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Article

Land Use Modeling and Predicted Ecosystem Service Value Under Different Development Scenarios: A Case Study of the Upper–Middle Yellow River Basin, China

by
Mingwei Ma
1,
Yuhuai He
1,
Yanwei Sun
1,
Huijuan Cui
2,* and
Hongfei Zang
1
1
College of Water Resources, North China University of Water Resources and Electric Power, Zhengzhou 450046, China
2
Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
*
Author to whom correspondence should be addressed.
Land 2025, 14(1), 115; https://doi.org/10.3390/land14010115
Submission received: 8 December 2024 / Revised: 2 January 2025 / Accepted: 7 January 2025 / Published: 8 January 2025
Browse Figures
Figure 1
<p>Geographic location of the upper and middle reaches of the Yellow River.</p> "> Figure 2
<p>Flow chart of the proposed methodology.</p> "> Figure 3
<p>Suitability map for different land types.</p> "> Figure 4
<p>Spatial and temporal distribution of land use types in the upper and middle reaches of the Yellow River in 2005, 2010, 2015, and 2020.</p> "> Figure 5
<p>Quantitative changes in land type transfer in the upper and middle reaches of the Yellow River, 2000–2020. <b>Note:</b> Different colored trajectory lines indicate the direction of flow of a site type during a specific period of time, and the thickness of the trajectory line represents the magnitude of the transformation.</p> "> Figure 6
<p>Spatial pattern of land use in the upper and middle reaches of the Yellow River in 2035 under different scenarios.</p> "> Figure 7
<p>Spatial distribution of the <span class="html-italic">ESV</span> in the upper and middle reaches of the Yellow River under multi-scenario simulation.</p> "> Figure 8
<p>Sensitivity coefficients of ecosystem service values for each category under different scenarios.</p> "> Figure 9
<p><span class="html-italic">ESV</span> based on land use type for different scenarios.</p> "> Figure A1
<p>Distribution of actual and modeled land use in the upper and middle reaches of the Yellow River in 2015 and 2020.</p> ">
Versions Notes

Abstract

:
Exploring the future ecosystem service value (ESV) of the upper–middle Yellow River Basin is of great significance to enhancing its ecological security and capacity. This is in response to the strategy for the ecological protection and high-quality development of the Yellow River Basin. In this study, the land use change from 2000 to 2020 was analyzed quantitatively. The land use pattern in 2035 was predicted using Cellular Automata and Markov models under business as usual (BAU), ecological protection (EPS), and high urbanization (HUS) scenarios. The future ESV was estimated and the impact of land use changes on the regional ESV was identified. The results indicate that the study area experienced a reduction (~12,139 km2) in cultivation and an expansion (~10,597 km2) of built-up land from 2000 to 2020. In 2035, under the BAU scenario, the area of construction land and water would expand by 24.52% and 11.51%, respectively, while the area of grassland and unused land would decrease by 18,520 km2 and 2770 km2, respectively. Under the EPS scenario, the area of forests, grasslands, and water would increase by 16.57%, 10.59%, and 4%, respectively. Under three different scenarios, the regional ESVs are estimated at from CNY 2475 to 2710 billion, while grasslands contribute the largest part accounting for from 57.98% to 59.21%. These findings could help to guide land development and protection through regional ecological construction.

1. Introduction

Ecosystem services, derived either directly or indirectly from the structural, functional, and processual characteristics of ecosystems, represent essential products and services that sustain human life [1]. Maintaining healthy ecosystem services is critical to regional ecological security. Unfortunately, global environmental changes, driven by climate dynamics and land use/cover changes (LUCC), natural ecosystems are deteriorating globally, rapidly reducing biodiversity and ecosystem services [2,3]. Therefore, assessing the value of ecosystem services (ESVs) offers a robust scientific foundation for designing ecological protection strategies, managing land resources, and promoting sustainable development. LUCC influences ESVs by affecting the structure and function of regional ecosystems [4]. Recently, scholars have made significant progress in studying land use change and the corresponding spatial response of ESVs. Akhtar et al. [5] investigated the spatial and temporal variability of ESVs in Pakistan’s drylands, identifying reductions in cultivated and grassland areas as primary factors for declining ESVs. Egoh et al. [6] have quantified the required grassland area to protect ecosystem services from grassland degradation in Africa. Kindu et al. [7] clarified nutrient cycling, erosion control, climate regulation, and water purification as major contributors to the overall ESVs in Ethiopian highlands. Research indicates that strengthening the protection of ecological lands including grasslands, forests, wetlands, water bodies, and agricultural lands is conducive to increasing the total ESV [8,9,10,11].
Land use changes can be analyzed by models such as PLUS [12,13], CLUE-S [14,15,16,17], logistic regression [18,19,20], and artificial neural networks [21,22]. Among these models, CLUE-S and PLUS are spatial models that can reflect dynamic changes in land but have limitations in quantity or spatial expansion. Logistic models and artificial neural networks are empirical statistical models that can handle complex operations but have limitations in the evolution of spatial complexity. On the other hand, the CA–Markov model combines the spatial simulation capabilities of cellular automata (CA) with the predictive power of Markov chains, enabling it to simulate and forecast future land use spatial distribution patterns with high accuracy. This approach has gained increasing prominence in land use simulation and predictive studies [8,11]. Using the above model, Kubiszewski et al. [23] predicted that the ESV may differ by USD 81 trillion annually by 2050 under different scenarios.
Previous studies have analyzed land use and ecosystem service value (ESV) in the Yellow River Basin [24,25]. However, discussions on future changes in land use and the ESV within the basin remain relatively limited. Furthermore, preceding studies have accorded comparatively limited attention to the ecological patterns that may emerge under prospective high-quality development strategies. Meanwhile, the Yellow River basin occupies a pivotal position in China’s national security and ecological layout. Especially in recent years, President Xi has proposed a strategy for ecological conservation and high-quality development in the Yellow River basin which has led to increased attention on ecological security and development in this region. This was in response to national policies and to gain a deeper understanding of the impact of the future development direction of the Yellow River basin on its ecosystem. Therefore, this study aims to evaluate the impact of future land use changes on the ESV in the upper and middle reaches of the Yellow River under three representative scenarios (business-as-usual, ecological protection, and high urbanization). The CA–Markov model and the Multi-Criteria Evaluation (MCE) method are combined with the equivalent factor method. The results may provide theoretical references for analyzing changes in ecosystem service function in the upper and middle reaches of the Yellow River basin, as well as for planning future ecological security construction of land resources in the study area.
The remainder of this article is structured as follows: Section 2 provides an overview of the materials and methods employed in this study while Section 3 presents the results, primarily through illustrative representations. Section 4 offers a detailed discussion of the findings, and, finally, Section 5 summarizes the main conclusions and their broader implications.

