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Article

An Integrated Model for Constructing Urban Ecological Networks and Identifying the Ecological Protection Priority: A Case Study of Wujiang District, Suzhou

by
Liyu Pan
1,
Wenquan Gan
2,3,*,
Jinliu Chen
2,4 and
Kunlun Ren
2,5
1
Department of Geography, National University of Singapore, Singapore 119077, Singapore
2
School of Design, Xi’an-Jiaotong Liverpool University, Suzhou 215123, China
3
School of Environmental Sciences, University of Liverpool, Liverpool L69 3BX, UK
4
School of Design and Art, Suzhou City University, Suzhou 215104, China
5
School of Architecture, University of Liverpool, Liverpool L69 3BX, UK
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(5), 4487; https://doi.org/10.3390/su15054487
Submission received: 3 February 2023 / Revised: 24 February 2023 / Accepted: 27 February 2023 / Published: 2 March 2023
(This article belongs to the Special Issue Ecological Sustainability and Landscape Ecology)
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Abstract

:
As a result of the rapid urbanisation in China, the ecological system in urban areas has become fragmented, posing a threat to ecological stability. Constructing ecological networks is considered a critical strategy to reconnect habitats, restore ecosystems and improve ecological capacity. This research aims to develop a GIS-based model that can inform urban ecological network construction and identify the priority areas for ecological protection in a given urban context. The methodological prototype considers land use, habitat conditions and human interventions from an integrated perspective and has been tested based on a case study in Wujiang District, Suzhou. The results show that (i) 30 critical ecological patches were identified, including 2 vital, 4 important and 24 general cores; (ii) 69 ecological corridors, including 15 vital, 36 important and 18 general corridors, 59 ecological nodes and 24 barriers were determined. Based on these results, this research identified priority restoration and protected areas that urgently require the restoration of ecological networks according to their importance. This research proposes further recommendations on management strategies for construction and protection guidance at macro and micro levels in accordance with existing territorial and spatial planning of Wujiang. The model developed in this research provides a scientific methodology for planning and optimising ecological networks and can serve as a basis for realising ecological protection.

1. Introduction

Urbanisation is a complicated process where the natural ecological environment has been turned into an artificial built environment [1]. During this process, urban landscapes progressively replace ecological landscapes, putting tremendous pressure on the functioning and structure of local ecosystems [2,3]. Human interventions, especially construction activities, can significantly alter geographic conditions, such as the density of blue-green network and permeability of the land [4,5]. This has not only broken the local ecological system into fragmented habitat patches but also reduced the overall ecological capacity of the landscape matrix [6,7,8]. Consequently, such environmental issues can even threaten ecological security and thereby undermine the sustainable urban development of highly urbanized regions or countries [9,10].
To mitigate these challenges, the concept of urban ecological networks provides a promising solution for ecological habitat protection, and it has been incorporated into urban ecological planning and policy-making [11,12]. By combining different landscape types organically, ecological networks can bring multiple benefits to the urban environment, such as restoring ecological systems [13], increasing biodiversity [14], reducing flood risk [15] and facilitating sustainable development [16]. The main principle of ecological network construction and optimisation is about increasing ecological corridors, reducing barriers and repairing ecological breakpoints according to the degree of connectivity of the existing landscape [17]. To be more specific, extra ecological corridors should be planned and constructed to connect the isolated habitat patches, and when the patches are far apart, steppingstone patches can be created within corridors to maximize the migration of biological species [18]. Since the urban ecological network consists of ecological cores, buffer zones and corridors, the main task for planners is to identify key ecological patches and then construct and optimise ecological corridors [19,20].
However, there is a lack of scientific and credible methods in the current research and practice of ecological network construction. The research mode of “source identification–resistance surface construction–extraction corridor” is the mainstream ecological network construction research method [16,21]. However, compared with previous studies, it has been found that most studies have obvious shortcomings in one or more parts. In identifying ecological sources, there is a considerable amount of research using qualitative methods to determine the ecological patches, which heavily depends on planners’ experience in evaluating the ecological value of different green spaces and deciding a region’s ecological sources [22]. In most studies that use quantitative methods to determine ecological elements, only one measure is used, such as the importance of ecosystem services [23,24], landscape connectivity [17,25], habitat quality [26] or ecological resources of a certain scale (e.g., the nature reserves and scenic spots) [27]. The lack of verification may lead to deviations in the identification of ecological sources. Constructing an ecological resistance surface is an essential prerequisite for extracting ecological corridors and building ecological networks. However, most previous research directly took land use as the evaluation standard and determined the resistance values of different types of land through expert evaluation [9,28,29]. Due to the inconsistent understanding of ecological resistance and the specific differences in ecological problems faced by various regions, different experts assign different values to the resistance of land-use types. Furthermore, due to the intricate interplay between land use and ecological processes, homogenous assignment based on land-use type unavoidably covers the spatial heterogeneity between the same land-use type. This method cannot accurately reflect the spatial difference of ecological resistance, which decreases the credibility of constructed ecological networks. Consequently, developing a scientific methodology that considers more comprehensive indicators to guide the identification of ecological patches, extraction of ecological corridors, and repair of ecological networks is necessary.
Given this background, this research aims to develop an integrated model that employs multiple approaches to guide the construction of ecological networks from a more scientific perspective. In addition, to verify the effectiveness of this methodological prototype, a case study was conducted in Wujiang District, the core area of the demonstration zone of green and integrated ecological development of the Yangtze River Delta (DZGIED). The Yangtze River Delta is one of the most economically vibrant and highly urbanised regions in China. Cities located in this area often have rich water resources such as rivers, water channels, lakes and ponds and so on, which provides a robust foundation for ecological networks. Wujiang District, Suzhou, is a typical case of this kind of Jiangnan water towns. During the rapid urbanisation process in this region, the unique townscape of Wujiang District has gradually disappeared [9]. The polder fields within the dense water network have been replaced by urban fabric, such as nonpermeable roads and residential and commercial buildings. Such transformations have resulted in the destruction of habitat patches and ecological corridors, and thereby made a negative impact on the local ecological circle. To promote ecological civilization and to explore sustainable urban ecological development patterns, the central government of China has decided to develop a demonstration zone of green and integrated ecological development of the Yangtze River Delta (DZGIED) in this region, including Wujiang District in Suzhou, Qingpu District in Shanghai and Jiashan County in Jiaxing, which are suitable for selected as the case to validate the proposed model. The case study aims to explore the following questions using the created model: (i) how to quantitatively assess and analyse the relative importance of ecological patches and identify their priority; (ii) how to use the proposed model to develop the ecological resistance surfaces and then build ecological corridors accordingly; (iii) how to use the existing ecological resources as steppingstones to optimise the ecological networks; (iv) what supplementary, detailed, and actionable suggestions can be put forward to improve the existing land use strategy.

