters for both street GVI and ecological resistance. Results revealed that areas with high ecological resistance values were primarily concentrated within the First and Second Ring Roads, with smaller clusters in the northern and southern areas within the Third Ring Road. Low ecological resistance values were mainly concentrated between the Second and Third Ring Roads, with relative clustering in the southwestern and southeastern regions. The spatial distribution of high and low GVI values aligned with the hot and cold spots identified in the street-level GVI analysis, indicating that the street GVI pattern effectively reflects the condition of urban street green space networks.
Bivariate spatial autocorrelation analysis between street GVI and ecological resistance (Global Moran’s I = −0.181, p = 0.001) revealed a degree of negative spatial correlation (Figure 15c), indicating that areas with high GVI values were often adjacent to areas with low ecological resistance, and vice versa. Based on the sample size percentage statistics (excluding grids in insignificant areas), local bivariate analysis further identified key areas for prioritized restoration and protection: (1) Areas with low GVI values and high urban ecological resistance values were the most extensively distributed and concentrated, accounting for 37.28% of the total grids; (2) Grids in areas with high GVI values and low ecological resistance values accounted for 27.20%; (3) Grids in the remaining areas, where high GVI values and high ecological resistance values, as well as low GVI values and low ecological resistance values, were concentrated, accounted for 20.82% and 14.69%, respectively (Figure 15f).
Building on the findings above, this study aims to identify critical areas for the collaborative construction of urban ecological and street green space networks. (1) Regions with low GVI and high ecological resistance, prioritized for restoration, are mainly concentrated in the central areas within the First Ring Road, as well as the northwestern areas between the Second and Third Ring Roads. These regions typically include commercial centers, residential zones, and areas with complex traffic infrastructure, such as primary roads and elevated highways (blue areas in Figure 15g). (2) Regions with high GVI and low ecological resistance, identified as key areas for future conservation, are primarily located in the northern, western, and southeastern parts between the Second and Third Ring Roads, with fewer occurrences within the First Ring Road. These areas are often situated near urban ecological sources, including urban parks and green spaces along rivers (red areas in Figure 15g).

4. Discussion

4.1. Synergistic Construction of Regional Ecological Network and Street Green Space Network

This study aims to explore strategies for coordinating regional ecological networks and street green space construction in high-density urban areas from a multi-scale spatial perspective, and to identify priority areas for ecological green space protection and restoration through spatial correlation analysis. The findings reveal that areas with high ecological resistance values in ecological network construction are primarily located in densely built-up and traffic-complex zones, where street green visibility also exhibits significant low-value clustering. This observation aligns with related studies, which demonstrate that the connectivity of ecological networks is often negatively influenced by traffic infrastructure, manifesting as fragmented ecological corridors and increased resistance to species migration [67,68].
Furthermore, previous research highlights the ecological benefits of street-level green spaces, which not only support human health and well-being but also act as ecological corridors connecting larger green spaces within and outside the city [6,8,30,69]. These spaces facilitate the exchange of ecosystem materials and energy, strengthen the connectivity of landscape patches, and serve as habitats for small bird, insect, and plant populations [34,69,70]. These findings highlight that in high-density urban areas with limited green space resources, enhancing street greening levels in regions with low ecological resistance values can significantly improve the connectivity of urban ecological-green space networks. Such efforts enhance urban resilience to the global climate crisis and support the achievement of the Sustainable Development Goals [7,71].
Building on these findings, this study proposes a conceptual framework for the synergistic construction of regional ecological networks and street green space networks, integrating MSPA, the InVEST model, circuit theory, and streetscape image analysis. This framework advances the theoretical understanding of the spatial integration of ecological networks and urban green spaces by: (1) identifying future research opportunities for coordinating ecological conservation and human settlements construction from a multi-scale perspective; (2) emphasizing the critical role of street green space network construction in addressing human well-being and species habitat needs within biophilic communities; and (3) offering nature-based solutions for optimizing limited land resources in high-density urban areas.

4.2. Implications for Urban Planning Practice

From the time Ebenezer Howard proposed the concept of the “Garden City” in 1898, which aimed to connect cities with the countryside through the planning of tree-lined avenues and urban green belts to promote the integration of nature into urban development, to the introduction of the “Park City” development concept by the Chinese government in 2018 [29]. Alongside urban modernization, societies have consistently sought solutions to achieve peaceful coexistence between urban environments and ecological nature [72]. Over the years, countries around the world have engaged in extensive research and discussions on this topic, particularly in urban settings. Examples include urban greenway planning, integrating city parks and community gardens [73], and rooftop greening [74], all aimed at optimizing urban ecological-green space networks across various spatial scales. Similar to the high-density urban development of Chengdu, the Korean government is currently implementing a plan to build a linear green infrastructure network in Seoul, which will improve roads in the city center that have been cut off by urbanization or are in need of greening through measures such as utilizing the underground space of elevated roads or renovating abandoned linear infrastructures. The project aims to create a greenway of more than 400 km connecting forests and urban parks to provide citizens with more green space, with plans to complete this network of ecological-green spaces by 2026 [75]. Additionally, a related study in Madrid, Spain, proposed a multi-scale planning approach to coordinate metropolitan and local areas. This study compared and analyzed changes in regional landscape connectivity under different development scenarios and made predictions for 2030. By integrating landscape ecology with urban green infrastructure planning while considering temporal and spatial changes, the study aimed to develop a comprehensive ecological network system. This hierarchical planning approach emphasizes the connectivity between green infrastructure development in different areas and urban sustainability [7].
Based on these research findings, this study offers the following specific recommendations for urban planners and policymakers: (1) Formulation of a policy program for the synergistic development of multi-scale ecological green space in high-density urban areas, and formation of a planning methodology for the integration of ‘regional ecological networks—parks green spaces—street green spaces’; (2) identify priority areas for protection and restoration, and implement comprehensive monitoring of ecological networks and street green spaces using remote sensing imagery and street-view image data; and (3) provide nature-based solutions for urban design. Examples include implementing vertical greening on walls and roofs in high-density building areas, planting climbing plants under bridges, overpasses, and elevated roads or along walls, and incorporating bio-friendly road designs.

