Dynamic coupling of habitat quality and landscape ecological risk for sustainable ecosystem management in open-pit mining area

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While balancing these factors is critical for sustainable mining management, integrated approaches remain limited. To address this gap, we propose a two-dimensional framework that integrates habitat quality and landscape ecological risk, offering a more detailed, tree-level assessment compared to conventional land-use-based approaches. The results indicatea that: (1) Low/lower-quality habitats persistently exceeded 69% across mining stages, with degradation dominating initial/developmental phases (1990–2010) and improvement emerging in the stable phase (2010–2020). (2) High LER areas correlated with forest/grassland fragmentation, whereas low LER zones linked to construction/bare land continuity. Notably, forest and farmland expansion in stable stages increased LER, requiring targeted land-use strategies to mitigate risks. (3) The key transitions in ecosystem coordination zones included the conversion of bare land and construction land to forestland_UP-RP, forestland_PT, and grassland. Although transitions (e.g., construction land to forestland_UP-RP, bare land to forestland_PT improved HQ, they still pose landscape ecological risks. These findings strengthen land-use planning's scientific basis and provide actionable ecological governance insights for mining areas, fragile cities, and resource-based regions, while their enhanced detail improves assessment accuracy and enables precise restoration strategies. Opencast coal mine Landscape transitions Ecological restoration Ecological management Ecological zoning Linear mixed-effects model Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction As the global population and economies expand, the rising demand for coal resources has led to significant challenges for surrounding environments and communities (Luckeneder et al. 2021 ; Maus and Werner 2024 ; Sonter et al. 2023a ). Open-pit mining induces geomorphic instability, leading to a decline in biotic community integrity, as indicated by habitat quality (HQ) index reduction, while also intensifying spatial fragmentation, which heightens ecosystem vulnerability thresholds and amplifies landscape ecological risks (LER) (Poskočilová et al. 2024 ). Ecological restoration is considered an important measure for combating ecosystem degradation in opencast mines (X. Liu et al. 2016 ). Given that ecological restoration in mining areas is a long-term endeavor, its effectiveness is more accurately evaluated at the landscape scale over extended periods (Betts et al. 2024 ). HQ indicates how well a habitat can sustain species survival(Ren et al. 2022a ), with habitat quality serving as a key measure of ecosystem health and biodiversity. The main research methods for studying HQ include field surveys and remote-sensing. For example, scholars typically record parameters such as plant species and abundance through field surveys, which are then used to construct relevant HQ indices (such as species richness, evenness, and diversity indices) to assess HQ (Aznarez et al. 2022 ; Joseph et al. 2018 ; Qu et al. 2025 ). However, short-term stand-scale forestry measures are insufficient to reveal HQ trends throughout the mining area's life cycle (Betts et al. 2024 ). Currently, some scholars use the InVEST model to assess long-term trends in HQ changes in plateaus, islands, cities, and mountainous regions (Qin et al. 2024 ; Tan et al. 2024 ; Tang et al. 2023 ). However, these studies mostly utilize land-use data, often treating the forest as a single entity without differentiating the tree species. Different vegetation patterns constitute various reclamation models in the mining areas, resulting in different HQ conditions in the region (H. Feng et al. 2024 ). Therefore, refining land use at the tree species level helps determine which reclamation model maintains a high HQ throughout the mine life cycle. LER assessment focuses on the risks induced by landscape structural changes and the negative impacts of their interactions with ecological processes (Xu and Matsushima 2024 ). Recently, scholars have explored the optimal granularity to conduct reasonable assessments of regional LER (Q. Wang 2024 ; S. Li 2024 ; Yaermaimaiti 2024 ). In addition, studies on LER have often examined ecological networks, safety patterns, and driving factors (L. Liu et al. 2024 ; W. Zeng et al. 2024 ; S. Zhang et al. 2024 ). However, these studies typically addressed only a single aspect of regional LER and did not integrate HQ with LER for effective ecological zoning management in mining areas. The former focuses on assessing ecosystem services but does not account for the risk transmission mechanisms driven by spatial heterogeneity (Du et al. 2025 ; K. Li et al. 2024 ). The latter focuses on changes in landscape indices while overlooking the ecological resilience differences among land cover types (Hui et al. 2024 ; J. Zeng et al. 2024 ). This fragmentation often leads to decision-making biases in mining area restoration, such as "hidden ecological risks under high vegetation coverage" or "critical habitat degradation masked by low-risk indices (X. Feng et al. 2024 )." Open-pit coal mining has a profound impact on regional landscapes, leading to significant changes in LER and HQ (Mei Zhang et al. 2025 ). This high-intensity disturbance not only disrupts the physical integrity of habitat units but also alters the spatial configuration of landscape elements (e.g., connectivity, dominance), triggering cascading ecological effects (Jin, Dong, et al. 2024 ). The spatial interface effects created by fragmentation serve as the key link between these two dimensions (S. Wang et al. 2025 ). For example, built-up land, through rigid expansion, forms stable low-risk patches, but its edge effects accelerate habitat degradation in adjacent forest and grassland areas (Wu et al. 2021a ). Meanwhile, forest and grassland fragmentation diminishes internal habitat suitability while also expanding ecologically fragile interfaces, creating a topological network for risk propagation (Joy et al. 2024 ; Zheng et al. 2023 ). An integrated framework can decouple the coupling mechanisms of "structural risk" and "functional risk." The habitat quality index identifies priority areas for ecological conservation, while the landscape risk index warns of potential spatial structural imbalances. The spatial coupling analysis of these two indices provides a dual-dimensional reference system for coordinated planning in mining area "ecological restoration–risk prevention and control." In this study we propose an ecological zoning approach that balances between LER and HQ throughout the complete mine life cycle. Specifically, our approach includes four key steps: (1) to monitor HQ at the vegetation landscape scale throughout the whole mine life cycle; (2) to analyze LER resulting from different vegetation landscape transitions; and (3) to identify key transitions under the HQ and LER dynamic framework and propose ecological management strategies for each zone. This study combines field surveys and remote sensing data to gather information at the tree species level, elucidating the patterns of change in HQ and LER in tree-level landscapes throughout the mine life cycle. We employ a linear mixed-effects model to identify key landscape transitions driven by dynamic shifts in habitat quality and ecological risk. We applied the new approach in a case study of the Pingshuo open-pit mining site in China. Our results show that this approach not only enhances the scientific foundation for land management decisions but also offers important insights for the long-term development and ecological management of ecologically vulnerable cities and resource-dependent areas. Materials and methods Study area The Pingshuo mine area is an integral part of the national planning and development of the northern Shanxi coal base, situated within the Pinglu and Shuocheng districts of Shuozhou City, Shanxi Province (Fig. 1 ). This mining area lies in the northern section of Shanxi Province, within the central-western region of Shuozhou City, with coordinates spanning from 112°14′E to 112°32′E longitude and 39°22′N to 39°37′N latitude (Yuan et al. 2016 ). The climate in this region is classified as temperate semi-arid continental monsoon, with annual temperatures ranging from 4.8 to 9.6°C and average yearly precipitation between 461.12 and 476.74 mm (Yuan et al. 2018 ). Owing to significant increases in rock and soil stripping caused by mining activities and repeated disturbances, the mine area implements a coordinated management mechanism throughout the processes of "land acquisition/lease - excavation/stripping - transportation - disposal - reclamation - restoration." Data sources and processing Tree-level landscape classification This research leveraged multispectral imagery (Landsat 5 TM/7 ETM+/8 OLI, Sentinel-2 MSI) accessed via Google Earth Engine's cloud platform, acquiring quadrennial observations (1990–2020). To maintain spectral integrity under sub-10% cloud contamination thresholds, the multisensor data streams underwent radiometric normalization, atmospheric compensation, and cloud masking algorithms. Field investigations were conducted in vegetated areas within the southern, western, and interior regions of Pingshuo mine. The sampling protocol systematically distributed 1,000 training and 300 validation points across geographically stratified zones, ensuring proportional representation of differently aged tree species and agricultural fields while mitigating spatial autocorrelation effects. This design explicitly resolved the "uniform spatial distribution" imperative and accounted for spectral variability challenges through iterative location adjustments. The random forest classification framework integrated seven optimized features (NDVI, EVI, NDWI, IBI, BSI, Elevation, Slope) derived from the literature (Y. Deng et al. 2023 ; Filip et al. 2025 ), with the specific calculation formulas found in the literature (Zhao et al. 2024 ). Feature importance quantification revealed NDVI and elevation as dominant discriminators, collectively explaining over 60% of predictive variance. Model validation through temporally stratified sampling demonstrated 90.31–94.20% overall accuracy (mean 92.78 ± 2.40%) and 0.88–0.93 Kappa coefficients (mean 0.91 ± 0.03) across the 30-year study period. HQ assessment of tree-level landscape This study was based on the HQ module of the InVEST model to estimate HQ in the Pingshuo mining area for 1990, 2000, 2010, and 2020. This module calculates habitat degradation based on input threat source data and derives HQ using habitat suitability data and degradation levels. The calculation formula for habitat quality in the InVEST module can be found in the literatures (Chen et al. 2025 ; Luo et al. 2024 ; Wei et al. 2022a ). Based on field survey data, this study employed Principal Component Analysis to calculate habitat suitability for different habitat types (Table 1 ), thereby enabling localized HQ assessment. The 10-variable dataset underwent Z-score normalization, followed by factorability validation using KMO-Bartlett diagnostics. Upon achieving sufficient metric interdependency (KMO > 0.616, Bartlett's sphericity p < 0.05), PCA-driven variable compression was executed. Cross-referencing established studies (Lei et al. 2022 ; Ren et al. 2022b ; Wei et al. 2022b ), we determined: (1) habitat-type specific susceptibility to anthropogenic stressors (Table 1 ), (2) threat weighting coefficients, and (3) spatial influence thresholds (Table 2 ). Table 1 Habitat category responsiveness to identified threat sources. Tree age Habitat types Habitat suitability Construction land Bare land Cultivated land 10 Forestland_PT 0.53 0.8 0.2 0.8 20 Forestland_PT 0.75 0.8 0.2 0.8 30 Forestland_PT 0.86 0.8 0.2 0.8 10 Forestland_UP 0.64 0.8 0.2 0.8 20 Forestland_UP 0.88 0.8 0.2 0.8 30 Forestland_UP-RP 0.97 0.8 0.2 0.8 10 Forestland_P 0.75 0.8 0.2 0.8 20 Forestland_P 0.78 0.8 0.2 0.8 30 Forestland_P 0.90 0.8 0.2 0.8 / Forestland_Shrub 0.56 0.8 0.2 0.8 / Grassland 0.42 0.7 0.5 0.7 / Cultivated land 0.27 0.4 