2. Materials and Methods

2.1. Study Area

The project site is located in Wujiang District, Suzhou (30°45′–31°13′ N, 120°21′–120°53′ E) (Figure 1), with a total area of 1227 km2. As a typical Jiangnan water town, Wujiang District has a dense water network system consisting of rivers, ditches, lakes and ponds and so on. There are more than 300 large and small lakes within the region, accounting for one-third of the total area, and it is also an important area of the DZGIED. The DZGIED was planned at the junction of Shanghai, Jiangsu and Zhejiang Province, including Qingpu District in Shanghai, Wujiang District in Suzhou, and Jiashan County in Jiaxing, covering an area of about 2300 km2. Wujiang District accounts for nearly half the total area, approximately 1092 km2. The planning of this core area intends to demonstrate the best practice of ecological protection and sustainable urban regeneration.

2.2. Data Source

The data are derived from remote sensing products, public datasets, and Amap API. Among them, (1) land use data was interpreted from a Sentinel 2 remote sensing image in 2022 (27 July 2022–02:35:29) with 10 m resolution. The satellite image was pre-processed, including image geometry correction and registration, image enhancement and cropping, and then the land use and land cover were identified using the method of supervised classification and random forest algorithm. The classification standard refers to the classification of land resources in China, taking into account the atlases, planning documents from the Wujiang planning bureau and actual land use situations as additional references. Eight land types were classified with a reliable result of accuracy assessment, and the results are shown in Figure 2 and Table 1; (2) NDVI data in 2020 with a resolution of 30 m were downloaded from National Ecosystem Science Data Center (http://www.nesdc.org.cn/ (accessed on 12 December 2022)); 100 m resolution gridded population density data in 2020 were obtained from Worldpop (https://www.worldpop.org/ (accessed on 12 December 2022)); (3) road network and POI data were acquired from AmapAPI (https://lbs.amap.com/ (accessed on 20 January 2023)) using Python.

2.3. Methods

The proposed integrated model in this research combines quantitative analysis and spatial analysis methods. As shown in Figure 3, this model consists of three steps. The first step was to identify ecological source, where the critical ecological patches were identified by analysing the land use data using the morphological spatial pattern analysis (MSPA) method. Subsequently, the priority of the identified ecological patches was determined using landscape connectivity analysis. The second step was to establish the ecological resistance surface. The resistance of ecological flow is primarily influenced by both natural and human interventions, and the spatial heterogeneity of the same land use type also has a great impact [30,31]. Based on previous research methods, an index system for assessing ecological resistance surfaces was constructed from a comprehensive perspective [32,33,34,35,36]. Specifically, this index system can be represented by habitat quality, habitat degradation and factional vegetation coverage (FVC), as well as population density and road network density. Following that, the analytic hierarchy process (AHP) was applied to assign the relative weight of the five types of influencing factors. According to these weight assignments, the corresponding resistance maps were integrated into one resistance surface in ArcGIS. The last step was to optimise the ecological network, including the identification of ecological corridors and critical ecological nodes. The significant potential corridors were identified based on minimum cumulative resistance, while the ecological nodes, barriers, and the priority of corridors were evaluated by circuit theory. The proposed model combines multiple approaches to optimise each step in the predominant “resource identification–construction of resistance surface–extraction corridor” method, which can provide the theoretical basis for future research. The detailed procedures for each part are presented in the following sections.

2.4. Identification of the Ecological Sources

2.4.1. Ecological Sources Identification based on the Morphological Spatial Pattern Analysis (MSPA)

The morphological spatial pattern analysis (MSPA) functions as a sequence of custom mathematical morphological operators developed by Soille and Vogt [37]. It is often used to describe the geometric structure and connectivity of image components. Based on a geometric concept, this method contains a sequence of morphological transformations, including erosion, geodesic dilations, and anchorage skeletonization, for analysing the geometry and connectivity of land cover patterns on raster maps [38]. The mathematical morphological operator allows MSPA to classify the raw binary image into pixel-level maps and assign each foreground pixel to one of the seven mutually exclusive categories defined in MSPA, including cores, islets, loops, perforations, edges, bridges and branches (Table 2). The utilization of this method has enhanced the scientific rigor of ecological patch and corridor identification [39]. Its validity has been demonstrated across a diverse range of studies, including investigations into forest fragmentation at the town-level in Massachusetts, USA [40], and in support of greenway design in Europe [41]. Therefore, MSPA is considered one of the essential methods for determining ecological elements in this research. The land use map of Wujiang District (Figure 2) was converted into a binary raster map using a reclassification tool in ArcGIS to distinguish the ecological area (foreground) and another land type (background). Specifically, wetlands, grassland, forest and water body are defined as foreground and assigned a value of 1, while others are assigned 0. The foreground has been further subdivided into seven categories, corresponding to different ecological landscapes. The “core” represents the relatively large patches serving as key ecological sources [42]. This study selected 50 core ecological patches in accordance with the classification results of MSPA. Further analysis will be conducted for connectivity to extract important core patches as ecological sources.