2. Materials and Methods

2.1. Study Area and Data

The Yellow River Basin originates from the Tibetan Plateau, traverses the Inner Mongolian Plateau, and extends to the Loess Plateau, encompassing three major geomorphological regions, each distinguished by unique geographical and geomorphological characteristics. Among these, the Loess Plateau holds particular importance as a critical water conservation zone and a major sediment source within the Yellow River Basin. However, soil erosion on the Loess Plateau poses a significant threat to the ecological security of both the basin and the nation. To address this challenge, the Chinese government has implemented a policy of returning farmland to forests, aiming to restore vegetation, mitigate soil erosion, and promote ecological balance and sustainable development within the region. The Yellow River Basin serves as a crucial ecological barrier, a key agricultural production area, and an economic hub in China; the primary agricultural products cultivated in the watershed are wheat, corn, and soybeans. This paper focuses on the upper and middle reaches of the river, from its source to the Huayuankou hydrological station, covering a distance of approximately 4700 km and accounting for 97% of the basin’s total area (Figure 1). The basin features a complex topography, characterized by a west-to-east gradient with mountainous terrain and diverse geomorphological formations. The region reaches of the Yellow River Basin are situated at the confluence of three major climatic zones in China, exhibiting considerable internal climatic variations. The basin’s climate exhibits a transition from arid to semi-arid to semi-humid conditions from northwest to southeast. The climatic diversity of the basin is predominantly shaped by the Himalayas and the Tibetan Plateau, resulting in lower temperatures at higher altitudes and warmer, drier climates at lower altitudes [26]. In recent years, the accelerated urbanization process in China has led to dramatic changes in the land cover of the upper and middle reaches of the Yellow River basin, resulting in a series of ecological and environmental problems.
This study utilized various datasets, including land use, food prices, a digital elevation model (DEM), and slope and road data, as summarized in Table 1. The land use types within the upper and middle reaches of the Yellow River were categorized into six primary classes: cultivated land, forest land, grassland, water bodies, construction land, and unused land [27]. This classification was based on the first-level criteria of the LUCC classification system in ArcGIS. To incorporate food prices into the valuation of ecosystem services, all datasets for the study area were resampled to a spatial resolution of 1 km. The spatial coordinate system employed was WGS_1984_UTM_Zone_48N.

2.2. Methodology

The proposed methodology has three steps (see Figure 2). Firstly, historical land use changes within the study area are systematically analyzed. Then, the CA–Markov model is employed to simulate future land use patterns under different scenarios, and the corresponding ESVs are estimated.

2.2.1. CA Model

Cellular automata (CA) is a dynamic modeling approach designed to simulate discrete spatial units. It is a class of tools based on the microscopic rules of the system’s evolution and is suitable for simulating complex systems [28]. Land use changes are represented through the interactions among individual spatial units. The formula is expressed as follows:
S t + 1 = f S t , N
In this context, S represents a finite and discrete set of states for a tuple. The variables t and t + 1 represent different points in time. The variable f represents a transformation rule for the states of a localized spatial tuple, while N represents the neighborhood of a tuple.

2.2.2. Markov Model

The Markov model is a mathematical tool used for predicting spatial probability. It is based on a system of Markov stochastic processes and is commonly used to predict the likelihood of future geographic events [29]. Equation (2) states that the Markov transfer state matrix comprises the proportion of areas transformed between feature types at time t and land types at time (t + 1). This transformation process is characterized by its non-sequential and discrete nature.
S t + 1 = P i j × S t
P i j = P 11 P 12 ⋯ P 1 n P 21 P 22 ⋯ P 2 n ⋮ ⋮ ⋮ ⋮ P n 1 P n 2 ⋯ P n n
The system’s state at moments t and t + 1 are denoted by St and St+1, respectively. The transfer probability is denoted by P, while n represents the various land use types. The initial and final land types at the beginning and end of the study period are denoted by i and j, respectively. Each element in the matrix must be non-negative, with the sum of all elements in each row equaling one.

2.2.3. CA–Markov Models

The CA–Markov model effectively predicts the dynamic evolution of both the quantity and spatial distribution of land-use types. It achieves this by integrating the strengths of the Cellular Automata (CA) model, which excels in simulating the spatial dynamics of complex systems, with the predictive capabilities of the Markov model for analyzing long-term sequences. The multi-criteria evaluation (MCE) is a method for comprehensively examining the impact of factors. In this study, it was used to produce land suitability mapping with a weighted linear combination and Boolean superposition method to make optimal choices based on objectives [30]. The factors are classified into two types: limiting factors and influencing factors. Limiting factors refer to land use types prohibited from converting to other types, represented as a Boolean mapping, i.e., a binary image with a background of 1 and a land type of 0. The influencing factors can enhance or reduce the suitability of land use type in state transformation. The values of the minimum and maximum suitable control points are determined by selecting the function form of its influence mode. Then the weight size in the Analytic Hierarchy Process (AHP) is determined to obtain the suitability image of the standardized data from 0 to 255.
Based on the topography and land use resources in the upper and middle reaches of the Yellow River, the watershed is identified as the limiting factor. Natural, transportation, socio–economic, and other influencing factors, i.e., setting elevation, slope, and distance from the road, are integrated as limiting conditions. The suitability atlas was obtained by the ‘Decision Wizard’ module in IDRISI Selva 17 (see Figure 3). A standard 5 × 5 CA filter was constructed based on this, with the number of iterations set to 10, to simulate and predict future land-use changes in the study area. The probability matrix of land-use transfer for 2005–2010 and 2010–2015 was constructed to simulate land use pattern for 2015 and 2020, which was compared with the actual land use for accuracy evaluation.

2.2.4. Accuracy Verification

This study employed the Kappa coefficient as an evaluation metric to assess the simulation accuracy of the CA–Markov model. The Kappa coefficient is widely utilized in land-use studies and quantifies classification accuracy by measuring the consistency between predicted outcomes and observed values in land-use patterns [31]. The accuracy assessment criteria are presented in Table 2 [8]. The formula for calculating the Kappa coefficient is as follows:
Kappa = P 0 − P e 1 − P e
In the given formula, P0 represents the probability of consistent prediction, while Pe represents the probability of correct prediction under stochastic simulation.

