Construction of a GEP-Based Ecological Security Pattern in the Henan Region Along the Yellow River: Integrating MSPA

Abstract

As a novel approach to address the lack of systematic studies on spatial Gross Ecosystem Product (GEP) accounting and Ecological Security Pattern construction, this study integrates GEP thresholds with Morphological Spatial Pattern Analysis (MSPA) to identify ecological sources. A resistance surface is constructed using five representative influencing factors, and the Minimum Cumulative Resistance (MCR) model is applied to extract ecological corridors, thereby establishing the Ecological Security Pattern for the Yellow River-Fronting Region of Henan in 2020. The results indicate the following: (1) GEP in the study area exhibits a spatial distribution of “high in the northwest, low in the southeast,” with regulating services accounting for more than 90% of the GEP. (2) A total of 11 ecological sources, 13 ecological corridors, and 7 ecological nodes were identified, primarily distributed in mountainous regions. (3) The Ecological Security Pattern exhibits spatial imbalance, with dense corridors in the western mountains and sparse distribution in the eastern plains. These findings provide scientific support for formulating ecological conservation measures and optimizing ecosystem management in the Yellow River Basin.

1. Introduction

With accelerated urbanization and the expanding human footprint, the risks of environmental pollution and ecological degradation continue to intensify. Ecological security issues such as soil erosion, soil contamination, landscape fragmentation, forest degradation, grassland degradation, and biodiversity loss have become increasingly prominent, impeding regional sustainable development [1]. Therefore, how to effectively safeguard socio-economic sustainable development while maintaining the stability of ecosystem structure and function has become an urgent and critical challenge [2]. Delineating a regional Ecological Security Pattern and achieving the rational development and scientific conservation of ecosystems have become key pathways for promoting socio-economic sustainable development, as well as vital components in implementing the national ecological civilization strategy [3], thereby facilitating the conservation of mountain, river, forest, farmland, lake, grassland, and desert ecosystems [4,5].

Scholars have extensively investigated Ecological Security Patterns, focusing on four core domains: design principles, planning methods, indicator construction, and pattern optimization [6]. As research advances, the Ecological Security Pattern construction framework has been continuously refined [7,8,9], with methodologies becoming increasingly diverse, streamlined, and sophisticated. In recent years, researchers have proposed various approaches to constructing Ecological Security Patterns, among which the Minimum Cumulative Resistance (MCR) model has been widely adopted [10,11,12,13]. The MCR model is commonly employed to delineate potential ecological corridors among different ecological sources. Compared with alternative approaches, the MCR model more effectively accounts for lateral linkages between ecological patches, simulates the resistance exerted by different patches on species migration and energy flow within ecosystems, and identifies the path with the minimum resistance cost as the potential ecological corridor, thereby markedly enhancing the scientific rigor and rationality of ecological sources and ecological corridors [14,15]. With more in-depth research, a fundamental framework for Ecological Security Pattern construction has gradually emerged—“ecological source extraction–resistance surface construction–ecological corridor extraction–ecological node identification”—yet unified standards for each step remain absent [16]. In constructing resistance surfaces, scholars commonly select resistance factors such as land use type, elevation, and slope and then refine these surfaces—taking into full account regional population and geographical characteristics [17,18,19]—by incorporating nighttime light data [20] and ecological sensitivity assessment [21] to reduce subjectivity. Ecological corridors and nodes, as critical connections among ecological sources, are essential for maintaining landscape connectivity [22]. These elements are primarily identified using the Minimum Cumulative Resistance (MCR) model, the gravity model, and circuit theory. The MCR model can directly delineate an ecological corridor network with Minimal Cumulative Resistance and can be readily integrated into other methods to evaluate the final Ecological Security Pattern [23,24].

Multiple methods are used to delineate ecological sources, often by integrating Morphological Spatial Pattern Analysis (MSPA) with recognized hotspots of typical ecosystem services [25,26], nature reserves [27,28], areas of ecosystem service supply–demand balance [29,30], and ecological barrier zones [31]. These approaches are largely founded on the holistic conservation concept that coordinates the protection of integral natural “mountain–water–forest–farmland–lake–grassland–desert” ecosystem components. Beyond natural factors, socio-economic factors influencing ecosystem management and ecological conservation must also be considered. The target of ecological conservation should therefore shift from single-natural-ecosystem elements to multi-factor socio-ecological systems [32]. Identifying ecological sources from the perspective of enhancing human well-being is an effective means of reconciling socio-economic development with ecological protection. Gross Ecosystem Product (GEP) represents the total value of ecosystem services and well-being provided to society and humanity. Delineating ecological sources and constructing Ecological Security Patterns from a GEP perspective can help balance the trade-offs between socio-economic development and ecological conservation [12]. The core concept of the Ecological Security Pattern is to maintain the integrity and sustainability of ecosystem structure and function through the protection of key spatial elements [33], while GEP provides a comprehensive measurement tool for the supply capacity of ecosystem services. GEP addresses the question of “what value to protect,” whereas the Ecological Security Pattern solves the problem of “where to protect.” GEP accounting can thus provide critical support for the construction of Ecological Security Patterns. In the identification of ecological sources, GEP accounting can precisely delineate the spatial differentiation of the importance of ecosystem services and identify areas with high ecological contributions. In terms of pattern control and policy integration, incorporating GEP into territorial spatial planning systems makes it possible to achieve linked management of ecological baselines and planning controls [34].