4.3. Limitations and Future Research Directions

Although this study investigates the construction and relationships between ecological and green space networks in high-density urban areas, focusing on the region within Chengdu’s Third Ring Road, several limitations remain, which also indicate directions for future research. First, the selection of ecological source areas in constructing ecological networks was based solely on area thresholds and landscape connectivity indices, which may have led to the omission of certain high-quality ecological patches. In the future, ecosystem service functions can be considered in various aspects, making the assessment of ecological source areas more comprehensive. Additionally, the absence of specific species considerations in this study meant that the ecological resistance value thresholds and the design of ecological corridors lacked precision. Previous studies have shown that pre-surveying biological species in the study area can enhance the effective management of ecological networks [3,5,76]. Second, in constructing the street green space network, GVI evaluation was only based on widely used standards. In the future, public participation could be used to conduct an in-depth investigation into local residents’ perceptions. Third, the correlation analysis between regional ecological networks and street green space networks did not fully account for the impacts of urban built environment factors or temporal development changes. Future research could incorporate socio-economic development factors and comprehensive urban planning into green space system planning to optimize the construction of multi-scale spatial ecological-green space networks in cities. Fourth, regarding data analysis, critical datasets such as land use and NDVI with a 10 m resolution remain limited when applied to the analysis of high-density urban areas with complex conditions. Future research will explore the integration of high-resolution imagery with existing classified datasets and optimize data resolution using machine learning techniques, such as random forest and deep learning algorithms, to enhance the accuracy of research outcomes.

5. Conclusions

This study takes the high-density urban area within the Third Ring Road of Chengdu City as the research object, and from the perspective of the synergistic development of regional ecological network and street green space at multiple scales, it provides a new idea to promote the development of urban biophilic communities in the future, and to realize the harmonious coexistence between human beings and nature. The study results indicate:
(1)
Urban ecological networks were built using ecological sources and corridors. In the study area, ecological landscape elements cover 28.77 km2, accounting for only 14.86% of the total area, indicating a high degree of landscape fragmentation. The study identified 51 potential ecological sources, 41 important ecological sources, and 94 ecological corridors with a total length of 173.62 km. Among these, the ecological pinch points, which are crucial protection areas for ecological corridors, cover 1.12 km2 and are primarily located in forested areas near rivers. The ecological barrier points, which are important restoration areas, cover 1.88 km2 and are mainly situated in densely built-up and traffic-complex regions.
(2)
Constructing the street green space network through the evaluation of urban street GVI. The average street GVI in the study area is 22.55%, which is categorized as ordinary based on the 5-level evaluation standard. Areas with higher GVI are concentrated around university campuses, urban parks, and rivers, while areas with lower GVI are mainly located near commercial streets and urban transportation hubs.
(3)
The bivariate spatial autocorrelation analysis of ecological resistance values and street GVI revealed a negative correlation between them. Areas with low ecological resistance and high GVI were designated as priority protection zones, primarily located in the northern, western, and southeastern sections between the Second and Third Ring Roads. Conversely, areas with high ecological resistance and low GVI were identified as priority restoration zones, mainly concentrated in the central area within the First Ring Road and the northwestern parts between the Second and Third Ring Roads.
(4)
In summary, this study proposes a conceptual framework for the implementation of multi-scale spatial synergy in building ecological-green space network construction in high-density urban areas. Combining the functionality and spatial relationship between ecological networks and street green spaces, the planning strategy of prioritizing protection and restoration is clarified. The application of this framework will help to complement and improve the regional ecological security construction and provide a planning reference for sustainable urban development.

Author Contributions

S.H.: Conceptualization, Methodology, Formal analysis, Data curation, Software, Visualization, Writing—original draft. Y.Y.: Formal analysis, Data curation, Software. T.J.: Methodology, Resources, Writing—review & editing, Supervision, Funding acquisition. X.H.: Methodology, Resources, Writing—review & editing, Funding acquisition. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

Data are contained within the article; further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A

Appendix A.1

Land 14 00026 g0a1
Figure A1. Comparison of edge width analysis results at 30 m, 60 m, and 90 m in MSPA analysis.
Figure A1. Comparison of edge width analysis results at 30 m, 60 m, and 90 m in MSPA analysis.
Land 14 00026 g0a1