2.2.5. Scenario Settings

Recognizing the strong connection between land use, social development, and human activities, as well as the temporal variability in land use patterns, this study establishes three scenarios for future land use evolution: business-as-usual, ecological protection, and high urbanization. Considering previous research on limiting the conversion of different land types by modifying the Markov transfer probability matrix [32], this study predicts the spatial distribution of land use patterns in the upper and middle reaches of the Yellow River basin in 2035 corresponding to these scenarios.
(1) Business as usual (BAU). This scenario aims to reflect future land patterns that follow current patterns without altering the rates of change and transition rules between land use types [33,34]. It serves as a basis for analyzing other evolutionary scenarios. The probabilistic matrices of land use transfer and transfer rates evolve naturally under the existing 2005–2020 phase without any constraints.
(2) Ecological Protection Scenarios (EPSs). This scenario emphasizes controlling the expansion of construction land while prioritizing the preservation of woodland and grassland to enhance the ecological functionality of the watershed and safeguard the ecosystem [35,36]. The transfer rate from controlled forest land, grassland, and unused land to arable land and construction land will decrease by 60% compared to 2005–2020. Additionally, the transfer rate from arable land to forest land will increase by 50%.
(3) High Urbanization Scenario (HUS). The scenario considers the development needs of cities in the basin to achieve greater economic benefits. Furthermore, this approach is consistent with the high-quality development strategy of the Yellow River Basin [37]. The rapid development of urban agglomerations can directly influence changes in regional land use patterns. The specific settings include a 30% increase in the transfer rate of unused land, arable land, forest land, and grassland to construction land and a 20% reduction in the transfer rate of construction land to land categories other than arable land.

2.2.6. Estimating the Value of Ecosystem Services

The value of ecosystem services is calculated by the products of ‘China’s Terrestrial Ecosystem Service Value Equivalent per Unit Area’ developed by Xie et al. [38]. However, the equivalent factor table established by Xie et al. did not include construction land in the study. In previous research, different scholars have proposed various methods to determine the ESV for construction land. For example, Yao et al. [39]. pointed out that construction land only has cultural value and set the cultural value of construction land as 2.176 times that of cultivated land. In this study, we refer to the research by Han et al. [40,41,42,43] and assign an ESV of 0 to construction land. The changes in the ESV under different scenarios were analyzed, with calculations performed using Equations (5) and (6).
E S V = ∑ i = 1 n A i × V C i
V C i = ∑ j = 1 k E C j × E a
Here, ESV denotes the ecosystem service value measured in Yuan/year, i represents the land use type, and j represents the ecosystem service type; VCi is the value of ecosystem services per unit area of land use type i in Yuan hm−2 a−1, and Ai is the area of land use type i in hm2. ECj represents the value equivalent of the jth ecosystem service for a specific land use type, k indicates the total number of ecosystem service types, and Ea refers to the economic value of one unit of ecosystem services, measured in Yuan hm−2 a−1.
Studies have shown that the economic value of an ecosystem service unit is equivalent to a seventh of the market value of the national average food production in that year [1]. To prevent fluctuations in the equivalent of the ESV per unit area in the study area caused by varying grain prices in different years, we unified the reference statistical yearbook to include the total output of a single type of grain crop (wheat, corn, soybean) and the average purchase price of grain crops (wheat, corn, soybean), which are the three main types of grain crops. In 2022, the ecosystem service equivalent coefficients were calculated for each province in the upper and middle reaches of the Yellow River using Equation (7).
E a = 1 7 ∑ i = 1 n m i p i q i M
where Ea represents the equivalent value of ecosystem services per unit area in Yuan hm−2 a−1, i denotes the type of food crop, mi is the average price of the ith food crop in the study area in Yuan kg−1, pi represents the yield of the ith food crop in kg hm−2, qi is the area planted with the ith food crop in hm2, and M is the total area under food crops in hm2.
The average value was taken as the equivalent of ecosystem services value per unit area in the study area, with each province using the Yellow River as a benchmark. The results indicated that the ecosystem service value of one standard equivalent factor in the study area was about 2029 Yuan hm−2 a−1. Then, based on ‘China’s terrestrial ecosystem unit area ecosystem service value equivalent table [44], we obtained the ecosystem service value equivalent per unit area for each land type in the study area (Table 3).

2.2.7. ESV Sensitivity Analysis

To assess the reliability of ESV evaluation results, this study utilized the concept of elasticity coefficient from economics to calculate ESV’s coefficient of sensitivity (CS). This can help to determine the degree of dependence of ESV changes over time on the coefficient of ecosystem service value [45]. The value coefficients for each category were adjusted by 50% to measure the change in the total ESV using the following formula:
C S = ( E S V j − E S V i ) / E S V i ( V C j k − V C i k ) / V C i k
where ESVi and ESVj represent the initial and adjusted ecosystem service values, respectively, and VCik and VCjk represent the initial and adjusted ecosystem service value coefficients, respectively. If CS is greater than 1, it indicates that the ESV is elastic to VC. It means that a 1% change in the independent variable will cause a greater than 1% change in the dependent variable, which is less accurate and credible. Conversely, if CS is less than 1, the ESV is inelastic to VC, and the findings are more plausible [45].

3. Results

3.1. Spatial and Temporal Variations of LUCC

The land use in the upper and middle reaches of the Yellow River is mainly dominated by grassland and cropland (see Figure 4 and Table 4), where grassland accounts for approximately 49% of the study area, followed by cropland and forest land, which account for approximately 25% and 14%, respectively. The majority of cultivated and construction land is located in the eastern region with lower terrain, while grassland and forestland are more widely distributed throughout the area. Between 2000 and 2020, the study area experienced an expansion of construction land and decreased cultivated land, where construction land increased by 73.5%, while cultivated land decreased by 12,139 km2. Specifically, the construction land area increased from 14,408 km2 in 2000 to 25,005 km2 in 2020, which is closely related to the national development of urbanization. Furthermore, the expansion of forest and grassland areas has been minimal, with only a 3.59% and 1.36% increase, respectively. Additionally, there has been no significant change in the water area.
Figure 5 and Table 5 show the quantitative changes in land use types in the study area from 2000 to 2020. The area underwent mutual transformation between 2000 and 2020 was about 284,547 km2. The transfer was more significant (280,504 km2) during 2010–2020, indicating that this was the main period of land transformation. From 2000 to 2010, the largest outflow was from arable land (6056 km2) and grassland to other categories, while the largest inflow was from forest land and grassland, which was 1509 km2 and 2670 km2, respectively. It might be related to the policy of returning arable land to forest and grassland at the Loess Plateau [46]. From 2010 to 2020, the area of mutual transfer was 280,504 km2, characterized by the reorganization of the internal structure of arable land and grassland. During this period, the inflow of construction land was 19,921 km2, almost twice the outflow, indicating a significant acceleration of urbanization. The above results suggest that cultivated land and grassland provide the foundation for promoting urbanization and industrialization. Additionally, due to the implementation of the policy of returning farmland to forests and grasslands, the transfer of forest land and grassland is also a significant factor resulting in the transfer of cultivated land.