Despite this logical coupling, few studies have integrated ecosystem service values into Ecological Security Pattern construction. Most current frameworks rely on individual or combined ecosystem service hotspots, which fail to capture the total societal value of ecosystems in a unified monetary metric, thereby constraining their utility in economic policy discourse. Furthermore, ecological source identification often emphasizes biophysical attributes while overlooking the socio-economic dimension of human well-being. Although prior GEP-based studies have advanced ecosystem service accounting, the systematic integration of GEP thresholds with landscape pattern analysis to define spatially explicit ecological networks, particularly for refining ecological source identification, remains largely unexplored. This study addresses these gaps by proposing a novel dual-source framework that integrates GEP thresholds with MSPA, thereby enhancing the socio-ecological relevance of ecological source identification and advancing the methodological integration of ecosystem service valuation with spatial conservation planning.

The Yellow River Basin holds a pivotal strategic position in China’s socio-economic development and ecological civilization construction, serving as a key region for building the national ecological security barrier and achieving harmonious coexistence between humans and nature. The Henan section of the Yellow River Basin is characterized by diverse topography and intensive territorial space development and utilization, serving as the socio-economic core of Henan Province [35]. It faces ecological security challenges such as ecological fragility incongruent with economic development and insufficient high-quality regional development. Research on Gross Ecosystem Product (GEP) accounting and Ecological Security Pattern construction in this region urgently needs to be enriched and deepened [36]. Therefore, this study focuses on the Yellow River-Fronting Region of Henan to quantify the GEP of 11 ecosystem services and analyze their spatiotemporal evolution, including provisioning services (agricultural and livestock products), regulating services (water retention, water purification, carbon sequestration, oxygen release, etc.), and cultural services (ecotourism). On this basis, ecological sources are identified by integrating the Morphological Spatial Pattern Analysis (MSPA) method. In accordance with the natural and human characteristics of the study area, five resistance factors—landscape pattern type, Normalized Difference Vegetation Index (NDVI), land use type, elevation, and slope—are selected to establish a comprehensive resistance surface. The Minimum Cumulative Resistance (MCR) model is then employed to extract ecological corridors and nodes and construct an Ecological Security Pattern, and corresponding ecological conservation measures and policies are then proposed. The goal is to provide reference and scientific support for ecological protection, high-quality development, and ecological civilization construction in the Yellow River Basin.

2. Materials and Methods

2.1. Study Sites

The Yellow River-Fronting Region of Henan comprises nine prefecture-level cities (Zhengzhou, Kaifeng, Luoyang, Anyang, etc.) and one municipal district, covering approximately 6.70 × 10⁴ km². In the west, the study area borders the Loess Plateau and the remaining ranges of the Qinling Mountains, while to the north lies the southern section of the Taihang Mountains, as shown in Figure 1. The marked elevation difference and complex topography give rise to diverse ecological types. The eastern part belongs to the North China Plain, where superior farming and animal husbandry foundations coexist under fragile ecological conditions, as well as a profound cultural heritage. In 2020, the study area had a population of roughly 47.88 million and a cultivated land area of about 9626 km², resulting in an acute human–land conflict and making it a representative zone for ecological conservation in the Yellow River Basin. Rapid urbanization and the associated exploitation of natural resources have significantly altered the land use pattern, leading to a reduction in ecosystem regulating services and imbalanced spatial distribution of ecosystem service supply and demand. These issues have become major constraints on high-quality and sustainable economic development. Therefore, as a typical coupled social–ecological system, the Henan region along the Yellow River serves as an ideal testing ground for validating the effectiveness and policy applicability of the GEP-based ESP framework.