Appendix A.2. Formula of the Habitat-Quality Module in the InVEST Model

(1)
Calculate the degree of habitat degradation using the formula shown below:
D x j = ∑ r = 1 R ∑ y = 1 Y r ω r / ∑ r = 1 R ω r r y i r x y β x S j r
where D x j represents the habitat degradation degree of grid x within land use type j; R is the number of threat factors; Y r represents a set of grids on the threat factor grid map r; ω r represents the weight of the threat factor; r y is the threat factor value for grid y; i r x y indicates the impact of threat factor r from grid y on habitat grid x; β x represents the accessibility level of the threat factor; and S j r represents the sensitivity of habitat type j to threat factor r.
(2)
Calculate the Habitat Quality Index, the formula for which is shown below:
Q x j = H j 1 − D x j z D x j z + k z
where Q x j is the habitat quality index of grid x in land use type j; H j is the habitat suitability of land use type j, with relative habitat suitability scores ranging from 0 to 1; D x j z represents the habitat threat level of patch x in land use type j; k is a half-saturation constant; and z is a constant.
Table A1. Treat factors and their maximum impact distance, weight, and attenuation types.
Table A1. Treat factors and their maximum impact distance, weight, and attenuation types.
Treat FactorsMaximum Impact
Distance		WeightAttenuation Types
cropland5.00.6Linear
built-up8.01.0Exponential
bare land6.00.5Linear
Table A2. Sensitivity of land use types to threat factors.
Table A2. Sensitivity of land use types to threat factors.
Land Use TypeHabitat SuitabilitySensitivity to Threat Factors
CroplandBuilt-UpBare Land
Tree cover1.00.60.80.5
Shrubland0.90.50.70.4
Grassland0.70.80.60.5
Cropland0.10.20.80.5
Built-up0.00.00.00.0
Bare land0.40.50.40.2
Water bodies0.60.40.40.2

Appendix A.3

Table A3. Resistance values and weights.
Table A3. Resistance values and weights.
FactorsClassificationValueWeight
Habitat quality≤0.290.2875
0.2–0.47
0.4–0.65
0.6–0.83
≥0.81
NDVI≤0.290.1715
0.2–0.47
0.4–0.65
0.6–0.83
≥0.81
Distance From Water≤500 m90.0410
500–1000 m7
1000–1500 m5
1500–2000 m3
≥2000 m1
Land Use TypeTree cover10.3403
Shrubland3
Grassland3
Cropland7
Built-up9
Bare5
Water7
Distance From Road≤120 m90.1007
120–240 m7
240–360 m5
360–480 m3
≥480 m1
Distance From Railroad≤400 m90.0590
400–800 m7
800–1200 m5
1200–1600 m3
≥1600 m1

Appendix A.4

Table A4. Ecological source and landscape connectivity assessment.
Table A4. Ecological source and landscape connectivity assessment.
No.Ecological SourcedIICdPCdI
1Sandy River and surrounding green areas16.7425.7921.26
2South River and Surrounding Green Space (Binjiang Middle Road)21.9917.7619.88
3Tazishan Park and surrounding green space (Tazishan South Street)14.9822.5318.75
4Wangjianglou Park and Jinjiang River16.3721.1118.74
5Foo River and surrounding green space13.4222.3017.86
6Shahe and Surrounding Green Space (Chui Kam Road East)12.0522.4017.23
7Du Fu Cao Tang and Raccoon Stream Park19.9313.8216.87
8Donghu Park10.3911.5110.95
9Foo River and surrounding green space (Shunjin Road)7.499.198.34
10Sandy River and Surrounding Green Space (Dongpu Road)6.0910.548.31
11Foo River and surrounding green space4.6411.217.93
12Shahe Dongli Cuihu Park3.5310.146.83
13Sandy River and Surrounding Green Space (Jumping Stirrup Village)4.277.565.92
14Sandy River and Surrounding Green Space (Tashan Road)2.927.425.17
15Sandy River and Surrounding Green Space (Tashan Road)2.626.834.73
16Green area around the confluence of the Foo River and the South
River		2.027.034.52
17Near Southwestern University of Finance and Economics Gymnasium5.323.234.28
18Ma Shi Qiao Sha He Park3.135.394.26
19Qing Shui River and green space around Chengdu Garden residential area3.743.733.74
20People’s Park3.263.553.40
21Shahe Park3.083.293.19
22Baihuatan Park in Beijing2.583.212.90
23Chengdu Wuhou Ancestral Hall2.952.562.75
24Dobaoji Park2.383.082.73
25Shahe Park Science and Technology Show Court2.492.652.57
26Shahe Park2.382.492.43
27Jinsha Site Museum2.941.832.38
28Yongling Park in Nanjing2.881.872.38
29Green space around New Hope Road2.142.012.08
30Shahe Park1.461.941.70
31Xinhua Park1.591.631.61
32Chengdu Youth Activity Center1.591.531.56
33Qing Shui River and Surrounding Green Space (Qing Feng Street)1.701.391.55
34Green space around Jingtian East Road1.591.411.50
35Xinqiao Park1.451.431.44
36Chenghua Park1.381.441.41
37Shahe Park1.341.461.40
38Sengxian Lake Park1.341.361.35
39Qingshuihe Park1.630.981.31
40Chengdu Zoo1.181.061.12
41Shahe Park0.991.161.08

