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Article

Towards an Integrated Framework for Understanding the Landscape Pattern of Coupled Urban Green and Blue Spaces

by
Lumeng Liu
1,
Jiajia Zhang
1,
Yilin Liu
1,
Yuchen Fan
1,
Baiting He
2 and
Chenwei Shang
2,*
1
Jiangsu Key Laboratory of Soil and Water Processes in Watershed, College of Geography and Remote Sensing, Hohai University, Nanjing 211100, China
2
Ministry of Education Key Laboratory of Ecology and Resource Use of the Mongolian Plateau, School of Ecology and Environment, Inner Mongolia University, Hohhot 010021, China
*
Author to whom correspondence should be addressed.
Land 2025, 14(12), 2311; https://doi.org/10.3390/land14122311
Submission received: 4 November 2025 / Revised: 20 November 2025 / Accepted: 21 November 2025 / Published: 24 November 2025
(This article belongs to the Special Issue Application of Multi-Source Geographical Big Data in Land Use Decision-Making)
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Versions Notes

Abstract

Urban green and blue spaces (UGBS) provide key ecosystem services, and growing research has sought to examine their synergistic effects using landscape metrics. However, inconsistent choices of indicators for characterizing the coupled UGBS patterns hinder comparability across studies. To address this, we developed a systematic framework that integrates key spatial relationships between green and blue spaces—such as blue-green distances and waterfront green areas—into UGBS landscape characterization. Using Nanjing as a case study, we quantified the integrated UGBS patterns at 500 m and 1 km scales and assessed their distributive equity. At the 500 m scale, the average distance from green space to the nearest blue space was 334 ± 292 m, and mixed blue–green areas accounted for 43% of the total UGBS landscape. Composition metrics of UGBS showed weak positive associations with the proportion of elderly residents and negative associations with socioeconomic indicators. Newly developed urban areas contained larger, less fragmented green spaces, shorter blue–green distances, and more extensive waterfront green zones. Our findings highlight the frequent co-occurrence of green and blue spaces in subtropical cities. The proposed framework offers methodological support for advancing the understanding of UGBS synergies.

1. Introduction

Rapid global urbanization is intensifying environmental and social challenges, including more frequent and severe heatwaves [1], biodiversity loss [2], and urban flooding [3,4]. Urban green and blue spaces (UGBS) are an effective nature-based solution that can help reduce air pollution and mitigate the heat island effect [5,6], while enhancing residents’ physical and mental well-being [7,8]. Growing evidence suggests that coupled UGBS—i.e., the spatially proximate and interacting green and blue elements—have synergistic effects on temperature reduction [9,10], air pollution mitigation [11,12], and health promotion [13]. For example, riverside trees provide shade that lowers both water and surrounding air temperatures [14], while humidity from water bodies increases leaf moisture, enlarging hygroscopic PM particles, and promoting their deposition [11,15]. Identifying the optimized landscape pattern of UGBS is crucial for improving ecosystem services and enhancing human well-being.
Numerous studies used landscape metrics to quantify the spatial patterns of UGBS such as area, shape, and connectivity [16,17,18]. However, green and blue spaces have typically been analyzed in isolation, thereby neglecting key facets of their spatial coupling—proximity and relative area proportion [19,20]. Although recent work has begun to quantify the synergistic effects of coupled UGBS using landscape metrics [10,11,20,21], the indicators remain highly inconsistent. Some studies focused on morphological characteristics—mean width and area of waterfront green spaces [20,22,23], while others on proximity measures, notably the distance from green-patch centroids to water edges [24,25].
Although current metrics provide valuable insights into specific aspects of the coupled UGBS landscape patterns, they often capture only isolated dimensions such as distance or area. Relying on one or two standalone indicators might overlook the joint influence of multiple factors, leading to biased or incomplete interpretations. Moreover, inconsistent indicator selections across studies hamper cross-study comparability and constrain the generalization of empirical findings. Recent studies attempted to integrate the multiple dimensions of the coupled UGBS. For instance, Feng [26] analyzed the distance, area ratios, and spatial orientation of UGBS at the community scale, whereas Mu [27] proposed a spatial coupling degree to assess the blue–green spatial relationships. However, such efforts remain fragmentary. A systematic framework that simultaneously captures the intrinsic attributes of green and blue landscapes and their spatial relationships is therefore imperative.
In addition, a growing body of research has examined the distribution equity of UGBS [28,29,30]. Evidence shows that the availability and accessibility of UGBS tend to disadvantage older adults [31], lower-income groups [32], and neighborhoods with higher social vulnerability [33]. However, improvements toward more adequate and equitable green space provision have been observed over the past decades [34]. Nevertheless, the spatial equity of the coupled UGBS, particularly regarding the co-occurrence and proximity of green and blue elements, remains insufficiently understood. Understanding the integrated equity of UGBS is crucial to capture their synergistic ecological and social benefits.
Therefore, we aimed to develop a comprehensive framework for characterizing the coupled UGBS by considering the spatial relationships between green and blue elements and examining their spatial equity. Specifically, we addressed two key research questions: (1) to what extent are green and blue spaces spatially coupled? and (2) how are the coupled green and blue spaces distributed among different sociodemographic groups? To answer these questions, we first proposed an analytical framework for quantifying the integrated landscape patterns of UGBS. Using Nanjing as a case study, we then quantified the landscape pattern of UGBS across multiple spatial scales and identified distinct types of the coupled UGBS. Finally, we explored the distribution equity of UGBS to evaluate how their ecological and social benefits were shared across populations.

