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Article

Identification of Township-Scale Ecological Restoration Priority Areas Based on Ecological Security Pattern and Multi-Method Integration

by
Tingyun Zhou
1,
Yutong Li
1,
Yu Zhang
2,
Lushuang Lin
2,
Rui Zhou
2,3,*,
Aimin Ma
1 and
Junying Chen
1
1
Taihu Basin Monitoring Central Station for Soil and Water Conservation, Shanghai 200434, China
2
School of Environmental and Geographical Sciences, Shanghai Normal University, Shanghai 200234, China
3
Yangtze River Delta Urban Wetland Ecosystem National Field Scientific Observation and Research Station, Shanghai 200234, China
*
Author to whom correspondence should be addressed.
Land 2026, 15(2), 274; https://doi.org/10.3390/land15020274
Submission received: 4 December 2025 / Revised: 30 January 2026 / Accepted: 2 February 2026 / Published: 6 February 2026
(This article belongs to the Topic Urban Climate Improvement and Spatial Pattern Optimization Under a Multi-Objective Orientation)
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Abstract

The scientific establishment of ecological security pattern and identification of ecological restoration priority areas are key for territorial space ecological restoration and people’s well-being enhancement. Although numerous studies have addressed this topic, most focused on regional and urban scales. As the most basic administrative units in China, townships serve as a crucial link between macro-ecological protection strategies and micro-ecological restoration practices and are essential for effectively implementing ecological restoration and supporting rural revitalization practices, but research at this scale is currently lacking. Therefore, taking a typical township in Shanghai as an example, this study incorporated the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, Morphological Spatial Pattern Analysis (MSPA), landscape connectivity analysis, and circuit theory to construct an ecological security pattern and identify ecological restoration priority areas at the township scale, as well as to discuss corresponding ecological restoration strategies. The results showed that: (1) The study area contained 19 significant ecological sources (area of approximately 4.85 km2), exhibiting a spatial pattern characterized by “north–south concentration, central dispersion”. High-resistance areas were mainly distributed in areas with dense human activity and high development intensity, reflecting the significant impact of human activities on ecological processes. There were 32 main ecological corridors with a total length of 58.06 km, showing significant spatial imbalance, with some northern ecological sources at the risk of forming ecological isolated islands. (2) The ecological restoration priority areas mainly consisted of 41 ecological pinch points (area of approximately 27.24 ha) and 30 ecological barrier points (area of approximately 25.67 ha), which were crucial for enhancing ecological network connectivity and maintaining ecological security. (3) Based on the current land use status and spatial distribution characteristics of key ecological restoration areas, a hierarchical and categorized ecological restoration strategy was formulated. This study can strengthen research on identifying ecological restoration priority areas at the township scale. The methodological system established can provide a theoretical framework for ecological restoration research in similar areas. Moreover, this study pinpointed key areas and the spatial layout for ecological restoration, which helped to enhance the level of refined ecological governance at the township level and can also provide precise spatial decision-making basis for ecological restoration of the township territorial space.