0.4 0 / Bare land 0.04 0.3 0 0.2 / Water 1 0.9 0.2 0.2 Table 2 Threat source magnitude and spatial influence thresholds. Threat factor Maximum influence distance (km) Weight Decay Cultivated land 4 0.6 exponential Construction land 8 0.9 linear Bare land 2 0.2 linear LER assessment of tree-level landscape The LER index indicates the degree to which the relationship between landscape patterns and ecological processes, shaped by natural or human factors, undermines the landscape's capacity to preserve its structure and function (Gu 2024 ). The calculation is based on the landscape vulnerability and disturbance indices (Fig. 2 ), which reflect the internal fragility of and external disturbances to the ecosystem, respectively (Guo et al. 2024 ). Framework of habitat quality and ecological risks with key transitions To further understand the dynamic relationship between habitat quality and landscape ecological risk in mining areas, we constructed four-quadrant diagrams for three developmental stages (1990–2000, 2000–2010, and 2010–2020) based on the rise and fall of HQ and LER. In these diagrams, simultaneous decreases in HQ and LER were categorized as deterioration, while simultaneous increases were classified as resistance. When HQ increased while LER decreased, the relationship was labeled as coordination, whereas the reverse (decreasing HQ and increasing LER) was defined as conflict. Based on this, the proportion of landscape transitions within each quadrant at different stages was quantified. Treating year as a random effect, a linear mixed-effects model was applied to assess the impact of key transitions within each quadrant on habitat quality and landscape ecological risk. The formula for the linear mixed-effects model is as follows: $${\text{H}}{{\text{Q}}_{{\text{ij}}}}{\text{\sim }}{\beta _{\text{0}}}{\text{+}}{\beta _{\text{1}}}{{\text{A}}_{{\text{1ij}}}}{\text{+}}{\beta _{\text{2}}}{{\text{A}}_{{\text{2ij}}}}{\text{+}}{\beta _{\text{3}}}{{\text{A}}_{{\text{3ij}}}}{\text{+}}{{\text{u}}_j}+{\varepsilon _{ij}}$$ 1 $${\text{LE}}{{\text{R}}_{{\text{ij}}}}{\text{\sim }}{\beta _{\text{0}}}{\text{+}}{\beta _{\text{1}}}{{\text{A}}_{{\text{1ij}}}}{\text{+}}{\beta _{\text{2}}}{{\text{A}}_{{\text{2ij}}}}{\text{+}}{\beta _{\text{3}}}{{\text{A}}_{{\text{3ij}}}}{\text{+}}{{\text{u}}_j}+{\varepsilon _{ij}}$$ 2 where HQ ij and LER ij are the dependent variable (response variable), which represents the first j Group in the first i observations. ​ β 0 is the intercept term of the fixed effect. The terms A 1 , A 2 , and A 3 represent different types of landscape transitions, digital elevation model, and slope gradient, respectively. U j is a random effect. ε ij is the error term. Research methods and framework Firstly, we mapped the geographic distribution of tree species and reclamation years within the restored areas of the study area. Based on the GEE platform, we utilized a random algorithm combined with geographic annotation points for visual interpretation, generating a 30-year tree-level landscape for the Pingshuo mining region. Secondly, to accurately assess habitat quality at the study area, we adjusted the parameters of the InVEST model using field survey data on tree species characteristics and soil physicochemical properties, and then mapped its spatial distribution. Subsequently, based on the landscape ecological risk index, we analyzed changes in landscape ecological risk in the mining area and identified key land use types at different stages and risk levels. Thirdly, we constructed a dynamic framework for habitat quality and landscape ecological risks, quantified the proportions of key landscape transitions during dynamic changes, and employed a linear mixed-effects model to evaluate the impact of key landscape transitions on both habitat quality and ecological risks during different zoneFinally, we propoesed ecological management strategies for ecological zones (Fig. 3 ). Results Analyzing HQ dynamics across the entire life cycle of the mine Assessing the temporal changes in habitat quality over the mine life cycle is crucial for gaining a holistic view of its status in mining areas. HQ in the Pingshuo mining area underwent significant changes between 1990 and 2020, primarily characterized by low and fairly low HQ levels. The combined area of these two categories accounted for more than 69% of the total area each year (Fig. 4a). Low HQ was mainly distributed across construction land and bare land, whereas fairly low HQ was primarily found in grasslands and cultivated land (Fig. 2 c, Fig. 4a, Table S1 ). Areas classified as fairly high- and high-quality accounted for less than 25% of the total area and predominantly consisted of forestland_PT, forestland_P, forestland_UP-RP, and forestland_shrub (Fig. 2 c, Fig. 4a). The average HQ indices in 1990, 2000, 2010, and 2020 were 0.53, 0.49, 0.37, and 0.42, respectively. Overall, HQ initially declined, and then recovered. Between 1990 and 2000 (the initial stage), the area of degraded HQ was greater than that of the improved areas (Fig. 4b). The areas of low-and fairly low-quality habitats increased by 12.88%, whereas those of high-and fairly high-quality habitats increased by 3.18% (Fig. 4a). In the western region, low-quality habitats transitioned to higher- and high-quality habitats, showing significant improvement. However, in the central and western regions, moderate- and high-quality habitats transitioned to lower-quality habitats, indicating a noticeable decline in HQ (Fig. 4a). Between 2000 and 2010 (development stage), the proportion of the degraded HQ reached a peak of 44.24%, which was the highest in 30 years, whereas the proportion of improved HQ was the lowest at 24.32% (Fig. 4b). The area of low-quality habitats expanded by 22.09% in the central and eastern regions, whereas habitats in the western region improved. Additionally, some high-quality habitats in the northern region transitioned to low-quality habitats (Fig. 4a). From 2010 to 2020 (the stable stage), the area of improved HQ exceeded that of degraded areas (Fig. 4b). High-and fairly high-quality habitats expanded in the western region, with an increase of 9.83%. Meanwhile, the areas of low- and low-quality habitats decreased by 11.55%. This change was primarily due to the transition of 32.36% of low- and low-quality habitats into higher- and high-quality habitats (Fig. 4a). Understanding LER caused by tree-level landscape transitions As changes in HQ occur due to landscape transitions, LER also change accordingly. From 1990 to 2020, the LER in the Pingshuo mining area underwent significant dynamic changes. Areas with high and fairly high risks were mainly located in forest and grassland regions with weak resilience to external pressures and high fragmentation. Moderate-risk areas are primarily found in cultivated land that experiences considerable human disturbance, whereas low-and fairly low-risk areas are distributed in construction and bare lands. These latter areas exhibit lower fragmentation and greater spatial continuity, and relatively intact patches are beneficial for maintaining ecosystem stability and resilience, thereby reducing LER (Fig. 5 ). The average LER indices for 1990, 2000, 2010, and 2020 were 0.23, 0.33, 0.24, and 0.58, respectively, indicating a fluctuating upward trend in the average LER index. From 1990 to 2000 (initial stage), the areas of low-and fairly low-risk zones significantly decreased by 29.47%, whereas those of high-and fairly high-risk landscape zones in the west increased by 18.44%. This change was primarily due to the transfer of low-, fairly low-, and moderate-risk zones to high-and fairly high-risk zones, with shifts of 33.95%, 15.99%, and 26.39%, respectively. The low-and fairly low-risk zones dominated by grassland gradually transitioned to landscapes co-dominated by construction land and grassland. The high-and fairly high-risk zones, initially dominated by bare land, shifted to a distribution that included bare land, forest land _PT, forest land _P, and forest land _UP-RP (Fig. 5 ). From 2000 to 2010 (development stage), the low-and fairly low-risk zones expanded into the central and eastern regions, with their proportions increasing by 26.52%. The moderate-risk zone area decreased by 11.15%, whereas the high-and fairly high-risk zone areas decreased by 15.37%. During this stage, 68.09% of the moderate-risk zones, 42.70% of the fairly high-risk zones, and 25.39% of the high-risk zones transitioned to low- and low-risk areas. Construction land in the low-risk zone increased by 23.76%, whereas forestland_PT, forestland_P, and forestland_UP-RP in the high-and fairly high-risk zones increased by 26.66% and 32.70%, respectively. Bare land in the high-risk and low-risk zones decreased by 40.28% and 22.37%, respectively (Fig. 5 ). From 2010 to 2020 (stable stage), the area of low and fairly low landscape risk zones decreased by 29.16%, moderate landscape risk zones increased by 14.09%, and high and fairly high landscape risk zones increased by 15.07%. During this stage, 40.52% of the low-risk zones and 38.77% of the fairly low-risk zones transitioned into moderate-risk landscape zones. Additionally, some low (266.80 ha), fairly low (544.45 ha), and moderate (470.32 ha) landscape risk zones shifted to high and fairly high landscape risk zones. Construction land in low-risk zones continued to increase by 7.95%, whereas forestland_PT, forestland_P, and forestland_UP-RP expanded in high-and fairly high-risk zones (225.25 ha), and cultivated land in high-and fairly high-risk zones increased by 13.55% and 15.55%, respectively (Fig. 5 ). Identifying main landscape transitions under dynamic HQ and LER We established a two-dimensional framework incorporating changes in HQ and LER to gain a thorough insight into how mining activities interact with the environment across three phases: 1990–2000, 2000–2010, and 2010–2020. In the stable stage, the resistance areas increased by 27.43% compared to the initial stage, and the conflict areas decreased by 31.27%. The coordination areas increased by 9.12% from the initial stage to the development stage but accounted for a very small proportion during the stable stage. The deterioration areas peaked during the development stage and decreased by 55.35% in the stable stage compared to the development stage (Fig. 6 ). From 1990 to 2000, the resistant area accounted for 21.40%, primarily in the southwestern region. The top three landscape transitions observed in these areas were from cultivated land and bare land to grassland, and from grassland to forestland_PT (Fig. 7 ). In the initial stage, conflict areas were dominant, covering 67% of the total area (Fig. 6 ). Between 2000 and 2010, deteriorated areas made up 69.86%, with the top three transitions being from grassland to bare land, construction land, and cultivated land to bare land (Fig. 7 ). The majority of resistance areas were concentrated in the western region, comprising 9.05% of the total area(Fig. 6 ). The primary transitions involve bare land shifting toward forestland_PT and forestland_P, and grassland transitioning toward forestland_PT (Fig. 8 ). From 2010 to 2020, the area of resistance accounted for 48.83% (Fig. 6 ). The three most significant transitions in these areas were from cultivated land and construction land to grassland, as well as from construction land to bare land (Fig. 7 ). The deteriorated area was predominantly located in the northeast, making up 14.51% of the total area (Fig. 6 ). The main transitions involve grassland, cultivated land, and bare land shifting toward construction land in these areas (Fig. 7 ). We applied a linear mixed-effects model to evaluate how landscape transitions influence HQ and LER dynamics within each dynamic partition. The transitions from foresland, grassland, and cropland to construction land, as well as from forestland_PT to bare land, significantly contributed to ecosystem deterioration in the mining area. Transitions from forestland_P to bare land and grassland led to declines in habitat quality accompanied by increased ecological risk. In contrast, key transitions in ecosystem coordination zones involved the transformation of bare land and construction land into forestland_UP-RP and