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Article

Constructing Ecological Security Patterns in Coal Mining Subsidence Areas with High Groundwater Levels Based on Scenario Simulation

by
Shiyuan Zhou
1,2,
Zishuo Zhang
1,
Pingjia Luo
1,2,*,
Qinghe Hou
1 and
Xiaoqi Sun
1
1
School of Architecture and Design, China University of Mining and Technology, Xuzhou 221116, China
2
Jiangsu Provincial Mining Area Territorial Space Ecological Restoration Engineering Technology Innovation Center, Xuzhou 221116, China
*
Author to whom correspondence should be addressed.
Land 2025, 14(8), 1539; https://doi.org/10.3390/land14081539
Submission received: 29 June 2025 / Revised: 25 July 2025 / Accepted: 25 July 2025 / Published: 27 July 2025
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Abstract

In mining areas with high groundwater levels, intensive coal mining has led to the accumulation of substantial surface water and significant alterations in regional landscape patterns. Reconstructing the ecological security pattern (ESP) has emerged as a critical focus for ecological restoration in coal mining subsidence areas with high groundwater levels. This study employed the patch-generating land use simulation (PLUS) model to predict the landscape evolution trend of the study area in 2032 under three scenarios, combining environmental characteristics and disturbance features of coal mining subsidence areas with high groundwater levels. In order to determine the differences in ecological network changes within the study area under various development scenarios, morphological spatial pattern analysis (MSPA) and landscape connectivity analysis were employed to identify ecological source areas and establish ecological corridors using circuit theory. Based on the simulation results of the optimal development scenario, potential ecological pinch points and ecological barrier points were further identified. The findings indicate that: (1) land use changes predominantly occur in urban fringe areas and coal mining subsidence areas. In the land reclamation (LR) scenario, the reduction in cultivated land area is minimal, whereas in the economic development (ED) scenario, construction land exhibits a marked increasing trend. Under the natural development (ND) scenario, forest land and water expand most significantly, thereby maximizing ecological space. (2) Under the ND scenario, the number and distribution of ecological source areas and ecological corridors reach their peak, leading to an enhanced ecological network structure that positively contributes to corridor improvement. (3) By comparing the ESP in the ND scenario in 2032 with that in 2022, the number and area of ecological barrier points increase substantially while the number and area of ecological pinch points decrease. These areas should be prioritized for ecological protection and restoration. Based on the scenario simulation results, this study proposes a planning objective for a “one axis, four belts, and four zones” ESP, along with corresponding strategies for ecological protection and restoration. This research provides a crucial foundation for decision-making in enhancing territorial space planning in coal mining subsidence areas with high groundwater levels.

1. Introduction

Coal mining serves as a primary global energy source, and the choice of mining techniques yields significant distinctions in environmental impacts. Large-scale surface subsidence constitutes the primary ecological disruption resulting from underground coal extraction [1]. For example, in the Ruhr region of Germany, the largest coal mining area in Europe, centuries of mining have caused large-scale surface subsidence, with some areas subsiding by more than 25 m. In the Appalachian mining region of the United States, surface subsidence of over 9 m has occurred in some areas after coal mining, resulting in significant damage to farmland and infrastructure [2]. As a result of mining-induced subsidence in areas with high groundwater levels, artificial secondary wetlands are frequently formed. Landscape transformations of this type alter the regional landscape configuration and pose significant challenges to ecosystem stability [3]. Meanwhile, resource-based cities face shrinking ecological spaces, habitat fragmentation, and biodiversity loss driven by urbanization [4]. The “Mining and Biodiversity Management Series Guidelines” published by the International Union for Conservation of Nature (IUCN) emphasize the restoration of habitat networks in mining areas and the recovery of species migration corridors [5]. In the ecological restoration of the Emscher River Basin in the Ruhr region, plans were developed for the restoration of riparian wetlands and the reconstruction of ecological networks [6]. Developing an ecological security pattern (ESP) can serve as a fundamental strategy for restoring biodiversity [7], as it not only connects fragmented habitats but also creates favorable conditions for species migration and dispersal, contributing to the maintenance of ecological stability in the region. This measure holds great significance for promoting ecological restoration and ensuring the sustainable development of resource-based cities.
The ESP refers to the potential landscape configuration, comprising critical components, strategic locations, and spatial connections, which represents a sustainable landscape pattern that integrates spatial arrangements with ecological processes [8]. Current research on ESP construction has developed a standard paradigm, “source area identification—resistance surface construction—corridor extraction” [9]. To optimize future ESPs, the temporal scale of research has shifted from static identification to dynamic simulation [10]. By simulating land use changes and exploring ESPs under multiple future scenarios, practical insights and valuable references can be provided for regional ecological environment protection. Land use change can be simulated using cellular automata CA, CLUE-S, SLEUTH, PLUS, and multi-agent systems. It is noteworthy that CA-based models have been applied most extensively [11]. However, they exhibit certain limitations with respect to the representation of driving factors and simulation accuracy. CLUE-S and SLEUTH perform better in simulating urban construction land changes. Compared to other models, the PLUS model demonstrates superior performance in transformation rule mining, dynamic change simulation strategies, and accuracy of results. In addition to providing more substantial support for regional planning and policy formulation, its simulation outcomes have been empirically validated as being more accurate [12].
In identifying ecological source areas, many studies primarily emphasize the intrinsic functions of habitat patches while overlooking their connections to the surrounding environment [13]. Some studies designate nature reserves and scenic areas as ecological sources using a single screening criterion, which introduces high subjectivity [14]. Using morphological spatial pattern analysis (MSPA), landscape types can be distinguished precisely, and structural connectivity is highlighted, which improves the scientific basis for identifying ecological source areas [15,16]. Research on ecological corridor construction has produced various theoretical approaches, including graph theory [17], the minimum cumulative resistance (MCR) model [18,19], and circuit theory [20,21,22], with the MCR model being the most widely applied [23]. However, the MCR model primarily reflects the direction of ecological corridors and offers limited insight into their width [24]. In contrast, circuit theory can simulate corridor current density [25], identify all potential paths for species movement between habitats to represent corridor width [26], and pinpoint ecological pinch points and ecological barrier points, thereby informing restoration strategies. This study aims to construct an ecological network from a landscape structural perspective based on the characteristics of ecological space degradation in coal mining subsidence areas with high groundwater levels. Additionally, it utilizes circuit theory to identify ecological pinch points and ecological barrier points, enabling the development of a more refined ESP.
Huaibei City is a significant coal-producing region in eastern China. The average burial depth of its coal seams exceeds 1000 m, with notably thick strata. Consequently, substantial surface subsidence occurs after coal mining. The Xiaosui mining area in the study region is the earliest mining area to be developed in Huaibei City. Over 60 years of large-scale mining have resulted in the formation of more than 20 coal mining subsidence areas in the region, with an average subsidence depth exceeding 5 m and a maximum subsidence depth of over 11 m. Huaibei is characterized by a high groundwater table in its mining areas, with depths ranging from −3.15 to −4.15 m. Over 70% of the subsidence zones are waterlogged. Over 70% of the subsidence areas are covered by accumulated water. Furthermore, the study area has a dense river network, with all subsidence areas situated next to rivers. As a city built around coal mining, Huaibei has experienced a significant rise in urbanization, with its urbanization rate soaring from 28% in 2000 to 66.81% in 2024, driving the rapid expansion of urban construction land. Early urban planning centered around mining districts has resulted in spatial conflicts between construction land and coal mining subsidence zones. As a result, mining activities and economic growth have had a profound impact on reshaping regional land use patterns and ecological security structures. This study seeks to address the following scientific questions: (1) How can dynamic land use changes in mining areas be simulated under the combined influence of subsidence from coal mining and urban expansion? (2) How can the evolution of ESPs be modeled to support decision-making for restoring river basin networks and enhancing biodiversity?
In this study, the northern area of Huaibei City is selected as the research area, the PLUS model is used to simulate land use changes under various scenarios in 2032, and MSPA, landscape connectivity analysis, and circuit theory are combined to construct multi-scenario ESPs. The research objectives are as follows: (1) to investigate the mechanisms driving land use change in coal mining subsidence areas with high groundwater levels and to simulate land use trends under various scenarios influenced by compound disturbances; (2) to compare ecological network dynamics under multiple scenarios between 2022 and 2032, identify ecological pinch points and ecological barrier points in the optimal scenario, and construct an ESP; (3) to propose planning goals and corresponding ecological protection and restoration strategies for the ESP based on the research findings. The findings provide transferable analytical methodologies for biodiversity restoration in global mining regions with high groundwater levels, as well as for developing ecological security frameworks in resource-dependent cities.