1. Introduction

Regional ecological security and ecological restoration are pivotal for addressing global challenges such as climate change, biodiversity loss, and the degradation of ecosystem services [1]. In China, urbanization has driven rapid socio-economic development but also triggered a series of ecological and environmental issues [2,3], necessitating effective territorial space ecological restoration [4]. It serves as a spatial pathway linking ecological conservation with sustainable development, providing fundamental support for territorial space planning, the establishment of biodiversity conservation networks, and the enhancement of human wellbeing [1,5]. The key challenge and focus of current ecological restoration work lies in how to scientifically and efficiently construct the ecological security pattern (ESP) of territorial space and identify priority areas for ecological restoration [6,7].
The ESP, grounded in the core landscape ecological principle of “pattern–process–function”, serves as an important spatial approach for harmonizing ecological protection with sustainable socioeconomic development and guiding ecological restoration [8]. By identifying and securing key ecological spaces, the ESP aims to maintain critical ecological processes and ecosystem services, thereby forming the spatial foundation for ecological restoration planning [9,10]. In recent years, significant progress has been made in the theoretical understanding, technical paradigms, and practical applications of ESP and territorial ecological restoration, advancing regional ecological security and restoration from scientific research to policy decision-making [11,12]. The prevailing paradigm of “ecological source identification–resistance surface construction–ecological corridor extraction” has become the fundamental framework for ESP research [13,14] and has been widely applied in urban ecological planning, ecological network construction, and territorial space ecological restoration [15,16]. Ecological sources are the foundation for constructing ESP and the vital ecological land for maintaining regional ecological security and stability, providing suitable habitats for living organisms. Previous studies mainly identified ecological sources by selecting areas with superior ecological integrity, such as protected areas, national parks, extensive forest ecosystems, and designated scenic sites [17,18]. Recently, methodological frameworks employing MSPA, InVEST model, ecological importance evaluation, ecological risk analysis, and landscape connectivity analysis have emerged as dominant techniques for ecological source identification [19,20,21]. The resistance surface refers to the resistance that ecological flows must overcome when moving between ecological sources. As a fundamental element in ecological corridor and ecological network studies, it is used to simulate the obstructive effects of landscape heterogeneity on ecological flows. Recent studies have shifted from assigning resistance values solely based on land-use types to refining resistance surfaces by integrating multiple indicators, thereby more accurately reflecting variations in landscape resistance within the study area [22,23,24]. Ecological corridors play a critical role in maintaining ecosystem stability [25], and their extraction commonly relies on approaches such as the minimum cumulative resistance (MCR) model [26], ant colony algorithm [27,28], and circuit theory [29,30,31]. Among these methods, the MCR model is the most widely used, as it constructs ecological corridors by identifying the least-cost paths between ecological sources. However, it overlooks the stochastic movement pattern of species and fails to identify key ecological nodes [32]. Circuit theory uses the characteristics of electrons performing random walks in a circuit to simulate the migration and dispersal processes of species across a given landscape [33]. It allows for the assessment of corridor significance through current magnitude, facilitating the identification of potential ecological corridors [34]. Consequently, circuit theory has become one of the mainstream approaches for corridor extraction. The core of ecological restoration lies in restoring the key processes of ecosystems, while the ESP represents the spatial mapping of these processes. Essentially, ESP is constructed based on the “pattern–process–function” logic of ecosystems, with a primary focus on overall ecological processes [35,36].
In recent years, identifying key areas for territorial ecological restoration based on ESP has attracted increasing scholarly attention due to its systematic, integrative, and ecological value [7,11,37]. However, a significant gap persists between ESP research and its on-ground application. Most existing studies focused on national, regional, and urban scales, with studies at the township scale being relatively scarce [3,13]. While research at the macro and meso scales is valuable for overall optimization and planning of regional ecological space, the relatively coarse spatial resolution of these studies often results in ecological elements being highly averaged, making it difficult to capture fine-scale spatial heterogeneity. Consequently, such research cannot be effectively down-scaled, rendering it insufficient for guiding the refined management and precise implementation of ecological restoration measures. It is worth noting that the ultimate effectiveness of ecological restoration depends on its precise on-the-ground implementation. As the most fundamental administrative unit in China, townships constitute the foundation for implementing ecological restoration policies and measures. At the same time, they face challenges such as ecological fragmentation, limited scope for ecological restoration, and a lack of detailed spatial guidance for implementing macro-level ecological strategies. However, research at this scale remains extremely scarce. In fact, research on constructing ESP and identifying priority areas for ecological restoration at the township scale serves as a critical link between macro-level ecological strategies and micro-level restoration practices. Such studies have significant theoretical and practical implications for the maintenance of ecological security and the effective implementation of ecological restoration measures and thus warrant further investigation.
Therefore, to fill the gap in previous research, this study took Zhuanghang Town, a typical township in Shanghai, as a case study. Drawing on land-use, DEM, meteorological, and nighttime light data, we integrated Morphological Spatial Pattern Analysis (MSPA), the InVEST model, landscape connectivity analysis, and circuit theory to explore the construction of ESP and the identification of ecological restoration priority areas at the township scale. This study pursued three main goals: (1) to construct a township-scale ESP by identifying ecological sources and corridors; (2) to precisely identify ecological restoration priority areas (e.g., ecological pinch points and barrier points) within the ESP framework; (3) to propose tailored ecological restoration strategies for priority areas, providing a spatially explicit decision-making basis for precise ecological restoration in township-level territorial spaces. This study aimed to strengthen the theoretical and practical framework for fine-scale ecological restoration, offering a replicable model for similar regions.

2. Materials and Methods

2.1. Study Area

Zhuanghang Town is located in Fengxian District, Shanghai (Figure 1). Bordered by Hangzhou Bay to the south and the Huangpu River to the north, it enjoys a favorable geographical location and convenient transportation. Covering an area of approximately 69.45 km2, the town comprises 16 administrative villages and 3 residential committees, with a permanent population of about 63,000. It boasts a dense and intricate network of waterways, complemented by 5 km of riverside shoreline and 120 hectares of ecological conservation forests, resulting in a favorable ecological foundation. Its land use pattern dominated by farmland and water bodies, complemented by forestland, grassland, and construction land. It experiences a subtropical monsoon climate characterized by distinct seasons with rainfall coinciding with the warm period. The terrain is generally flat, belonging to the alluvial plain of the Yangtze River Delta. The annual average temperature is approximately 15 °C, with annual precipitation around 1100 mm.