forestland_PT. In ecosystem resistance zones, while transitions from construction land to forestland_UP-RP and bare land to forestland_PT resulted in some improvement in habitat quality, they still posed ecological risks (Fig. 8 , Table S2). Proposing ecological management strategies for mining areas Based on the quantified impacts of landscape transitions on habitat quality and ecological dynamics (Table 3 , Table S2), the following targeted management strategies are proposed: Deterioration Zones: Designate mining-essential zones where transitions from forestland, grassland, or cropland to construction land are permitted only under strict "destruction-compensation" protocols. For every unit area of forestland_PT/P converted to bare land for mining, mandate pre-planned revegetation of equivalent ecosystem service value in adjacent coordination zones (e.g., bare land to forestland_UP-RP). In addition, implement phased mining schedules to minimize simultaneous large-scale clearing, prioritizing retention of intact forestland_PT buffers around extraction sites to reduce edge effect risks. Conflict (2) zones: In conflict zones where forestland_P-to-bare land/grassland transitions significantly degrade habitat quality, prioritized interventions should focus on dual-goal restoration that balances ecological stability and land-use demands. First, implement mycorrhizal inoculation in degraded forestland_P areas to enhance soil retention and accelerate native tree regeneration, targeting a minimum 40% canopy cover recovery within 5 years. Second, enforce smart zoning policies using real-time satellite monitoring to block further poplar conversion in high-risk clusters (e.g., slopes > 15° or near watercourses). Third, introduce community stewardship programs that incentivize locals to maintain transitional grasslands as biodiversity buffers through payment-for-ecosystem-services (PES) schemes, conditional on achieving habitat connectivity metrics (e.g., 500-m corridor width between forest patches). These measures directly counter model-identified drivers while aligning with sustainable mining-urban interfaces. Coordination zones: To enhance ecological outcomes in the coordination zones of mining areas, prioritize multi-layered vegetation restoration using site-specific native species (e.g., Ulmus pumila, Pinus tabuliformis, and grassland communities) to improve biodiversity and soil stability. Implement adaptive management, including phased planting (e.g., pioneer grasses for erosion control followed by tree saplings) and real-time monitoring of soil nutrients and pollutants to address legacy contamination. Establish buffer corridors to support wildlife movement and genetic exchange. Engage local communities in restoration planning by integrating traditional ecological knowledge with modern techniques like mycoremediation for heavy metal absorption. Additionally, develop circular economy incentives, such as carbon credit programs linked to vegetation growth, to ensure long-term ecological and socioeconomic sustainability. Resistance zones: Firstly, in the process of forest _UP-RP and forest _PT restoration, priority should be given to planting native tree species with strong stress resistance, deep roots and adaptation to the local environment to improve ecosystem stability. Secondly, adopt mixed forest models (e.g., tree-shrub-grass combination) to improve ecosystem resilience and reduce the risk of pests and diseases and ecological vulnerability brought by a single tree species. Finally, before vegetation restoration, proceed soil remediation (e.g., organic matter supplementation, soil microbial remediation) to enhance soil and water conservation capacity and reduce the risk of erosion due to vegetation conversion. Table 3 Policy toolkit for ecological partitions integrating model drivers and spatial targeting. Ecological partition Key landscape transitions Dominant Spatial Clusters Policy Tools Deterioration Zones Foresland/Grassland/Cultivated land → Construction land Central Region (2000–2010) Restoration and management Forestland_PT → Bare land Eastern Region (2010–2020) Conflict Zones Forestland_P → Bare land Western reclamation area Risk mitigation Forestland_P → Grassland Coordination Zones Bare land/Construction land → Forestland_UP-RP and forestland_PT Western reclamation area(1990–2010) Optimization and promotion Bare land/Construction land → Grassland Central Region (2010–2020) Resistance Zones Construction land → Forestland_UP-RP Western reclamation area(1990–2010) Preventive conservation Bare land → Forestland_PT Central Region (2010–2020) Discussion The response of habitat quality to tree-level landscape changes The dynamic assessment of habitat quality in the study area over the past three decades reveals a clear pattern of degradation and partial recovery, driven by land-use changes and mining activities. Over the 30-year period, low and fairly low HQ consistently dominated (> 69% of the total area), primarily linked to construction land, bare land, grasslands, and cultivated land. In contrast, high-quality habitats (forestland types) remained limited (< 25%), underscoring the vulnerability of ecosystems in mining-affected regions. The average HQ index declined sharply from 0.53 to 0.37 between 1990 and 2010, reflecting cumulative degradation during intensive mining expansion, followed by a modest recovery to 0.42 by 2020. This nonlinear trend highlights the interplay between anthropogenic pressures and localized restoration efforts, particularly in later stages. This is consistent with the findings of previous studies (Guan et al. 2023 ; H. Wang et al. 2023 ). Notably, the spatial-temporal heterogeneity of HQ shifts corresponded to mining phases. During initial development (1990–2000), HQ degradation outpaced improvement, with central and western zones experiencing habitat downgrades despite western recovery. The peak degradation (2000–2010) coincided with accelerated mining, as low-quality habitats expanded by 22.09%, outweighing improvements in the west. This trend was closely linked to the expansion of the central mining area, which encroached upon a significant portion of cultivated land (Min Zhang et al. 2020 ). By the stable stage (2010–2020), ecological interventions likely contributed to HQ recovery, evidenced by a 9.83% expansion of high-quality habitats in the west and reduced low-quality areas (-11.55%). These findings emphasize the need for phased management strategies to mitigate habitat fragmentation and prioritize rehabilitation in critical zones during mining operations (Min Zhang et al. 2023 ). LER patterns under landscape change This study reveals the spatiotemporal variation of LER in the Pingshuo mining area across the entire mine life cycle, along with its driving mechanisms. The results show that the LER index of the mining area exhibited a fluctuating upward trend (from 0.23 to 0.58), with a significant positive correlation between high-risk areas and the fragmentation of forests/grasslands, while low-risk areas are closely related to the spatial continuity of construction land/bare land. These results are consistent with the LER levels constructed based on the 'source–sink' landscape theory (Wu et al. 2021b ), but further reveals the staged impact of human activities on LER: in the early stages of mining development (1990–2000), the rapid conversion of low-risk areas to high-risk areas (transfer rate > 15%) was mainly driven by the development of bare land and occupation of forest land; whereas in the stable phase (2010–2020), the expansion of construction land and conversion of farmland became the main driving factors for the increase in LER. This dynamic feature suggests that traditional "static zoning" management strategies are insufficient to meet the needs of landscape evolution in mining areas, and there is an urgent need to establish a differentiated and adaptive risk regulation system. Building on these findings, this study suggests the following management approaches: First, ecological restoration in high-risk areas should prioritize the restoration of connectivity in western forests/grasslands. Data shows that through the replanting of native tree species and the construction of ecological corridors, the area of forest patches can be increased by 26.66% (2000–2010), significantly reducing the LER index (with a 15.37% reduction in high-risk areas during the same period). Second, in the stable phase of mining areas, the significant increase in ecological risk caused by the expansion of forest and farmland should be addressed through targeted land use management. This includes implementing measures such as controlling deforestation, preventing further farmland fragmentation, promoting sustainable agricultural practices, and enhancing ecological restoration, particularly in areas of land conversion (Ríos-Alvear et al. 2024 ). Additionally, establishing protective buffers and improving landscape connectivity can help mitigate the adverse effects on the local ecosystem (Zhu et al. 2024 ). Finally, the long-term effectiveness of LER management in mining areas relies on institutional innovation and technological integration. It is recommended to incorporate the LER index into the ecological compensation standards for mining development (e.g., mining rights in high-risk areas should be accompanied by a 3%-5% restoration budget) and use remote sensing monitoring systems to identify risk hotspot areas to implement "monitoring-early warning-intervention" closed-loop management (Jin, Bian, et al. 2024 ). This proposes a staged regulation framework based on risk transfer paths, providing a basis for formulating differentiated strategies for similar mining areas in their "development" and "stability" phases. However, the LER index constructed based on landscape patterns focuses more on the integrated response of the ecosystem structure to human activities and the natural environment and lacks consideration of ecological and geological risks. In the future, integrating ecological and geological risks with economic activities could improve the comprehensive calculation of LER (H. Wang et al. 2023 ). In addition, future research could place more focus on the "landscape pattern-disturbance intensity-risk response" in mining areas. Significance and limitations of the dynamic HQ and LER framework Compared with prior studies integrating HQ and LER into ecological zoning (H. Liu and Tang 2024 ; Xie et al. 2024 ; Y. Zhang et al. 2023 ), this study emphasized the landscape transitions driving dynamic changes in both factors. It revealed that construction land encroachment on grassland, cultivated land, and forestland land degrades mining area ecosystems, underscoring the need for rational resource use and land planning. Early mining stages saw ecosystem conflicts from grassland replacement by bare and cultivated land, while later phases involved further conflicts from transformations into construction land. Sustainable mining technologies are essential to mitigate such conflicts (Tomassi and Kinyondo 2024 ). During the initial and development stages, transitions from bare land, cropland, and construction land to forest and grassland promote ecosystem coordination. In the development stage, reclaimed forest land itself further enhances ecosystem coordination. Restoration and reconstruction should be prioritized during early mining stages, while reclaimed area maintenance becomes critical later (Sonter et al. 2023b ). Finally, in ecosystem resistance zones, transitions from construction land to forestland_UP-RP and bare land to forestland_PT improved habitat quality but still posed ecological risks. This underscores the need for targeted soil stabilization and native species selection to mitigate unintended cascading effects of afforestation. (Yuan et al. 2018 ). HQ and LER are key indicators for assessing ecosystem health in mining areas (G. Deng et al. 2024 ; Qian et al. 2022 ). The two-dimensional framework of HQ and LER developed in this study offers insights into the ecological management of other fragile cities and resource-based regions. The dynamic framework for evaluating habitat quality and landscape ecological risk in mining areas offers a structured method for