3.2. Projections of Future Land Use Change

This study utilized 2020 as the base period image and employed the CA–Markov model to project the land use image in 2035 under three representative scenarios (BAU, EPS, and HUS) for the upper and middle reaches of the Yellow River (Figure 6). The reliability of the predictions is verified by simulating land use in 2015 and 2020 showing Kappa coefficients of 0.897 and 0.913 (see Appendix A). Table 6 shows the quantitative area of each land use category under the three proposed scenarios. Under the BAU scenario, which follows the previous land evolution pattern, there is a significant increase in the area of built-up land (24.52%) and water (11.51%) in the study area in 2035 compared to 2020, while grassland and unused land decrease by 18,520 km2 and 2770 km2, respectively. Arable land and forest land show a slight increase of 4.75% and 4.64%, respectively. Under the EPS, the expansion of construction land would be effectively controlled and the area of arable land would be significantly reduced in response to the national ecological security policy. The construction land area shows a reduction of 288 km2 in comparison to 2020, and the forest, grassland, and water areas would increase from BAU, with 129,636 km2, 401,515 km2, and 15,235 km2, respectively. Under HUS, the study area tends to experience an increase of 11,963 km2 and 8495 km2 in construction land and cultivated land, while a decrease of 359,055 km2 and 59,091 km2 in grassland and unused land, compared to 2020.

3.3. Projections of Ecosystem Service Value Under Multiple Scenarios

The value of land ecosystem services is primarily determined by the area of each land use type, which shows the EPS has the highest ESV (CNY 2710 billion) in the study area in 2035, followed by the BAU scenario (CNY 2501 billion) and the HUS (CNY 2475 billion) (Table 7). Under the BAU scenario, there is an increase in built-up land area and a decrease in grassland area by 2035 due to the continuation of previous land transfer trends, which is detrimental to the ESV; the total ESV has decreased from 2020, which can be mainly attributed to the decline in the grasslands ESV (CNY ~739.99 billion). On the other hand, the increase in the regional ESV under EPS is mainly due to the protection of forest and grassland, constraints on expanding construction land, and strengthened protection of ecological land. The ESV under HUS is CNY 25,589 million lower than that of the BAU scenario, which is due to its high rate of urbanization.
To analyze the spatial variations in the ESV under multiple scenarios, due to the large size of the study area, with reference to previous related research [10], we created a 20 km × 20 km square grid to calculate the ESV by counting the land area of each land-use type in each grid. As seen from Figure 7, except for the scenarios related to ecological protection, the total economic value of ecosystem services shows a downward trend, which is consistent with decreased projections in the ESV in Figure 6. The spatial distribution of ESV in 2035 under the three scenarios seems to be consistently high in the west and low in the east, which is in line with the elevation, topography, and geomorphology. The distribution of the high-value area is consistent with the distribution of waters, while the second high-value area is mainly distributed in the west and south–central parts of the upper and middle reaches of the Yellow River, which is consistent with the distribution of forest and grassland. Furthermore, more than half of the low-value zones in BAU and HUS have upgraded to median and second-lowest zones under the EPS due to the increase in forest and water areas.

3.4. Impacts of Land-Use Change on ESV

Furthermore, the sensitivity coefficients of all land use types are examined to be less than one (Figure 8), which indicates that the results are credible. It suggests that the ESV for each category in the study area is relatively stable and inelastic compared to the adjusted coefficients. Grassland, the dominant land use type in the study area, showed the highest sensitivity for all three scenarios in 2035, suggesting that changes in the ESV of grassland play a decisive role in the total ESV changes in the study area. The decrease in the ESV resulting from changes in grassland area offset the increase in the ESV of other land use types under the BAU scenario, resulting in a decrease in the regional ESV compared to 2020. The significant decrease in grassland area is also the main reason for the reduction in the total ESV under the HUS.
To sum up, the contribution order of land use types to the total value of the ESV is as follows (see Figure 9): grassland (58.0~60.8%), forest (19.7~22.3%), water (13.4~14.9%), cultivated land (4.1~6.3%), and unused land (0.1%). The impact of changes in the ESV on unused land under the three scenarios is relatively insignificant.

4. Discussion

Between 2000 and 2020, the upper and middle reaches of the Yellow River witnessed a significant urbanization process, with a marked expansion of construction land. This urban growth has not come without consequences for the regional ecosystem, which is now facing increasing pressure from the conversion of land for urban and infrastructure development [47,48]. In light of this, future development strategies must carefully balance the needs of urbanization with the imperative to protect ecosystems with high ecological value. It is critical to propose a sustainable development pathway in this region. One of the most critical findings of this study is that grasslands and forests contribute most significantly to the ecosystem service value (ESV) of the region. To ensure that future development aligns with ecological preservation, it is recommended to adopt the principle of “lucid waters and lush mountains are invaluable assets”—a concept that underscores the value of environmental conservation alongside economic growth. Specifically, strategies should focus on restoring grasslands, which provide some of the highest ecosystem services, by continuing efforts to return ploughland to grasslands and forests. This ecological restoration can help to reinforce the integrity of the ecosystem, providing vital services such as carbon sequestration, water retention, and biodiversity support. Further, promoting forest coverage is essential for strengthening the region’s ecological security barriers [49]. The expansion of forests can help mitigate soil erosion, improve air quality, and enhance biodiversity, all while contributing to the overall health of the watershed. These efforts will provide the foundation for building a resilient, sustainable ecosystem that can support both the region’s urbanization and ecological goals.
This study provides valuable insights into the changes in land use and the ESV over time using 1 km resolution land use raster data. However, as the Yellow River basin is characterized by its ecological complexity and diversity [50,51], future research could delve deeper into specific ecosystems, such as grasslands and woodlands, and the secondary land classes they support. This would allow for a more detailed understanding of the different ecological functions provided by these land types. Additionally, future studies could explore the relationship between ecosystem services and human well-being in greater detail. For example, examining the trade-offs and synergies between provisioning services (such as food and water) and regulating services (such as flood control and climate regulation) will provide a more comprehensive picture of how land use changes impact both the environment and local communities. Furthermore, the use of higher-resolution land-use data would allow for a finer-scale analysis of the ESV supply and demand, potentially at the level of counties or even smaller units within the watershed. This could provide more targeted and actionable insights for local-scale ecosystem management and planning.