2.2. Data Sources and Preprocessing

The foundational data utilized in this study primarily consist of: (1) land-use and land-cover data for 2020 with a spatial resolution of 30 m, obtained from the Resource and Environment Science and Data Center of the Chinese Academy of Sciences (https://www.resdc.cn/ accessed on 8 August 2025); (2) high-resolution remote sensing imagery (GF-6) for 2020, with a spatial resolution of 2 m (https://data.cresda.cn/#/home accessed on 5 September 2025); (3) DEM data with a spatial resolution of 30 m, sourced from the Geospatial Data Cloud (http://www.gscloud.cn/ accessed on 16 September 2025); (4) monthly average air temperature and precipitation data for 2020, with annual averages calculated from the monthly values for model input, obtained from the China Meteorological Data Service Center (http://data.cma.cn/ accessed on 25 September 2025); and (5) monthly composite VIIRS/DNB nighttime light data for 2020. After cloud removal and outlier processing, annual composite data were used to characterize human activity intensity (https://ncc.nesdis.noaa.gov/VIIRS/ accessed on 28 September 2025).
All spatial data were uniformly converted to the WGS 1984 UTM Zone 51N projected coordinate system. Land use and DEM data at 30 m resolution, as well as GF-6 imagery at 2 m resolution, were resampled to a 30 m grid cell size using bilinear interpolation. Nighttime light data were also resampled to the same resolution. All raster data were clipped using the boundary of the study area. NDVI was calculated using Landsat 8 OLI imagery from the 2020 growing season (May–September), which underwent atmospheric correction and cloud masking. Nighttime light data were normalized to a 0–1 range using the Min-Max method for adjusting the resistance surface.

2.3. Methods

This study aims to construct an ESP, identify priority areas for ecological restoration, and propose corresponding strategies for conservation and restoration. The research framework consists of four main steps. First, key ecological sources in the study area were identified using MSPA, the InVEST model, and landscape connectivity analysis. Second, a resistance surface was constructed based on land-use data, NDVI, and nighttime light data, and ecological corridors were extracted using circuit theory to establish the ESP (ESP). Third, priority areas for ecological restoration were delineated based on circuit-theory metrics. Finally, targeted restoration strategies were proposed for different types of priority areas. The schematic workflow diagram of this study is shown in Figure 2.

2.3.1. Identification of Ecological Sources

Two main approaches are commonly used to identify ecological sources. The first directly designates nature reserves, national forest parks, scenic areas, and similar sites as ecological sources, but this approach is highly subjective. The second relies on methods such as ecosystem service importance assessment, landscape connectivity analysis, and MSPA to identify sources. However, this approach remains limited in its comprehensiveness. Ecosystem service assessment excels at identifying zones with high human well-being value but is limited in diagnosing spatial morphological characteristics. MSPA, on the other hand, is less suitable for evaluating habitat quality or ecosystem service provision yet highly effective in delineating landscape structural pattern. Therefore, to overcome the complementary limitations of these individual approaches, this study integrates ecosystem services, MSPA, and landscape connectivity to identify patches of high ecological value as ecological sources. The specific integration criteria involve spatially overlaying the top 20% high-value zones identified in the ecosystem services assessment with the core areas extracted from the MSPA. The resulting intersection is designated as preliminary ecological source candidate zones. Subsequently, landscape connectivity analysis (Patch Importance Index, dPC > 1) is applied to select the final ecological sources from these zones. This integrated approach enhances both the analytical rigor and the comprehensiveness of functional assessment. In terms of ecosystem services, four types of services (habitat quality, soil retention, carbon storage, and water yield) were selected based on the natural conditions and ecological status of the study area. These services basically cover key ecological functions such as biodiversity conservation, soil erosion prevention, climate regulation and water resource protection. They can comprehensively reflect the overall ecosystem service value and provide multi-dimensional basis for scientifically identifying ecological sources. Subsequently, ecosystem services were quantitatively evaluated using the InVEST model, a widely used tool for ecosystem service quantitative assessment that supports the identification of ecological sources from the perspective of landscape functional attributes [38,39,40]. During the computational process, the parameter settings for threat factors, sensitivity values, biophysical tables, and carbon pools were primarily based on the research findings of Chen et al. (2023) [41], Xu et al. (2018) [42], Ding et al. (2023) [43], and Sun et al. (2023) [44], respectively. MSPA is an image processing method based on mathematical morphology that measures, identifies, and segments spatial pattern in raster images, enabling the precise differentiation of landscape types and structural components [23]. In recent years, it has been widely applied in studies on ecological networks and the construction of ESP [45,46]. In this study, forest land, grassland, and water bodies were designated as foreground elements, while all other land-use types were treated as background elements. Subsequently, seven mutually exclusive landscape categories (core, island, perforation, edge, loop, bridge, and branch) were generated using the eight-neighborhood analysis in the Guidos Toolbox, among which core areas served as the basis for selecting ecological sources. Landscape connectivity analysis was conducted to evaluate the importance of each patch by quantifying changes in connectivity before and after patch removal [47,48,49]. Patch importance was calculated using Conefor Sensinode, and patches with an importance index greater than 1 were identified as key ecological sources. This threshold is commonly used in previous studies to identify patches that have a significant impact on maintaining landscape connectivity [24,36]. The software versions used in this study were as follows: InVEST 3.13.0, Guidos Toolbox 3.0, and Conefor Sensinode 2.6. All analyses were conducted based on the above versions.