2.2. Data Sources and Processing

• Land use/cover data: Land use data for the years 2000, 2010, and 2020 were obtained from the Globeland30 global land cover product at a spatial resolution of 100 m. Following the product’s classification scheme, the land use data were reclassified into eight ecosystem types: cropland, forest, grassland, shrubland, wetland, water body, artificial surface, and bare land.
• Statistical Data: In this study, data on the output of agriculture, animal husbandry, and fishery products, as well as tourism revenue, were primarily obtained from the Henan Statistical Yearbook, the Henan Survey Yearbook, the China City Statistical Yearbook, and the statistical yearbooks and statistical communiqués issued by prefectures and counties. Tourism revenue data were additionally sourced from the above statistical yearbooks and statistical communiqués released by local governments. To ensure accuracy and completeness, sector-specific datasets published by the Department of Agriculture, the Department of Natural Resources, and the Statistical Bureau of Henan Province were also consulted.
• Ecological Parameters: Digital Elevation Model (DEM) data were downloaded from the Geospatial Data Cloud from the ASTER GDEM (30 m resolution), and then mosaicked, clipped, and projected to generate the study-area DEM. Hydrology tools were used for depression filling and flow accumulation to delineate watershed boundaries. Normalized Difference Vegetation Index (NDVI) data were provided by the Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (RESDC). Soil texture, soil organic matter content, and solar radiation data were acquired from the National Tibetan Plateau Data Center. Meteorological data for the study area and its surroundings (1986–2022) include daily precipitation, mean air temperature, maximum air temperature, minimum air temperature, and sunshine duration, and were obtained from the China Meteorological Administration’s China Surface Climate Data Daily Dataset (http://data.cma.cn/). Spatial interpolation was performed with the ANUSPLIN package to generate monthly, annual, and multi-year mean precipitation datasets for evapotranspiration and water retention calculations. To match the resolution of different land use types, all of the above data were uniformly resampled or interpolated to a 100 m raster. Road data were extracted from the National Road Dataset of RESDC and used to estimate the biodiversity index. Locations of scenic spots within the study area were collected using a Baidu-Maps POI crawler; duplicate records and culturally oriented sites were manually excluded to retain only natural attractions. The remaining parameters were taken from the recommended values in the InVEST model manual, supplemented by the relevant literature.
• Price Parameters: By combining information collected from agriculture, development-and-reform, environmental, price-supervision, and health departments with the literature review and market surveys, we obtained price benchmarks for crop, livestock, and fishery products, carbon-trading prices, average oxygen prices, air purification costs, water quality purification fees, and pollutant treatment costs. These unit values serve as conversion standards for quantifying the monetary value of ecosystem services when calculating Gross Ecosystem Product (GEP).
• Road data: This dataset was obtained from the National Road dataset of the Resource and Environment Science Data Centre of the Chinese Academy of Sciences (http://www.resdc.cn) and includes highways, railways, national roads, provincial roads, and county roads.

2.3. Ecosystem Service Assessment and Gross Ecosystem Product (GEP) Accounting

Ecosystem service assessment considers provisioning, regulating, and cultural services [37]. In light of the well-developed agricultural base in the Yellow River-Fronting Region of Henan, provisioning services were quantified using agricultural and livestock outputs. Agricultural products focused on grain and oil crop yields, while livestock products included pork, beef, mutton, poultry eggs, and milk. Regulating services were assessed using eight indicators, including water retention, soil conservation, carbon sequestration, oxygen release, and average species abundance [32,38]. Cultural services were represented by the value of ecotourism generated from natural landscape tourism within the region, evaluated on the basis of natural density distribution. According to the Comprehensive Analysis Report on Inbound Tourist Sampling Survey conducted by the China National Tourism Administration from 2010 to 2020, the proportion of ecotourism to total tourism revenue is 24.6%. Specific models and methods are listed in

Table 1. Framework and accounting methods for Gross Ecosystem Product (GEP) in the Yellow River-Fronting Region of Henan.

Service Type	Ecological Indicator	Material-Quantity Spatial Assessment Method	Value Accounting Method
Supply services	Agricultural products	County-level grain and oil crop yields from statistical yearbooks are disaggregated to 100 m grid cells based on cropland NDVI	Market price method
	Livestock products	County-level meat, egg, and milk yields from statistical yearbooks are disaggregated to cropland and grassland grid cells using NDVI
Regulating services	Water retention	Assessed with the InVEST model’s Annual Water Yield module (water balance principle)	Replacement cost method
	Water purification	Assessed with the InVEST model’s Nutrient Delivery Ratio (NDR) module for total N and P removal	Replacement cost method
	Soil conservation	Assessed with the InVEST model’s Sediment Delivery Ratio (SDR) module	Replacement cost method
	Carbon sequestration	Assessed with the InVEST model’s Carbon Storage and Sequestration module	Replacement cost method
	Oxygen release	Estimated from carbon sequestration using the stoichiometric ratio MO2/MC = 32/44	Replacement cost method
	Air purification	Estimated from research-based adsorption capacities of forests, shrublands, and grasslands for SO2, NOx, and dust	Replacement cost method
	Climate regulation	Evapotranspiration estimated with the modified Hargreaves model	Replacement cost method
	Biodiversity conservation	Habitat quality (0–1) assessed with the InVEST model’s Habitat Quality module; nature reserves are assigned a value of 1	Conservation value method
Cultural services	Ecotourism	Kernel density of natural scenic sites	Tourism revenue substitution method

.

To capture the spatial heterogeneity of GEP results, a 100 m × 100 m grid-based GEP assessment model was built. The GEP was calculated from both ecosystem service material quantities and monetary values, converting material products and functional services into monetary terms and aggregating them to obtain the Gross Ecosystem Product. Monetary values were calculated using the average prices of products in 2020.