2. Materials and Methods

2.1. Study Area

Nanjing, the capital city of Jiangsu Province, consists of 11 administrative districts, including the main urban areas—Xuanwu, Qinhuai, Jianye, Gulou, Qixia, and Yuhuatai—and the suburban districts of Luhe, Pukou, Jiangning, Lishui, and Gaochun [35]. Our study focused on the six main urban districts (Figure 1), which account for approximately 11.97% of Nanjing’s total area, 51.38% of its GDP, and 46.22% of the total population (https://tjj.nanjing.gov.cn/ accessed on 20 November 2025).
Renowned for its natural landscapes, Nanjing features iconic sites such as Zhongshan (Purple Mountain), Qixia Mountain, and the Yangtze and Qinhuai Rivers. Nanjing lies in the northern subtropical humid climate zone, characterized by four distinct seasons. According to Nanjing Statistical Yearbook (2024), the mean annual temperature was 17.1 °C, and the average annual precipitation was 1276.8 mm in 2023 (https://tjj.nanjing.gov.cn accessed on 20 November 2025). The third national land survey showed that cultivated land, forest land, urban land, and water area accounted for 21.98%, 23.58%, 22.63%, and 22.94% of Nanjing’s territory in 2019, respectively [35]. Although green spaces declined during the early stages of rapid urbanization, Nanjing has implemented a series of greening strategies, including “inserting green into gaps” and “increasing green to fill gaps” [36,37]. As of 2023, official statistics from the Nanjing Greening and Landscape Bureau indicate that the green coverage rate in built-up areas has reached 45.04% [38], with 178 urban parks distributed across the central districts [37].

2.2. A Framework for Analyzing the Integrated Landscape Pattern of the Coupled Green and Blue Spaces

Building on previous studies examining the synergistic effects of green and blue spaces [9,20,39], we developed a comprehensive framework to analyze the integrated landscape patterns of coupled UGBS (Table 1, Figure 2). The indicator system consists of two main components: landscape metrics describing the spatial characteristics of single green or blue spaces and landscape metrics reflecting the spatial relationships between green and blue spaces. Each group of indicators was analyzed from three complementary perspectives: composition, configuration, and quality.
For landscape metrics characterizing individual green or blue spaces, we adopted several commonly used composition and configuration indices, while also including green space quality to represent overall green space conditions (Table 1, Figure 2). For indicators representing the spatial relationships between green and blue spaces, we considered the ratios of total area (AR), mean patch size (MPSR), and largest patch size between green and blue spaces (LPR), to capture their compositional differences. We also introduced the blue-green combination type (CT) to indicate whether a grid cell contains only blue, only green, or both types of spaces. In addition, we incorporated metrics describing green spaces adjacent to water bodies, including the length of waterfront edges contacted with blue spaces (LE), the area of waterfront green spaces (WGS300A), and the distance between green spaces and water bodies (D). We also calculated the vegetation quality of waterfront green spaces (NDVIWGS) to represent the visual quality of green-blue interactions. Waterfront green spaces were defined as green areas located within 300 m of water bodies. We selected a 300 m buffer because comparable studies at similar latitudes showed that green patches within this distance from lake boundaries can enhance temperature–relative humidity effects [9]. This buffer width has also been employed to quantify the cooling and carbon-saving effects of UGBS [22]. Larger LE and WGS300A values, as well as smaller D, indicate a greater interface between green and blue spaces. Higher values of AR, MPSR, and LPR reflect a greater dominance of water bodies over green spaces.