2. Materials and Methods

2.1. Study Area

The study area is situated in Huaibei City, Anhui Province, in the eastern Huanghuaihai Plain, which is rich in coal resources (Figure 1). This study area is typical of coal mining subsidence areas with high groundwater levels and abundant internal water systems, and changes in water accumulation have affected the spatial distribution of watershed systems. The study defines the Xiaosuixinhe Basin and the Tuohe Basin as its spatial scope to examine changes in the ESP of coal mining subsidence areas. The study area spans 1136.53 km2 and falls within the temperate semi-humid monsoon climate zone, characterized by distinct seasonal variations. It encompasses Duji District, Xiangshan District, Lieshan District, and the towns of Suixi, Liuqiao, Tiefo, Baishan, and Sipu. The mining area extends in a northeast–southwest direction. The area features a dense river network, primarily composed of secondary and tertiary tributaries of the Huaihe River. The terrain is divided mainly into hilly and plain regions, with hilly areas located in the east and north, ranging in elevation from 60 to 400 m. As a result of mining activities and rapid economic development, conflicts between ecological protection and land development and exploitation have become increasingly pronounced, posing significant threats to the stability and integrity of regional ecosystems.

2.2. Data Sources

Land use data were obtained from the Geospatial Data Cloud (http://www.gscloud.cn, accessed on 1 December 2024) at a 30 m resolution. Major road data were sourced from the Resources and Environmental Science Data Center, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences (http://www.resdc.cn, accessed on 1 December 2024). River system data were obtained from OpenStreetMap (https://www.openstreetmap.org, accessed on 3 December 2024). DEM data were also obtained from the Geospatial Data Cloud (http://www.gscloud.cn, accessed on 5 December 2024) at a 30 m resolution. Vector data, including administrative boundaries and subsidence depth, were provided by the Huaibei Bureau of Natural Resources and Planning. Raster data were resampled to a 30 m resolution using bilinear interpolation, with the aim of preserving spatial continuity and minimizing interpolation errors.

2.3. Methods

2.3.1. Simulation of Spatio-Temporal Changes in Land Use

PLUS simulates land use changes driven by both human activity and natural factors, as well as projects’ future land use scenarios. A more comprehensive understanding of potential future land use dynamics can be achieved by combining the land expansion analysis strategy (LEAS) and the CA based on multi-type random seeds (CARS) [12]. The simulation process for predicting land use patterns in the study area by 2032 involves the following steps. (1) The LEAS module is applied to extract land expansion areas in 2002 and 2012. (2) Land use demand is predicted using Markov chains. (3) Neighborhood weight parameters are determined, and the land use pattern for 2022 is simulated using the CARS module. (4) The simulation results are compared with actual 2022 land use data, and model accuracy is assessed by calculating the Kappa coefficient using the verification module. The validated parameters are then used to predict land use in 2032 under different scenarios, defined by the research objectives.
Based on previous studies [27,28,29], a total of 22 driving factors were selected (Figure 2), including DEM, slope, aspect, groundwater depth, precipitation, evaporation, coal mining production scale, urbanization rate, farmland productivity, GDP, population density, wetland nature reserve, ecological control area, basic farmland conservation area, comprehensive land consolidation area, subsidence depth, water accumulation rate in the subsidence area, average annual rate of change in water accumulation over the past decade, water accumulation depth, distance to main road, distance to major river, and distance to built-up area.