2.4.2. Identify the Priority of Ecological Cores Based on the Landscape Connectivity

Landscape connectivity is an essential precondition for maintaining patch environmental function and ecological flow diffusion within the core source area and a necessary embodiment of how the landscape promotes or obstructs ecological flow [43]. Given its paramount significance for biodiversity conservation and ecosystem stability maintenance, landscape connectivity is a crucial indicator for measuring ecological processes [44]. The probability of connectivity (PC) and the important values of the patches (dPCs) have been seen as critical indicators for assessing landscape connectivity, reflecting the connectivity degree between core patches [17]. The probability connectivity index (PC) reveals the change in landscape structure and connectivity as the result of removing a particular patch to express the importance of that patch [45]. The two variables are frequently used in ecological planning to define connectivity by measuring the probability of direct migration between two patches and are widely used to assess species migration’s intensity, frequency and flexibility [46]. The higher landscape connectivity index represents the higher priority of this patch in the ecological networks and the more significant biological migration and interaction between patches [45]. In order to evaluate the probability connectivity index (PC) and the important values of the patches (dPCs) of each core area, the classification of ecological elements obtained from the MSPA analysis was imported into ArcGIS10.8 as the critical data. Subsequently, the Conefor Sensinode 2.6 software package (A GIS extension) has been used to quantify the degree to which habitat areas and their linkages are essential for maintaining or improving the connectivity and evaluating impacts on the connectivity of habitat and landscape changes [46]. The calculation method is shown below:
This is example 1 of an equation:
P C = ∑ i = 1 n ∑ j = 1 n a i × a j × p i j * A L 2
Among them, n denotes the number of patches within the region; a i and a j represent the areas of patch i and j; p i j * is the maximum probability of species dispersion between patch i and patch j; A L is the total area of Wujiang District.
d P C = P C − P C r e m o v e P C × 100 %
where PC is the probability of the connectivity index of a specific patch in Wujiang District, which ranges from 0 to 1; P C r e m o v e represents the probability of the connectivity index after removing this patch. In this research, the ecological cores with the 30 highest dPCs were determined as significant ecological cores for the optimisation of the ecological network.

2.5. Construction of Ecological Resistance Surface

Ecological resistance refers to the extent to of species are hindered when propagating and migrating between different habitat patches [47,48]. Landscape resistance between source and destination significantly affects landscape connectivity and species migration, exhibiting inherent spatial variability [49]. Regarding data availability and previous research, this study considered the habitat conditions and the influence of human disturbance in the research site, and selected habitat quality, habitat degradation, fractional vegetation cover (FVC), population density and road density as the impact factor to construct the ecological resistance surface. Based on the analytic hierarchy process method (AHP), the weights of the different impact factors are determined as 0.33, 0.13, 0.04, 0.38 and 0.13, respectively. The impact of each factor was differentiated by the natural breaks classification and scored with 1, 3, 5, 7 and 9, representing resistance from low to high. The raster calculator was used to overlay the resistance factors in ArcGIS to form the ecological resistance surface.
Habitat quality and habitat degradation maps were generated using InVEST (3.12.0), a well-established software tool that combines information on land use threats and ecological sensitivity to assess habitat quality and ecosystem service value under different land use scenarios [50]. Its effectiveness has been widely validated worldwide, including in case studies conducted in China [51,52,53,54], Iran [55], Ethiopia [56], Portugal [57] and Nicaragua [58]. Habitat quality refers to the availability of living resources and the capability of the environment to sustain the survival and persistence of populations, the higher value of which represents the more beneficial maintenance of liveability and biodiversity [59,60]. Habitat degradation reflects the extent of the impact of threat factors on habitat, taking values from 0 to 1 to reveal the likelihood of habitat destruction and biodiversity detriment from low to high [59]. Given its effectiveness in assisting natural resource management, this research attempts to use habitat quality and degradation as constituent factors of ecological resistance surface, representing natural conditions. These two indexes can be calculated based on land use data, in which the land use changes directly contribute to habitat loss and landscape fragmentation [61]. The intensity of land use and the proximity of an ecological patch to land with frequent human activities largely determine habitat quality [62]. Depending on land use information, the InVEST habitat quality module operates on the relative impact of and distance from threats, habitat suitability, and habitat sensitivity [50]. In this research, built-up areas, bare land, and cultivated land were regarded as threats and exported as raster layers separately after reclassifying in ArcGIS. The assessment of their influence refers to research on ecological risks in the Grand Canal (Suzhou section) [50], urban ecological security in Suzhou [63], and software user manuals. The specific assignments are shown in Table 3 and Table 4.
FVC is the percentage of the surface area of the vertical projection area of vegetation, which can characterize the landscape pattern and the ecosystem status [64]. Its value ranging from 0 to 1 signifies the vitality of vegetation, corresponding to vegetation coverage from sparse to prosperous [65]. The calculation method is as follows:
F V C = N D V I − N D V I m i n N D V I m a x − N D V I m i n
Human disturbance significantly causes the decline in connectivity and quantity of natural habitats [66]. Higher population density suggests more substantial human interference, deserving higher resistant value. Furthermore, road networks constructed in tandem with urban expansion, directly and indirectly, result in habitat losses and disturb the distribution and abundance of flora and fauna [67]. In this sense, road density might reflect the intensity of human activities but also the fragmentation of the landscape to some extent. It can be calculated using the ratio of road network length to unit area. Considering the possible impact range of the road, this research used ArcGIS to create a fishing net with a unit of 1000 m and used the field calculator for calculation.