exploring the spatial and temporal relationships between landscape transitions and ecological processes. By categorizing ecological zones into degradation, conflict, coordination, and resistance areas, this framework helps identify critical landscape transitions that drive ecological change. The use of a linear mixed-effects model further strengthens its applicability by quantifying the major landscape shifts affecting habitat quality and ecological risk. However, limitations exist, including potential uncertainties in model assumptions, the influence of external socio-economic factors, and the need for long-term validation. Future studies should incorporate finer-scale ecological indicators, consider ecosystem carbon sequestration, and integrate human-induced disturbances to enhance the robustness and applicability of this framework in diverse mining landscapes (Bayer et al. 2023 ). Conclusion This study demonstrates how habitat quality (HQ) and landscape ecological risk (LER) evolve dynamically through different phases of mine development and reclamation. By integrating both HQ and LER within a unified framework for ecological zoning, it becomes clear that the expansion of mining operations initially reduces habitat quality and amplifies ecological risks, whereas targeted reclamation efforts and land-use changes can reverse certain degradation trajectories. Nonetheless, some transitions designed to improve one aspect (e.g., habitat quality) may introduce new landscape ecological risk, for example, converting bare land to forestland_PT. More holistic approaches for ecological zoning of mining areas can evidence this trade-offs and strengthen ecological planning to improve the sustainable development of resource-dependent landscapes. Declarations Acknowledgements We acknowledge the editors and anonymous reviewers for processing and improving the quality of this manuscript. Author contribution Yang G.T.: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing. Zhang H.: Funding acquisition, Project administration, Resources, Supervision, Validation, Writing – review & editing. Maus V.: Formal analysis, Validation, Writing – review & editing. Su C.: Funding acquisition, Supervision, Validation. Zhang X.Y.: Supervision, Validation. Funding This study was supported by the National Natural Science Foundation of China (grant numbers U1910207 and 42107420) and the Natural Science Foundation for Young Scientists of Shanxi Province (grant number 20210302124363). Data availability The authors declare that the data supporting the findings of this study are available within the paper and its supplementary files. Competing interest The authors declare no competing interests. References Aznarez, C., Svenning, J.-C., Taveira, G., Baró, F., & Pascual, U. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7244001","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":498787895,"identity":"3a43ff8c-7d3d-4bb9-a187-a59d21344bde","order_by":0,"name":"Guoting Yang","email":"","orcid":"","institution":"Shanxi University","correspondingAuthor":false,"prefix":"","firstName":"Guoting","middleName":"","lastName":"Yang","suffix":""},{"id":498787896,"identity":"a39b5c08-f017-431b-a143-ba81a9193ad0","order_by":1,"name":"Hong Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYDACCSBmbAAS7A1gPmMD8Vp4DpCsRSKBSC3ys5ufPfy6wyZPPvL5M2keBhvZDQeYnz3Ap4VxzjFzY9kzacWGt3OMjXkY0ow3HGAzN8CnhVkiwUxasu1w4sbZOYyPeRgOJ244wMMmgU8Lm0T6N4iWmccfHOZh+E9YC49EjpnkR6CW+RIMhkBbDhDWIiGRUybN2JaWuIEnx9hwjkGy8czDbGZ4tcjPSN8m+bPNJnF++/FnEm8q7GT7jjc/w6sFBJh5gITBARATFFTMhNQDAeMPkHUNRKgcBaNgFIyCkQkAL8JH4iVbrpEAAAAASUVORK5CYII=","orcid":"","institution":"Shanxi University","correspondingAuthor":true,"prefix":"","firstName":"Hong","middleName":"","lastName":"Zhang","suffix":""},{"id":498787898,"identity":"a2dbf023-636a-4e61-82dc-6e756a083ccb","order_by":2,"name":"Victor Maus","email":"","orcid":"","institution":"Shanxi University","correspondingAuthor":false,"prefix":"","firstName":"Victor","middleName":"","lastName":"Maus","suffix":""},{"id":498787900,"identity":"5cdec534-125e-4c0c-be39-32483148b420","order_by":3,"name":"Chao Su","email":"","orcid":"","institution":"Shanxi University","correspondingAuthor":false,"prefix":"","firstName":"Chao","middleName":"","lastName":"Su","suffix":""},{"id":498787902,"identity":"30022afb-3bc8-41ec-9cd8-4a6a2b594161","order_by":4,"name":"Xiaoyu Yang","email":"","orcid":"","institution":"Shanxi University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyu","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2025-07-29 13:53:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7244001/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7244001/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10661-025-14923-5","type":"published","date":"2026-01-06T15:58:07+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":89071956,"identity":"0c0b2341-de05-4aef-9c6b-bfa35ee84bca","added_by":"auto","created_at":"2025-08-14 11:22:23","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":343587,"visible":true,"origin":"","legend":"\u003cp\u003eGeospatial distribution of the research domain: (a) Administrative boundaries of Shanxi Province's Shouzhou municipal region; (b) Delineated extent of the Pingshuo coal extraction zone; (c) Structural changes in tree-level landscape patterns in the Pingshuo mining area (1990-2020, 30-year chronosequence).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7244001/v1/7c29030afd52ea42dbca24b4.png"},{"id":89071957,"identity":"b50483cf-47d7-4afb-af64-49091d65a19e","added_by":"auto","created_at":"2025-08-14 11:22:23","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":289739,"visible":true,"origin":"","legend":"\u003cp\u003eThe calculation method for landscape ecological risk.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7244001/v1/a1816f42c6f2387f64e1c4dc.png"},{"id":89071960,"identity":"09898410-73f0-4a58-ba5f-e47b637a32a6","added_by":"auto","created_at":"2025-08-14 11:22:23","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":513430,"visible":true,"origin":"","legend":"\u003cp\u003eResearch framework.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7244001/v1/b4ed345963b665602ad56bc9.png"},{"id":89072750,"identity":"50921235-b37e-4738-92d9-4b07517a88bd","added_by":"auto","created_at":"2025-08-14 11:30:23","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":499736,"visible":true,"origin":"","legend":"\u003cp\u003eDynamics of habitat quality changes across the entire life cycle of the study area. (a) Habitat quality classification map of the Pingshuo mining area throughout its entire life cycle; (b) Habitat quality improvement or degradation areas of the Pingshuo mining area.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7244001/v1/a08264004e1c411b1e8b8ff1.png"},{"id":89072754,"identity":"008e312b-8efc-49ca-bb47-df5e33e921e5","added_by":"auto","created_at":"2025-08-14 11:30:23","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":302813,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of landscape types across LER levels.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7244001/v1/a094226eeb90e9cbe1a07942.png"},{"id":89071974,"identity":"545eba64-4088-4cd9-a704-70163f84b60d","added_by":"auto","created_at":"2025-08-14 11:22:24","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":235536,"visible":true,"origin":"","legend":"\u003cp\u003eA two-dimensional dynamic framework of HQ and LER.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7244001/v1/b49f8be3e715e631e47d8d26.png"},{"id":89073221,"identity":"eaef3aa3-780a-4339-800f-4ecdc17efc03","added_by":"auto","created_at":"2025-08-14 11:38:23","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":281078,"visible":true,"origin":"","legend":"\u003cp\u003eThe proportion of key landscape transitions in the four quadrants during different developmental stages of the mining area.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7244001/v1/54fec386c900aafa454577be.png"},{"id":89072751,"identity":"1766437f-bdeb-4b35-8b2b-0d8f1ae847fb","added_by":"auto","created_at":"2025-08-14 11:30:23","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":328722,"visible":true,"origin":"","legend":"\u003cp\u003eThe influence of landscape transitions within the four quadrants on HQ and LER.\u003c/p\u003e\n\u003cp\u003eNote: The gray and red lines in the figure represent landscape transitions within the quadrant that have significant impacts on habitat quality and landscape ecological risk. The depth and thickness of the lines indicate the magnitude of the impact, with deeper and thicker lines representing greater influence. The direction of the arrow points to the land-use type after the transition. Red lines specifically represent key transitions that have identical effects on both habitat quality and landscape ecological risk.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-7244001/v1/4a3f4df789f1583567a2dc7f.png"},{"id":100069806,"identity":"b4fc5c21-b7d6-43d5-a729-19b7cb3177a0","added_by":"auto","created_at":"2026-01-12 16:15:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3850083,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7244001/v1/3825c9a4-c77e-4cc4-812c-9008886e5e4a.pdf"},{"id":89072748,"identity":"1e828859-c45d-4663-bc88-d337eda31eaf","added_by":"auto","created_at":"2025-08-14 11:30:23","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":31895,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixA.Supplementarydata.docx","url":"https://assets-eu.researchsquare.com/files/rs-7244001/v1/2e5bf37a2ee1fa42b09270ef.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dynamic coupling of habitat quality and landscape ecological risk for sustainable ecosystem management in open-pit mining area","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAs the global population and economies expand, the rising demand for coal resources has led to significant challenges for surrounding environments and communities (Luckeneder et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Maus and Werner \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Sonter et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e). Open-pit mining induces geomorphic instability, leading to a decline in biotic community integrity, as indicated by habitat quality (HQ) index reduction, while also intensifying spatial fragmentation, which heightens ecosystem vulnerability thresholds and amplifies landscape ecological risks (LER) (Poskočilov\u0026aacute; et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Ecological restoration is considered an important measure for combating ecosystem degradation in opencast mines (X. Liu et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Given that ecological restoration in mining areas is a long-term endeavor, its effectiveness is more accurately evaluated at the landscape scale over extended periods (Betts et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHQ indicates how well a habitat can sustain species survival(Ren et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e), with habitat quality serving as a key measure of ecosystem health and biodiversity. The main research methods for studying HQ include field surveys and remote-sensing. For example, scholars typically record parameters such as plant species and abundance through field surveys, which are then used to construct relevant HQ indices (such as species richness, evenness, and diversity indices) to assess HQ (Aznarez et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Joseph et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Qu et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, short-term stand-scale forestry measures are insufficient to reveal HQ trends throughout the mining area's life cycle (Betts et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Currently, some scholars use the InVEST model to assess long-term trends in HQ changes in plateaus, islands, cities, and mountainous regions (Qin et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Tan et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Tang et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, these studies mostly utilize land-use data, often treating the forest as a single entity without differentiating the tree species. Different vegetation patterns constitute various reclamation models in the mining areas, resulting in different HQ conditions in the region (H. Feng et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Therefore, refining land use at the tree species level helps determine which reclamation model maintains a high HQ throughout the mine life cycle.