5. Conclusions

Focusing on the upper and middle reaches of the Yellow River basin, this study uses the CA–Markov model to predict land use in 2035 under different development scenarios. The regional ecosystem services value (ESV) in 2035 was calculated and analyzed through the equivalent factor approach. The key findings and conclusions are summarized as follows.
(1) In the upper and middle reaches of the Yellow River basin, grassland and cultivated land are the predominant land use types, accounting for approximately 49% and 25% of the area, respectively. Between 2000 and 2020, land use changes revealed a reduction in cultivated land by 12,139 km2 and an expansion of construction land by 10,597 km2 (73.5%). From 2000 to 2010, cultivated land was transferred to forest or grassland due to the policy of returning farmland to forest and grassland on the Loess Plateau. However, between 2010 and 2020, a significant acceleration of urbanization accelerated land use change, and the expanded area of the construction land was almost twice as high as its loss.
(2) In 2035, it is predicted that, under the BAU scenario, the area of construction land and water will expand by 24.52% and 11.51%, respectively, while the area of grassland and unused land will decrease by 18,520 km2 and 2770 km2, respectively. Under the ecological protection scenario, the area of ecological land, such as forests, grasslands, and water, would increase by 16.57%, 10.59%, and 4%, respectively. The regional ecological conditions tend to improve progressively, and the expansion of construction land could be effectively controlled with a reduction of 20.62% compared to the BAU scenario. The high urbanization scenario would expect the largest increase in construction land and a reduction in both grassland and unused land.
(3) Land-use change altering the areas and locations of ecosystems affects the value of ecosystem services. In terms of the total amount, the regional ecosystem service values of the BAU scenario, EPS, and HUS in 2035 were estimated as CNY 2,500,714 million, CNY 2,709,727 million, and CNY 2,475,125 million, respectively. These, under BAU and HUS, are lower than those of 2020. The spatial distribution of ecosystem service values in the upper and middle reaches of the Yellow River basin shows a pattern of high values in the west and low values in the east. The low-value areas increase significantly under the BAU scenario and the HUS, which may be caused by the increase in construction land and decrease in forest and water areas.
In conclusion, this study underscores the importance of integrating ecological considerations into land use planning, particularly in rapidly urbanizing areas like the upper and middle reaches of the Yellow River. By fostering a balance between urban development and ecosystem conservation, the region can achieve sustainable development that benefits both people and the environment. Moreover, they serve as a scientific foundation for responding to the high-quality development strategy of the Yellow River Basin.

Author Contributions

Conceptualization, H.C.; formal analysis, Y.S.; data curation, H.Z.; funding acquisition, H.C. and M.M.; methodology, M.M.; resources, H.Z.; writing—original draft, Y.H.; writing—review and editing, M.M. All authors have read and agreed to the published version of the manuscript.

Funding

This study is supported by the Innovation Project of Ecological Environmental Technology, Chinese Research Academy of Environmental Sciences (2024-XDZX-01-0701), National Natural Science Foundation of China (41701022).

Data Availability Statement

The data presented in this study are available upon request from the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A. Prediction Accuracy Test

This study simulated the land use distribution patterns in the upper and middle reaches of the Yellow River Basin for 2015 and 2020 using the CA–Markov model. The simulation period was based on the spatial distribution of land use, and transfer probability matrices were created for the study area in 2005–2010 and 2010–2015 using the Markov module of the IDRISI Selva 17 software. The suitability atlas of each cover type was produced according to the Decision Wizard module. The simulated land use distribution patterns were compared with the actual ones (see Figure A1). Figure A1 shows that the spatial distribution of land use in the two periods obtained from the simulation is consistent with the actual years. The Kappa coefficients of the simulation for 2015 and 2020 are 0.897 and 0.913 respectively, indicating high simulation accuracy for the change of land use in the upper and middle reaches of the Yellow River. The model realistically reflects the state of the change of land use in the area and can be used to predict the future spatial distribution of land use with certain reliability.
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Figure A1. Distribution of actual and modeled land use in the upper and middle reaches of the Yellow River in 2015 and 2020.
Figure A1. Distribution of actual and modeled land use in the upper and middle reaches of the Yellow River in 2015 and 2020.
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References