2.3.2. Construction of the Ecological Resistance Surface

Ecological flows between source patches are primarily influenced by factors such as topography, land-use characteristics, and the intensity of human activities [3,7]. Drawing on previous studies [50,51,52], and considering the local conditions of the study area, this study used land-use types and the normalized difference vegetation index (NDVI) to construct the basic resistance surface. The classification and resistance values of each resistance factor in the study area are presented in Table 1. Higher values indicate greater costs for ecological flows to traverse a given patch. Night-time light data can reflect spatial variations in human activity intensity, including population density, urbanization level, and economic status [30,53,54]. In this study, night-time light data were used to adjust the basic resistance surface, enabling a more scientifically and objectively constructed comprehensive resistance surface for the study area. The adjustment formula is as follows:
R i = N L i N L c × R
where Ri is the resistance coefficient of raster i adjusted by the night-time light index; NLi is the night-time light index of raster i; NLc is the mean night-time light index corresponding to the resistance class of raster i; and R is the basic resistance coefficient of raster i.

2.3.3. Extraction of Ecological Corridors

As crucial channels for the movement of ecological elements between regional ecological sources, ecological corridors play an important role in maintaining regional ecological security and enhancing landscape connectivity. Ecological corridors serve as low-resistance pathways for ecological flows among source areas. Circuit theory simulates species dispersal across a landscape by leveraging the random-walk behavior of electrons in electrical circuits, thereby identifying multiple alternative pathways of a certain width across the landscape. The magnitude of current density represents the probability of species dispersal along a given path, and it enables the assessment of the relative importance of corridors and the identification of potential ecological corridors [34]. Based on this principle, the present study employs the Linkage Pathways Tool within the Linkage Mapper toolbox, which is grounded in circuit theory, to identify ecological corridors in the study area. In the analysis, the corridor width was set to 100 m, the current threshold was determined using the natural breaks method, and connectivity was calculated between all ecological sources in pairwise mode. In this study, the 19 identified ecological sources were paired, resulting in 171 source-pair combinations for analysis. The Circuitscape model was used to calculate the current density for each potential path. The current density value reflects the probability of species using that path and directly indicates the corridor’s importance. Ultimately, paths with a current density higher than the top 20% average of all corridors were identified as significant ecological corridors, and these are presented in the results.

2.3.4. Identification of Key Areas for Ecological Restoration

(1) Ecological pinch point
According to circuit theory, areas with high current density indicate locations where species are highly likely to traverse during movement. Degradation or loss of such areas may disrupt the connectivity between ecological sources, thus making them priority targets for protection and restoration, and being identified as ecological pinch points. In this study, the Pinch Point Mapper tool in the Linkage Mapper toolbox was used in ‘pairwise’ mode to identify ecological pinch points, which calls the Circuitscape model to compute the current density between habitat patches. Current density values were classified into five levels using the Natural Breaks method, with the highest level defined as ecological pinch points. Areas with elevated current density represent zones with high probabilities of species movement but are also more susceptible to surrounding disturbances, indicating that their adjacent areas require targeted restoration.
(2) Ecological barrier point
Ecological barrier points are areas where species movement between ecological sources is hindered. Removing or mitigating these barriers can enhance connectivity among ecological sources, making such areas priorities for ecological restoration. In this study, the Barrier Mapper tool was applied with a moving window radius of 100 m for the search, which was determined to be the optimal radius through repeated iterative testing. Improvement scores were used to quantify the contribution of barrier removal to landscape connectivity, with higher values indicating a greater increase in landscape connectivity that would result from removing the barrier.