\u003c/p\u003e\u003cp\u003eLER assessment focuses on the risks induced by landscape structural changes and the negative impacts of their interactions with ecological processes (Xu and Matsushima \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Recently, scholars have explored the optimal granularity to conduct reasonable assessments of regional LER (Q. Wang \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; S. Li \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Yaermaimaiti \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In addition, studies on LER have often examined ecological networks, safety patterns, and driving factors (L. Liu et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; W. Zeng et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; S. Zhang et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, these studies typically addressed only a single aspect of regional LER and did not integrate HQ with LER for effective ecological zoning management in mining areas. The former focuses on assessing ecosystem services but does not account for the risk transmission mechanisms driven by spatial heterogeneity (Du et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; K. Li et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The latter focuses on changes in landscape indices while overlooking the ecological resilience differences among land cover types (Hui et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; J. Zeng et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This fragmentation often leads to decision-making biases in mining area restoration, such as \"hidden ecological risks under high vegetation coverage\" or \"critical habitat degradation masked by low-risk indices (X. Feng et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\"\u003c/p\u003e\u003cp\u003eOpen-pit coal mining has a profound impact on regional landscapes, leading to significant changes in LER and HQ (Mei Zhang et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This high-intensity disturbance not only disrupts the physical integrity of habitat units but also alters the spatial configuration of landscape elements (e.g., connectivity, dominance), triggering cascading ecological effects (Jin, Dong, et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The spatial interface effects created by fragmentation serve as the key link between these two dimensions (S. Wang et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). For example, built-up land, through rigid expansion, forms stable low-risk patches, but its edge effects accelerate habitat degradation in adjacent forest and grassland areas (Wu et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e). Meanwhile, forest and grassland fragmentation diminishes internal habitat suitability while also expanding ecologically fragile interfaces, creating a topological network for risk propagation (Joy et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zheng et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). An integrated framework can decouple the coupling mechanisms of \"structural risk\" and \"functional risk.\" The habitat quality index identifies priority areas for ecological conservation, while the landscape risk index warns of potential spatial structural imbalances. The spatial coupling analysis of these two indices provides a dual-dimensional reference system for coordinated planning in mining area \"ecological restoration\u0026ndash;risk prevention and control.\"\u003c/p\u003e\u003cp\u003eIn this study we propose an ecological zoning approach that balances between LER and HQ throughout the complete mine life cycle. Specifically, our approach includes four key steps: (1) to monitor HQ at the vegetation landscape scale throughout the whole mine life cycle; (2) to analyze LER resulting from different vegetation landscape transitions; and (3) to identify key transitions under the HQ and LER dynamic framework and propose ecological management strategies for each zone. This study combines field surveys and remote sensing data to gather information at the tree species level, elucidating the patterns of change in HQ and LER in tree-level landscapes throughout the mine life cycle. We employ a linear mixed-effects model to identify key landscape transitions driven by dynamic shifts in habitat quality and ecological risk. We applied the new approach in a case study of the Pingshuo open-pit mining site in China. Our results show that this approach not only enhances the scientific foundation for land management decisions but also offers important insights for the long-term development and ecological management of ecologically vulnerable cities and resource-dependent areas.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cb\u003eStudy area\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe Pingshuo mine area is an integral part of the national planning and development of the northern Shanxi coal base, situated within the Pinglu and Shuocheng districts of Shuozhou City, Shanxi Province (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This mining area lies in the northern section of Shanxi Province, within the central-western region of Shuozhou City, with coordinates spanning from 112\u0026deg;14\u0026prime;E to 112\u0026deg;32\u0026prime;E longitude and 39\u0026deg;22\u0026prime;N to 39\u0026deg;37\u0026prime;N latitude (Yuan et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The climate in this region is classified as temperate semi-arid continental monsoon, with annual temperatures ranging from 4.8 to 9.6\u0026deg;C and average yearly precipitation between 461.12 and 476.74 mm (Yuan et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Owing to significant increases in rock and soil stripping caused by mining activities and repeated disturbances, the mine area implements a coordinated management mechanism throughout the processes of \"land acquisition/lease - excavation/stripping - transportation - disposal - reclamation - restoration.\"\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eData sources and processing\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eTree-level landscape classification\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis research leveraged multispectral imagery (Landsat 5 TM/7 ETM+/8 OLI, Sentinel-2 MSI) accessed via Google Earth Engine's cloud platform, acquiring quadrennial observations (1990\u0026ndash;2020). To maintain spectral integrity under sub-10% cloud contamination thresholds, the multisensor data streams underwent radiometric normalization, atmospheric compensation, and cloud masking algorithms. Field investigations were conducted in vegetated areas within the southern, western, and interior regions of Pingshuo mine. The sampling protocol systematically distributed 1,000 training and 300 validation points across geographically stratified zones, ensuring proportional representation of differently aged tree species and agricultural fields while mitigating spatial autocorrelation effects. This design explicitly resolved the \"uniform spatial distribution\" imperative and accounted for spectral variability challenges through iterative location adjustments.\u003c/p\u003e\u003cp\u003eThe random forest classification framework integrated seven optimized features (NDVI, EVI, NDWI, IBI, BSI, Elevation, Slope) derived from the literature (Y. Deng et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Filip et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), with the specific calculation formulas found in the literature (Zhao et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Feature importance quantification revealed NDVI and elevation as dominant discriminators, collectively explaining over 60% of predictive variance. Model validation through temporally stratified sampling demonstrated 90.31\u0026ndash;94.20% overall accuracy (mean 92.78\u0026thinsp;\u0026plusmn;\u0026thinsp;2.40%) and 0.88\u0026ndash;0.93 Kappa coefficients (mean 0.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03) across the 30-year study period.\u003c/p\u003e\u003cp\u003e\u003cb\u003eHQ assessment of tree-level landscape\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study was based on the HQ module of the InVEST model to estimate HQ in the Pingshuo mining area for 1990, 2000, 2010, and 2020. This module calculates habitat degradation based on input threat source data and derives HQ using habitat suitability data and degradation levels. The calculation formula for habitat quality in the InVEST module can be found in the literatures (Chen et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Luo et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wei et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBased on field survey data, this study employed Principal Component Analysis to calculate habitat suitability for different habitat types (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), thereby enabling localized HQ assessment. The 10-variable dataset underwent Z-score normalization, followed by factorability validation using KMO-Bartlett diagnostics. Upon achieving sufficient metric interdependency (KMO\u0026thinsp;\u0026gt;\u0026thinsp;0.616, Bartlett's sphericity p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), PCA-driven variable compression was executed. Cross-referencing established studies (Lei et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ren et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e; Wei et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e), we determined: (1) habitat-type specific susceptibility to anthropogenic stressors (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), (2) threat weighting coefficients, and (3) spatial influence thresholds (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eHabitat category responsiveness to identified threat sources.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTree age\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHabitat types\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHabitat suitability\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eConstruction land\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBare land\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCultivated land\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eForestland_PT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eForestland_PT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eForestland_PT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eForestland_UP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eForestland_UP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eForestland_UP-RP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eForestland_P\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eForestland_P\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eForestland_P\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eForestland_Shrub\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGrassland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCultivated land\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBare land\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWater\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThreat source magnitude and spatial influence thresholds.