References

  1. Geng, J.; Yu, K.; Sun, M.; Xie, Z.; Huang, R.; Wang, Y.; Zhao, Q.; Liu, J. Construction and Optimisation of Ecological Networks in High-Density Central Urban Areas: The Case of Fuzhou City, China. Remote Sens. 2023, 15, 5666. [Google Scholar] [CrossRef]
  2. Huang, X.; Wang, H.; Shan, L.; Xiao, F. Constructing and Optimizing Urban Ecological Network in the Context of Rapid Urbanization for Improving Landscape Connectivity. Ecol. Indic. 2021, 132, 108319. [Google Scholar] [CrossRef]
  3. Li, C.; Huang, L.; Xu, Q.; Cao, Z. Synergistic Ecological Network Approach for Sustainable Development of Highly Urbanized Area in the Bay Bottom Region: A Study in Chengyang District, Qingdao. Ecol. Indic. 2024, 158, 111443. [Google Scholar] [CrossRef]
  4. Xiu, N.; Ignatieva, M.; Van Den Bosch, C.K.; Zhang, S. Applying a Socio-Ecological Green Network Framework to Xi’an City, China. Landsc. Ecol. Eng. 2020, 16, 135–150. [Google Scholar] [CrossRef]
  5. Yang, Y.; Feng, Z.; Wu, K.; Lin, Q. How to Construct a Coordinated Ecological Network at Different Levels: A Case from Ningbo City, China. Ecol. Inform. 2022, 70, 101742. [Google Scholar] [CrossRef]
  6. Tzoulas, K.; Korpela, K.; Venn, S.; Yli-Pelkonen, V.; Kaźmierczak, A.; Niemela, J.; James, P. Promoting Ecosystem and Human Health in Urban Areas Using Green Infrastructure: A Literature Review. Landsc. Urban Plan. 2007, 81, 167–178. [Google Scholar] [CrossRef]
  7. Vilanova, C.; Ferran, J.S.; Concepción, E.D. Integrating Landscape Ecology in Urban Green Infrastructure Planning: A Multi-Scale Approach for Sustainable Development. Urban For. Urban Green. 2024, 94, 128248. [Google Scholar] [CrossRef]
  8. Ying, J.; Zhang, X.; Zhang, Y.; Bilan, S. Green Infrastructure: Systematic Literature Review. Econ. Res. -Ekon. Istraživanja 2022, 35, 343–366. [Google Scholar] [CrossRef]
  9. Nie, W.; Shi, Y.; Siaw, M.J.; Yang, F.; Wu, R.; Wu, X.; Zheng, X.; Bao, Z. Constructing and Optimizing Ecological Network at County and Town Scale: The Case of Anji County, China. Ecol. Indic. 2021, 132, 108294. [Google Scholar] [CrossRef]
  10. Li, Y.-Y.; Zhang, Y.-Z.; Jiang, Z.-Y.; Guo, C.-X.; Zhao, M.-Y.; Yang, Z.-G.; Guo, M.-Y.; Wu, B.-Y.; Chen, Q.-L. Integrating Morphological Spatial Pattern Analysis and the Minimal Cumulative Resistance Model to Optimize Urban Ecological Networks: A Case Study in Shenzhen City, China. Ecol. Process 2021, 10, 63. [Google Scholar] [CrossRef]
  11. Hüse, B.; Szabó, S.; Deák, B.; Tóthmérész, B. Mapping an Ecological Network of Green Habitat Patches and Their Role in Maintaining Urban Biodiversity in and around Debrecen City (Eastern Hungary). Land Use Policy 2016, 57, 574–581. [Google Scholar] [CrossRef]
  12. Xie, H.; Yao, G.; Liu, G. Spatial Evaluation of the Ecological Importance Based on GIS for Environmental Management: A Case Study in Xingguo County of China. Ecol. Indic. 2015, 51, 3–12. [Google Scholar] [CrossRef]
  13. Yu, Q.; Yue, D.; Wang, Y.; Kai, S.; Fang, M.; Ma, H.; Zhang, Q.; Huang, Y. Optimization of Ecological Node Layout and Stability Analysis of Ecological Network in Desert Oasis: A Typical Case Study of Ecological Fragile Zone Located at Deng Kou County (Inner Mongolia). Ecol. Indic. 2018, 84, 304–318. [Google Scholar] [CrossRef]
  14. Hu, C.; Wang, Z.; Wang, Y.; Sun, D.; Zhang, J. Combining MSPA-MCR Model to Evaluate the Ecological Network in Wuhan, China. Land 2022, 11, 213. [Google Scholar] [CrossRef]
  15. Wei, Q.; Halike, A.; Yao, K.; Chen, L.; Balati, M. Construction and Optimization of Ecological Security Pattern in Ebinur Lake Basin Based on MSPA-MCR Models. Ecol. Indic. 2022, 138, 108857. [Google Scholar] [CrossRef]
  16. Xie, Y.; Ying, J.; Zou, J.; Li, R.; Zhang, H.; Shi, Q.; Li, Y. Habitat Protection in Urban–Rural Fringes through Coordinated Ecological Network Construction and Territorial Planning. Land 2024, 13, 935. [Google Scholar] [CrossRef]
  17. Wang, Y.; Qu, Z.; Zhong, Q.; Zhang, Q.; Zhang, L.; Zhang, R.; Yi, Y.; Zhang, G.; Li, X.; Liu, J. Delimitation of Ecological Corridors in a Highly Urbanizing Region Based on Circuit Theory and MSPA. Ecol. Indic. 2022, 142, 109258. [Google Scholar] [CrossRef]
  18. Zhang, R.; Zhang, Q.; Zhang, L.; Zhong, Q.; Liu, J.; Wang, Z. Identification and Extraction of a Current Urban Ecological Network in Minhang District of Shanghai Based on an Optimization Method. Ecol. Indic. 2022, 136, 108647. [Google Scholar] [CrossRef]
  19. Kong, F.; Yin, H.; Nakagoshi, N.; Zong, Y. Urban Green Space Network Development for Biodiversity Conservation: Identification Based on Graph Theory and Gravity Modeling. Landsc. Urban Plan. 2010, 95, 16–27. [Google Scholar] [CrossRef]
  20. Urban, D.L.; Minor, E.S.; Treml, E.A.; Schick, R.S. Graph Models of Habitat Mosaics. Ecol. Lett. 2009, 12, 260–273. [Google Scholar] [CrossRef]