2.3.5. Validations

To enhance the reliability of the results, the following validation approaches were applied: (1) the identified ecological sources were visually compared with high-resolution remote sensing imagery (GF-6) to confirm consistency with actual vegetation cover and water body distribution; (2) in constructing the resistance surface, resistance values were cross-referenced with those from similar regions in existing studies to ensure reasonableness; (3) extracted ecological corridors were overlaid with the current land-use map to examine whether they avoid high-resistance built-up areas and align with ecological connectivity logic.

3. Results

3.1. Construction of the ESP

3.1.1. Ecological Source Analysis

By overlaying the core areas identified by MSPA with high-value ecosystem service zones assessed by InVEST, and further refining based on landscape connectivity analysis (dPC > 1), 19 ecological sources were identified, with a combined area of approximately 4.85 km2, accounting for 6.99% of the total area of the study region. This reflected the intense pressure of land development faced by the study area as a suburban township. These sources are primarily composed of forest, grassland, and water bodies, and exhibit a spatial pattern characterized by dense clusters in the north and south and a more dispersed distribution in the central part (Figure 3). From a village-level perspective, the ecological source areas are primarily concentrated in the northern villages of Puxiu, Yuli, and Xinye, characterized by high vegetation cover, relatively strong ecosystem service functions, and high landscape connectivity, as well as in the southern Pandian Village, which contains extensive water bodies. Among these, Puxiu Village has the largest source area, representing 30.10% of the total ecological source area in the study region, followed by Pandian Village, accounting for 14.07%.

3.1.2. Resistance Surface Construction and Ecological Corridor Analysis

Nighttime light data were used to modify the basic resistance surface constructed from land-use types and the normalized difference vegetation index (NDVI), resulting in the final integrated ecological resistance surface (Figure 4). The high-resistance areas are predominantly concentrated in regions with clustered built-up land, showing strong spatial consistency with construction land distribution. Areas with relatively high ecological resistance values are mainly located in Changbang Village, Zhuanghang Community, the junction of Xinhua, Lvqiao, and Dongfeng villages, the boundary between Lvqiao and Changbang villages, and the convergence of Yuli, Wuqiao, and Huaian villages, regions characterized by intensive human activities and high levels of village and township development. These areas are primarily dominated by construction land. In contrast, low-resistance areas are mainly distributed in the northern part of the study area, where land-use types are dominated by forest, grassland, and cropland.
Ecological corridors connect major ecological sources and provide pathways for ecological flows as well as species dispersal and migration. Based on the integrated ecological resistance surface, the spatial distribution of ecological corridors in the study area was identified using circuit theory (Figure 5). A total of 32 ecological corridors were detected, with a combined length of approximately 58.06 km and an average length of 1.81 km. Considerable variation exists in corridor distribution among villages. The villages of Xinhua, Pandian, Lujing, Zhangtang, and Xinye each contain more than 5 km of corridors, significantly higher than other villages. These corridors play a crucial role in maintaining structural connectivity and functional stability of the regional ecosystem, and their significance in this study primarily manifested in the subsequent identification of pinch points and barrier points. The results concerning corridor counts, lengths, and spatial distributions aimed to reflect the network structure and connectivity deficiencies, providing spatial justification for delineating priority restoration zones. In addition, several core ecological sources in the northern part of the study area exhibit weak connectivity with other sources, posing a risk of ecological isolation and island formation, indicating that the overall ecological pattern requires further optimization.

3.2. Identification of Priority Areas for Ecological Restoration

3.2.1. Identification of Ecological Pinch Points

Based on the construction of the ESP in the study area, ecological “pinch points” were identified using the Pinch point Mapper tool in pairwise mode. The current distribution along ecological corridors is shown in Figure 6a, with colors ranging from blue to red indicating increasing current density. Current density was classified into five levels using the natural breaks method, with the highest level designated as ecological pinch points (red areas in Figure 6b). Natural breaks method optimizes classification boundaries based on data distribution, proving suitable for ecological flow simulation data exhibiting spatial heterogeneity. It is also more commonly employed and robust in ecological corridor analyses that emphasize local variation. A total of 41 ecological pinch points were identified in the study area, covering approximately 27.24 ha, and were mainly distributed across 14 villages. The ecological pinch points in Zhangtang and Xinye villages had the largest areas, accounting for 71.84% of the total pinch point area. Ecological pinch points hold significant ecological importance for sustaining landscape connectivity across the study area. Overlaying them with the integrated ecological resistance surface shows that most pinch points (85.64%) are located in low-resistance areas. Ecological pinch-point areas should be protected and restored to maintain ecosystem integrity and enhance regional landscape connectivity, thereby strengthening the ESP.