2.4. Ecological Source Identification

Ecological sources are patches of critical ecological value or patches exerting strong ecological spill-over effects on their surroundings, and they play a pivotal role in ecological conservation. Using GEP estimation results and the STARS mutation test, we aimed to identify the mutation points of GEP values as they vary with area, which are used as area thresholds. Following the principle of protecting greater GEP with smaller spatial coverage, the mutation point for GEP in 2020 was identified at the top 22.6% [4,39], i.e., areas with values above approximately 170,000 yuan ha⁻¹, which were delineated as Type-A ecological sources. By identifying the mutation point, the core areas of high-GEP contribution can be delineated, which are regions that achieve a relatively high total ecological value within a comparatively small spatial extent. This approach follows the benefit maximization principle in ecological conservation planning, which prioritizes areas with the highest ecological benefit per unit area under the realistic constraint of limited conservation resources.

We conducted Morphological Spatial Pattern Analysis (MSPA), designating forests, water bodies, grasslands, and wetlands as foreground and cropland and artificial surfaces—land use types disturbed based on human activities—as background. This yielded seven landscape classes: core, edge, islet, perforation, loop, bridge, and branch. Large core areas were extracted as Type-B ecological sources. After merging Type-A and Type-B sources, based on the patch area frequency distribution characteristics of the study area, ecological functional integrity, and similar area thresholds used in Ecological Security Pattern construction, we obtained the initial ecological-source layer and retained patches larger than 100 km² as the final ecological sources.

2.5. Resistance Surface Construction

The integration of an ecological resistance surface is a crucial step in constructing the Ecological Security Pattern; the higher the resistance value, the greater the expansion cost. Based on local conditions, we selected five influencing factors—land use type, landscape pattern type, NDVI, elevation, and slope—and assigned resistance values based on previous studies (

Table 2. Resistance values and weighting factors for the study area.

Resistance Factor	Resistance Value							Weight
	1	2	3	4	5	6	7
Land use type	Forest, water	Grassland	Wetland	Cropland	—	Bare land	Artificial surface	0.35
Landscape pattern type	Core	Islet	Perforation	Edge	Loop	Bridge	Branch	0.12
NDVI	0.88–1	0.82–0.88	0.75–0.82	0.67–0.75	0.56–0.67	0.42–0.56	0.10–0.42	0.15
Elevation	34–266	185–385	385–592	592–826	826–1077	1077–1370	1370–2387	0.2
Slope	0–2.6	2.6–7.5	7.5–13.03	13.03–18.83	18.83–25.2	25.2–33.3	33.3–74.1	0.18

). Individual resistance surfaces for each factor were generated, and a weighted-sum approach was used to construct the comprehensive resistance surface. The weights were determined using the Analytic Hierarchy Process (AHP) and were calibrated and validated based on existing research results in the study area [40,41], ensuring regional applicability. Furthermore, to further validate the robustness of the weighting system, multiple sets of weight simulations were conducted. The results showed no significant changes in the spatial pattern of the resistance surface under different weight combinations, indicating that the weighting system possesses good robustness and can be reliably used for the subsequent construction of the Ecological Security Pattern.

2.6. Extraction of Ecological Corridors and Nodes

Ecological corridors serve as vital pathways connecting ecological sources, enabling energy flow and material exchange; they are indispensable ecological spaces that ensure the smooth operation of ecological processes and realization of ecological functions. In this study, the geometric centroid of each ecological source was designated as an ecological source point. The geometric centroid represents the average spatial position of the source patch, effectively avoiding uncertainties in source point selection caused by boundary fluctuations. It aligns with the description of the “core–periphery” structure in ecological processes and is well suited for application in the Minimum Cumulative Resistance (MCR) model, which was employed to compute the Minimum Cumulative Resistance distance between source points, thereby extracting ecological corridors. The intersections of these corridors are defined as ecological nodes. The calculation formula is as follows:

$$\mathrm{M}={\mathrm{f}}_{\mathrm{m}\mathrm{i}\mathrm{n}}{\sum }_{\mathrm{j}=\mathrm{n}}^{\mathrm{i}=\mathrm{m}}({\mathrm{D}}_{\mathrm{i}\mathrm{j}}\times {\mathrm{R}}_{\mathrm{i}})$$

(1)

where f is a function expressing the direct proportionality between M and variables D_ij and R_i, D_ij denotes the spatial distance traveled by an ecological source from j to i, and R_i represents the resistance value encountered by species when traversing landscape surface i.

The technical flowchart is shown in Figure 2.

3. Results

3.1. Temporal Dynamics of GEP

From 2000 to 2020, the total Gross Ecosystem Product (GEP) of the Yellow River-Fronting Region of Henan was 1044 billion yuan in 2000, 1075 billion yuan in 2010, and 1018 billion yuan in 2020, showing an overall trend of an initial increase followed by decline. As shown in

Table 3. GEP changes in the Yellow River-Fronting Region of Henan, 2000–2020.