  1. Xie, G.; Lu, C.; Leng, Y. Valuation of ecological assets on the Tibetan Plateau. J. Nat. Resour. 2023, 18, 189–196. [Google Scholar]
  2. Yang, D.; Zhu, C.; Li, J.; Li, Y.; Zhang, X.; Yang, C.; Chu, S. Exploring the supply and demand imbalance of carbon and carbon-related ecosystem services for dual carbon goal ecological management in the Huaihe River Ecological Economic Belt. Sci. Total Environ. 2024, 912, 169169. [Google Scholar] [CrossRef]
  3. Yu, S.; Zhou, W.; Wang, J. Impact of urbanization on regional ecosystem services---a case study in Guangdong-Hong Kong-Macao Greater Bay Area. Ecol. Indic. 2024, 159, 111633. [Google Scholar]
  4. Mooney, H.A.; Duraiappah, A.; Lariqauderie, A. Evolution of natural and social science interactions in global change research programs. Proc. Natl. Acad. Sci. USA 2013, 110, 3665–3672. [Google Scholar] [CrossRef]
  5. Akhtar, M.; Zhao, Y.; Gao, G.; Gulzar, Q.; Hussain, A. Assessment of spatiotemporal variations of ecosystem service values and hotspots in a dryland: A case-study in Pakistan. Land Degrad. Dev. 2022, 33, 1383–1397. [Google Scholar] [CrossRef]
  6. Egoh, B.N.; Revers, B.; Rouget, M.; Richardson, D.M. Identifying priority areas for ecosystem service management in South African grasslands. J. Environ. Manag. 2011, 92, 1642–1650. [Google Scholar] [CrossRef]
  7. Kindu, M.; Schneider, T.; Teketay, D.; Knoke, T. Changes of ecosystem service values in response to land use/land cover dynamics in Munessa–Shashemene landscape of the Ethiopian highlands. Sci. Total. Environ. 2016, 547, 137–147. [Google Scholar] [CrossRef]
  8. Beroho, M.; Briak, H.; Cherif, E. Future Scenarios of Land Use/Land Cover (LULC) Based on a CA-Markov Simulation Model: Case of a Mediterranean Watershed in Morocco. Remote Sens. 2023, 15, 1162. [Google Scholar] [CrossRef]
  9. Mamat, A.; Halik, Ü.; Rouzi, A. Variations of ecosystem service value in response to land-use change in the Kashgar region, northwest China. Sustainability 2018, 10, 200. [Google Scholar] [CrossRef]
  10. Qu, Y.; Ding, J.; Bao, Q. Valuation of ecosystem services in Xinjiang under multi-scenario modeling. Environ. Sci. Technol. 2023, 46, 195–204. [Google Scholar]
  11. Xu, X.; Liu, H.; Lin, Z. Scenario analysis of coastal land use change in Jiangsu based on CA-MARKOV model. Res. Soil Water Conserv. 2017, 24, 213–218. [Google Scholar]
  12. Shawky, M.; Mohammed, A.B.; Talal, A.A. Monitoring land use and land cover changes in the mountainous cities of Oman using GIS and CA-Markov modelling techniques. Land Use Policy 2020, 91, 104414. [Google Scholar]
  13. Wang, Z.; Li, X.; Mao, Y.; Li, L.; Wang, X.; Lin, Q. Dynamic simulation of land use change and assessment of carbon storage based on climate change scenarios at the city level: A case study of Bortala, China. Ecol. Indic. 2022, 134, 108499. [Google Scholar] [CrossRef]
  14. Yu, Z.; Zhao, M.; Gao, Y. Multi-scenario Simulation and Prediction of Land Use in Huaibei City Based on CLUE-S and PLUS Models. Appl. Sci. 2023, 13, 7142. [Google Scholar] [CrossRef]
  15. Stanley, C.O.L.O.; Lewis, S. Modelling of land-use/cover change trajectories in a transboundary catchment of the Sio-Malaba-Malakisi Region in East Africa using the CLUE-s model. Ecol. Model. 2023, 476, 110256. [Google Scholar]
  16. Qin, J.; Ye, H.; Lin, K. Assessment of water-related ecosystem services based on multi-scenario land use changes: Focusing on the Poyang Lake Basin of southern China. Ecol. Indic. 2024, 158, 111549. [Google Scholar] [CrossRef]
  17. Nasiakou, S.; Vrahnakis, M.; Chouvardas, D.; Mamanis, G.; Kleftoyanni, V. Land Use Changes for Investments in Silvoarable Agriculture Projected by the CLUE-S Spatio-Temporal Model. Land 2022, 11, 598. [Google Scholar] [CrossRef]
  18. Changyeon, L.; Sugie, L. Analyzing spatiotemporal land use change using an urban growth model based on multilevel logistic regression and future land demand scenarios. Appl. Geogr. 2023, 160, 103099. [Google Scholar]
  19. Jiang, M.; Wu, Z.; Guo, X. Study on the Contribution of Land Use and Climate Change to Available Water Resources in Basins Based on Vector Autoregression (VAR) Model. Water 2023, 15, 2130. [Google Scholar] [CrossRef]
  20. Yang, Y.; Bao, W.; Liu, Y. Scenario simulation of land system change in the Beijing-Tianjin-Hebei Region. Land Use Policy 2020, 96, 104677. [Google Scholar] [CrossRef]
  21. Manoj, D.; Nazrul, I.; Kumar, S.G. Prediction of spatio-temporal (2030 and 2050) land-use and land-cover changes in Koch Bihar urban agglomeration (West Bengal), India, using artificial neural network-based Markov chain model. Model. Earth Syst. Environ. 2023, 9, 3621–3642. [Google Scholar]
  22. Feng, Y.; Tong, X. A new cellular automata framework of urban growth modeling by incorporating statistical and heuristic methods. Int. J. Geogr. Inf. Sci. 2020, 34, 74–97. [Google Scholar] [CrossRef]
  23. Kubiszewski, I.; Costanza, R.; Anderson, S.; Sutton, P. The future value of ecosystem services: Global scenarios and national implications. Ecosyst. Serv. 2017, 26, 289–301. [Google Scholar] [CrossRef]
  24. Guo, M.; Liu, W. Analysis of land use and ecosystem service value in energy-rich areas of the Yellow River Basin. People’s Yellow River 2023, 45, 98–104+110. [Google Scholar]
  25. Li, Y.; Chang, J.; Wang, Y. Research on land use simulation prediction in the Yellow River Basin based on CA-Markov model. J. Northwest Agric. For. Univ. Nat. Sci. Ed. 2020, 48, 107–116. [Google Scholar]
  26. Zhang, Q.; Cui, B.; John, R. Spatiotemporal pattern of cropland change in the Yellow River Basin from 1985 to 2015. J. Geogr. Sci. 2018, 28, 767–786. [Google Scholar]
  27. Liu, J. Research on Macro-Survey and Dynamic Monitoring of National Resources and Environment by Remote Sensing. J. Remote Sens. 1997, 1, 225–230. [Google Scholar]
  28. Rimal, B.; Zhang, L.; Keshtkar, H.; Haack, B.N.; Rijal, S.; Zhang, P. Land use/land cover dynamics and modeling of urban land expansion by the integration of cellular automata and Markov chain. ISPRS Int. J. Geo Inf. 2018, 7, 154–221. [Google Scholar] [CrossRef]
  29. He, Y.; Wu, W.; Xie, X.; Ke, X.; Song, Y.; Zhou, C.; Li, W.; Li, Y.; Jing, R.; Song, P.; et al. Land Use/Cover Change Prediction Based on a New Hybrid Logistic-Multicriteria Evaluation-Cellular Automata-Markov Model Taking Hefei, China as an Example. Land 2023, 12, 1899. [Google Scholar] [CrossRef]
  30. Mohamed, A.; Worku, H. Simulating urban land use and cover dynamics using cellular automata and Markov chain approach in Addis Ababa and the surrounding. Urban Clim. 2020, 31, 100545. [Google Scholar] [CrossRef]