3.2.2. Identification of Ecological Barrier Points

Based on the Barrier Mapper tool and using a moving-window search approach, ecological barrier points in the study area were identified (Figure 7a), with colors from blue to red indicating increasing cumulative current recovery values. Current recovery values were classified into five levels using the natural breaks method, and the highest level was designated as ecological barrier points (red areas in Figure 7b). A total of 30 ecological barrier points were identified, covering approximately 25.67 ha and concentrated in nine villages (Figure 7b). Among these, Pandian Village contained the largest number (14) and area (7.25 ha) of barrier points, accounting for 28.24% of the total. This was followed by Lvqiao and Xinhua villages, with a combined barrier area of approximately 11.25 ha, representing 43.83% of the total. Ecological barrier points are areas that hinder ecological flows between source areas. Locations where barrier points intersect ecological corridors are particularly vulnerable and should be prioritized for maintenance and restoration to sustain ecosystem stability and connectivity.

4. Discussion

4.1. Rationality and Scientific Basis of Methods for Constructing the ESP and Identifying Priority Areas for Ecological Restoration

This study developed an integrated technical framework. Specifically, it was reflected in the following aspects: (1) Combining structural integrity (MSPA), ecosystem service value (InVEST), and connectivity to identify comprehensive ecological sources. This approach accounts for both the “internal quality” of source patches (i.e., functional attributes) and their “external linkages” (i.e., spatial configuration), ensuring that the final ecological sources are not only structural cores but also functionally important nodes. Consequently, they contributed to the scientific rigor and comprehensiveness of the ecological source identification. (2) Enhancing the resistance surface with night-time light data to better reflect human activity impacts. This approach provides a more realistic representation of ecological flow resistance between source patches, thereby generating a resistance surface that more closely reflects on-the-ground conditions and supports subsequent corridor extraction and identification of priority restoration areas. (3) Applying circuit theory instead of traditional MCR to model species movement, identify multiple corridors, and pinpoint restoration priority areas (such as pinch and barrier point) through process-based simulation, thereby improving scientific accuracy. This approach provides deeper insight into the underlying mechanisms of ecological space [3,34] by simulating ecological processes and inversely identifying the key spatial structures that support them. Consequently, the identification of priority areas for ecological restoration shifts from “empirical judgment” to “process-based simulation” enhancing the scientific rigor of the results. Although this research framework exhibits logical coherence, certain uncertainties remain. Future work could incorporate multi-scenario threshold sensitivity analyses and field-based species trajectory data to validate the model, thereby enhancing the robustness of the results. Furthermore, it is also necessary to draw extensively upon theories and methodologies concerning European green infrastructure, landscape graph theory, and urban–rural ecological corridors.

4.2. Implications for Territorial Spatial Ecological Restoration

This study constructed an ESP and identified key ecological restoration areas at the township scale, providing insights for ecological restoration planning. First, bridging macro-strategy with local implementation, townships represent the fundamental administrative units where these restoration projects are ultimately implemented. Identifying ESP and priority restoration areas at the township scale helps translate broader ecological strategies into actionable and manageable spatial plans, forming a restoration project deployment pathway that integrates top-down and bottom-up approaches, effectively addressing the challenge of implementing macro-level strategies. Second, identifying ecological pinch points (critical nodes requiring protection) and ecological barrier points (key impediments requiring restoration) enables the precise spatial targeting of restoration efforts and guides decision-makers to focus interventions on areas that maximize overall ecological function and network connectivity, thereby optimizing the allocation of resources and funding. Finally, a hierarchical ecological restoration strategy should be implemented, with the following implementation details: (1) Align the identified ecological restoration priority areas with relevant planning like Zhuanghang Town Territorial Spatial Plan (2021–2035) and the Small Watershed Ecological Restoration Plan, through a “multi-plan integration” approach to ensure their spatial implementation. Critical ecological sources should be strengthened as urban green cores, and structural ecological networks should be constructed by reinforcing riparian and roadside forest belts along the Punan Canal, the Huangpu River, and major transport routes. Ecological pinch point areas should be incorporated into ecological conservation redlines or equivalent strictly regulated areas, where development activities are prohibited. (2) Convert the priority areas into specific ecological restoration projects, incorporate them into the government’s annual investment plan, and actively seek policy support such as upper-level ecological compensation and special funds for ecological restoration. Ecological engineering measures such as vegetation restoration and habitat enhancement should be implemented to improve habitat quality and ecological functions, thereby ensuring the effectiveness of ecological corridors. For ecological barrier point areas, ecological modifications to the corridors traversing these points are recommended, such as constructing ecological bridges, underground ecological culverts and restoring natural riverbanks, directly reducing resistance to ecological flows. (3) During the delineation of urban development boundaries and project approvals, important ecological corridors and pinch points should be proactively avoided, thereby reducing the occupation of ecological space by development and construction at the source. In village planning, ecological development should be strengthened in the central part of the study area by establishing corridors connecting northern and southern ecological patches to form a complete ecological network. Special attention should be given to connecting isolated northern sources with other sources to prevent the formation of ecological islands. (4) Utilize technologies such as remote sensing and the Internet of Things to build an intelligent ecological monitoring platform, providing technical support for targeted ecological restoration measures.
Considering that high-resistance areas and ecological barrier points are often located at village boundaries, it is recommended to establish cross-village collaborative ecological governance mechanisms to ensure unified planning and joint restoration, avoiding fragmented ecological management caused by administrative divisions. The specific implementation details were as follows: (1) created a cross-village coordination mechanism led by the township government, involving relevant departments, village committees, residents, and enterprises to oversee transboundary restoration efforts; (2) formulated a dedicated cross-village ecological restoration plan to clarify tasks and responsibilities for each village; (3) introduced ecological compensation and benefit-sharing systems (e.g., fiscal transfers, industrial support) to balance interests between conservation and beneficiary areas; (4) engaged social organizations, enterprises, and villagers in project design, implementation, and supervision, fostering a collaborative governance model led by the government with broad public involvement.
Additionally, it should be noted that although this study has identified priority areas through model simulations and quantitative analysis, the actual ecological effectiveness of restoration measures must be evaluated over the long term in conjunction with future field monitoring data. The specific measures were as follows: developed a comprehensive monitoring indicator system that encompasses ecosystem structure, functions, and services, established fixed monitoring plots and transects and integrate technologies such as remote sensing and unmanned aerial vehicles to create an integrated “sky-air-ground” monitoring network, built an ecological restoration monitoring database to standardize the collection, storage, and management of data, based on the monitoring results, identified areas with sub-optimal restoration outcomes, analyze the underlying causes, and adjust restoration measures accordingly. This helped establish a closed-loop management system encompassing planning, implementation, assessment, and adjustment.