2.3. Quantifying the Integrated Landscape Pattern of Green and Blue Spaces

Based on the proposed framework (Table 1), we calculated landscape metrics of urban green and blue spaces in Nanjing mainly using Fragstats 4.2. All metrics were computed at the class level using the 8-cell neighborhood rule. Considering the strong scale sensitivity of landscape metrics, we conducted calculations at two fishnet scales—500 m and 1 km—resulting in 3324 and 890 fishnet units, respectively. Data on green and blue spaces were obtained from Xu and Zhao [44], who produced a high-resolution (3 m) urban blue-green-gray landscape dataset (UBGG-3m) for 36 major Chinese cities using a transferable multi-scale convolutional neural network and 336 Planet satellite images. The dataset was rigorously validated against existing products, achieving an overall classification accuracy of 91.2% [44].
Based on the landscape indices, we applied Partitioning Around Medoids (PAM) clustering to classify different types of urban green and blue spaces. Unlike K-means, whose objective function rests on Euclidean distance and is thus highly sensitive to outliers, PAM uses representative medoids, is more robust to outliers, and can accommodate mixed data types [45,46]. Our landscape indices contain both outliers and categorical variables; hence, PAM offers a more appropriate and stable clustering solution. All variables were first standardized using Z-score transformation. The optimal number of clusters was determined using the Silhouette Coefficient, with candidate cluster numbers (k) evaluated from 1 to 10.
To visualize the characteristics of each cluster, we used rose–wind diagrams to display the mean values of landscape indices across groups. To identify the driving factors behind the different types of UGBS landscape patterns, we selected six influencing factors representing both natural and socioeconomic dimensions (Table 2). We used the Kruskal–Wallis test with Dunn’s post hoc adjustments to compare these factors of different blue and green landscapes.

2.4. Analyzing the Spatial Equity of the Coupled Green and Blue Spaces

We used Spearman’s rank correlation analysis to examine the relationships between landscape metrics and variables representing spatial equity. Previous studies in the Yangtze River Delta assessed the equity of UGBS through social stratifications such as age, gender, ethnicity, and income [35,49,50]. However, most of these variables (e.g., income, gender, and ethnicity) are restricted to administrative units (subdistrict or district) in the national census, precluding fine-scale spatial analysis. Consequently, we focused only on the elderly (aged 65 and above) and children (aged 0–14), together with economic development proxied by nighttime-lights intensity, building height, and GDP.
Data on the proportion of elderly and children populations were derived from the Age-Stratified Population Estimation from the 2020 China Census by Township (ASPECT), a 100 m resolution gridded dataset of age-structured populations [51,52]. ASPECT integrates township census data with high-resolution spatial covariates using random forest models to produce fine-scale, age-stratified population estimates. Compared with coarser datasets such as WorldPop, ASPECT likely provides improved accuracy in capturing the spatial distribution of different age groups [52]. Building heights were estimated from all-weather earth observations (radar, optical, and night lights) using a Random Forest model, showing strong agreement with national-scale observations [53]. Nighttime light data were obtained at 500 m resolution [54], building height at 10 m resolution [53], and GDP at 1000 m resolution (available at http://www.resdc.cn/ accessed on 20 November 2025). To ensure consistent spatial resolutions, all variables were aggregated to 500 m and 1 km grids by averaging, whereas population was summed within each grid cell.
To compare differences in UGBS among urban areas across different periods, we used built-up area boundary data [55] to differentiate old urban areas, recently developed areas, and newly developed areas. Specifically, the old urban area refers to the urban extent within the 1992 city boundary; the recently developed area corresponds to the expansion between 1992 and 2000; and the newly developed area represents the expansion from 2000 to 2020. We compared landscape metrics among these urban regions across different periods via the Kruskal–Wallis test, with Dunn’s post hoc test for pairwise comparisons.

3. Results

3.1. Integrated Landscape Pattern of the Coupled Green and Blue Spaces at Multiple Scales

Generally, metrics representing the composition and quality of green and blue spaces (CA, PLAND, LPI, MPS, NDVI) were positively correlated, but negatively correlated with configuration metrics (PARA, DIVISION) (Figure 3). Green space composition metrics showed a negative relationship with blue space composition metrics, indicating that regions with larger green spaces tend to have smaller blue spaces. Spatially, blue space composition values were higher along the Yangtze River (Figure 4 and Figure S1), while metrics representing green space quantity and quality were higher around Zhongshan (Purple Mountain).
Metrics representing the relative composition of green and blue spaces (AR, MPSR, LPR, LE) were positively correlated, whereas negatively correlated with metrics reflecting distances between green and blue spaces (D) and waterfront green spaces (WGS300A and NDVIWGS) (Figure 3). Spatially, the area ratio (AR), mean patch size ratio (MPSR), and largest patch index ratio (LPR) showed relatively high values in waterfront areas (Figure 5 and Figure S2). The average edge length (LE) was approximately 430 m at the 500 m scale and 984 m at the 1 km scale. The distance from green spaces to the nearest blue spaces (D) was 334 ± 292 m at the 500 m scale and 317 ± 259 m at the 1 km scale. Across both spatial scales, blue–green spaces constituted the dominant landscape type. At the 500 m scale, green-only spaces accounted for 43%, whereas blue–green mixed spaces accounted for 49%. At the 1 km scale, the proportion of blue–green spaces rose to 75%, while green-only spaces fell below 20%.