  21. 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]
  22. 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]
  23. Kwon, O.-S.; Kim, J.-H.; Ra, J.-H. Landscape Ecological Analysis of Green Network in Urban Area Using Circuit Theory and Least-Cost Path. Land 2021, 10, 847. [Google Scholar] [CrossRef]
  24. Hu, C.; Wang, Z.; Huang, G.; Ding, Y. Construction, Evaluation, and Optimization of a Regional Ecological Security Pattern Based on MSPA–Circuit Theory Approach. Int. J. Environ. Res. Public Health 2022, 19, 16184. [Google Scholar] [CrossRef]
  25. Dickson, B.G.; Albano, C.M.; Anantharaman, R.; Beier, P.; Fargione, J.; Graves, T.A.; Gray, M.E.; Hall, K.R.; Lawler, J.J.; Leonard, P.B.; et al. Circuit-theory Applications to Connectivity Science and Conservation. Conserv. Biol. 2019, 33, 239–249. [Google Scholar] [CrossRef] [PubMed]
  26. McRae, B.H.; Hall, S.A.; Beier, P.; Theobald, D.M. Where to Restore Ecological Connectivity? Detecting Barriers and Quantifying Restoration Benefits. PLoS ONE 2012, 7, e52604. [Google Scholar] [CrossRef] [PubMed]
  27. Hansen, R.; Olafsson, A.S.; Van Der Jagt, A.P.; Rall, E.; Pauleit, S. Planning Multifunctional Green Infrastructure for Compact Cities: What Is the State of Practice? Ecol. Indic. 2019, 96, 99–110. [Google Scholar] [CrossRef]
  28. Boulton, C.; Dedekorkut-Howes, A.; Byrne, J. Factors Shaping Urban Greenspace Provision: A Systematic Review of the Literature. Landsc. Urban Plan. 2018, 178, 82–101. [Google Scholar] [CrossRef]
  29. Haaland, C.; van Den Bosch, C.K. Challenges and Strategies for Urban Green-Space Planning in Cities Undergoing Densification: A Review. Urban For. Urban Green. 2015, 14, 760–771. [Google Scholar] [CrossRef]
  30. Jim, C.Y.; Chen, S.S. Comprehensive Greenspace Planning Based on Landscape Ecology Principles in Compact Nanjing City, China. Landsc. Urban Plan. 2003, 65, 95–116. [Google Scholar] [CrossRef]
  31. Liu, Y.; Huang, T.-T.; Zheng, X. A Method of Linking Functional and Structural Connectivity Analysis in Urban Green Infrastructure Network Construction. Urban Ecosyst. 2022, 25, 909–925. [Google Scholar] [CrossRef]
  32. Caneva, G.; Bartoli, F.; Zappitelli, I.; Savo, V. Street Trees in Italian Cities: Story, Biodiversity and Integration within the Urban Environment. Rendiconti Lincei. Sci. Fis. e Nat. 2020, 31, 411–417. [Google Scholar] [CrossRef]
  33. Graça, M.; Queirós, C.; Farinha-Marques, P.; Cunha, M. Street Trees as Cultural Elements in the City: Understanding How Perception Affects Ecosystem Services Management in Porto, Portugal. Urban For. Urban Green. 2018, 30, 194–205. [Google Scholar] [CrossRef]
  34. Russo, A.; J Escobedo, F.; Zerbe, S. Quantifying the Local-Scale Ecosystem Services Provided by Urban Treed Streetscapes in Bolzano, Italy. AIMS Environ. Sci. 2016, 3, 58–76. [Google Scholar] [CrossRef]
  35. Aoki, Y. Evaluation Methods for Landscapes with Greenery. Landsc. Res. 1991, 16, 3–6. [Google Scholar] [CrossRef]
  36. Aikoh, T.; Homma, R.; Abe, Y. Comparing Conventional Manual Measurement of the Green View Index with Modern Automatic Methods Using Google Street View and Semantic Segmentation. Urban For. Urban Green. 2023, 80, 127845. [Google Scholar] [CrossRef]
  37. Kumakoshi, Y.; Chan, S.Y.; Koizumi, H.; Li, X.; Yoshimura, Y. Standardized Green View Index and Quantification of Different Metrics of Urban Green Vegetation. Sustainability 2020, 12, 7434. [Google Scholar] [CrossRef]
  38. Zhang, W.; Zeng, H. Spatial Differentiation Characteristics and Influencing Factors of the Green View Index in Urban Areas Based on Street View Images: A Case Study of Futian District, Shenzhen, China. Urban For. Urban Green. 2024, 93, 128219. [Google Scholar] [CrossRef]
  39. Huang, Z.; Tang, L.; Qiao, P.; He, J.; Su, H. Socioecological Justice in Urban Street Greenery Based on Green View Index-A Case Study within the Fuzhou Third Ring Road. Urban For. Urban Green. 2024, 95, 128313. [Google Scholar] [CrossRef]
  40. Lu, Y.; Ferranti, E.J.S.; Chapman, L.; Pfrang, C. Assessing Urban Greenery by Harvesting Street View Data: A Review. Urban For. Urban Green. 2023, 83, 127917. [Google Scholar] [CrossRef]
  41. Dong, R.; Zhang, Y.; Zhao, J. How Green Are the Streets within the Sixth Ring Road of Beijing? An Analysis Based on Tencent Street View Pictures and the Green View Index. Int. J. Environ. Res. Public Health 2018, 15, 1367. [Google Scholar] [CrossRef] [PubMed]
  42. Berland, A.; Lange, D.A. Google Street View Shows Promise for Virtual Street Tree Surveys. Urban For. Urban Green. 2017, 21, 11–15. [Google Scholar] [CrossRef]
  43. Li, X. Examining the Spatial Distribution and Temporal Change of the Green View Index in New York City Using Google Street View Images and Deep Learning. Environ. Plan. B Urban Anal. City Sci. 2021, 48, 2039–2054. [Google Scholar] [CrossRef]