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThreat factor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMaximum influence distance (km)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWeight\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDecay\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCultivated land\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eexponential\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstruction land\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003elinear\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBare land\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003elinear\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003eLER assessment of tree-level landscape\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe LER index indicates the degree to which the relationship between landscape patterns and ecological processes, shaped by natural or human factors, undermines the landscape's capacity to preserve its structure and function (Gu \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The calculation is based on the landscape vulnerability and disturbance indices (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), which reflect the internal fragility of and external disturbances to the ecosystem, respectively (Guo et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eFramework of habitat quality and ecological risks with key transitions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo further understand the dynamic relationship between habitat quality and landscape ecological risk in mining areas, we constructed four-quadrant diagrams for three developmental stages (1990\u0026ndash;2000, 2000\u0026ndash;2010, and 2010\u0026ndash;2020) based on the rise and fall of HQ and LER. In these diagrams, simultaneous decreases in HQ and LER were categorized as deterioration, while simultaneous increases were classified as resistance. When HQ increased while LER decreased, the relationship was labeled as coordination, whereas the reverse (decreasing HQ and increasing LER) was defined as conflict. Based on this, the proportion of landscape transitions within each quadrant at different stages was quantified. Treating year as a random effect, a linear mixed-effects model was applied to assess the impact of key transitions within each quadrant on habitat quality and landscape ecological risk. The formula for the linear mixed-effects model is as follows:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$${\\text{H}}{{\\text{Q}}_{{\\text{ij}}}}{\\text{\\sim }}{\\beta _{\\text{0}}}{\\text{+}}{\\beta _{\\text{1}}}{{\\text{A}}_{{\\text{1ij}}}}{\\text{+}}{\\beta _{\\text{2}}}{{\\text{A}}_{{\\text{2ij}}}}{\\text{+}}{\\beta _{\\text{3}}}{{\\text{A}}_{{\\text{3ij}}}}{\\text{+}}{{\\text{u}}_j}+{\\varepsilon _{ij}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$${\\text{LE}}{{\\text{R}}_{{\\text{ij}}}}{\\text{\\sim }}{\\beta _{\\text{0}}}{\\text{+}}{\\beta _{\\text{1}}}{{\\text{A}}_{{\\text{1ij}}}}{\\text{+}}{\\beta _{\\text{2}}}{{\\text{A}}_{{\\text{2ij}}}}{\\text{+}}{\\beta _{\\text{3}}}{{\\text{A}}_{{\\text{3ij}}}}{\\text{+}}{{\\text{u}}_j}+{\\varepsilon _{ij}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere HQ\u003csub\u003eij\u003c/sub\u003e and LER\u003csub\u003eij\u003c/sub\u003e are the dependent variable (response variable), which represents the first j Group in the first i observations. ​ β\u003csub\u003e0\u003c/sub\u003e is the intercept term of the fixed effect. The terms A\u003csub\u003e1\u003c/sub\u003e, A\u003csub\u003e2\u003c/sub\u003e, and A\u003csub\u003e3\u003c/sub\u003e represent different types of landscape transitions, digital elevation model, and slope gradient, respectively. U\u003csub\u003ej\u003c/sub\u003e is a random effect. ε\u003csub\u003eij\u003c/sub\u003e is the error term.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResearch methods and framework\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFirstly, we mapped the geographic distribution of tree species and reclamation years within the restored areas of the study area. Based on the GEE platform, we utilized a random algorithm combined with geographic annotation points for visual interpretation, generating a 30-year tree-level landscape for the Pingshuo mining region. Secondly, to accurately assess habitat quality at the study area, we adjusted the parameters of the InVEST model using field survey data on tree species characteristics and soil physicochemical properties, and then mapped its spatial distribution. Subsequently, based on the landscape ecological risk index, we analyzed changes in landscape ecological risk in the mining area and identified key land use types at different stages and risk levels. Thirdly, we constructed a dynamic framework for habitat quality and landscape ecological risks, quantified the proportions of key landscape transitions during dynamic changes, and employed a linear mixed-effects model to evaluate the impact of key landscape transitions on both habitat quality and ecological risks during different zoneFinally, we propoesed ecological management strategies for ecological zones (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eAnalyzing HQ dynamics across the entire life cycle of the mine\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAssessing the temporal changes in habitat quality over the mine life cycle is crucial for gaining a holistic view of its status in mining areas. HQ in the Pingshuo mining area underwent significant changes between 1990 and 2020, primarily characterized by low and fairly low HQ levels. The combined area of these two categories accounted for more than 69% of the total area each year (Fig.\u0026nbsp;4a). Low HQ was mainly distributed across construction land and bare land, whereas fairly low HQ was primarily found in grasslands and cultivated land (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec, Fig.\u0026nbsp;4a, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Areas classified as fairly high- and high-quality accounted for less than 25% of the total area and predominantly consisted of forestland_PT, forestland_P, forestland_UP-RP, and forestland_shrub (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec, Fig.\u0026nbsp;4a). The average HQ indices in 1990, 2000, 2010, and 2020 were 0.53, 0.49, 0.37, and 0.42, respectively. Overall, HQ initially declined, and then recovered.\u003c/p\u003e\u003cp\u003eBetween 1990 and 2000 (the initial stage), the area of degraded HQ was greater than that of the improved areas (Fig.\u0026nbsp;4b). The areas of low-and fairly low-quality habitats increased by 12.88%, whereas those of high-and fairly high-quality habitats increased by 3.18% (Fig.\u0026nbsp;4a). In the western region, low-quality habitats transitioned to higher- and high-quality habitats, showing significant improvement. However, in the central and western regions, moderate- and high-quality habitats transitioned to lower-quality habitats, indicating a noticeable decline in HQ (Fig.\u0026nbsp;4a).\u003c/p\u003e\u003cp\u003eBetween 2000 and 2010 (development stage), the proportion of the degraded HQ reached a peak of 44.24%, which was the highest in 30 years, whereas the proportion of improved HQ was the lowest at 24.32% (Fig.\u0026nbsp;4b). The area of low-quality habitats expanded by 22.09% in the central and eastern regions, whereas habitats in the western region improved. Additionally, some high-quality habitats in the northern region transitioned to low-quality habitats (Fig.\u0026nbsp;4a).\u003c/p\u003e\u003cp\u003e From 2010 to 2020 (the stable stage), the area of improved HQ exceeded that of degraded areas (Fig.\u0026nbsp;4b). High-and fairly high-quality habitats expanded in the western region, with an increase of 9.83%. Meanwhile, the areas of low- and low-quality habitats decreased by 11.55%. This change was primarily due to the transition of 32.36% of low- and low-quality habitats into higher- and high-quality habitats (Fig.\u0026nbsp;4a).\u003c/p\u003e\u003cp\u003e\u003cb\u003eUnderstanding LER caused by tree-level landscape transitions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAs changes in HQ occur due to landscape transitions, LER also change accordingly. From 1990 to 2020, the LER in the Pingshuo mining area underwent significant dynamic changes. Areas with high and fairly high risks were mainly located in forest and grassland regions with weak resilience to external pressures and high fragmentation. Moderate-risk areas are primarily found in cultivated land that experiences considerable human disturbance, whereas low-and fairly low-risk areas are distributed in construction and bare lands. These latter areas exhibit lower fragmentation and greater spatial continuity, and relatively intact patches are beneficial for maintaining ecosystem stability and resilience, thereby reducing LER (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The average LER indices for 1990, 2000, 2010, and 2020 were 0.23, 0.33, 0.24, and 0.58, respectively, indicating a fluctuating upward trend in the average LER index.\u003c/p\u003e\u003cp\u003eFrom 1990 to 2000 (initial stage), the areas of low-and fairly low-risk zones significantly decreased by 29.47%, whereas those of high-and fairly high-risk landscape zones in the west increased by 18.44%. This change was primarily due to the transfer of low-, fairly low-, and moderate-risk zones to high-and fairly high-risk zones, with shifts of 33.95%, 15.99%, and 26.39%, respectively. The low-and fairly low-risk zones dominated by grassland gradually transitioned to landscapes co-dominated by construction land and grassland. The high-and fairly high-risk zones, initially dominated by bare land, shifted to a distribution that included bare land, forest land _PT, forest land _P, and forest land _UP-RP (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFrom 2000 to 2010 (development stage), the low-and fairly low-risk zones expanded into the central and eastern regions, with their proportions increasing by 26.52%. The moderate-risk zone area decreased by 11.15%, whereas the high-and fairly high-risk zone areas decreased by 15.37%. During this stage, 68.09% of the moderate-risk zones, 42.70% of the fairly high-risk zones, and 25.39% of the high-risk zones transitioned to low- and low-risk areas. Construction land in the low-risk zone increased by 23.76%, whereas forestland_PT, forestland_P, and forestland_UP-RP in the high-and fairly high-risk zones increased by 26.66% and 32.70%, respectively. Bare land in the high-risk and low-risk zones decreased by 40.28% and 22.37%, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFrom 2010 to 2020 (stable stage), the area of low and fairly low landscape risk zones decreased by 29.16%, moderate landscape risk zones increased by 14.09%, and high and fairly high landscape risk zones increased by 15.07%. During this stage, 40.52% of the low-risk zones and 38.77% of the fairly low-risk zones transitioned into moderate-risk landscape zones. Additionally, some low (266.80 ha), fairly low (544.45 ha), and moderate (470.32 ha) landscape risk zones shifted to high and fairly high landscape risk zones. Construction land in low-risk zones continued to increase by 7.95%, whereas forestland_PT, forestland_P, and forestland_UP-RP expanded in high-and fairly high-risk zones (225.25 ha), and cultivated land in high-and fairly high-risk zones increased by 13.55% and 15.55%, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eIdentifying main landscape transitions under dynamic HQ and LER\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe established a two-dimensional framework incorporating changes in HQ and LER to gain a thorough insight into how mining activities interact with the environment across three phases: 1990\u0026ndash;2000, 2000\u0026ndash;2010, and 2010\u0026ndash;2020. In the stable stage, the resistance areas increased by 27.43% compared to the initial stage, and the conflict areas decreased by 31.27%. The coordination areas increased by 9.12% from the initial stage to the development stage but accounted for a very small proportion during the stable stage. The deterioration areas peaked during the development stage and decreased by 55.35% in the stable stage compared to the development stage (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFrom 1990 to 2000, the resistant area accounted for 21.40%, primarily in the southwestern region. The top three landscape transitions observed in these areas were from cultivated land and bare land to grassland, and from grassland to forestland_PT (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003e). In the initial stage, conflict areas were dominant, covering 67% of the total area (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Between 