4.3. Limitations and Future Research Directions

Although integrating multiple mainstream methods to systematically explore the construction of ESP and the identification of priority ecological restoration areas at the township scale, certain limitations remain in this study. First, as a static analysis based on a single-phase dataset, it cannot capture the dynamic process of ecological restoration under pressures like land-use change and climate variation. Future studies should incorporate multi-temporal and multi-source data to analyze the spatiotemporal evolution of ESP and priority restoration areas, potentially integrating land-use simulation and prediction models for dynamic modeling [49]. Second, the circuit theory model assumes species movement is random and isotropic, which may not fully align with actual species migration behavior. This simplifying assumption may introduce potential biases. It is recommended that species-specific resistance surfaces be incorporated for key indicator species across different regions to correct this bias, thereby enhancing the model’s adaptability. Furthermore, by integrating the ecological security patterns of multiple species, a more comprehensive ecological network oriented towards biodiversity conservation can be established [55,56,57]. Moreover, since no universal resistance values exist, relative values were adopted based on prior research, with less consideration given to uncertainty, confidence intervals or validation metrics. Nighttime light data were additionally used to account for human disturbance and refine the resistance surfaces, thereby reducing potential effects on the results [20,58]. However, more accurate resistance values warrant further investigation in future studies. Third, the models used in this study were based on a series of key assumptions. These assumptions, whilst simplifying complex ecological processes, may also exert a certain influence on the research findings. For instance, the circuit theory model assumes that species movement within a landscape is random and isotropic, whereas actual species dispersal often exhibits directionality, path dependency, and learning behavior. However, the models and methods adopted in this study have been widely used in similar research, and their simplified assumptions are generally considered acceptable in ecological planning at meso- and micro-scales [29]. The findings reflected relative priorities rather than absolute values, so the systematic bias caused by the assumptions could be partially offset to some extent. Nevertheless, when applying these results to specific ecological restoration projects, verification and adjustment through field investigations remain essential. Fourth, this study represents a case study of a single township. The applicability and generalizability of its conclusions and methods at the township scale remain to be further validated. Future work could involve comparative analyses across multiple townships of different types (e.g., industrial-dominated, agriculture-dominated, peri-urban integrated) to summarize the commonalities and unique characteristics, thereby developing a more widely applicable technical framework for constructing township-scale ESP and identifying key ecological restoration areas.