  44. Zhang, T.; Wang, L.; Hu, Y.; Zhang, W.; Liu, Y. Measuring Urban Green Space Exposure Based on Street View Images and Machine Learning. Forests 2024, 15, 655. [Google Scholar] [CrossRef]
  45. Han, X.; Wang, L.; He, J.; Jung, T. Restorative Perception of Urban Streets: Interpretation Using Deep Learning and MGWR Models. Front. Public Health 2023, 11, 1141630. [Google Scholar] [CrossRef]
  46. Wang, L.; Han, X.; He, J.; Jung, T. Measuring Residents’ Perceptions of City Streets to Inform Better Street Planning through Deep Learning and Space Syntax. ISPRS J. Photogramm. Remote Sens. 2022, 190, 215–230. [Google Scholar] [CrossRef]
  47. Dai, W.; Yuan, S.; Liu, Y.; Peng, D.; Niu, S. Measuring Equality in Access to Urban Parks: A Big Data Analysis from Chengdu. Front. Public Health 2022, 10, 1022666. [Google Scholar] [CrossRef]
  48. Soille, P.; Vogt, P. Morphological Segmentation of Binary Patterns. Pattern Recognit. Lett. 2009, 30, 456–459. [Google Scholar] [CrossRef]
  49. Wang, F.; Yuan, X.; Zhou, L.; Zhang, M. Integrating Ecosystem Services and Landscape Connectivity to Construct and Optimize Ecological Security Patterns: A Case Study in the Central Urban Area Chongqing Municipality, China. Environ. Sci. Pollut. Res. 2022, 29, 43138–43154. [Google Scholar] [CrossRef]
  50. Correa Ayram, C.A.; Mendoza, M.E.; Etter, A.; Salicrup, D.R.P. Habitat Connectivity in Biodiversity Conservation: A Review of Recent Studies and Applications. Prog. Phys. Geogr. Earth Environ. 2016, 40, 7–37. [Google Scholar] [CrossRef]
  51. Moreira, M.; Fonseca, C.; Vergílio, M.; Calado, H.; Gil, A. Spatial Assessment of Habitat Conservation Status in a Macaronesian Island Based on the InVEST Model: A Case Study of Pico Island (Azores, Portugal). Land Use Policy 2018, 78, 637–649. [Google Scholar] [CrossRef]
  52. Wu, Z.; Cheng, S.; Xu, K.; Qian, Y. Ecological Network Resilience Evaluation and Ecological Strategic Space Identification Based on Complex Network Theory: A Case Study of Nanjing City. Ecol. Indic. 2024, 158, 111604. [Google Scholar] [CrossRef]
  53. Wang, T.; Li, H.; Huang, Y. The Complex Ecological Network’s Resilience of the Wuhan Metropolitan Area. Ecol. Indic. 2021, 130, 108101. [Google Scholar] [CrossRef]
  54. Zhou, Y.; Zheng, Z.; Wu, Z.; Guo, C.; Chen, Y. Construction and Evaluation of Ecological Networks in Highly Urbanised Regions: A Case Study of the Guangdong-Hong Kong-Macao Greater Bay Area, China. Ecol. Indic. 2023, 152, 110336. [Google Scholar] [CrossRef]
  55. Vogt, P.; Ferrari, J.R.; Lookingbill, T.R.; Gardner, R.H.; Riitters, K.H.; Ostapowicz, K. Mapping Functional Connectivity. Ecol. Indic. 2009, 9, 64–71. [Google Scholar] [CrossRef]
  56. 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] [PubMed]
  57. Saura, S.; Torné, J. Conefor Sensinode 2.2: A Software Package for Quantifying the Importance of Habitat Patches for Landscape Connectivity. Environ. Model. Softw. 2009, 24, 135–139. [Google Scholar] [CrossRef]
  58. Wang, B.; Fu, S.; Hao, Z.; Zhen, Z. Ecological Security Pattern Based on Remote Sensing Ecological Index and Circuit Theory in the Shanxi Section of the Yellow River Basin. Ecol. Indic. 2024, 166, 112382. [Google Scholar] [CrossRef]
  59. Badrinarayanan, V.; Kendall, A.; Cipolla, R. Segnet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 2017, 39, 2481–2495. [Google Scholar] [CrossRef] [PubMed]
  60. Wang, J.; Liu, W.; Gou, A. Numerical Characteristics and Spatial Distribution of Panoramic Street Green View Index Based on SegNet Semantic Segmentation in Savannah. Urban For. Urban Green. 2022, 69, 127488. [Google Scholar] [CrossRef]
  61. Yao, Y.; Liang, Z.; Yuan, Z.; Liu, P.; Bie, Y.; Zhang, J.; Wang, R.; Wang, J.; Guan, Q. A Human-Machine Adversarial Scoring Framework for Urban Perception Assessment Using Street-View Images. Int. J. Geogr. Inf. Sci. 2019, 33, 2363–2384. [Google Scholar] [CrossRef]
  62. Abdollahi, S.; Ildoromi, A.; Salmanmahini, A.; Fakheran, S. Evaluating Spatial Patterns of Ecosystem Services Based on a Comparative Approach on Spatial Statistics in the Central Part of Isfahan Province. Geogr. Environ. Sustain. 2021, 11, 41–54. [Google Scholar]
  63. Statistic, G. A Novel Hybrid Method for Measuring the Spatial Autocorrelation of Vehicular Crashes: Combining Moran’s Index and Getis-Ord. Open J. Civ. Eng. 2017, 7, 208–221. [Google Scholar]
  64. Zhang, J.; Zhang, K.; Zhao, F. Research on the Regional Spatial Effects of Green Development and Environmental Governance in China Based on a Spatial Autocorrelation Model. Struct. Change Econ. Dyn. 2020, 55, 1–11. [Google Scholar] [CrossRef]