3.2. Types of Coupled Green and Blue Spaces

Landscape indices of UGBS were clustered into five major types, with consistent patterns observed at both the 500 m and 1 km scales (Figure 6a,b). Blue-dominated blue–green landscapes and blue landscapes were characterized by high values of blue space indices (UBS_LPI, UBS_MPS, UBS_PLAND, UBS_CA). However, blue landscapes exhibited the largest distances to the nearest green space (D), whereas blue-dominated blue–green landscapes had higher values for waterfront green space metrics (NDVI_WGS, A_WGS). Green-dominated blue–green landscapes were distinguished by larger and greener patches. Both green and blue–green landscapes comprised smaller green spaces, but blue–green landscapes displayed more complex blue space configurations (UBS_PARA, UBS_SHAPE, UBS_DIVISION) and higher values of waterfront green composition. Overall, blue–green landscapes were the dominant type at both 500 m and 1 km scales.
The five landscape types exhibited distinct socioecological characteristics (p < 0.001, Figure 6c). Blue–green and green landscapes were associated with higher total population, road density, and number of points of interest (POIs). Green-dominated blue–green landscapes occurred in higher-elevation, steeper areas with lower temperatures. Pure blue landscapes consisted primarily of grids dominated entirely by water and showed lower values across all socioeconomic variables.

3.3. Spatial Equity of the Coupled Green and Blue Spaces

In general, different sociodemographic groups exhibited similar relationships with the metrics representing blue, green, and coupled blue–green spaces (Figure 7). However, compared with blue spaces, socioeconomic status showed stronger negative associations with green spaces, the distance between green and blue spaces, and metrics of waterfront green spaces. Specifically, the proportion of elderly residents showed significant but weak positive correlations with metrics representing the composition of green and blue spaces and with indicators describing their interrelationships (e.g., WGS300A and NDVIWGS). In contrast, the proportion of children showed significant but weak negative correlations with metrics reflecting the composition of green and blue spaces, as well as with WGS300A and NDVIWGS. Socioeconomic variables (GDP per capita, building height, and nighttime light) exhibited significant negative correlations with the composition of green and blue spaces and with WGS300A and NDVIWGS.
Landscape metrics differed among cities developed before 1992, between 1992 and 2000, and between 2000 and 2020, with broadly consistent patterns observed at both the 500 m and 1000 m scale (Table S1, Figure 8 and Figure S3). Compared with cities developed in other periods, those urbanized between 1992 and 2020 had larger, more abundant, and less fragmented blue spaces, as indicated by higher values of composition metrics (UBS_CA, UBS_PLAND, UBS_LPI, UBS_MPS) and lower values of configuration metrics (UBS_SHAPE, UBS_DIVISION) (Figure 8a). For green spaces, newly developed urban areas also showed higher composition metrics (UGS_CA, UGS_PLAND, UGS_LPI, UGS_MPS) as well as higher vegetation quality (UGS_NDVI), indicating that more recent urban development tended to incorporate greener landscapes (Figure 8b). Metrics describing the spatial relationships between green and blue spaces further revealed that recently developed areas had lower values of AR, MPSR, LPR, and D, along with higher values of WGS300A and NDVIWGS (Figure 8c). This suggests that new urban regions are characterized by green-space–dominated landscapes more closely integrated with water bodies.