5. Conclusions

This study takes typical townships in Shanghai as a case study and integrates MSPA, the InVEST model, landscape connectivity analysis, and circuit theory to explore township-scale ESP construction, the identification of priority areas for ecological restoration, and corresponding restoration strategies. The main conclusions are as follows: (1) The proposed “structure–function–process” multidimensional integration framework effectively overcomes the limitations of single methodologies. This framework provides a replicable technical pathway for other studies examining ecological security patterns at the township scale. (2) The pattern of ecological source areas being “concentrated in the north and south, dispersed in the central region” coupled with the risk of isolation faced by northern source areas reveals the imbalance and fragility of ecological spaces within townships. This necessitates ecological restoration extending beyond administrative boundaries, establishing collaborative governance mechanisms across villages. The high overlap between areas of high ecological resistance and dense human activity clearly identifies built-up urban and rural areas as key barriers to ecological connectivity. Consequently, future territorial space planning need prioritize optimizing the spatial layout of construction land, embedding ecological corridors and nodes. By identifying ecological pinch points (prioritizing protection) and barrier points (prioritizing restoration), precise spatial allocation guidance is provided for limited ecological restoration resources. This facilitates a shift from “area-based conservation” to “targeted restoration”, enhancing the cost-effectiveness of restoration projects. (3) Furthermore, townships serve as the “last mile” implementation units, acting as the critical scale for translating provincial- or municipal-level macro strategies, such as ecological conservation redlines, into actionable plans. The research findings can directly inform ecological restoration planning for Zhuangxing Township and similar localities. Specific measures include integrating ecological pinch points into refined redline management and implementing ecological bridges or culverts at barrier points.

Author Contributions

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

Funding

This research was funded by the Philosophy and Social Sciences Planning fund of Shanghai (2024BCK011), the National Natural Science Foundation of China (42471118, 42571353) and the Natural Science Foundation of Shanghai, China (grant no. 25ZR1402409).

Data Availability Statement

Data derived from public domain resources.

Acknowledgments

The authors are deeply thankful to the anonymous reviewers for their insightful comments and suggestions, which significantly contributed to the improvement of the manuscript.

Conflicts of Interest

The authors declare no conflicts of interest.

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Land 15 00274 g001
Figure 1. Location map of the study area, village boundaries, and land-use map.
Figure 1. Location map of the study area, village boundaries, and land-use map.
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Figure 2. The schematic workflow diagram of this study.
Figure 2. The schematic workflow diagram of this study.
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Figure 3. Spatial distribution of major ecological sources in the study area.
Figure 3. Spatial distribution of major ecological sources in the study area.
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Figure 4. Integrated ecological resistance surface of the study area.
Figure 4. Integrated ecological resistance surface of the study area.
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Figure 5. Spatial distribution of ecological corridors in the study area.
Figure 5. Spatial distribution of ecological corridors in the study area.
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Figure 6. Spatial distribution of: (a) cumulative current in ecological corridors, and (b) ecological pinch points in the study area.
Figure 6. Spatial distribution of: (a) cumulative current in ecological corridors, and (b) ecological pinch points in the study area.
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Figure 7. Spatial distribution of: (a) cumulative current recovery in ecological corridors, and (b) ecological barrier points in the study area.
Figure 7. Spatial distribution of: (a) cumulative current recovery in ecological corridors, and (b) ecological barrier points in the study area.
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Table 1. Assigned base resistance coefficients for the study area.
Table 1. Assigned base resistance coefficients for the study area.
Resistance FactorResistance Coefficient
110203050100300
Land use typesForest landGrasslandGarden plotFarmlandWater
body		Other construction LandUrban construction Land, Rural construction Land
NDVI[0.411, 1][0.349, 0.411][0.273, 0.349][0.192, 0.273][0.109, 0.192][−0.005, 0.109][−1, −0.005]
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MDPI and ACS Style

Zhou, T.; Li, Y.; Zhang, Y.; Lin, L.; Zhou, R.; Ma, A.; Chen, J. Identification of Township-Scale Ecological Restoration Priority Areas Based on Ecological Security Pattern and Multi-Method Integration. Land 2026, 15, 274. https://doi.org/10.3390/land15020274

AMA Style

Zhou T, Li Y, Zhang Y, Lin L, Zhou R, Ma A, Chen J. Identification of Township-Scale Ecological Restoration Priority Areas Based on Ecological Security Pattern and Multi-Method Integration. Land. 2026; 15(2):274. https://doi.org/10.3390/land15020274

Chicago/Turabian Style

Zhou, Tingyun, Yutong Li, Yu Zhang, Lushuang Lin, Rui Zhou, Aimin Ma, and Junying Chen. 2026. "Identification of Township-Scale Ecological Restoration Priority Areas Based on Ecological Security Pattern and Multi-Method Integration" Land 15, no. 2: 274. https://doi.org/10.3390/land15020274

APA Style

Zhou, T., Li, Y., Zhang, Y., Lin, L., Zhou, R., Ma, A., & Chen, J. (2026). Identification of Township-Scale Ecological Restoration Priority Areas Based on Ecological Security Pattern and Multi-Method Integration. Land, 15(2), 274. https://doi.org/10.3390/land15020274

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