  31. Pontius, R.G. Quantification error versus location error in comparison of categorical maps. Photogramm. Eng. Remote Sens. 2000, 66, 1011–1016. [Google Scholar]
  32. Zeng, H.; Gao, L.; Xia, J. Urban landscape dynamics based on the modified transfer probability method: A case study of Nanchang city. J. Ecol. 2003, 11, 2201–2209. [Google Scholar]
  33. Wang, Y.; Zhang, Z.; Chen, X. Spatiotemporal change in ecosystem service value in response to land use change in Guizhou Province, southwest China. Ecol. Indic. 2022, 144, 109514. [Google Scholar] [CrossRef]
  34. Li, Q.; Yang, L.; Jiao, H.; He, Q. Spatiotemporal Analysis of the Impacts of Land Use Change on Ecosystem Service Value: A Case from Guiyang, China. Land 2024, 13, 211. [Google Scholar] [CrossRef]
  35. Feng, Z.; Yang, X.; Li, S. New insights of eco-environmental vulnerability in China’s Yellow River Basin: Spatio-temporal pattern and contributor identification. Ecol. Indic. 2024, 167, 112655. [Google Scholar] [CrossRef]
  36. Sun, H.; Di, Z.; Sun, P.; Wang, X.; Liu, Z.; Zhang, W. Spatiotemporal Differentiation and Its Attribution of the Ecosystem Service Trade-Off/Synergy in the Yellow River Basin. Land 2024, 13, 369. [Google Scholar] [CrossRef]
  37. An, S.; Zhang, S.; Hou, H.; Zhang, Y.; Xu, H.; Liang, J. Coupling Coordination Analysis of the Ecology and Economy in the Yellow River Basin under the Background of High-Quality Development. Land 2022, 11, 1235. [Google Scholar] [CrossRef]
  38. Xie, G.; Zhang, C.; Zhang, L. Improvement of ecosystem service valorization method based on unit area value equivalent factor. J. Nat. Resour. 2015, 30, 1243–1254. [Google Scholar]
  39. Yao, S.; Li, Y.; Quan, X.; Xu, J. Applying the driver-pressure-state-impact-response model to eco-logical restoration: A case study of comprehensive zoning and benefit assessment in Zhejiang Province, China. Glob. Ecol. Conserv. 2024, 55, e03222. [Google Scholar]
  40. Han, X.; Yu, J.; Shi, L.; Zhao, X.; Wang, J. Spatiotemporal evolution of ecosystem service values in an area dominated by vegetation restoration: Quantification and mechanisms. Ecol. Indic. 2021, 131, 108191. [Google Scholar] [CrossRef]
  41. He, N.; Zhou, Y.; Wang, L.; Li, Q.; Zuo, Q.; Liu, J. Spatiotemporal differentiation and the coupling analysis of ecosystem service value with land use change in Hubei Province, China. Ecol. Indic. 2022, 145, 109693. [Google Scholar] [CrossRef]
  42. Luan, C.; Liu, R.; Li, Y.; Zhang, Q. Comparison of various models for multi-scenario simulation of land use/land cover to predict ecosystem service value: A case study of Harbin-Changchun Urban Agglomeration, China. J. Clean. Prod. 2024, 478, 144012. [Google Scholar] [CrossRef]
  43. Yang, M.; Luo, J.; Zhu, L.; Lu, P. Impact of Land Use Change on the Spatiotemporal Evolution of Ecosystem Services in Tropical Islands: A Case Study of Hainan Island, China. Land 2024, 13, 1244. [Google Scholar] [CrossRef]
  44. Luo, W.; Yi, H.; Li, H.; Jia, W.; Feng, Z. Spatiotemporal variability of land use and its impact on the value of ecological service functions in the Yanghe River Basin. J. Ecol. 2017, 37, 5342–5351. [Google Scholar]
  45. Yang, S.; Su, H.; Zhao, G. Multi-scenario simulation of urban ecosystem service value based on the PLUS model—A case study of Hanzhong City. Arid Zone Resour. Environ. 2022, 36, 86–95. [Google Scholar]
  46. Lin, Q.; Su, H.; Sammonds, P.; Xu, M.; Yan, C.; Zhu, Z. Evaluation and Prediction of Ecosystem Services Value in Urban Agglomerations Using Land Use/Cover Change Analysis: Case Study of Wuhan in China. Land 2024, 13, 1154. [Google Scholar] [CrossRef]
  47. Guo, P.; Zhang, F.; Wang, H. The response of ecosystem service value to land use change in the middle and lower Yellow River: A case study of the Henan section. Ecol. Indic. 2022, 140, 109019. [Google Scholar] [CrossRef]
  48. Liu, B.; Pan, L.; Qi, Y.; Guan, X.; Li, J. Land Use and Land Cover Change in the Yellow River Basin from 1980 to 2015 and Its Impact on the Ecosystem Services. Land 2021, 10, 1080. [Google Scholar] [CrossRef]
  49. Liu, J.; Wang, J.; Tan, Y. Spatiotemporal pattern and influencing mechanism of the flood-regulating ecosystem service capacity and demand in China’s Yellow River basin, 2000–2020. J. Hydrol. Reg. Stud. 2023, 45, 101312. [Google Scholar] [CrossRef]
  50. Yang, Q.; Liu, G.; Lombardi, G.V.; Yang, Z. Attribution of upstream-downstream transitive natural and human imprint on watershed ecosystem services variations. J. Clean. Prod. 2023, 413, 137421. [Google Scholar] [CrossRef]
  51. Wu, C.; Ma, G.; Yang, W.; Zhou, Y.; Peng, F.; Wang, J.; Yu, F. Assessment of Ecosystem Service Value and Its Differences in the Yellow River Basin and Yangtze River Basin. Sustainability 2021, 13, 3822. [Google Scholar] [CrossRef]
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Figure 1. Geographic location of the upper and middle reaches of the Yellow River.
Figure 1. Geographic location of the upper and middle reaches of the Yellow River.
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Figure 2. Flow chart of the proposed methodology.
Figure 2. Flow chart of the proposed methodology.
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Figure 3. Suitability map for different land types.
Figure 3. Suitability map for different land types.
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Figure 4. Spatial and temporal distribution of land use types in the upper and middle reaches of the Yellow River in 2005, 2010, 2015, and 2020.
Figure 4. Spatial and temporal distribution of land use types in the upper and middle reaches of the Yellow River in 2005, 2010, 2015, and 2020.
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Figure 5. Quantitative changes in land type transfer in the upper and middle reaches of the Yellow River, 2000–2020. Note: Different colored trajectory lines indicate the direction of flow of a site type during a specific period of time, and the thickness of the trajectory line represents the magnitude of the transformation.
Figure 5. Quantitative changes in land type transfer in the upper and middle reaches of the Yellow River, 2000–2020. Note: Different colored trajectory lines indicate the direction of flow of a site type during a specific period of time, and the thickness of the trajectory line represents the magnitude of the transformation.