4. Discussion

4.1. To What Degree Are Green and Blue Spaces Spatially Coupled?

We found that even at a fine spatial resolution of 500 m × 500 m—roughly corresponding to the scale of an urban block—the proportion of areas where green and blue spaces coexist reaches up to 50% (Figure 5). This high degree of spatial overlap underscores the importance of identifying optimal blue and green space combinations that can enhance both ecosystem services and human well-being. The average distance from green space to the nearest water body is around 300 m (Figure 5), which falls within typical cooling distances reported for green spaces. For instance, the average cooling range of green spaces is about 570 m in Shanghai [56], 347 m in Bengaluru [57], and 60 to 270 m in Nanning [58]. Such spatial proximity suggests that the cooling effect of green spaces is likely to interact with nearby water bodies, potentially amplifying microclimatic regulation through synergistic interactions between green and blue spaces.
Nanjing’s green–blue co-occurrence aligns with values reported for other southeastern Chinese cities. For example, 80.33% of scattered water bodies in Fuzhou are fringed by green space [59], consistent with the about 50% co-occurrence rate observed herein. The low blue–green area ratio in Nanjing is also consistent with Fuzhou [59], with about 80% of values below 0.5. In Hangzhou, the weighted average nearest distance between green and blue spaces is about 500 m, ranging from 100 m to nearly 900 m [60], exceeding ours despite a comparable range. Although many studies quantified distance, edge length, or area ratios, few provide an explicit blue–green coupling index. This hampers cross-city comparisons across typologies or regions and underscores the necessity for a unified, region-wide framework.
The five types of blue–green landscape combinations exhibited distinct socioecological signatures (Figure 6). Higher population density in green-dominated landscapes corroborates earlier evidence linking demographic growth to UGBS enhancement [61]. The widespread presence of mixed blue–green spaces in Nanjing is largely due to the subtropical climate and dense river network, which creates numerous small, fragmented areas where green and blue spaces co-occur [62]. We also found that blue-dominated blue–green landscapes were concentrated in low-elevation, flat regions with relatively low population density but a high density of POIs. Such areas often serve as waterfront recreational spaces, providing unique physical and mental health benefits compared to other urban open spaces [63,64,65].
Nevertheless, our findings are potentially sensitive to the choice of indicators and the spatial scale [66,67,68]. The modifiable areal unit problem (MAUP) suggests that both the variables per se and their interrelationships can vary with zoning scheme, indicating that the results require cautious interpretation [69,70]. We found that landscape indices varied with the size of the analytical grid, and that correlations among indices were stronger at the 500 m scale than at 1 km (Figure 3). This is likely because the 500 m grid captures more local details (e.g., small ponds or fragmented riparian green belts) and thus represents more homogeneous landscape units. In contrast, aggregating these fine-scale features into a 1 km grid may obscure or average out local heterogeneity, thereby weakening inter-metric correlations. This also indicates that, at finer spatial scales, a small number of indices may effectively represent overall landscape characteristics; however, as the analysis scale increases, relationships among indices tend to weaken, and a greater number of metrics may be needed to adequately describe landscape patterns.

4.2. How Are the Coupled Green and Blue Spaces Distributed Among Different Sociodemographic Groups?

We found that in terms of exposure to the coupled green and blue spaces, older populations are relatively—though only slightly—advantaged, whereas children are relatively disadvantaged (Figure 7). This pattern is consistent with previous findings on equity in ES provision in Nanjing [35] and park access equity in Shanghai [71]. One possible explanation is that children typically reside in areas well-served by schools and commercial amenities, whereas older residents—often longer-term settlers—favor open public green space for recreation [71].
In contrast, socially and economically developed areas generally contain smaller green space areas and exhibit greater distances between green and blue spaces, suggesting potential ecological disadvantages associated with higher urban development intensity (Figure 7). This may be because urban land expansion associated with economic growth has encroached upon green and blue spaces [72]. However, the observed disparities may not reflect actual inequality. Socioeconomic development at the neighborhood level does not always correspond to individual income. Moreover, our analysis focused primarily on the morphological characteristics of green spaces, neglecting accessibility dimensions. Existing studies show that higher-status residents benefit from superior vegetation maintenance [73], richer streetscape greenery [74,75], and enhanced park accessibility, rather than from greater UGBS quantity alone [76].
We found that newly developed urban areas exhibited larger and higher-quality green spaces, along with lower fragmentation (Figure 8 and Figure S3). Similarly, Yu [77] reported that green space exposure is generally more evenly distributed in newly built districts compared with older urban areas, and Zhang [78] found that in most megacities and large cities experiencing rapid urbanization after 2010, urban tree canopy cover within newly developed built-up areas increased significantly. These patterns may reflect a growing emphasis on green space provision in response to urbanization-driven loss of natural land. In Nanjing, early efforts included the 2002 “Opinions on Building a Green Nanjing” and the spatial development strategy of “preserving the old city while constructing the new city” during 2000–2005 [36]. Later, after 2015, park and landscape development projects in mountainous, peri-urban, and waterfront areas further promoted the expansion and improvement of green spaces [36].