Service Type	2000	Contribution	Ranking	2010	Contribution	Ranking	2020	Contribution	Ranking
	(Billion Yuan)			(Billion Yuan)			(Billion Yuan)
Supply services	1.67	0.0600%	7	2.84	0.2612%	8	1.85	0.1795%	8
Water retention	234.31	22.2038%	2	199.94	18.3863%	3	212.43	20.6084%	3
Water purification	0.05	0.0048%	9	0.05	0.0046%	10	0.05	0.0049%	10
Soil conservation	0.22	0.0208%	8	0.23	0.0212%	9	0.23	0.0223%	9
Carbon sequestration	42.81	4.0568%	6	42.11	3.8724%	7	40.56	3.9348%	6
Oxygen release	311.36	29.5052%	1	306.22	28.1597%	1	294.96	28.6149%	1
Air purification	166.62	15.7893%	4	169.24	15.5632%	4	160.48	15.5686%	4
Climate regulation	75.18	7.1242%	5	72.88	6.7020%	5	76.3	7.4021%	5
Biodiversity conservation	224.05	21.2315%	3	221.14	20.3358%	2	212.84	20.6482%	2
Ecotourism	No data	——	—	72.79	6.6937%	6	31.09	3.0161%	7

and Figure 3, regulating services constitute the core component of the GEP in this region, accounting for 99.94%, 93.04%, and 96.80% in 2000, 2010, and 2020, respectively.

Among regulating services, oxygen release, biodiversity maintenance, water retention, and air purification consistently rank as the top four individual ecosystem services in terms of their contribution to GEP, each valued at hundreds of billions of yuan. Climate regulation and carbon sequestration also make substantial contributions, with values ranging from 72.88 to 76.30 and 40.56 to 42.81 billion yuan, respectively. This dominance is primarily attributable to forest, grassland, shrubland, and water ecosystems [42,43]. In contrast, water purification and soil conservation services contribute relatively little and show smaller fluctuations. The value of supply services exhibited an initial increase followed by a decline, driven chiefly by the continuous reduction in cropland and grassland area. While food output still grew between 2000 and 2010 due to improved farming practices and technologies, from 2010 to 2020, the rise in yield per unit area could no longer offset the loss of cropland area, leading to a decline in food supply capacity [44]. Cultural service values reflect shifts in public demand. With rising living standards and economic development, the esthetic value provided by ecosystems has gained increasing attention, directly meeting spiritual and cultural needs while indirectly reinforcing conservation awareness [45]. In 2010, cultural services reached 72.79 billion yuan, but their value dropped sharply in 2020, a decline closely linked to the COVID-19 pandemic. Without the COVID-19 pandemic, ecotourism services would have shown a significant growth trend in line with China’s economic development. However, given that a relatively large proportion of the GEP comprises regulating services, such as water retention, air purification, oxygen release, and biodiversity, which have all been declining, the GEP in 2020 may have remained unchanged or increased slightly compared to 2010.

3.2. Spatial Variation Characteristics of Gross Ecosystem Product (GEP)

Figure 4 shows that GEP in the study area, which exhibits a typical west-high, east-low spatial pattern, with the eastern portion displaying a high–low–medium stratification. High-GEP zones are concentrated in mountainous areas with high elevation, dense vegetation, and favorable ecological conditions, notably the southern foothills of the Taihang Mountains in the north, and the Funiu Mountains in the west, and the Central Song Mountains. Additionally, scattered urban artificial surfaces south of the Yellow River also register low GEP values, and low-GEP areas are gradually expanding from the core region outwards, most prominently in the central districts of the Zhengzhou–Kaifeng–Luoyang urban agglomeration. This is likely linked to rapid urbanization-driven population concentration, urban sprawl, and an increase in artificial surface area. Through spatial autocorrelation analysis, the Moran’s I indexes for 2000, 2010, and 2020 were 0.785, 0.762, and 0.747, respectively, with p-values of 0 in all cases. The gradual decline in the Moran’s I index indicates an overall weakening of the spatial clustering effect of the GEP, suggesting that urban expansion has led to increasing landscape fragmentation. The GEP in the loess hilly region of western Henan gradually increases, indicating that ecological programs such as the Grain for Green Project have effectively restored the local ecological environment and enhanced ecosystem services.

3.3. Distribution Characteristics of Landscape Pattern Types

Morphological Spatial Pattern Analysis (MSPA) was applied to delineate the seven landscape pattern types in the Yellow River-Fronting Region of Henan, and their spatial distribution and area proportions are illustrated in Figure 5. In 2020, the combined area of the seven landscape pattern types reached 1.92 × 10⁴ km², accounting for approximately 28.66% of the total study area. Among them, core areas—large natural patches with favorable ecological conditions—represent 67% of the total landscape pattern area and show a high degree of overlap with high-GEP zones. Islets are small, highly fragmented patches that lack connectivity to their surroundings, occupying roughly 4% of the landscape pattern area. Perforations, edges, loops, branches, and bridges function primarily as transition zones between core areas and non-ecological land cover types, facilitating connectivity between the core and wider landscape [46]; each of these components constitutes no more than 10% of the total landscape pattern area.