2000 and 2010, deteriorated areas made up 69.86%, with the top three transitions being from grassland to bare land, construction land, and cultivated land to bare land (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The majority of resistance areas were concentrated in the western region, comprising 9.05% of the total area(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The primary transitions involve bare land shifting toward forestland_PT and forestland_P, and grassland transitioning toward forestland_PT (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003e). From 2010 to 2020, the area of resistance accounted for 48.83% (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The three most significant transitions in these areas were from cultivated land and construction land to grassland, as well as from construction land to bare land (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The deteriorated area was predominantly located in the northeast, making up 14.51% of the total area (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The main transitions involve grassland, cultivated land, and bare land shifting toward construction land in these areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWe applied a linear mixed-effects model to evaluate how landscape transitions influence HQ and LER dynamics within each dynamic partition. The transitions from foresland, grassland, and cropland to construction land, as well as from forestland_PT to bare land, significantly contributed to ecosystem deterioration in the mining area. Transitions from forestland_P to bare land and grassland led to declines in habitat quality accompanied by increased ecological risk. In contrast, key transitions in ecosystem coordination zones involved the transformation of bare land and construction land into forestland_UP-RP and forestland_PT. In ecosystem resistance zones, while transitions from construction land to forestland_UP-RP and bare land to forestland_PT resulted in some improvement in habitat quality, they still posed ecological risks (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003e, Table S2).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eProposing ecological management strategies for mining areas\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBased on the quantified impacts of landscape transitions on habitat quality and ecological dynamics (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Table S2), the following targeted management strategies are proposed:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eDeterioration Zones: Designate mining-essential zones where transitions from forestland, grassland, or cropland to construction land are permitted only under strict \"destruction-compensation\" protocols. For every unit area of forestland_PT/P converted to bare land for mining, mandate pre-planned revegetation of equivalent ecosystem service value in adjacent coordination zones (e.g., bare land to forestland_UP-RP). In addition, implement phased mining schedules to minimize simultaneous large-scale clearing, prioritizing retention of intact forestland_PT buffers around extraction sites to reduce edge effect risks.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConflict\u003c/strong\u003e\u003cp\u003e(2) zones: In conflict zones where forestland_P-to-bare land/grassland transitions significantly degrade habitat quality, prioritized interventions should focus on dual-goal restoration that balances ecological stability and land-use demands. First, implement mycorrhizal inoculation in degraded forestland_P areas to enhance soil retention and accelerate native tree regeneration, targeting a minimum 40% canopy cover recovery within 5 years. Second, enforce smart zoning policies using real-time satellite monitoring to block further poplar conversion in high-risk clusters (e.g., slopes\u0026thinsp;\u0026gt;\u0026thinsp;15\u0026deg; or near watercourses). Third, introduce community stewardship programs that incentivize locals to maintain transitional grasslands as biodiversity buffers through payment-for-ecosystem-services (PES) schemes, conditional on achieving habitat connectivity metrics (e.g., 500-m corridor width between forest patches). These measures directly counter model-identified drivers while aligning with sustainable mining-urban interfaces.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003col start=\"3\"\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eCoordination zones: To enhance ecological outcomes in the coordination zones of mining areas, prioritize multi-layered vegetation restoration using site-specific native species (e.g., Ulmus pumila, Pinus tabuliformis, and grassland communities) to improve biodiversity and soil stability. Implement adaptive management, including phased planting (e.g., pioneer grasses for erosion control followed by tree saplings) and real-time monitoring of soil nutrients and pollutants to address legacy contamination. Establish buffer corridors to support wildlife movement and genetic exchange. Engage local communities in restoration planning by integrating traditional ecological knowledge with modern techniques like mycoremediation for heavy metal absorption. Additionally, develop circular economy incentives, such as carbon credit programs linked to vegetation growth, to ensure long-term ecological and socioeconomic sustainability.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eResistance zones: Firstly, in the process of forest _UP-RP and forest _PT restoration, priority should be given to planting native tree species with strong stress resistance, deep roots and adaptation to the local environment to improve ecosystem stability. Secondly, adopt mixed forest models (e.g., tree-shrub-grass combination) to improve ecosystem resilience and reduce the risk of pests and diseases and ecological vulnerability brought by a single tree species. Finally, before vegetation restoration, proceed soil remediation (e.g., organic matter supplementation, soil microbial remediation) to enhance soil and water conservation capacity and reduce the risk of erosion due to vegetation conversion.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePolicy toolkit for ecological partitions integrating model drivers and spatial targeting.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEcological partition\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKey landscape transitions\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDominant Spatial Clusters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePolicy Tools\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDeterioration Zones\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eForesland/Grassland/Cultivated land \u0026rarr; Construction land\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCentral Region (2000\u0026ndash;2010)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eRestoration and management\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eForestland_PT \u0026rarr; Bare land\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEastern Region (2010\u0026ndash;2020)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConflict Zones\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eForestland_P \u0026rarr; Bare land\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eWestern reclamation area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eRisk mitigation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eForestland_P \u0026rarr; Grassland\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCoordination Zones\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBare land/Construction land \u0026rarr; Forestland_UP-RP and forestland_PT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWestern reclamation area(1990\u0026ndash;2010)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eOptimization and promotion\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBare land/Construction land \u0026rarr; Grassland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCentral Region (2010\u0026ndash;2020)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResistance Zones\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eConstruction land \u0026rarr; Forestland_UP-RP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWestern reclamation area(1990\u0026ndash;2010)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePreventive conservation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBare land \u0026rarr; Forestland_PT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCentral Region (2010\u0026ndash;2020)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cb\u003eThe response of habitat quality to tree-level landscape changes\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe dynamic assessment of habitat quality in the study area over the past three decades reveals a clear pattern of degradation and partial recovery, driven by land-use changes and mining activities. Over the 30-year period, low and fairly low HQ consistently dominated (\u0026gt;\u0026thinsp;69% of the total area), primarily linked to construction land, bare land, grasslands, and cultivated land. In contrast, high-quality habitats (forestland types) remained limited (\u0026lt;\u0026thinsp;25%), underscoring the vulnerability of ecosystems in mining-affected regions. The average HQ index declined sharply from 0.53 to 0.37 between 1990 and 2010, reflecting cumulative degradation during intensive mining expansion, followed by a modest recovery to 0.42 by 2020. This nonlinear trend highlights the interplay between anthropogenic pressures and localized restoration efforts, particularly in later stages. This is consistent with the findings of previous studies (Guan et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; H. Wang et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eNotably, the spatial-temporal heterogeneity of HQ shifts corresponded to mining phases. During initial development (1990\u0026ndash;2000), HQ degradation outpaced improvement, with central and western zones experiencing habitat downgrades despite western recovery. The peak degradation (2000\u0026ndash;2010) coincided with accelerated mining, as low-quality habitats expanded by 22.09%, outweighing improvements in the west. This trend was closely linked to the expansion of the central mining area, which encroached upon a significant portion of cultivated land (Min Zhang et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). By the stable stage (2010\u0026ndash;2020), ecological interventions likely contributed to HQ recovery, evidenced by a 9.83% expansion of high-quality habitats in the west and reduced low-quality areas (-11.55%). These findings emphasize the need for phased management strategies to mitigate habitat fragmentation and prioritize rehabilitation in critical zones during mining operations (Min Zhang et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eLER patterns under landscape change\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study reveals the spatiotemporal variation of LER in the Pingshuo mining area across the entire mine life cycle, along with its driving mechanisms. The results show that the LER index of the mining area exhibited a fluctuating upward trend (from 0.23 to 0.58), with a significant positive correlation between high-risk areas and the fragmentation of forests/grasslands, while low-risk areas are closely related to the spatial continuity of construction land/bare land. These results are consistent with the LER levels constructed based on the 'source\u0026ndash;sink' landscape theory (Wu et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e), but further reveals the staged impact of human activities on LER: in the early stages of mining development (1990\u0026ndash;2000), the rapid conversion of low-risk areas to high-risk areas (transfer rate\u0026thinsp;\u0026gt;\u0026thinsp;15%) was mainly driven by the development of bare land and occupation of forest land; whereas in the stable phase (2010\u0026ndash;2020), the expansion of construction land and conversion of farmland became the main driving factors for the increase in LER. This dynamic feature suggests that traditional \"static zoning\" management strategies are insufficient to meet the needs of landscape evolution in mining areas, and there is an urgent need to establish a differentiated and adaptive risk regulation system.