4.3. Implications and Research Limitations

As China transitions to slower, stock-oriented urbanization, spatial planning is pivotal to urban sustainability [79,80]. We found that although green and blue spaces frequently coexisted in Nanjing, approximately 40% of green spaces were located more than 300 m from the nearest waterbody (Figure 5 and Figure S2). This suggests that many green spaces cannot directly benefit from adjacent water bodies. Urban planning must therefore transcend single-element approaches and explicitly integrate blue–green coupling. A blue–green infrastructure network linking parks, heritage sites, recreation areas and rivers, for example, would enhance connectivity and multifunctionality [81,82,83]. Merely increasing large patch numbers is insufficient; coordinated blue–green arrangement is equally critical. For air purification and microclimate regulation, dense blue spaces perform best alongside dispersed green spaces that sustain airflow, whereas sparse blue spaces benefit from clustered green patches [11].
We found that in higher socioeconomic areas, green spaces tend to be smaller and more fragmented, and blue–green integration indices correspondingly decline. Relative to green spaces, waterbodies are less amenable to restoration, relocation or artificial construction. Although residents with higher socioeconomic status can finance managed vegetation, this advantage does not readily offset urbanization-induced hydrological degradation. Therefore, strategically locating green spaces within a constrained buffer of existing waterbodies will ensure equitable access to blue–green synergistic benefits across the population.
Our study established a framework for characterizing the coupled green and blue spaces; however, several limitations should be acknowledged. First, while our framework offers guidance for future indicator selection, diverse metrics across regions and space types make capturing all dimensions challenging. Waterfront green space, for example, was variously defined as either the narrow strip directly abutting waterbodies [22] or as any green area within a 30 m buffer [9]. These discrepancies stem from contextual differences, waterbody morphology, and the scale of interest (e.g., park design versus citywide planning). Beyond horizontal patterns, vertical interactions—such as tree canopies shading water—also influence the synergistic effects of green and blue spaces. Given strong intercorrelations among many landscape metrics [84], sensitivity testing is imperative to distill a parsimonious, representative subset for typical blue–green scenarios (wetlands, lakes, rivers) [85,86]. Future studies should incorporate a wider set of equity-related variables—such as educational attainment, economic status, and ethnicity—to more comprehensively assess the spatial distribution and fairness of blue–green spaces.

5. Conclusions

Our study proposed a framework for analyzing the integrated landscape pattern of coupled urban green and blue spaces. Applying this framework to Nanjing, we found that the co-occurrence of green and blue spaces is a typical feature of subtropical urban landscapes. Even at the urban-block scale, up to 50% of areas contain both blue and green elements, and the average distance from green spaces to the nearest blue spaces remains relatively short (approximately 300–350 m). Elderly residents experience relatively equitable coverage of green and blue spaces, while communities with higher socioeconomic development tend to have fragmented green spaces and fewer opportunities for blue and green integration. Newly developed districts provide larger and higher-quality green spaces, shorter blue–green distances, and more extensive waterfront green areas. Given the high frequency of blue–green co-occurrence, our findings underscore the need to prioritize integrated blue–green planning and management in subtropical cities. Policies in socioeconomically advantaged communities and older urban districts should prioritize improving access to green and blue spaces, enhancing connectivity, and promoting coordinated spatial planning. The proposed analytical framework provides a more comprehensive perspective for assessing the degree of blue–green integration and enables comparative analyses across regions.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/land14122311/s1.

Author Contributions

Conceptualization, Methodology, Investigation, Formal analysis, Writing—original draft, Writing—review and editing, L.L.; Methodology, Investigation, Visualization, Formal analysis, J.Z.; Methodology, Investigation, Visualization, Formal analysis, Y.L.; Visualization, Y.F.; Visualization, B.H.; Conceptualization, Supervision, Funding acquisition, Writing—review and editing, C.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the National Natural Science Foundation of China (42401370), Natural Science Foundation of Jiangsu Province (BK20241536), the MOE (Ministry of Education in China) Liberal arts and Social Sciences Foundation (24YJCZH174), and Natural Science Foundation of Inner Mongolia (2024QN04026).