2.6. Optimisation of the Ecological Network

Corridors are indispensable for integrating ecological sources and sustaining ecological flows. The minimum cumulative resistance model (MCR) can identify the optimal path of ecological flow diffusion by calculating the minimum cumulative resistance distance between patches, reflecting the tendency and possibility of biological movement [68]. In addition, the electronic random walk phenomenon based on graph theory and circuit theory has been employed to output ecological nodes and barriers, and distinguish the priority of ecological corridors [69]. It stimulates the process of energy flow and species migration by assigning ecological meanings to physical quantities and abstracting the landscape into a circuit composed of nodes and resistors [70,71]. A linkage mapper combines the least-cost corridor analysis and circuit theory to map ecological corridors, pinpoint landscape restoration opportunities and detect important corridors [72]. This research thus adopted the Linkage Mapper toolkit (LM 3.0.0) in ArcGIS to construct ecological corridors, identify ecological nodes and barriers, and distinguish the priority of corridors.
Depending on the identified ecological cores and constructed resistance surface, the Linkage Pathways tool was used to extract the minimum cost path of species migration and diffusion between multiple paired sources. The cost-weighted distance threshold in this study is set to 2 km. After that, the established corridors were further classified by their importance for landscape connectivity and ecological improvement using the Linkage Priority tool. This ArcGIS tool quantifies the relative priority of network linkages based on weighted combinations among multiple factors, including shape, size, proximity, and resistance value [73]. This study used corridor-specific priority (CSP) to determine the importance of ecological corridors and to prioritize conservation according to the resulting blended priority.
With the assistance of the circuit space programs, Pinchpoint Mapper and Barrier Mapper, we simulated the transfer pathways and their key points relying on circuit theory. The cumulative current map generated by Pinchpoint Mapper can estimate the net number of times energy flows and species migration that reaches the target habitat through the ecological corridor, with a higher value meaning higher passing frequency and a more significant impact on landscape connectivity [74]. In terms of calculation modes, the effective resistance is iteratively calculated between all pairs of focal nodes in “Pairwise”, while in “all-to-one”, it is to connect an ecological source to the ground and traverse all ecological sources [75]. This study selected the “one-to-all” mode for calculation. Given that the width of corridors will not affect the position of the nodes and the landscape connectivity, this study set a weighted cost of 2000 m as the corridor width and divided the current density into four categories in line with the natural break method, extracting the highest to be the ecological nodes.
On the contrary, the Barrier Mapper identifies significant barriers that impact the corridors’ quality and location, using a circular search window to provide the minimum cost distance between sources and presenting the improvement of connectivity after removing the barrier [76]. This study selected the barrier considering both the situation of calculating least-cost distance (LCD) and not calculating LCD, setting the minimum detection radium as 250 m, maximum detection radius as 1250m, and radius step value as 250 m, and using the maximum method for combining across multiple core area pairs. Similarly, the barriers were screened as the highest scores from the four value categories using the natural break method.

3. Results

3.1. The Identified Ecological Patches and Their Distribution in the Research Area

Each MSPA category identified in Figure 4 was calculated and listed in Table 5. The results show that the core area, most critical for ecological network connectivity, is the main MSPA category in the research area, with a total area of 224.14 km2, accounting for 55.84% of the entire ecological land. The distribution of the core area is relatively uniform and widely distributed along the coast of Taihu Lake and the northeast, south and central part of Wujiang District, which is basically consistent with the results from previous research [77,78]. By comparing the land use classification in Figure 2, it is apparent that the core area is predominantly composed of water bodies and forests, which diverges from the results of Zhang et al. [9]. Specifically, the number and density of the “core” exhibit substantial disparities between the two studies. Zhang et al. study [9] identified densely connected ecological habitats in the central region, including Pingwang, Songling and Tongli Town, which markedly contrasts the results shown in Figure 3. This discrepancy in findings may be attributed to several factors. Firstly, the data utilized in this study were acquired in 2022, whereas Zhang et al.’s data date back to 2019. This time difference coincides with a period of rapid urbanization, which may have led to a reduction in ecological elements. Moreover, another significant reason for the discrepancies in results is the difference in methodologies employed. Specifically, Zhang et al. utilized MSPA for ecological element extraction and introduced cultivated land, including paddy and dry land, into MSPA as “foregrounds”, which resulted in identifying a dense core in the Wujiang region. However, it is worth noting that cultivated land is widely recognized for having greater resistance to biological migration in most studies that aim to identify ecological sources and assess the value of ecological services [79,80]. Consequently, this study designated cultivated land as the background to minimize deviations in identifying ecological resources.
Additionally, the Taipu River is an essential core linking the ecological network of Wujiang and connecting many ecological patches in the central area (Pingwang Town). Due to urban expansion, Songling Town and Shengze Town, the main urban areas of Wujiang, have almost no core areas, becoming areas dominated by artificial surfaces and lacking ecological patches, consistent with previous research [9]. Furthermore, the number of bridges is significantly correlated with ecological connectivity. As a critical structural corridor connecting the core area, it is essential to contribute to the spread of species and biodiversity conservation [37,44]. In the study area, bridges mainly concentrate in the southern part (Taoyuan Town and Zhenze Town). However, a large number of small core areas distributed along the axis from the southwest to the northeast lack bridge connections, revealing a high degree of landscape fragmentation and little contribution to the connectivity of the entire ecological network.
Following the results of landscape connectivity analysis in the identified ecological cores, the core area of dPC > 1 was selected as an essential ecological patch, a total of 30 (Figure 4). In line with the importance and contribution of each patch to maintaining the ecological network connectivity, an evaluation of the ecological connectivity index of core patches was obtained based on dPC (Table 6). Moreover, the importance of patches was divided into five levels using the natural break classification method. There are two patches for level 1 (29.31–73.70), four for level 2 (6.78–29.30), six for level 3 (3.93–6.77), and nine patches for levels 4 (2.22–3.92) and nine (1.07–2.21), respectively (Figure 4). The core patches are mainly distributed along the coast of Taihu Lake, the central and north-eastern parts of the Wujiang District. Overlapping the priority of the ecological patches (Figure 5) with the land use analysis (Figure 2), the results revealed that essential ecological patches are mainly lakes, rivers and other water bodies, such as Taihu Lake, Taipu River, Xueluo, Chang and Beima Lake. Comparing the priority of the ecological patches, it turns out that the patch area is not directly proportional, meaning that the small patches were not less essential than the larger ones, and vice versa, which supports the previous findings [47]. For instance, compared to other patches, number 2, Taipu River, and 3, Xuelong Lake, have a smaller area but have a higher value in the order of importance than larger patches such as 5 and 7.