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Figure 6. Spatial pattern of land use in the upper and middle reaches of the Yellow River in 2035 under different scenarios.
Figure 6. Spatial pattern of land use in the upper and middle reaches of the Yellow River in 2035 under different scenarios.
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Figure 7. Spatial distribution of the ESV in the upper and middle reaches of the Yellow River under multi-scenario simulation.
Figure 7. Spatial distribution of the ESV in the upper and middle reaches of the Yellow River under multi-scenario simulation.
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Figure 8. Sensitivity coefficients of ecosystem service values for each category under different scenarios.
Figure 8. Sensitivity coefficients of ecosystem service values for each category under different scenarios.
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Figure 9. ESV based on land use type for different scenarios.
Figure 9. ESV based on land use type for different scenarios.
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Table 1. Data information and sources.
Table 1. Data information and sources.
Data TypeData DescriptionData ResolutionSource of Data
Land dataLand use1 kmData Centre for Resource and Environmental Sciences, Chinese Academy of Sciences (http://www.resdc.cn, accessed on 10 November 2023)
DEM30 mGeospatial data cloud (http://www.gscloud.cn, accessed on 10 November 2023)
Slope30 mObtained from DEM data using ArcGIS
Food dataGrain production per unit area/Statistical Yearbook of the Provinces in the Upper and Middle Reaches of the Yellow River
Crop sown area/Statistical Yearbook of the Provinces in the Upper and Middle Reaches of the Yellow River
Unit cost of crops/National Compendium of Cost–Benefit Information on Agricultural Products
Spatial dataRoad data/OpenStreetMap (https://www.openstreetmap.org, accessed on 27 November 2023)
Table 2. Classification criteria for Kappa coefficient accuracy evaluation.
Table 2. Classification criteria for Kappa coefficient accuracy evaluation.
Kappa Factor0.00~0.200.20~0.400.40~0.600.60~0.800.80~1.00
Analogue accuracyMediocreCommonMediumPreferablyRare
Table 3. Ecosystem service value per unit area of different land types in the study area (Unit: Yuan/hm2).
Table 3. Ecosystem service value per unit area of different land types in the study area (Unit: Yuan/hm2).
Level 1 TypeSecondary TypeCultivated LandForestGrasslandWaterConstruction LandUnused Land
Supply serviceFood production1724.88588.49771.121623.420.000.00
Raw material production811.711339.321136.39466.730.000.00
Water supply40.59689.95629.0716,822.680.000.00
Regulatory servicesGas regulation1359.614403.523997.671562.540.0040.59
Climate regulation730.5413,190.2810,572.524647.040.000.00
Clean up the environment202.933916.503490.3511,262.470.00202.93
Hydrological regulation547.909618.767751.83207,472.950.0060.88
Support servicesSoil conservation2090.155377.584870.261887.220.0040.59
Maintaining nutrient cycling243.51405.85365.27142.050.000.00
Biodiversity263.814890.554423.825174.650.0040.59
Cultural serviceAesthetic landscape121.762151.031948.103835.330.0020.29
Table 4. Land use change from 2000 to 2020.
Table 4. Land use change from 2000 to 2020.
Land Use TypeCultivated LandForestGrasslandWaterConstruction LandUnused Land
2000Area (km2)195,516102,591376,46112,35314,40870,373
Proportion25.34%13.29%48.78%1.60%1.87%9.12%
2005Area (km2)191,851104,842374,76812,53215,42672,283
Proportion24.86%13.59%48.56%1.62%2.00%9.37%
2010Area (km2)191,373105,138375,11312,55615,88771,635
Proportion24.80%13.62%48.61%1.63%2.06%9.28%
2015Area (km2)190,075105,220373,90112,85419,47670,176
Proportion24.63%13.63%48.45%1.67%2.52%9.09%
2020Area (km2)183,377106,272381,58513,13725,00562,326
Proportion23.76%13.77%49.45%1.70%3.24%8.08%
Table 5. Area of land use flows in different historical periods (unit: km2).
Table 5. Area of land use flows in different historical periods (unit: km2).
Land Use TypeCultivated LandForestGrasslandWaterConstruction LandUnused Land
2000–2010Outflow60565455562782641596
Inflow19153092421498415432857
2010–2020Outflow82,33841,262107,044695610,79232,112
Inflow74,33142,401113,505754419,92122,802
2000–2020Outflow86,09439,726109,335700210,12532,265
Inflow73,94643,412114,448779220,73324,216
Table 6. Area of each category in 2035 under the three modeled scenarios (Unit: km2).
Table 6. Area of each category in 2035 under the three modeled scenarios (Unit: km2).
Land Use TypeCultivated LandForestGrasslandWaterConstruction LandUnused Land
2020183,377106,272381,58513,13725,00562,326
Business As Usual Scenario (BAU)192,088111,207363,06514,64931,13759,556
Ecological Protection Scenario (EPS)136,069129,636401,51515,23524,71764,530
Highly Urbanization Scenario (HUS)191,872110,262359,05514,45436,96859,091
Table 7. Values of ecosystem services of various land types under different scenarios for 2035 (unit: 108 yuan).
Table 7. Values of ecosystem services of various land types under different scenarios for 2035 (unit: 108 yuan).
Land Use TypeCultivated LandForestGrasslandWaterConstruction LandUnused LandTotal
2020 ESV1492.214949.2815,246.763348.58025.3025,062.13
BAU ESV1563.095179.1114,506.773733.99024.1725,007.14
EPS ESV1107.256037.3916,043.093883.36026.1927,097.27
HUS ESV1561.345135.1014,346.553684.28023.9824,751.25
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Ma, M.; He, Y.; Sun, Y.; Cui, H.; Zang, H. Land Use Modeling and Predicted Ecosystem Service Value Under Different Development Scenarios: A Case Study of the Upper–Middle Yellow River Basin, China. Land 2025, 14, 115. https://doi.org/10.3390/land14010115

AMA Style

Ma M, He Y, Sun Y, Cui H, Zang H. Land Use Modeling and Predicted Ecosystem Service Value Under Different Development Scenarios: A Case Study of the Upper–Middle Yellow River Basin, China. Land. 2025; 14(1):115. https://doi.org/10.3390/land14010115

Chicago/Turabian Style

Ma, Mingwei, Yuhuai He, Yanwei Sun, Huijuan Cui, and Hongfei Zang. 2025. "Land Use Modeling and Predicted Ecosystem Service Value Under Different Development Scenarios: A Case Study of the Upper–Middle Yellow River Basin, China" Land 14, no. 1: 115. https://doi.org/10.3390/land14010115

APA Style

Ma, M., He, Y., Sun, Y., Cui, H., & Zang, H. (2025). Land Use Modeling and Predicted Ecosystem Service Value Under Different Development Scenarios: A Case Study of the Upper–Middle Yellow River Basin, China. Land, 14(1), 115. https://doi.org/10.3390/land14010115

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