2.3.2. Scenario Setting

Three scenarios were simulated: the natural development scenario (ND), the land reclamation scenario (LR), and the economic development scenario (ED). These scenarios represent distinct development orientations for coal mining subsidence areas and are designed to assess the impact of land use change on ESPs under varying policy contexts.
ND Scenario: This scenario reflects the long-term effects of multiple disturbance factors on the evolution of wetland landscapes under current economic, industrial, and territorial spatial planning policies. Water accumulation in dynamic subsidence areas is identified based on the development zone settings in the PLUS model. In basic stable subsidence areas (subsidence rate < 50 mm/month), conversion to construction land is restricted; however, in stable subsidence areas (subsidence rate < 30 mm/month), transformation of subsidence water areas is permitted. During simulation, the transformation of water areas within ecological redlines and urban blue zones is restricted based on the “three zones and three lines” delineation in national land planning. Land use demand for other categories is predicted using Markov chains and adjusted according to projected subsidence-induced water accumulation.
LR Scenario: This scenario integrates technical standards for coal mining subsidence area management and future subsidence trends to simulate the evolution of wetlands when reclamation is prioritized for cultivated land. In this scenario, the transformation of coal mining subsidence water areas in dynamic subsidence areas is restricted. According to the principle of concurrent mining and reclamation, areas with water depths exceeding 3 m are designated as long-term water areas [30]. The overall reclamation target for 2032 is set at 75%, compared to approximately 54% in Huaibei in 2022.
ED Scenario: This scenario reflects the impact of urbanization-driven construction land expansion on the distribution of coal mining subsidence water areas. In this scenario, based on Huaibei’s territorial spatial planning and development goals, construction land is set to increase by 10% (the actual growth from 2012 to 2022 was 11.77%).

2.3.3. Model Verification

Based on the expansion characteristics of different land use types in the study area, neighborhood weight values were assigned accordingly (see Table 1). Model accuracy was verified using the Kappa coefficient, which yielded a value of 0.869, indicating a high degree of consistency and precision between the predicted results and the actual land use conditions [31]. The model was, therefore, deemed suitable for simulating land use in 2032.

2.3.4. Identification of Ecological Source Areas

Ecological source areas refer to relatively stable biological habitat patches in ecosystems [10], which play a crucial role in maintaining ecosystem stability and functionality. The MSPA method is well-suited for examining spatial patterns and is particularly effective in analyzing landscape morphology and spatial distribution [32]. In delineating the scope of ecological source areas, multiple factors were comprehensively considered. According to the Work Plan for Accelerating the Resolution of Outstanding Issues in Comprehensive Governance of Coal Mining Subsidence Areas issued by Anhui Province [33], areas with subsidence exceeding 1500 mm are designated as long-term water bodies due to their ecological quality and potential to support wildlife habitats. Based on these criteria, the study selected forest land, rivers, coal mining subsidence water areas (subsidence > 1500 mm), artificial lakes, reservoirs, and grassland as foreground data in the MSPA analysis, assigning them a value of 2. Construction land, cultivated land, unused land, and ponds were used as background data and assigned a value of 1 during land use reclassification. The analysis was conducted using Guidos Toolbox 2.8, which classified the landscape into seven types: core, islet, perforation, edge, bridge, loop, and branch [34]. Core areas larger than 0.1 km2 were extracted separately as candidate ecological source areas [32].
Landscape connectivity is defined as the extent to which a landscape facilitates or hinders movement among habitat patches [35], and it serves as a quantitative indicator of whether exchanges of matter, energy, and species migration are ecologically favorable [36]. This study employed three key indices: the probability of connectivity (PC), the integral index of connectivity (IIC), and the delta probability of connectivity (dPC). A higher index value indicates stronger landscape connectivity, which facilitates the migration of species. Conefor Sensinode 2.6 was used to calculate these indices for each patch, with the corresponding formulas presented as follows:
I I C = ∑ i = 1 n ∑ j = 1 n a i a j 1 + n l i j A L 2
P C = ∑ i = 1 n ∑ j = 1 n a i a j P i j * A L 2
d P C = P C − P C r e m o v e P C × 100 %
where n is the total number of patches; aᵢ and aⱼ represent the areas of patches i and j, respectively; P i j * is the most significant probability of species dispersal across patches i and j; AL is the total landscape area; and PCremove is the connectivity index after removing a single patch. Core areas initially extracted via MSPA were further refined, and patches with dPC values greater than 1.0 were identified as ecological source areas [32].

2.3.5. Construction of Resistance Surface

The resistance surface describes the extent of impediments to ecological flow processes within an area [37], and it is shaped primarily by land use patterns and anthropogenic activities [38,39]. To accurately assess the resistance surface, this study integrated relevant literature [18,19] with site-specific characteristics of the study area. Nine resistance factors were selected, including DEM, slope, land use type, distance to river, vegetation coverage, distance to built-up area, distance to main road, subsidence depth, and distance to coal mining subsidence area (Table 2). The resistance values were derived from relevant literature [10,18,32] and integrated with policy documents [33]. The weights of each factor were determined by combining the expert scoring method with the analytic hierarchy process.