3.2. Construction of Ecological Resistance Surface

As shown in Figure 6, ecological resistance surfaces are generated from habitat quality, habitat degradation, FVC, population and road density based on the assignment weight of AHP. Among them, habitat quality, FVC and human disturbance factors appear to be strongly correlated. Highly urbanized areas with dense population, such as Songling and Shengze Town, are characterized by poor habitat quality and low levels of FVC. This leads to high ecological resistance, a phenomenon that reinforces the findings from previous studies [9], even though the index system utilized for ecological resistance surface construction is different. From the perspective of habitat degradation, many lakes on the axis that runs through Wujiang from the southwest to the northeast are at increased risk due to poor landscape connectivity resulting from lacking bridges, which supports the conclusion in Section 3.1.
The overall ecological resistance surface indicates a significant positive correlation between ecological resistance and human interference factors in space, thereby supporting the argument that greater human activity intensity is associated with higher ecological resistance pointed out by Li et al. [81]. By comparing Figure 2 and Figure 6, the results indicated that the areas with high ecological resistance values were concentrated in the south (Shengze Town) and the north (Songling Town) areas of Wujiang District. Additionally, the high ecological resistance tended to spread north and south. Other areas with high resistance values are located in the centre of the town, such as the town centre of Zhenze, Pingwang, Lili and Qidu. The northern part of the Taipu River also disperses areas with high ecological resistance values that are small but spread together. In contrast, the areas with low resistance values were distributed along the shores of Taihu Lake, the southern part of the study area (Taoyuan Town with a large area of forest cover) and the northeast (Tongli National Wetland Park).

3.3. Optimising the Ecological Network for Wujiang District

In line with the identified essential ecological cores and the constructed comprehensive resistance surface, the ecological networks of Wujiang District based on the “patch-corridor-matrix” were optimised by the MCR model (Figure 7). There are 69 ecological corridors with minimal cost of species migration and dispersal extracted using the Linkage Pathways tool. Furthermore, depending on circuit theory, Pinchpoint Mapper and Barrier Mapper identified 59 ecological nodes and 24 barriers distributed in the northern, north-eastern and central regions of Wujiang District. Among them, the ecological nodes in the north and east are located in the intersecting area of urban land and ecological land in Songling and Lili Town. As shown in Figure 6, the results indicate an apparent overlap between ecological nodes and barriers, which means that the connection between ecological nodes and patches is the main obstacle to enhancing the connectivity of the ecological network. Subsequently, the dPC, CSP and Linkage Priority tool were used to prioritize ecological patches, corridors, and protected areas. Acquired from the results of the priority evaluation, ecological patches, ecological corridors and protected areas were divided into three categories. Among them, the first-level protected areas are mainly located along the coast of Taihu Lake (southern and western areas of Qidu and Songling Town) and northeast (east of Tongli and Lili Town). The secondary protected areas are located in the middle of Wujiang, including Zhenze, the central and southern parts of Pingwang, the west of Lili and Tongli Town. The third-level protection area is located in Shengze Town (southern Wujiang). From the perspective of landscape types in protected areas, lakes and wetlands deserve paramount preservation.
There were two vital cores within the research area: Taihu Lake and the western Taipu River (the connecting section of Taipu River and Taihu Lake), with an area of 57.93 km2. Furthermore, four important cores were identified, mainly near Shengze Wetland Park and Changyang Lake, including Xueluo, Changyang, Beima and Sanbai lakes, with an area of 22.20 km2; 24 general cores, with an area of 62.65 km2. Among the 69 ecological corridors, 15 are vital corridors, mainly distributed in the first-level protected area, connecting Taihu Lake–Qidu Town Center–Jinyu Lake in the west, strengthening the connection between Taihu Lake and the lakes on the land. The ecological security along the shores of Taihu Lake was optimised. In the central part, the connection between the vital core, Taipu River, and the important cores surrounding it was strengthened, and the connectivity of the eastern and western parts of the Wujiang ecological network was enhanced. In the northeast, with Baijian Lake as the core, the southern ecological cores of Yuan and Sanbai Lakes and the northern core of Tongli National Wetland Park were connected. Additionally, there are 36 important ecological corridors, mainly concentrated in the central and north-eastern regions, improving the connectivity between Taihu Lake and the significant patches in the central area and the interconnection between cores in the north-eastern regions. Eighteen general ecological corridors were identified, distributed in the middle of the research area, connecting the ecological core of Taihu Lake in the west, cores in Pingwang Town, and northeast.
In general, the optimised ecological networks of Wujiang District show the “two axes, three cores and multiple belts” spatial pattern. Among them, the east–west axis relies on the Taipu River, connecting Taihu Lake–Pingwang Town–Lili Town; the southwest-northeast axis optimizes the landscape connectivity between Qidu, Pingwang and Tongli Town. The three cores are the shore of Taihu Lake (including Qidu and west of Songling Town), Pingwang Town in the central area and the Tongli–Lili Town group in the northeast. Multiple belts connect the ecological patches within the three cores, and the regional ecological networks have been significantly optimized.