\u003c/p\u003e\u003cp\u003eBuilding on these findings, this study suggests the following management approaches: First, ecological restoration in high-risk areas should prioritize the restoration of connectivity in western forests/grasslands. Data shows that through the replanting of native tree species and the construction of ecological corridors, the area of forest patches can be increased by 26.66% (2000\u0026ndash;2010), significantly reducing the LER index (with a 15.37% reduction in high-risk areas during the same period). Second, in the stable phase of mining areas, the significant increase in ecological risk caused by the expansion of forest and farmland should be addressed through targeted land use management. This includes implementing measures such as controlling deforestation, preventing further farmland fragmentation, promoting sustainable agricultural practices, and enhancing ecological restoration, particularly in areas of land conversion (R\u0026iacute;os-Alvear et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Additionally, establishing protective buffers and improving landscape connectivity can help mitigate the adverse effects on the local ecosystem (Zhu et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Finally, the long-term effectiveness of LER management in mining areas relies on institutional innovation and technological integration. It is recommended to incorporate the LER index into the ecological compensation standards for mining development (e.g., mining rights in high-risk areas should be accompanied by a 3%-5% restoration budget) and use remote sensing monitoring systems to identify risk hotspot areas to implement \"monitoring-early warning-intervention\" closed-loop management (Jin, Bian, et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis proposes a staged regulation framework based on risk transfer paths, providing a basis for formulating differentiated strategies for similar mining areas in their \"development\" and \"stability\" phases. However, the LER index constructed based on landscape patterns focuses more on the integrated response of the ecosystem structure to human activities and the natural environment and lacks consideration of ecological and geological risks. In the future, integrating ecological and geological risks with economic activities could improve the comprehensive calculation of LER (H. Wang et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In addition, future research could place more focus on the \"landscape pattern-disturbance intensity-risk response\" in mining areas.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSignificance and limitations of the dynamic HQ and LER framework\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCompared with prior studies integrating HQ and LER into ecological zoning (H. Liu and Tang \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Xie et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Y. Zhang et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), this study emphasized the landscape transitions driving dynamic changes in both factors. It revealed that construction land encroachment on grassland, cultivated land, and forestland land degrades mining area ecosystems, underscoring the need for rational resource use and land planning. Early mining stages saw ecosystem conflicts from grassland replacement by bare and cultivated land, while later phases involved further conflicts from transformations into construction land. Sustainable mining technologies are essential to mitigate such conflicts (Tomassi and Kinyondo \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). During the initial and development stages, transitions from bare land, cropland, and construction land to forest and grassland promote ecosystem coordination. In the development stage, reclaimed forest land itself further enhances ecosystem coordination. Restoration and reconstruction should be prioritized during early mining stages, while reclaimed area maintenance becomes critical later (Sonter et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e). Finally, in ecosystem resistance zones, transitions from construction land to forestland_UP-RP and bare land to forestland_PT improved habitat quality but still posed ecological risks. This underscores the need for targeted soil stabilization and native species selection to mitigate unintended cascading effects of afforestation. (Yuan et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHQ and LER are key indicators for assessing ecosystem health in mining areas (G. Deng et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Qian et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The two-dimensional framework of HQ and LER developed in this study offers insights into the ecological management of other fragile cities and resource-based regions. The dynamic framework for evaluating habitat quality and landscape ecological risk in mining areas offers a structured method for exploring the spatial and temporal relationships between landscape transitions and ecological processes. By categorizing ecological zones into degradation, conflict, coordination, and resistance areas, this framework helps identify critical landscape transitions that drive ecological change. The use of a linear mixed-effects model further strengthens its applicability by quantifying the major landscape shifts affecting habitat quality and ecological risk. However, limitations exist, including potential uncertainties in model assumptions, the influence of external socio-economic factors, and the need for long-term validation. Future studies should incorporate finer-scale ecological indicators, consider ecosystem carbon sequestration, and integrate human-induced disturbances to enhance the robustness and applicability of this framework in diverse mining landscapes (Bayer et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrates how habitat quality (HQ) and landscape ecological risk (LER) evolve dynamically through different phases of mine development and reclamation. By integrating both HQ and LER within a unified framework for ecological zoning, it becomes clear that the expansion of mining operations initially reduces habitat quality and amplifies ecological risks, whereas targeted reclamation efforts and land-use changes can reverse certain degradation trajectories. Nonetheless, some transitions designed to improve one aspect (e.g., habitat quality) may introduce new landscape ecological risk, for example, converting bare land to forestland_PT. More holistic approaches for ecological zoning of mining areas can evidence this trade-offs and strengthen ecological planning to improve the sustainable development of resource-dependent landscapes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge the editors and anonymous reviewers for processing and improving the quality of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYang G.T.: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing. Zhang H.: Funding acquisition, Project administration, Resources, Supervision, Validation, Writing \u0026ndash; review \u0026amp; editing. Maus V.: Formal analysis, Validation, Writing \u0026ndash; review \u0026amp; editing. Su C.: Funding acquisition, Supervision, Validation. Zhang X.Y.: Supervision, Validation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the National Natural Science Foundation of China (grant numbers U1910207 and 42107420) and the Natural Science Foundation for Young Scientists of Shanxi Province (grant number 20210302124363).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the data supporting the findings of this study are available within the paper and its supplementary files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAznarez, C., Svenning, J.-C., Taveira, G., Bar\u0026oacute;, F., \u0026amp; Pascual, U. (2022). 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Construction of landscape eco-geological risk assessment framework in coal mining area using multi-source remote sensing data. \u003cem\u003eEcological Informatics\u003c/em\u003e, \u003cem\u003e81\u003c/em\u003e, 102635. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ecoinf.2024.102635\u003c/span\u003e\u003cspan address=\"10.1016/j.ecoinf.2024.102635\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Opencast coal mine, Landscape transitions, Ecological restoration, Ecological management, Ecological zoning, Linear mixed-effects model","lastPublishedDoi":"10.21203/rs.3.rs-7244001/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7244001/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOpen-pit mining disrupts landscape structure and ecological functions, directly affecting habitat quality (HQ) and landscape ecological risk (LER). While balancing these factors is critical for sustainable mining management, integrated approaches remain limited. To address this gap, we propose a two-dimensional framework that integrates habitat quality and landscape ecological risk, offering a more detailed, tree-level assessment compared to conventional land-use-based approaches. The results indicatea that: (1) Low/lower-quality habitats persistently exceeded 69% across mining stages, with degradation dominating initial/developmental phases (1990\u0026ndash;2010) and improvement emerging in the stable phase (2010\u0026ndash;2020). (2) High LER areas correlated with forest/grassland fragmentation, whereas low LER zones linked to construction/bare land continuity. Notably, forest and farmland expansion in stable stages increased LER, requiring targeted land-use strategies to mitigate risks. (3) The key transitions in ecosystem coordination zones included the conversion of bare land and construction land to forestland_UP-RP, forestland_PT, and grassland. Although transitions (e.g., construction land to forestland_UP-RP, bare land to forestland_PT improved HQ, they still pose landscape ecological risks. These findings strengthen land-use planning's scientific basis and provide actionable ecological governance insights for mining areas, fragile cities, and resource-based regions, while their enhanced detail improves assessment accuracy and enables precise restoration strategies.\u003c/p\u003e","manuscriptTitle":"Dynamic coupling of habitat quality and landscape ecological risk for sustainable ecosystem management in open-pit mining area","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-14 11:22:18","doi":"10.21203/rs.3.rs-7244001/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-17T14:58:58+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-14T05:37:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"138188548167936518435359420896708765555","date":"2025-08-24T03:03:38+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-24T01:10:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"26820106677854120340495079982819928917","date":"2025-08-08T11:16:16+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-08T01:21:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-04T05:03:06+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-04T05:02:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Monitoring and Assessment","date":"2025-07-29T13:44:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b787d524-572b-4e9a-8b57-5943c035ea2d","owner":[],"postedDate":"August 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-12T16:08:14+00:00","versionOfRecord":{"articleIdentity":"rs-7244001","link":"https://doi.org/10.1007/s10661-025-14923-5","journal":{"identity":"environmental-monitoring-and-assessment","isVorOnly":false,"title":"Environmental Monitoring and Assessment"},"publishedOn":"2026-01-06 15:58:07","publishedOnDateReadable":"January 6th, 2026"},"versionCreatedAt":"2025-08-14 11:22:18","video":"","vorDoi":"10.1007/s10661-025-14923-5","vorDoiUrl":"https://doi.org/10.1007/s10661-025-14923-5","workflowStages":[]},"version":"v1","identity":"rs-7244001","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7244001","identity":"rs-7244001","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}