3.4. Distribution Characteristics of Ecological Sources

After merging the 2020 high-GEP areas (Type-A sources) with the MSPA core areas (Type-B sources) and considering both patch area and connectivity, eleven patches larger than 100 km² were ultimately extracted as the principal ecological sources in the Yellow River-Fronting Region of Henan, whose geometric centroids were identified as ecological source points (Figure 6). The results show that the ecological sources exhibit the spatial pattern of “dense in the west and sparse in the east, clustered along mountains, and distributed around cities,” forming a gradient from mountains to plains. A single core was found in the central Songshan Mountains, while belt-like distributions occur in the Taihang Mountains and continuous patches dominate the western mountains. The west-dense and east-sparse pattern is jointly shaped by natural features and human impact. In particular, urban sprawl has eroded ecological space in the eastern plains. At the municipal level, Puyang and Kaifeng are devoid of ecological sources, mainly because their land-use is dominated by built-up land and cropland subject to intensive anthropogenic disturbance.

3.5. Distribution Characteristics of the Integrated Resistance Surface

The integrated resistance surface of the study area exhibits a stepwise spatial pattern characterized by “low in the west and high in the east, with clustered transitions” (Figure 7), displaying pronounced spatial heterogeneity. Resistance values range from 1 to 5.8, with an average of approximately 2.62, indicating that the overall resistance of the ecosystem is relatively low. In the northern and western mountainous areas, low-resistance surface bands are interlaced, and low-resistance zones (<2.5) account for 62.7% of the region. Dominated by forestland and the first-order tributaries of the Yellow River, these areas benefit from natural barriers created by topographic relief that effectively block anthropogenic disturbance. The eastern plains are generally low in resistance, yet the highest resistance values are also concentrated in the urban core areas of these plains, because the main urban districts are primarily covered by artificial surfaces that impede bio-energy flow. Particularly evident is the Zhengzhou–Kaifeng reach, which presents a “high–low–high” sandwich structure centered on the Yellow River’s main channel. Here, riparian wetlands form a low-resistance ecological trough, whereas a 5–10 km buffer on both banks experiences a sharp increase in resistance to over 4.5 under urban sprawl. The contiguous built-up cores of Zhengzhou City proper and Kaifeng City generate a unified high-resistance nucleus. Moreover, axial expansion along the Zhengzhou–Kaifeng Avenue distorts the resistance isopleths westward, indicating that the expansion of construction land directly raises ecological flow costs.

3.6. Ecological Corridors and Nodes

Based on the final ecological sources, integrated resistance surface, and internal course of the Yellow River, thirteen ecological corridors and seven ecological nodes were extracted to construct the overall Ecological Security Pattern of the study area (Figure 8). The pattern is characterized by “multiple cores driving, corridors linking, and sources in the west forming networks toward the east.” Three major ecological barriers—the western mountain belt, central Yellow River belt, and northern hill belt—anchor biodiversity conservation and water retention functions. The corridor system interlaces longitudinally, laterally, and diagonally: The east–west Yellow River main corridor links Yellow River wetlands from Sanmenxia to Kaifeng, creating a continuous ecological artery. The South-Taihang corridor traverses the region from northeast to southwest, connecting the hilly mountains of Anyang–Xinxiang–Jiaozuo–Jiyuan–Sanmenxia, and secondary longitudinal corridors extend along the Xiao–Funiu and Xiong’er–Waifang ranges. The Yellow River–Songshan corridor effectively couples mountain sources with riparian wetlands. Ecological nodes are mainly located at corridor intersections (e.g., western Luoyang, eastern Sanmenxia) and urban fringes (northern Zhengzhou, western Kaifeng), acting as stepping stones for species migration and resistance regulation points [47]. Low resistance in the western mountains and Yellow River wetlands ensures strong ecological flow, whereas abrupt resistance increases in built-up areas narrow or even sever corridors. The current pattern faces fragmentation risks from urban expansion; priority should thus be given to restoring corridor continuity in high-resistance zones and enhancing node resilience to safeguard regional ecological security.

4. Discussion

4.1. Rationality of GEP-Based Ecological Security Pattern Construction

Determining the most appropriate method for ESP construction has become a prominent research focus [48]; through ESP frameworks, the negative impacts of environmental degradation can be reduced by preventing and mitigating ecological risks. Accurate identification of ecological sources is the core step of ESP construction. Currently, ecological sources are mostly identified from the perspective of natural ecosystem element protection, whereas identification methods that explicitly consider human well-being is still scarce. Given that rapid urbanization continues to encroach upon cropland, both provisioning services and food security must be safeguarded, while cultural ecosystem services such as recreation are becoming increasingly important; these must therefore be incorporated into the assessment framework to fully capture the diverse values of the ecosystems [49], and the GEP can represent the total value of final goods and services that ecosystems provide to human society [50]. In this study, 11 indicators across three service categories—provisioning, regulating, and cultural services—were selected to spatialize and quantify GEP in the Yellow River-Fronting Region of Henan, revealing the region’s economic and ecological trajectories and the spatiotemporal evolution of the GEP in the twenty-first century. Type-A ecological sources extracted from GEP hotspots (>170,000 yuan ha⁻¹) (Figure 9) show a high degree of overlap with MSPA core areas (Figure 6) and also complement MSPA. In comparing Figure 9 with the core area (Type-B ecological sources) in Figure 5, it is evident that ecological sources near Mount Song and Kaifeng City have increased significantly. This is due to the dense distribution of natural scenic spots and ecological parks in these areas, as well as high tourism revenue, which highlights the value of ecotourism services and has led to their identification as ecological sources. This indicates that the GEP can effectively capture ecological function hotspots, avoiding the limitations of traditional methods that focus solely on natural elements (such as forest land and water bodies) while neglecting human welfare needs. This approach simultaneously meets the dual requirements of human activity and ecological conservation and offers practical significance for improving regional ecological protection, spatial identification methods and advancing natural–economic–social sustainability [51]. The dual-source identification method ensures that the various ecosystem services within ecological sources are balanced and comprehensive, demonstrating scientific rigor and rationality [52].