4. Discussion

In accordance with the simulated comprehensive ecological resistance surface, the identified ecological cores were reconnected through the constructed ecological corridors, which allowed for determining ecological conservation priorities in different regions. The findings of this research are discussed below in terms of methodological advantages and limitations, as well as the potential applications in land use control and urban landscape construction.

4.1. The Methodological Advantages Are Based on the Proposed Integrated Model

In order to repair fragmented ecological patches, improve ecological capacity, and achieve sustainable urban development, an integrated model, including a comprehensive index system, was proposed in this research. The aim is to provide a more scientific method for the construction of ecological networks. The development model is optimised for each step based on the method of “source identification–resistance surface construction–ecological corridor extraction”. The model utilised a combination of MSPA and landscape connectivity analysis methods to pinpoint ecological sources. It took into account the impact of the ecosystem’s functional characteristics and patch connectivity on ecological processes and functions comprehensively, which aims to improve the reliability of identifying results compared with the previous research [27,82,83]. When using MSPA to identify ecological sources, the classification of “foreground” and “background” by land type and the setting of pixel size are important factors affecting the result [84]. Most previous studies took cultivated land as the “foreground”, resulting in a large density and number of identified cores, which may affect the construction of resistance surfaces and extraction of ecological corridors [9,19]. This research argued that using cultivated land as the “background” may improve the accuracy of the final results of ecological network construction. Still, its effectiveness needs to be discussed in future studies. The setting of the MSPA pixel size directly affects its results [85]. Specifically, an increase in the pixel size results in the disappearance of more minor landscape elements or their transformation into noncore areas, as defined. Therefore, when using MSPA, an appropriate pixel scale should be selected for calculation according to the scale, urbanisation degree and other factors of different study cases. When constructing the ecological resistance surface, this research proposes a more integrated approach to evaluating it by constructing a comprehensive index system. This approach aims to visualise the spatial distribution characteristics of regional resistance values more accurately and in detail. The development model combines habitat quality, habitat degradation, fractional vegetation coverage (FVC), population density and road network density as a measure index. Moreover, the model combines the MCR model with the Linkage Mapper toolkit to extract the ecological corridor. In addition to extracting ecological corridors by simulating the minimum cost path, ecological nodes and barriers can also be identified, and the ecological protection priority can be evaluated.
The proposed integrated model optimises ecological network construction methods, making the results more scientific and accurate. The model takes Wujiang as a case for empirical study, constructs the ecological network of Wujiang through simulation, and determines the priority area for ecological protection. Territorial and spatial planning, as the critical guiding principles behind the urban development of Wujiang District, have regulated the urban functional, agricultural, and ecological areas by designating boundaries of urban construction areas, ecological protection areas, and permanent primary farmland. The ecological network constructed by the model is superimposed with the territorial spatial planning of Wujiang District, it was found that the results of optimised ecological networks, including the ecological cores, the priority of conservation areas was consistent with the legally ecological protected area. The overlap between the 30 defined ecological cores and the areas within the ecological protection line is approximately 60%, which verifies the validity of the model’s simulation results to some extent.