  65. Orihara, N. Study on the Evaluation of Green Landscapes: Consideration of Green Evaluation Methods for Good Landscape Formation. Build. Environ. Energy Conserv. Inf. 2006, 27, 32–35. [Google Scholar]
  66. Gomes, V.; Ribeiro, R.; Carretero, M.A. Effects of Urban Habitat Fragmentation on Common Small Mammals: Species versus Communities. Biodivers. Conserv. 2011, 20, 3577–3590. [Google Scholar] [CrossRef]
  67. Pan, J.; Wang, Y.; Zhang, Z. Identification and Optimization of Ecological Network in Arid Inland River Basin Using MSPA and Spatial Syntax: A Case Study of Shule River Basin, NW China. Land 2023, 12, 292. [Google Scholar] [CrossRef]
  68. Wang, Y.; Zhang, L.; Song, Y. Study on the Construction of the Ecological Security Pattern of the Lancang River Basin (Yunnan Section) Based on inVEST-MSPA-Circuit Theory. Sustainability 2022, 15, 477. [Google Scholar] [CrossRef]
  69. Manes, F.; Silli, V.; Salvatori, E.; Incerti, G.; Galante, G.; Fusaro, L.; Perrino, C. Urban Ecosystem Services: Tree Diversity and Stability of PM10 Removal in the Metropolitan Area of Rome. Ann. Bot. 2014, 4, 19–26. [Google Scholar]
  70. Tan, X.; Hirabayashi, S.; Shibata, S. Estimation of Ecosystem Services Provided by Street Trees in Kyoto, Japan. Forests 2021, 12, 311. [Google Scholar] [CrossRef]
  71. Li, F.; Liu, X.; Zhang, X.; Zhao, D.; Liu, H.; Zhou, C.; Wang, R. Urban Ecological Infrastructure: An Integrated Network for Ecosystem Services and Sustainable Urban Systems. J. Clean. Prod. 2017, 163, S12–S18. [Google Scholar] [CrossRef]
  72. Fang, X.; Li, J.; Ma, Q. Integrating Green Infrastructure, Ecosystem Services and Nature-Based Solutions for Urban Sustainability: A Comprehensive Literature Review. Sustain. Cities Soc. 2023, 98, 104843. [Google Scholar] [CrossRef]
  73. Middle, I.; Dzidic, P.; Buckley, A.; Bennett, D.; Tye, M.; Jones, R. Integrating Community Gardens into Public Parks: An Innovative Approach for Providing Ecosystem Services in Urban Areas. Urban For. Urban Green. 2014, 13, 638–645. [Google Scholar] [CrossRef]
  74. Dong, J.; Guo, F.; Lin, M.; Zhang, H.; Zhu, P. Optimization of Green Infrastructure Networks Based on Potential Green Roof Integration in a High-Density Urban Area—A Case Study of Beijing, China. Sci. Total Environ. 2022, 834, 155307. [Google Scholar] [CrossRef] [PubMed]
  75. Kim, E.J.; Lee, D.H.; Kang, Y. Explorations on Cooling Effect of Small Urban Linear Park Design in Low-Rise, High-Density District: The Case of Gyeongui Line Forest Park in Seoul. Urban For. Urban Green. 2024, 100, 128461. [Google Scholar] [CrossRef]
  76. Qian, M.; Huang, Y.; Cao, Y.; Wu, J.; Xiong, Y. Ecological Network Construction and Optimization in Guangzhou from the Perspective of Biodiversity Conservation. J. Environ. Manag. 2023, 336, 117692. [Google Scholar] [CrossRef] [PubMed]
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Figure 1. Location and land use of the study area.
Figure 1. Location and land use of the study area.
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Figure 2. Research framework and technical route.
Figure 2. Research framework and technical route.
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Figure 3. Spatial characteristics of each resistance surface. (a) habitat quality; (b) NDVI; (c) distance from rivers; (d) land use type; (e) distance from railroads; (f) distance from roads.
Figure 3. Spatial characteristics of each resistance surface. (a) habitat quality; (b) NDVI; (c) distance from rivers; (d) land use type; (e) distance from railroads; (f) distance from roads.
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Figure 4. The process of collecting and calculating the GVI of streets. (a) The process of collecting street view images; (b) Image segmentation process of street view images.
Figure 4. The process of collecting and calculating the GVI of streets. (a) The process of collecting street view images; (b) Image segmentation process of street view images.
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Figure 5. Landscape classification of MSPA. (a–c) Examples from the northern, western, and southeastern regions.
Figure 5. Landscape classification of MSPA. (a–c) Examples from the northern, western, and southeastern regions.
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Figure 6. Selection of ecological source areas. Numbers indicate extracted ecological source areas.
Figure 6. Selection of ecological source areas. Numbers indicate extracted ecological source areas.
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Figure 7. Integrated ecological resistance surfaces.
Figure 7. Integrated ecological resistance surfaces.
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Figure 8. Ecological corridors and important levels. Numbers indicate extracted ecological source areas.