2.3.6. Extraction of Ecological Corridors

Ecological corridors play a crucial role in facilitating the flow of energy and materials across a region [40,41], as well as in maintaining ecosystem stability and safeguarding regional ecological security [42]. Circuit theory conceptualizes the landscape as a conductive surface and simulates species migration and diffusion by modeling the random walk of electrons [43]. Based on the comprehensive ecological resistance surface, Circuitscape 4.0 was employed to extract ecological corridors for all three scenarios in both 2022 and 2032.

2.3.7. Identification of Ecological Pinch Points and Ecological Barrier Points

Ecological pinch points refer to specific areas within an ecological network that are crucial for species migration or contain irreplaceable pathways [44]. These nodes exhibit high ecological flow and are relatively fragile, making them vulnerable within the ecological network. Therefore, in the construction of ecological networks, priority should be given to protecting ecological pinch points [45]. This study utilized the Pinchpoint Mapper tool within the Circuitscape 4.0 software to identify and extract ecological pinch points. Through experimental simulation, the “all-to-one” mode was selected, and a weighted cost distance of 4000 m was set as the corridor width for the precise identification of ecological pinch points.
Ecological barrier points hinder ecological flows between ecological source areas, and their restoration can significantly enhance landscape connectivity [45,46]. To identify ecological barrier points, the Barrier Mapper tool in Circuitscape was employed. The minimum search radius was set to 30 m, the maximum search radius to 300 m, and the search radius increment to 15 m. The overall research framework of the article is shown in Figure 3.

3. Results

3.1. Simulation Results of Spatio-Temporal Changes in Land Use

The simulation results (Figure 4 and Table 3) indicate that, compared to 2022, the area of cultivated land exhibits a declining trend across all scenarios in 2032 but remains the dominant type of land use. The forest land area demonstrates an increasing trend under each scenario; specifically, in the ND scenario of 2032, it increases from 181.56 km2 to 194.45 km2, corresponding to a growth rate of 7.10%. Additionally, the grassland area shows an increase in all scenarios, with the most substantial rise of 21.40 km2 occurring under the LR scenario in 2032, representing a growth rate of 4.19%. Compared with 2022, water areas are projected to increase from 97.04 km2 to 100.97 km2 and 99.96 km2 in 2032 under the ND and ED scenarios, respectively, with growth rates of 4.05% and 3.01%. Conversely, under the LR scenario in 2032, the water areas decrease to 87.27 km2, reflecting a reduction rate of 10.07%. Construction land expands significantly in all scenarios, with the most pronounced growth observed under the ED scenario in 2032, reaching 322.46 km2 and indicating a growth rate of 9.92%. In terms of unused land, the area increases by 4.26% and 2.08% under the ND and ED scenarios in 2032, respectively, compared with 2022. However, under the LR scenario in 2032, the unused land area decreases by 2.79%. Based on the analysis above, the ecological space within the study area is expected to expand under the ND scenario in 2032. Spatially, land use changes between 2022 and 2032 are primarily concentrated in urban fringe areas and coal mining subsidence water areas. Construction land expands primarily in the southeast under all 2032 scenarios, with the ED scenario exhibiting the highest growth rate, while the ND scenario shows a comparatively moderate increase. Under the ND scenario, coal mining subsidence water areas expand significantly, show a decreasing trend under the LR scenario, and experience progressively increasing risks of encroachment by construction land under the ED scenario.

3.2. Identification of Ecological Source Areas

3.2.1. Results of Ecological Source Areas Identified Based on MSPA

Based on the MSPA analysis, the areas and proportions of seven types of ecological landscape elements were calculated (Table 4). In 2022, the core area was approximately 171.53 km2, representing 69.28% of all landscape types. The edge and perforation area, serving as transitional areas adjacent to the core area, accounted for 17.79% and 1.15%, respectively. Islets are fragmented habitat patches, comprising 3.94% of all landscape types. Branches, bridges, and loops all serve as critical components in facilitating connectivity within the ecological network. Among these, the branches function as strip-shaped areas that connect core areas with other landscape types, covering an area of approximately 11.01 km2, which accounts for 4.45% of all landscape types. The bridges serve as strip-shaped areas linking individual core areas, spanning approximately 5.77 km2, or 2.33% of all landscape types. The loops serve as shortcuts for species migration within the core area, which encompasses an area of approximately 2.62 km2, equivalent to 1.06% of the total landscape types.
Compared with 2022, the core area expanded under the ND and LR scenarios but declined to approximately 134.77 km2 under the ED scenario. The ND scenario had the largest core area, reaching approximately 191.87 km2. In the LR scenario, the core area reached about 187.99 km2. Overall, both the ND scenario and the LR scenario demonstrate improvements in the ecological environment compared to the current situation. In contrast, the ED scenario results in a reduction of the core area due to the expansion of construction land, leading to increased pressure on the regional ecological environment.

3.2.2. Results of Extracting Ecological Source Areas Based on Landscape Connectivity

Based on the MSPA results, core patches larger than 0.1 km2 were extracted for landscape connectivity analysis, and those with a dPC value greater than 1.0 were identified as ecological source areas. As shown in Figure 5, the identified ecological source areas measured 120.13 km2 in 2022 and 133.52 km2, 127.82 km2, and 129.45 km2 in 2032 under the ND, LR, and ED scenarios, respectively. Compared to 2022, the number of ecological source areas increases across all three 2032 scenarios, with most new patches located in the Lieshan and Xiangshan districts. According to the ND scenario, ecological source areas represent approximately 69.59% of the total core area, a significantly higher percentage than in other scenarios. The ecological source area comprises diverse ecosystem types, such as forests and wetlands, which are critical habitats for numerous rare and endangered species, as well as key ecological indicators. Spatially, the ecological source areas exhibit a pattern of being “numerous and scattered in the center, sparse and clustered at the periphery.” Owing to the high density of construction land and significant human activity interference in the central part of the study area, the ecological source areas exhibit relatively dispersed spatial distributions.