4.2. The Potential Applications in Land Use Control and Urban Landscape Construction

Based on the results of the constructed ecological network (Figure 7), potential application suggestions are proposed. Firstly, ecological sources need to be increased to balance the distribution of ecological resources. Derived from the results of the constructed comprehensive ecological resistance surface, Songling, Tongli, Shengze and the town centres have high ecological resistance, low FVC and relatively weak ecological functions. Moreover, Songling–Tongli and Shengze Townships are located at the northern and southern ends of the central Wujiang region with dense populations and intensive urban development and they expand to the north and south, respectively, which poses a potential threat to the ecological network. In line with the previous analysis (Figure 6), the ecological patches in the urban development zones are highly fragmented, facing the threat of increased resistance between the patches and a lack of connectivity. Therefore, increasing ecological spatial allocation and balancing the distribution of ecological patches should be considered in the central areas of towns with high ecological resistance. While protecting the existing ecological cores, it is also necessary to add some secondary ecological patches to form a completer and more functional ecological network. Specifically, additional parks are recommended to be built in Songling–Lili and Shengze Town, and protective green spaces on both sides of the existing water system network can be added.
Secondly, construct the ecological corridors to optimized ecological network need to be considered. There is a lack of ecological corridors connecting the shores of Taihu Lake in the west, Pingwang Town in the middle and numerous lakes and wetlands in the northeast of Wujiang District, which caused the water network on the southwest–northeast axis, is facing a high risk of ecological degradation. Therefore, it is essential to establish ecological corridors connecting inside and outside different towns and repair the breakpoint of the ecological network for the optimized ecological network. Concretely, along the southwest–northeast axis, ecological corridors need to be constructed relying on Xueluo, Chang and Beima Lakes in the west and northeast directions to integrate the water system of Wujiang District and enhance the connectivity of ecological patches in each region. A northwest–southeast corridor will be constructed in Qidu, Pingwang, Songling and Tongli Town to strengthen the ecological connectivity with Taihu Lake and improve the ecological resilience of the lake’s coastal areas. Moreover, an ecological corridor connecting the north and south based on the Beijing–Hangzhou Grand Canal needs to be taken into account, more specifically, taking the Beijing–Hangzhou Grand Canal as the core to increase the number of parks and public spaces along it and increase the vegetation cover of the protective green spaces on both sides.
In addition, the scenic spots in Wujiang, such as Tongli, Zhouzhuang, Lili Ancient Town and other historical or cultural towns, are identified ecological nodes in the optimised ecological networks and should be listed as ecological resources under protection (Figure 7). Therefore, when constructing ecological corridors to optimize ecological networks, connecting ecological nodes such as these scenic spots with corridors can not only restore ecological networks but also optimize the quality of the urban landscape. Due to the ecological corridors are not singular lines, but strip-shaped spaces with widths varying depending on species [33]. According to the previous research reveals that an appropriate width adequate for supporting the dispersal and migration of small animals and most species is at least 1000 m [33]. This research thus set 500 m buffer on both sides of each ecological corridor (1000 m wide in total) and intersect with scenic spots in POI data in ArcGIS to identify cultural resources that can be connected with ecological corridors. The POI data was classified and manually screened to ensure the selected points are representative of local cultures, such as historical relics, former residences of celebrities, and traditional Jiangnan Watertown. As the results illustrated in the Figure 8, the attractions such as Tongli and Zhouzhuang Ancient Town, Shuangta Temple, Xulingtai Tomb, etc., can be connected to the ecological corridors as the significant nodes.

4.3. The Limitation of this Study

The model proposed in this study not only optimizes the method of ecological corridor construction but also has some shortcomings. First, the model developed in this study lacks consideration of elevation when constructing resistance surfaces, which affects its applicability. The effectiveness of the ecological resistance surface generated by this model will be reduced in mountainous and hilly environments with a large elevation difference. Secondly, this study equates the ecological corridor with a homogeneous linear space. In reality, corridors may have different shapes and widths due to the heterogeneity of the land use and built environment. Therefore, the widths of ecological corridors in different spaces should be studied and improved in the following studies.

5. Conclusions

Constructing the ecological network can enhance landscape connectivity, increase biodiversity protection, and contribute to ecological conservation and regional sustainable development. In order to construct ecological network more scientifically and accurately, an integrated model is proposed to optimize the construction methods. The effectiveness of the model was simulated in Wujiang, and the results showed that
(1)
Thirty ecological cores were identified, with a total area of 142.77 km2, accounting for 11.63% of the study area. Forest, lake and wetland were the primary land use types for ecological cores. Sixty-nine ecological corridors were also extracted. Moreover, 59 nodes and 24 barriers provide the potential for further optimisation. The optimised ecological network is expected to promote the connectivity and stability of the ecological system. However, the distribution of ecological cores is uneven, concentrated in the western, central and north-eastern regions, and relatively lacking in the north and south. The unbalanced ecological cores caused the uneven distribution of ecological corridors, which might limit the performance of the ecological network in improving the urban ecosystem, so that topic still needs further consideration in future research;
(2)
The conservation of ecological cores and the construction of ecological corridors are divided into three phases according to priority, including 2 vital, 4 important, 24 general cores and 15 vital, 36 important and 18 general corridors, respectively. Vital corridors are distributed in the southwest and northeast, strengthening the connection between vital and important ecological cores. Since they significantly contribute to improving biological migration and environmental processes, the construction of vital corridors could be prioritised when optimising the ecological network, which is conducive to forming a virtuous circle of ecological restoration. Moreover, the ecological corridors constructed in Tongli, Lili and Qidu Town can be combined with scenic spots to improve the urban landscape while restoring ecological networks;
(3)
The conservation priority is divided into three levels. The first level includes Songling, Tongli, Lili and Qidu Town; the second comprises Pingwang and Zhenze Town; and the third is Shengze and Taoyuan Town. There are noticeable differences among towns in the area in terms of ecological cores and the number of corridors. Among them, there are fewer ecological cores and corridors in Songling, Shengze and Taoyuan Town, which suggests they should pay more attention to constructing ecological patches in the area in future urban planning. Additionally, there are fewer ecological cores near the ecological corridor connecting Pingwang, Lili and Tongli Town, so it might be worth considering developing steppingstones and other biological habitats when constructing the corridors;
(4)
In accordance with the findings of this study, the analysis is carried out at the town scale, and the recommendations proposed for each town can contribute to improving the implementation of ecological network construction and the distribution of responsibilities between different towns.
However, the integrated model proposed in this research was only tested in Wujiang District, and its validity and applicability are limited to the administrative boundary at a district level. Future research based on this model at larger scales and in more complex environments is necessary. Additionally, applying the theoretical optimization conducted by the developed model in practice requires further field investigations and expert consultation in the future.