4.2. Impacts of LULC and Policies on GEP and ESP Patterns

The GEP in the Henan region along the Yellow River exhibits significant spatial heterogeneity, with its distribution demonstrating a certain correlation with land use/land change (LULC), shaped by the interplay of natural background conditions, urbanization processes, and policy interventions [53]. Land cover types with higher vegetation coverage possess higher GEP values, with wetlands and water bodies also registering high GEPs. As shown in Figure 10, forest land has the highest mean GEP value, followed by wetlands, water bodies, and grassland, while artificial surfaces and bare land have the lowest mean GEP values. This finding is highly consistent with similar studies, both domestically and internationally. For instance, Ouyang et al. [34] found in their GEP accounting for Qinghai Province that forest ecosystems contributed 47.73% of the total GEP, and although water bodies and wetlands are limited in area, their GEP per unit area is the highest. From a mechanistic perspective, forest land, grassland, and wetlands exhibit outstanding functions in water retention, carbon sequestration, and biodiversity maintenance through their complex vertical structures and underground root systems. High-coverage vegetation also provides significant oxygen release, air purification, and climate regulation functions. Furthermore, forest land, wetlands, or water bodies within or around some cities can provide high-value cultural tourism functions. Guo et al. [8] further confirmed in their study on the Loess Plateau that in arid and semi-arid regions, vegetation coverage is significantly positively correlated with ecosystem service supply capacity, and vegetation degradation directly leads to increased ecological risks. Conversely, artificial surfaces and bare land, due to the degradation or absence of ecological functions, locally create ecological resistance that impedes the flow of ecological connectivity [54].

Further analysis reveals that the forest land, grassland, and wetlands with high GEP in this study precisely constitute the core ecological sources and ecological corridor belts of the regional Ecological Security Pattern. This aligns with the conclusions of Chen et al. [25] regarding the Guanzhong Plain’s urban agglomeration and of Peng et al. [55] on rapidly urbanizing areas, namely that ecological sources are typically composed of areas with extremely important ecosystem service functions. High-GEP ecological patches are not only “supply stations” for ecosystem services but also “transfer stations” and “refueling stations” for species migration and energy flow, playing an irreplaceable “source” function in maintaining regional ecological network connectivity. The high GEP values of forest land, grassland, wetlands, and water bodies in the Henan region along the Yellow River indicate that they should be prioritized as first-level ecological sources in future Ecological Security Patterns.

Examining these findings within the policy framework of the “Henan Provincial Territorial Spatial Plan (2021–2035)” highlights their methodological significance and practical value. The plan explicitly proposes constructing an ecological spatial pattern characterized by “one belt, three screens, four corridors, and multiple green cores” and delineates the “three zones and three lines” as the fundamental baseline for territorial spatial development and protection. The ecological sources identified in this study demonstrate good spatial coupling with the ecological protection redlines delineated in the plan, ensuring the effective protection of core ecological spaces. The construction of the Yellow River Ecological Belt should prioritize the restoration and expansion of high-GEP wetlands along its banks. The delineation of urban development boundaries should be integrated with the regulation of low-GEP areas. Within urban development boundaries, green infrastructure construction should be strengthened to enhance the ecosystem service supply capacity of built-up areas, achieving synergy between urban development and ecological protection. Therefore, integrating GEP assessment with Ecological Security Pattern construction realizes a methodological innovation from “functional evaluation” to “spatial optimization.” Through its integration with LULC and territorial spatial planning policies, the practical value of the research findings is enhanced, providing a scientific basis for ecological protection and high-quality development in the Yellow River Basin.