3.3. Resistance Surface Construction Results

The integrated resistance surface (Figure 6), constructed based on nine factors including DEM, slope, landscape type, distance to river, vegetation coverage, distance to built-up area, distance to main road, subsidence depth, and distance to coal mining subsidence area, indicates that resistance distribution varies significantly across different scenarios. As a whole, it exhibits a spatial pattern characterized by “high resistance at the center and low resistance at the periphery.” High-resistance areas are primarily concentrated in the central area. This area is heavily urbanized, with dense construction land that has fragmented previously continuous ecological spaces. Additionally, due to the influence of coal mining, coal mining subsidence areas exhibit relatively high ecological resistance, significantly impeding material and energy flows within the ecosystem.

3.4. Ecological Corridor Extraction Results

Based on the ecological source areas and the integrated resistance surface, ecological corridors were identified (Figure 7). These corridors are mainly concentrated in the central and northern areas of the study area and are predominantly composed of water, forest land, and cultivated land. In 2022, 30 ecological corridors were identified, totaling 87.54 km in length. Compared to 2022, both the number and total length of corridors increased significantly under all three 2032 scenarios. Among them, the ND scenario yielded the highest number of corridors—40 in total—with a combined length of 95.74 km. Under the ED scenario, 36 corridors were identified, totaling 93.98 km, while 27 corridors were identified under the LR scenario, with a total length of 69.79 km. According to the results, the ecological environment will improve by 2032. Ecological corridors are expected to be longer and more widely distributed in the ND scenario, indicating a greater ecological potential. For example, in this scenario, the corridors exhibit a spatial pattern of “dense in the north and sparse in the south.” In the southern area, most ecological sources are rivers with high levels of connectivity, resulting in fewer corridors. The corridors connecting Zhonghu Lake–Qianlonghu Lake–Suihe River and Longjishan Mountain–Donghu Lake–Xiangshan Mountain are primarily composed of natural rivers. These corridors facilitate the circulation of materials and energy, supporting the survival of species and aquatic migration. Therefore, in the subsequent construction of the ESP, emphasis should be placed on enhancing the protection and restoration of such corridors.

3.5. Identification of Ecological Pinch Points and Barrier Points

According to the aforementioned research, the ecological source areas identified under the ND scenario contain a greater number of nature reserves and exhibit a more structurally integrated ecological corridor system that facilitates species migration and dispersal. Consequently, identifying ecological pinch points and ecological barrier points in the 2022 and 2032 ND scenarios, as well as conducting comparative analyses of current and future ecological network configurations, can provide a scientific basis for development planning in coal mining subsidence areas with high groundwater levels.
The study applied the natural break point method to classify five levels, extracting the highest level as ecological pinch points (Figure 8). To ensure integrity, small patches were excluded [10]. Ultimately, two ecological pinch points were identified in 2022, covering a total area of 1.466 km2. These areas primarily comprised forest land and water, accounting for 46.73% and 24.86%, respectively. In the 2032 ND scenario, 18 ecological pinch points were identified, covering 0.843 km2, and mainly consisted of forest land (27.90%) and water (20.16%). Compared to 2022, the number and total area of ecological pinch points decreased by 33.3% and 42.5%, respectively, in 2032. The spatial extent of ecological pinch points is increasingly compressed, weakening material and energy exchange between ecosystems. Additionally, ecological pinch points are predominantly distributed in elongated, strip-like patterns along ecological corridors in the central and northern areas. Rivers and forest lands support these areas as significant nodes within the ecological network and pathways for species migration.
Ecological barrier points (Figure 9) are primarily distributed along the boundaries of adjacent ecological source areas and identified ecological corridors. In 2022, the maximum cumulative recovery values of the unimproved score and improved score modes were 62.710 and 0.650, respectively. The natural break point method was applied to classify them into five levels, and the highest level was extracted as ecological barrier points. Excluding smaller patches, a total of 39 ecological barrier points were identified, covering 2.369 km2, primarily located in the central area [10]. The dominant land use types were construction land (77.29%), grassland (7.35%), and cultivated land (5.19%). In the 2032 ND scenario, the maximum cumulative recovery values reached 72.837 and 0.651, respectively. A total of 43 ecological barrier points were identified, covering an area of 3.846 km2. The dominant land use types were construction land (74.75%), cultivated land (9.69%), and unused land (7.24%), while the coal mining subsidence water area accounted for 2.31%. In general, construction land constitutes the primary category of ecological barrier points, which are characterized by extensive spatial distribution as well as significantly high ecological resistance values, which accelerate the fragmentation of ecosystems. Furthermore, mining activities have indirectly altered the ecosystem’s background conditions, significantly hindering regional ecological flows.

4. Discussion

4.1. Optimization of Key Issues and Strategies for Ecological Protection and Restoration in Coal Mining Subsidence Areas with High Groundwater Levels