Data Availability Statement

The dataset is available from the authors upon request.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Spatial distribution of land use and land cover in Nanjing, illustrating city divisions and analytical grids at 500 m and 1000 m spatial resolutions.
Figure 1. Spatial distribution of land use and land cover in Nanjing, illustrating city divisions and analytical grids at 500 m and 1000 m spatial resolutions.
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Figure 2. Venn diagram illustrating the landscape metrics for individual green and blue spaces, as well as the coupled relationships between them.
Figure 2. Venn diagram illustrating the landscape metrics for individual green and blue spaces, as well as the coupled relationships between them.
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Figure 3. Spearman correlations among landscape indicators at the 500 m and 1 km scales. Blue labels indicate metrics related to urban blue spaces, green labels indicate metrics related to urban green spaces, and orange labels indicate metrics representing the spatial relationships between urban green and blue spaces. Blank cells indicate non-significant results (p > 0.05).
Figure 3. Spearman correlations among landscape indicators at the 500 m and 1 km scales. Blue labels indicate metrics related to urban blue spaces, green labels indicate metrics related to urban green spaces, and orange labels indicate metrics representing the spatial relationships between urban green and blue spaces. Blank cells indicate non-significant results (p > 0.05).
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Figure 4. Spatial patterns and frequency distributions of standardized landscape metrics for individual urban green and blue spaces at 500 m scale.
Figure 4. Spatial patterns and frequency distributions of standardized landscape metrics for individual urban green and blue spaces at 500 m scale.
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Figure 5. Spatial patterns and histogram distributions of the indicators representing the spatial relationship between green and blue spaces at 500 m scale. AR: Area ratio; MPSR: Mean patch size ratio; LPR: Largest patch index ratio; LE: Length of edge in contact with blue spaces; D: Distance from green spaces to the nearest blue space; WGS300A: Area of waterfront green spaces within a 300 m buffer; NDVIWGS: NDVI of waterfront green spaces; CT: Combination type.
Figure 5. Spatial patterns and histogram distributions of the indicators representing the spatial relationship between green and blue spaces at 500 m scale. AR: Area ratio; MPSR: Mean patch size ratio; LPR: Largest patch index ratio; LE: Length of edge in contact with blue spaces; D: Distance from green spaces to the nearest blue space; WGS300A: Area of waterfront green spaces within a 300 m buffer; NDVIWGS: NDVI of waterfront green spaces; CT: Combination type.
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Figure 6. Types of coupled green and blue spaces identified by cluster analysis: Spatial distribution and the normalized values of landscape metrics in each type of blue and green bundle at the scale of 500 m (a) and 100 m (b). Comparison of natural and socioeconomic factors among different types of blue and green landscape at 500 m scale (c). Different letters above theboxplots indicate significant different levels of the influencing factors (p < 0.05), with the medians decreasing from a to e.
Figure 6. Types of coupled green and blue spaces identified by cluster analysis: Spatial distribution and the normalized values of landscape metrics in each type of blue and green bundle at the scale of 500 m (a) and 100 m (b). Comparison of natural and socioeconomic factors among different types of blue and green landscape at 500 m scale (c). Different letters above theboxplots indicate significant different levels of the influencing factors (p < 0.05), with the medians decreasing from a to e.
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Figure 7. Spearman correlations of sociodemographic groups and landscape indicators at 500 m (a) and 1 km scales (b). Blue labels indicate metrics related to urban blue spaces, green labels indicate metrics related to urban green spaces, orange labels indicate metrics representing the relationships between urban green and blue spaces, and purple labels indicate metrics related to population characteristics. Blank cells indicate non-significant results (p > 0.05).
Figure 7. Spearman correlations of sociodemographic groups and landscape indicators at 500 m (a) and 1 km scales (b). Blue labels indicate metrics related to urban blue spaces, green labels indicate metrics related to urban green spaces, orange labels indicate metrics representing the relationships between urban green and blue spaces, and purple labels indicate metrics related to population characteristics. Blank cells indicate non-significant results (p > 0.05).
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Figure 8. Comparison of landscape metrics describing single blue spaces (a), green spaces (b), and the relationship between green and blue spaces (c) across built-up areas developed before 1992, during 1992 and 2000, and during 2000 and 2020 (500 m scale). Different letters above theboxplots indicate significant different levels of the influencing factors (p < 0.05), with the medians decreasing from a to c.