4.3. Constructing a “Three-Core, Two-Belt, Two-Zone” Ecological-Protection Optimization Pattern

The key to constructing an Ecological Security Pattern lies in the identification and analysis of ecosystem services and their interactions with landscape patterns and ecological processes [56]. Based on the study area’s Ecological Security Pattern, the Yellow River-Fronting Region of Henan is delineated into a “Three-Core, Two-Belt, Two-Zone” ecological protection and optimization scheme (Figure 11). The “Three Cores” are the Western Mountain Group Core Ecological Source (A), Central Songshan Core Ecological Source (B), and Northern Taihang Mountain Core Ecological Source (C). The western mountain group hosts numerous ecological corridors with favorable internal connectivity; however, increasing resistance from urban expansion has resulted in corridor blockage or rupture between this group and the adjacent Taihang ecological barrier belt and central Songshan core. Priority should therefore be given to restoring ecological barrier points between the Xiao Mountains and Taihang Mountains and between the Xiong’er Mountains and Songshan Mountains. The “Two Belts” are the Taihang Mountain Ecological Barrier Belt (1) and Yellow River Wetland Ecological Corridor Belt (2), together forming a “<”-shaped ecological skeleton. The Taihang Mountain Ecological Barrier Belt extends along an elevation gradient of 400–1200 m and effectively intercepts wind–sand flux; nevertheless, expressway construction in the Anyang–Xinxiang section has created multiple ruptures, reducing ecological connectivity and posing corridor fracture risks with Core C. The Yellow River Wetland Ecological Corridor Belt currently connects the Taihang Ecological Barrier Belt and Songshan core ecological source, with the aims of restoring riparian ecosystems, conserving biodiversity, and simultaneously providing flood control, landscape, and recreational functions. Although recent ecological governance has achieved certain successes, riparian wetlands and floodplains along the Yellow River still suffer from overlapping industrial and agricultural pollution, solid waste encroachment on river channels, wetland shrinkage, and biodiversity decline; integrated “water–sediment–pollution–ecology” governance and strengthened basin-wide supervision and ecological compensation mechanisms are urgently required. The “Two Zones” are the Northern Yellow River Low-Resistance Zone (I) and Southern Yellow River Resistance-Interlaced Zone (II), which exhibit differentiated ecological processes. The northern agriculture–urban transition belt is dominated by cropland, where both MCR and GEP values are low, especially for water retention services, which are far below those in the Southern Yellow River Resistance-Interlaced Zone. A “farmland–forest–channel” composite system should be established, together with biological and soil-and-water retention facilities, to reduce runoff and enhance water retention. The southern urbanized zone (Luoyang–Zhengzhou–Kaifeng, etc.) has large resistance spans, moderate GEP, and intense heat island effects; space can be released through ventilation corridor construction and ecological land substitution. By reinforcing the functions of core source areas, restoring corridor connectivity, and implementing precise zoning regulation, regional ecosystem resilience can be enhanced, providing paradigm support for Ecological Security Pattern construction in the Yellow River Basin [57].

4.4. Limitations and Future Directions

Using market price, replacement cost, and conservation value methods, the GEP results reflect only regional and market conditions within the study period. In GEP accounting, each of these valuation assessment methods has inherent sensitivities and limitations. Market prices are susceptible to short-term fluctuations and fail to capture non-market ecosystem services, while replacement cost methods rely on strong technological assumptions that may overstate the substitutability of natural functions. Conservation value estimates, often derived from willingness-to-pay surveys, are influenced by socio-economic contexts and subjective preferences. These uncertainties highlight the need for cautious interpretation of GEP results and suggest that future research should incorporate cross-method validation or sensitivity analysis to enhance the robustness of ecosystem service valuations for policy support.

Because many indicators were selected and spatialized models such as InVEST were employed—supplemented by terrain, soil, climate, and statistical yearbook data beyond land use types—assessment results are more refined, and the total GEP is therefore higher than in previous studies [58,59]. Uncertainty in resistance weighting is inherent to ecological corridor modeling, as resistance values for land use types and weighting schemes for influencing factors are often derived from expert judgment rather than empirical data. Different weighting combinations may alter corridor configurations, although the relative resistance rankings among land cover types tend to be more influential than absolute values. Additionally, spatial resolution and subjective factor selection introduce further variability. To improve robustness, future studies should incorporate sensitivity analyses to test the stability of corridors under varying weight scenarios and, where possible, validate modeled pathways against field observations or species movement data. Despite these limitations, the ecological spatial network constructed in this study is significant as it helps prioritize protection and restoration in high-GEP areas and provides concrete guidance for ecological spatial planning and security conservation in the Yellow River-Fronting Region of Henan.

5. Conclusions

Taking the Yellow River-Fronting Region of Henan as the study area, this study quantified the monetary value of ecosystem services, identified high-value ecological areas through the GEP-MSPA dual framework, constructed an Ecological Security Pattern, and emphasized the necessity of incorporating natural ecology and socio-economic factors into regional ecological security planning. The main conclusions are as follows: (1) The GEP exhibited significant spatiotemporal variation. Specifically, the total GEP from 2000 to 2020 followed a “first rise, then fall” trend, with regulating services being the dominant contributor, and the spatial pattern showed “high in the northwest, low in the southeast.” (2) The Ecological Security Pattern structure is clear. By integrating GEP hotspots with MSPA core areas, this study identified 11 ecological sources that cover 67% of the landscape-pattern type area, forming a gradient pattern of “dense in the west and sparse in the east, clustered along mountains.” Using the MCR model, 13 key ecological corridors and 7 ecological nodes were extracted; corridors are dense in the western mountains but fragmented in the eastern plains, indicating insufficient spatial balance.