The ecosystems of mining areas are among the most severely affected by human economic and social activities. Variability in geological environments, mining practices, and climatic conditions has resulted in significant instability in the evolution of regional landscapes. As a result, traditional territorial spatial planning, which relies on current status monitoring, struggles to adapt promptly to dynamic changes in subsidence, which ultimately limits the effectiveness of ecological restoration measures. Therefore, identifying key ecological risk areas and developing restoration strategies with spatio-temporal adaptability have become central challenges in contemporary ecological conservation and spatial planning. In order to address the aforementioned challenges, this study integrates the construction of ESPs with outcomes derived from ND scenarios and proposes an integrated planning framework for ESPs that aims to balance ecological restoration with economic and social development through the use of “one axis, four belts, and four zones.” (Figure 10).
The “one axis” refers to the water system corridor formed by a series of coal mining subsidence wetlands extending from Shuoxihu Lake to Qianlonghu Lake. This axis not only shapes the urban landscape but also enhances the conservation of water sources. A comparison between the 2022 and 2032 ND scenarios shows that ecological restoration efforts in mining areas have significantly increased urban green space and optimized ecological structures. However, ecological source areas along this axis continue to face challenges such as reduced landscape connectivity and weak ecological foundations. In future ecological restoration, flood and stormwater management systems should be improved as part of future efforts to enhance urban resilience.
The “four belts” refer to ecological corridors formed along the Suihe River, Xinbianhe River, Wangyinhe River, and Leihe River. The research findings demonstrate that the area is intricately interconnected with surrounding mountainous forests, cultivated land, and coal mining subsidence water areas, collectively contributing to the enhancement and safeguarding of regional ecological security. The Suihe River, Xinbianhe River, and Wangyinhe River, as major ecological source areas, exhibit relatively high landscape connectivity. However, these rivers face challenges from excessive exploitation of water resources. Therefore, water resource use should be regulated to maintain ecological water demand and ecological base flow. The Leihe River, as both an ecological corridor and an ecological pinch point, serves as a critical route for species migration. As the river flows through the urban core, it suffers from severe ecological fragmentation, compromising its basic ecological functions. In order to address this issue, river habitat restoration measures should be implemented, ecological buffer zones established, natural revetment rehabilitated, and riparian areas restored.
The “four zones” comprise the natural ecological conservation zone, the key urban restoration zone, the farmland ecological improvement zone, and the wetland protection zone. Xiangshan and Longjishan mountains are identified as key ecological patches with high landscape connectivity based on the analysis of current conditions and future ND scenarios in ecological source areas. These areas have been designated as the core areas of the natural ecological conservation zone, with planning priorities focused on protecting mountains and improving forest carbon sequestration. Key urban restoration zones are crucial for transforming resource-based cities into sustainable and livable environments. This zone currently faces several ecological issues, including deteriorating ecological quality, increasing landscape fragmentation, and mining-induced land degradation that elevates ecological barrier points. In areas where construction land causes ecological barrier points, urban infrastructure should be enhanced through rooftops and vertical greening to increase ecological nodes. In regions dominated by unused land and ecological barrier points, development should be rationally planned, with pocket parks and street greenery introduced to regulate the microclimate. Secondly, based on the ecological environment’s carrying capacity, functional zones should be scientifically demarcated to enhance the integrity and connectivity of regional ecological resources. In addition, in the ecological barrier points areas mainly composed of coal mining subsidence water areas, habitat diversity should be increased, wetland water replenishment mechanisms improved, and wetland resilience enhanced. In the farmland ecological improvement zone, intensive agriculture has weakened ecological functions, limiting sustainable agricultural development. Ecological circular agriculture should be promoted to support a green, low-carbon transformation. The wetland protection zone is impacted by coal mining subsidence, with an increasing share of artificial wetlands and a continuously expanding subsidence area. In response, wetland habitat quality should be improved, biodiversity enriched, and climate regulation functions enhanced. To support the sustainable development of the green economy, dynamic pre-reclamation management measures should be implemented, the wetland compensation mechanism should be refined, and the wetland mitigation banking system should be actively promoted.
In summary, based on the ESP analysis of the optimal scenario, this study presents a territorial spatial planning framework characterized by “one axis, four belts, and four zones.” Optimizing the ESP serves as a crucial foundation for reconstructing ecological networks in mining areas and maintaining ecosystem integrity and sustainability. It also plays a key role in enhancing regional ecosystem services, particularly by promoting biodiversity and improving hydrological connectivity.
The findings address several critical gaps in current territorial spatial planning for resource-based cities, including: (1) the failure to account for land use dynamics induced by coal mining subsidence in ecological network identification; (2) an inadequate response to multi-risk source disturbances; and (3) the lack of an empirical basis for ecological restoration zoning. In line with IUCN’s biodiversity restoration objectives for mining areas, priority should be given to rehabilitating ecological networks at watershed or landscape scales. A regional ecological restoration framework, incorporating scenario simulations, should be integrated into territorial spatial planning, supported by macro-level strategies for systematic implementation. Additionally, transitioning from static planning mechanisms (relying on status quo analysis) to dynamic early warning assessment systems for territorial ecological security is essential. This shift will optimize functional zoning, refine restoration strategies, and strengthen institutional safeguards.

4.2. Exploring the Applicability and Optimization of Methods for Constructing ESPs

Methodologies for building ESPs have reached a relatively mature state, with a standardized technical framework encompassing ecological source area identification, resistance surface construction, and ecological corridor extraction. According to this study, the research area is profoundly affected by coal mining activities and exhibits pronounced dynamic evolutionary characteristics, which makes traditional static approaches to ESP construction insufficient for capturing comprehensive spatio-temporal changes. To dynamically simulate the evolution of ESP, multi-temporal remote sensing data were incorporated into the study. Based on these results, it is demonstrated that this approach can reveal changes in ecological network structures caused by coal mining subsidence, thereby providing more timely and effective scientific support for dynamic ecological governance. The conventional framework of ESP construction has been significantly enhanced by incorporating this temporal dimension. Regarding the selection of resistance factors, prior research has utilized land use types to construct ecological resistance surfaces [47]. However, it is challenging to accurately represent regional landscape connectivity in land use types within coal mining subsidence areas with high groundwater levels. In this study, two indicators reflecting the characteristics of the ecological environment and anthropogenic disturbance, namely, subsidence depth and distance to coal mining subsidence area, were introduced. Based on the Work Plan for Accelerating the Resolution of Outstanding Issues in Comprehensive Governance of Coal Mining Subsidence Areas issued by the Anhui Province [33], areas with subsidence depths exceeding 1500 mm frequently develop into deep waterlogged areas. These areas exhibit potential aquatic ecological functions and, consequently, are assigned lower resistance values. In this study, the traditional methods of ESP construction were refined through the incorporation of a temporal dimension and improved resistance factor selection, thereby enhancing the model’s applicability in coal mining subsidence areas with high groundwater levels. This research provides a methodological framework for constructing ESPs in coal mining subsidence areas with high groundwater levels, as well as providing a solid empirical foundation for developing dynamic ecological management and optimizing ecological space in comparable regions for future applications in comparable areas.