Author Contributions

Conceptualization, W.G. and L.P.; methodology, W.G. and L.P.; software, L.P.; validation, W.G., L.P., K.R. and J.C.; formal analysis, W.G. and L.P.; investigation, W.G., L.P., K.R. and J.C.; resources, L.P. and J.C.; data curation, W.G., L.P., K.R. and J.C.; writing—original draft preparation, W.G. and L.P.; writing—review and editing, W.G.; visualization, L.P. and J.C.; supervision, W.G.; project administration, W.G.; funding acquisition, W.G., K.R. and J.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

The authors would like to thank Bing Chen for his suggestions and help in proofreading this manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. The geographical location of Wujiang District.
Figure 1. The geographical location of Wujiang District.
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Figure 2. The land use map of the Wujiang District.
Figure 2. The land use map of the Wujiang District.
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Figure 3. The methodology framework of this research.
Figure 3. The methodology framework of this research.
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Figure 4. The ecological elements identified by MSPA in Wujiang District.
Figure 4. The ecological elements identified by MSPA in Wujiang District.
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Figure 5. The priority of the ecological patches in Wujiang District.
Figure 5. The priority of the ecological patches in Wujiang District.
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Figure 6. The corrected ecological resistance surface of Wujiang District.
Figure 6. The corrected ecological resistance surface of Wujiang District.
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Figure 7. The pattern of the ecological network of Wujiang District.
Figure 7. The pattern of the ecological network of Wujiang District.
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Figure 8. Strategies for developing ecological networks by combining historical and cultural resources.
Figure 8. Strategies for developing ecological networks by combining historical and cultural resources.
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Table
Table 1. Different categories of landscape elements.
Table 1. Different categories of landscape elements.
Pattern ClassArea/km2Percentage of Total Land/%
Wetland10.800.88
Grassland3.200.26
Forest142.7111.62
Water body244.7519.93
Paddy field74.116.04
Non-irrigated farmland217.6617.73
Built-up area512.0841.70
Bare land22.671.85
Total1227.98100.00
Table
Table 2. The classification of the MSPA and its definitions [42].
Table 2. The classification of the MSPA and its definitions [42].
Pattern ClassDefinition
CoreAreas with larger scales can serve as habitat for species, which is essential for biodiversity conservation and can serve as source areas.
IsletIsolated small-scale broken patches, fragmented and poor internal connectivity, providing a place for species to communicate.
LoopEcological corridors connect patches within the same core area and are shortcuts for species to diffuse and energy exchange.
BridgeThe narrow area connects two or more core areas, serving as the ecological network’s corridor and providing the necessary pathways for species to migrate.
PerforationNonnatural areas within the core patches serve as the transition zone and have edge effects.
EdgeTransition zones between cores and nonecological landscape areas have an edge effect that can protect the core area’s ecological process.
BranchAreas connected to the edge, bridge, loop or perforation area at only one side.
Table
Table 3. Threat attributes data of different land use types.
Table 3. Threat attributes data of different land use types.
Threat Factors (Land Use)Max Distance/kmWeightDecay
Built-up area81Exponential
Bare land40.2Exponential
Paddy field50.3Linear
Dryland50.6Linear
Table
Table 4. Habitat suitability of different landscape types and sensitivity of land use types to each threat.
Table 4. Habitat suitability of different landscape types and sensitivity of land use types to each threat.
Land Use TypeHabitat
Suitability		Sensitivity to Threat Factors
Paddy FieldDrylandBuilt-Up AreaBare Land
Wetland10.50.60.80.5
Grassland0.80.40.40.60.5
Forest0.90.40.40.70.5
Water bodies0.90.70.60.80.4
Paddy field0.30.30.30.50.4
Dryland0.20.30.30.50.3
Built-up area00000
Bare land0.10.10.10.30.2
Table
Table 5. The area of each identified MSPA category.
Table 5. The area of each identified MSPA category.
MSPA CategoryArea (km2)Percentage of Foreground (%)MSPA CategoryArea (km2)Percentage of Foreground (%)
Core224.1455.84Loop8.072.01
Islet36.539.10Bridge22.785.68
Perforation2.770.69Branch35.688.89
Edge71.4117.79Total401.38100.00
Table
Table 6. The ecological connectivity index of 30 identified ecological cores.
Table 6. The ecological connectivity index of 30 identified ecological cores.
Importance RankingArea of Patches/km2dPCImportance RankingArea of Patches/km2dPC
157.1773.68164.202.86
20.7629.38171.702.51
33.2617.25182.132.50
45.6812.11191.682.41
59.8110.90201.912.40
63.447.04213.192.39
79.795.83223.271.94
81.265.56231.661.82
91.814.78240.731.74
105.384.58251.411.64
111.724.25263.161.63
120.724.15271.211.60
133.223.57281.881.46
142.353.48292.331.40
154.222.87301.721.07
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Pan, L.; Gan, W.; Chen, J.; Ren, K. An Integrated Model for Constructing Urban Ecological Networks and Identifying the Ecological Protection Priority: A Case Study of Wujiang District, Suzhou. Sustainability 2023, 15, 4487. https://doi.org/10.3390/su15054487

AMA Style

Pan L, Gan W, Chen J, Ren K. An Integrated Model for Constructing Urban Ecological Networks and Identifying the Ecological Protection Priority: A Case Study of Wujiang District, Suzhou. Sustainability. 2023; 15(5):4487. https://doi.org/10.3390/su15054487

Chicago/Turabian Style

Pan, Liyu, Wenquan Gan, Jinliu Chen, and Kunlun Ren. 2023. "An Integrated Model for Constructing Urban Ecological Networks and Identifying the Ecological Protection Priority: A Case Study of Wujiang District, Suzhou" Sustainability 15, no. 5: 4487. https://doi.org/10.3390/su15054487

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