Figure 8. Comparison of landscape metrics describing single blue spaces (a), green spaces (b), and the relationship between green and blue spaces (c) across built-up areas developed before 1992, during 1992 and 2000, and during 2000 and 2020 (500 m scale). Different letters above theboxplots indicate significant different levels of the influencing factors (p < 0.05), with the medians decreasing from a to c.
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Table 1. A framework for characterizing the integrated landscape patterns of the coupled UGBS.
Table 1. A framework for characterizing the integrated landscape patterns of the coupled UGBS.
CategoryMetrics, Abbreviations, and ReferencesDefinition
Single green and blue spaceCompositionClass area (CA) [20]The total area of all patches of green or blue spaces
C A = ∑ j = 1 n a i j
aij is the area of the jth patch of type i
Percentage of landscape (PLAND) [23,40]The proportion of the total area occupied by blue or green spaces
P L A N D = ∑ j = 1 n a i j A × 100
aij is the area of the jth patch of type i, and A is the total landscape area
Largest patch index (LPI) [41]The proportion of the landscape area occupied by the largest blue or green patch
L P I = max j = 1 n ( a i j ) A × 100
aij is the area of the jth patch of type i, and A is the total landscape area
Mean patch size (MPS) [9,40]The average size of individual blue or green space patches
M P S = ∑ j = 1 n a i j N i
aij is the area of the jth patch of type i, and Ni is the total number of patches of type i
Configuration Division index (DIVISION) [39]Degree of fragmentation among blue or green space patches
D I V I S I O N = 1 − ∑ i = 1 m ∑ j = 1 n a i j A 2
aij is the area of the jth patch of type i, and A is the total landscape area
Shape index (SHAPE) [20,42]Complexity of patch shape
S H A P E = ∑ i = 1 m ∑ j = 1 n 0.25 P i j a i j N i
aij is the area of the jth patch of type i, Ni is the total number of patches of type i, and Pij is the perimeter of the jth patch of type i
Perimeter-area ratio (PARA) [42]The average ratio of the perimeter to the area of blue or green space patches
P A R A = ∑ i = 1 m ∑ j = 1 n P i j a i j N i
aij is the area of the jth patch of type i, Ni is the total number of patches of type i, and Pij is the perimeter of the jth patch of type i
QualityNormalized Difference Vegetation Index (NDVI)Indicator of vegetation quality
Relationship between blue and green space CompositionArea ratio (AR) [43]Ratio of the total area of blue spaces to green spaces
A R = C A U B S C A U G S
CAUBS and CAUGS are the areas of blue and green spaces, respectively
Mean patch size ratio (MPSR)Ratio of the mean patch size of blue spaces to that of green spaces
M P S R = M P S U B S M P S U G S
MPSUBS and MPSUGS are the mean patch size of blue and green spaces, respectively
Largest patch index ratio (LPR)Ratio of the largest patch size of blue spaces to that of green spaces
L P R = L P I U B S L P I U G S
LPIUBS and LPIUGS are the largest patch size of blue and green spaces, respectively
Combination type (CT)Type of blue and green spaces combination, including blue-green space, blue space, and green space
Configuration Length of edge contacted with blue spaces (LE) [20,43]Total length of the waterfront shoreline within the blue and green spaces
Distance from green spaces to blue spaces (D) [25]Average shortest distance from the centroid of green patches to the nearest water bodies
Area of waterfront green spaces (WGS300A) Total area of green spaces within 300 m buffer zones of blue spaces
QualityNDVI of waterfront green spaces (NDVIWGS)Vegetation characteristics of waterfront green spaces
Table 2. Influencing factors for the green and blue landscape.
Table 2. Influencing factors for the green and blue landscape.
CategoryInfluencing FactorUnitSources
Natural factorDEMmhttps://www.earthdata.nasa.gov/ (accessed on 20 November 2025)
Slope°https://www.earthdata.nasa.gov/ (accessed on 20 November 2025)
Annual average temperature°C[47]
Socioeconomic factorPopulationperson[48]
Road densitykm/km2http://www.resdc.cn/ (accessed on 20 November 2025)
Points of interestcounthttps://lbs.amap.com/ (accessed on 20 November 2025)
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Liu, L.; Zhang, J.; Liu, Y.; Fan, Y.; He, B.; Shang, C. Towards an Integrated Framework for Understanding the Landscape Pattern of Coupled Urban Green and Blue Spaces. Land 2025, 14, 2311. https://doi.org/10.3390/land14122311

AMA Style

Liu L, Zhang J, Liu Y, Fan Y, He B, Shang C. Towards an Integrated Framework for Understanding the Landscape Pattern of Coupled Urban Green and Blue Spaces. Land. 2025; 14(12):2311. https://doi.org/10.3390/land14122311

Chicago/Turabian Style

Liu, Lumeng, Jiajia Zhang, Yilin Liu, Yuchen Fan, Baiting He, and Chenwei Shang. 2025. "Towards an Integrated Framework for Understanding the Landscape Pattern of Coupled Urban Green and Blue Spaces" Land 14, no. 12: 2311. https://doi.org/10.3390/land14122311

APA Style

Liu, L., Zhang, J., Liu, Y., Fan, Y., He, B., & Shang, C. (2025). Towards an Integrated Framework for Understanding the Landscape Pattern of Coupled Urban Green and Blue Spaces. Land, 14(12), 2311. https://doi.org/10.3390/land14122311

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