4.3. Limitations and Prospects

Using multi-scenario simulations and the construction of ESPs, the study fully considers the influence of coal mining subsidence factors on ecological resistance factors. This study investigates the optimal ESP in coal mining subsidence areas with high groundwater levels in the future and identifies key areas for environmental protection and restoration. The findings provide a scientific basis for territorial spatial planning in coal mining subsidence areas with high groundwater levels, facilitating coordination between land use and ecological networks and promoting a balance between urban development and ecological protection. However, this study has certain limitations. Firstly, the study integrates the disturbance patterns of mining, urbanization, and reclamation on land use to simulate three scenarios. In practice, scenario simulations should be broadened to accommodate a variety of planning objectives, thereby enhancing the applicability of the results. Secondly, practical factors driving land use changes—such as planning policies, economic conditions, and social dynamics—exhibit inherent unpredictability. Thus, scenario designs must be optimized according to specific development goals. Thirdly, disturbance factors vary significantly across mining areas, necessitating the adaptation of land use change drivers to regional environmental, economic, social, and policy-specific contexts. Fourth, due to the lack of a unified standard for ecological corridor widths, the threshold parameters in this study were determined through multiple simulations and references to related studies. Future research will focus on exploring multi-scale ecological corridors from a biodiversity perspective.

5. Conclusions

This study investigates the evolution of ESPs in coal mining subsidence areas with high groundwater levels, adopting a basin-scale perspective. The PLUS model is employed to simulate land use change trends under three future scenarios. These simulations are integrated with ESP construction for comprehensive analysis. Changes in ecological networks under different scenarios are analyzed, and by comparing the current and optimal future ESPs, targeted ecological protection and restoration strategies are proposed.
The main findings of the study are as follows. Land use changes from 2022 to 2032 are mainly concentrated in urban fringe areas and coal mining subsidence water areas. Land use projections indicate a declining trend in cultivated land, although it remains the dominant land type in the study area. The LR scenario exhibits the most minor reduction in cultivated land. In the ED scenario, the construction land area shows a significant increase. Under the ND scenario, the areas of forest land and coal mining subsidence water areas increase the most, and the overall ecological space shows an expanding trend, indicating that the existing relevant ecological protection and restoration measures have a positive effect on ecological restoration.
Compared to 2022, the ecological environment in 2032 shows improvement, marked by a significant increase in both the number and area of ecological source areas. Under the ND scenario in 2032, ecological source areas are more numerous and extensive than in other scenarios. Ecological corridors have also increased in number and length, with the widest spatial distribution. The ecological network is more integrated, providing greater ecological benefits by enhancing corridor connectivity.
By comparing and analyzing the ESP in the ND scenario of 2032 and the ESP in 2022, it is observed that both the number and total area of ecological pinch points decrease by 2032. These pinch points are predominantly distributed in elongated, narrow strips along ecological corridors and primarily consist of water and forest land. In contrast, the number and area of ecological barrier points increase, mainly concentrated in the central part of the study area. Construction land is the predominant type of land in ecological barrier points, and mining activities indirectly contribute to their formation. Therefore, greater emphasis should be placed on the ecological protection and restoration of these critical areas.
The findings indicate that the study area, under the combined pressures of coal mining subsidence and urbanization, faces significant challenges in balancing ecological restoration with economic and social development. This study proposes an ecological security framework based on the concept of “one axis, four belts, and four zones,” along with targeted strategies for ecological protection and restoration. These results offer valuable references for promoting ecological recovery and sustainable development in coal mining subsidence areas with high groundwater levels.

Author Contributions

Conceptualization, P.L. and S.Z.; methodology, Z.Z.; software, Z.Z.; validation, Z.Z. and Q.H.; formal analysis, X.S.; resources, S.Z. and Q.H.; writing—original draft preparation, Z.Z.; visualization, Z.Z.; project administration, P.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China, Grant/Award Number: 52208091, 52378082.

Data Availability Statement

The data presented in this study are available on request from the authors. The data are not publicly available due to privacy restrictions.

Acknowledgments

We would like to thank the reviewers for their constructive comments on this paper.

Conflicts of Interest

The authors declare no conflicts of interest.