Figure 8. Ecological corridors and important levels. Numbers indicate extracted ecological source areas.
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Figure 9. Pinch point identification. Numbers indicate extracted ecological source areas.
Figure 9. Pinch point identification. Numbers indicate extracted ecological source areas.
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Figure 10. Barrier point identification. Numbers indicate extracted ecological source areas.
Figure 10. Barrier point identification. Numbers indicate extracted ecological source areas.
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Figure 11. Spatial distribution of pinch points and Barrier points. Numbers indicate extracted ecological source areas. (a–d) Typical examples of ecological pinch points, mainly located in forested areas along urban rivers; (e–h) Typical examples of ecological barrier points, predominantly found in densely built-up areas and road intersections.
Figure 11. Spatial distribution of pinch points and Barrier points. Numbers indicate extracted ecological source areas. (a–d) Typical examples of ecological pinch points, mainly located in forested areas along urban rivers; (e–h) Typical examples of ecological barrier points, predominantly found in densely built-up areas and road intersections.
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Figure 12. GVI distribution of Street Sites.
Figure 12. GVI distribution of Street Sites.
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Figure 13. GVI distribution at the subdistrict level.
Figure 13. GVI distribution at the subdistrict level.
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Figure 14. GVI hot and cold spots analysis. (a,b) Examples of hot spot areas near parks and universities; (c,d) Examples of cold spot areas near commercial streets and transport hubs.
Figure 14. GVI hot and cold spots analysis. (a,b) Examples of hot spot areas near parks and universities; (c,d) Examples of cold spot areas near commercial streets and transport hubs.
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Figure 15. Spatial correlation analysis of GVI and ecological resistance values: (a) Global Moran’s I for GVI; (b) Global Moran’s I for ecological resistance; (c) Global Moran’s I for ecological resistance and GVI; (d) Local autocorrelation analysis of GVI; (e) Local autocorrelation analysis of ecological resistance; (f) Local autocorrelation analysis of GVI and ecological resistance; (g) Identification of priority restoration and conservation areas.
Figure 15. Spatial correlation analysis of GVI and ecological resistance values: (a) Global Moran’s I for GVI; (b) Global Moran’s I for ecological resistance; (c) Global Moran’s I for ecological resistance and GVI; (d) Local autocorrelation analysis of GVI; (e) Local autocorrelation analysis of ecological resistance; (f) Local autocorrelation analysis of GVI and ecological resistance; (g) Identification of priority restoration and conservation areas.
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Table 1. Ecological landscape characterization by MSPA.
Table 1. Ecological landscape characterization by MSPA.
Landscape ElementAre/km2Percentage of Land Used for Ecological Landscapes/%
Core10.33035.89
Edge9.30932.35
Islet3.52912.27
Branch3.52812.26
Bridge1.5985.55
Loop0.2690.94
Perforation0.2120.74
Total28.771100
Table 2. Land use area and percentage of land use at pinch points and barrier.
Table 2. Land use area and percentage of land use at pinch points and barrier.
Tree CoverShrublandGrasslandCroplandBuilt-UpBare LandWater BodiesTotal
Pinch PointsArea/km20.710.000.000.010.210.070.121.12
Ratios/%63.390.000.000.8918.756.2510.71100
BarriersArea/km20.010.000.000.041.620.170.041.88
Ratios/%0.530.000.002.1386.179.042.13100
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Hou, S.; Yu, Y.; Jung, T.; Han, X. How to Coordinate Urban Ecological Networks and Street Green Space Construction? Insights from a Multi-Scale Perspective. Land 2025, 14, 26. https://doi.org/10.3390/land14010026

AMA Style

Hou S, Yu Y, Jung T, Han X. How to Coordinate Urban Ecological Networks and Street Green Space Construction? Insights from a Multi-Scale Perspective. Land. 2025; 14(1):26. https://doi.org/10.3390/land14010026

Chicago/Turabian Style

Hou, Shujun, Ying Yu, Taeyeol Jung, and Xin Han. 2025. "How to Coordinate Urban Ecological Networks and Street Green Space Construction? Insights from a Multi-Scale Perspective" Land 14, no. 1: 26. https://doi.org/10.3390/land14010026

APA Style

Hou, S., Yu, Y., Jung, T., & Han, X. (2025). How to Coordinate Urban Ecological Networks and Street Green Space Construction? Insights from a Multi-Scale Perspective. Land, 14(1), 26. https://doi.org/10.3390/land14010026

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