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Land 14 01539 g001
Figure 1. Location of the research area.
Figure 1. Location of the research area.
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Figure 2. Spatial distribution of driving factors. (a) DEM in meters; (b) slope in degrees; (c) aspect in degrees; (d) groundwater depth in meters; (e) precipitation in millimeters per year; (f) evaporation in millimeters per year; (g) coal mining production scale in metric tons per year; (h) urbanization rate in percent; (i) farmland productivity in kilograms per hectare; (j) GDP in 10000 yuan per square kilometer; (k) population density in people per square kilometer; (l) wetland nature reserve; (m) ecological control area; (n) basic farmland conservation area; (o) comprehensive land consolidation area; (p) subsidence depth in millimeters; (q) water accumulation rate in subsidence areas in percent; (r) average annual rate of change in water accumulation over the past decade in percent; (s) water accumulation depth in millimeters; (t) distance to main road in meters; (u) distance to major river in meters; (v) distance to built-up area in meters.
Figure 2. Spatial distribution of driving factors. (a) DEM in meters; (b) slope in degrees; (c) aspect in degrees; (d) groundwater depth in meters; (e) precipitation in millimeters per year; (f) evaporation in millimeters per year; (g) coal mining production scale in metric tons per year; (h) urbanization rate in percent; (i) farmland productivity in kilograms per hectare; (j) GDP in 10000 yuan per square kilometer; (k) population density in people per square kilometer; (l) wetland nature reserve; (m) ecological control area; (n) basic farmland conservation area; (o) comprehensive land consolidation area; (p) subsidence depth in millimeters; (q) water accumulation rate in subsidence areas in percent; (r) average annual rate of change in water accumulation over the past decade in percent; (s) water accumulation depth in millimeters; (t) distance to main road in meters; (u) distance to major river in meters; (v) distance to built-up area in meters.
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Figure 3. Research framework.
Figure 3. Research framework.
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Figure 4. Land use in 2022 and 2032.
Figure 4. Land use in 2022 and 2032.
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Figure 5. Ecological source areas in 2022 and 2032.
Figure 5. Ecological source areas in 2022 and 2032.
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Figure 6. The integrated resistance surfaces for 2022 and 2032.
Figure 6. The integrated resistance surfaces for 2022 and 2032.
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Figure 7. Ecological corridors in 2022 and 2032.
Figure 7. Ecological corridors in 2022 and 2032.
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Figure 8. Ecological pinch points.
Figure 8. Ecological pinch points.
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Figure 9. Ecological barrier points.
Figure 9. Ecological barrier points.
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Figure 10. ESP planning goals.
Figure 10. ESP planning goals.
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Table 1. Neighborhood weight values under different scenarios.
Table 1. Neighborhood weight values under different scenarios.
Land Use TypeND Scenario
(2032)		LR Scenario
(2032)		ED Scenario
(2032)
Cultivated land0.50.80.4
Forest land0.70.50.3
Grassland0.30.50.3
Construction land0.80.71
Unused land0.010.010.01
Coal mining subsidence water area0.90.80.8
River0.60.60.6
Artificial lake0.40.40.3
Pond0.30.30.2
Reservoir111
Table 2. Resistance values and weights of resistance factors.
Table 2. Resistance values and weights of resistance factors.
Resistance FactorsResistance ValuesUnitWeights
1255075100
DEM−0.5–16
21–42		42–8316–21
83–135		135–200>200m0.056
Slope0–33–77–1313–22>22°0.054
Land use typeWater, forest landGrasslandCultivated landUnused landConstruction land/0.304
Distance to river0–10001000–15001500–30003000–5000>5000m0.104
Vegetation coverage0.8–10.6–0.80.4–0.60.2–0.40–0.2/0.099
Distance to built-up area>40003000–40002000–30001000–20000–1000m0.033
Distance to main road>40003000–40002000–30001000–20000–1000m0.033
Subsidence depth0–200
>1500		 200–500 500–1500mm0.213
Distance to coal mining subsidence area>75755160–75753077–51601231–30770–1231m0.104
Table 3. Land use type areas in 2022 and 2032.
Table 3. Land use type areas in 2022 and 2032.
Cultivated Land Forest LandGrasslandWaterConstruction LandUnused Land
Area
/km2		Proportion
/%		Area
/km2		Proportion
/%		Area
/km2		Proportion
/%		Area
/km2		Proportion
/%		Area
/km2		Proportion
/%		Area
/km2		Proportion
/%
2022524.30 46.13 181.5615.9820.541.8197.048.54293.3725.8119.721.73
ND scenario (2032)489.5943.09194.9517.1521.301.87100.978.88309.1627.2020.561.81
LR scenario (2032)505.6644.49194.2117.0921.401.8887.277.68308.8227.1719.171.69
ED scenario (2032)479.1442.16193.7717.0621.071.8599.968.79322.4628.3720.131.77
Table 4. Statistics for landscape type classification.
Table 4. Statistics for landscape type classification.
Landscape Type2022ND Scenario
(2032)		LR Scenario
(2032)		ED Scenario
(2032)
Area
/km2		Proportion
/%		Area
/km2		Proportion
/%		Area
/km2		Proportion
/%		Area
/km2		Proportion
/%
Core171.5369.28191.8772.22187.9972.63134.7751.4
Islet9.753.946.832.576.432.486.532.49
Perforation2.861.152.871.082.550.993.151.2
Edge44.0517.7946.4517.4845.0317.446.3317.67
Loop2.621.061.480.561.170.451.760.67
Bridge5.772.334.171.574.011.5557.722.01
Branch11.014.4512.014.5211.664.511.964.56
Total247.59100265.68100258.84100262.2100
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Zhou, S.; Zhang, Z.; Luo, P.; Hou, Q.; Sun, X. Constructing Ecological Security Patterns in Coal Mining Subsidence Areas with High Groundwater Levels Based on Scenario Simulation. Land 2025, 14, 1539. https://doi.org/10.3390/land14081539

AMA Style

Zhou S, Zhang Z, Luo P, Hou Q, Sun X. Constructing Ecological Security Patterns in Coal Mining Subsidence Areas with High Groundwater Levels Based on Scenario Simulation. Land. 2025; 14(8):1539. https://doi.org/10.3390/land14081539

Chicago/Turabian Style

Zhou, Shiyuan, Zishuo Zhang, Pingjia Luo, Qinghe Hou, and Xiaoqi Sun. 2025. "Constructing Ecological Security Patterns in Coal Mining Subsidence Areas with High Groundwater Levels Based on Scenario Simulation" Land 14, no. 8: 1539. https://doi.org/10.3390/land14081539

APA Style

Zhou, S., Zhang, Z., Luo, P., Hou, Q., & Sun, X. (2025). Constructing Ecological Security Patterns in Coal Mining Subsidence Areas with High Groundwater Levels Based on Scenario Simulation. Land, 14(8), 1539. https://doi.org/10.3390/land14081539

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