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Article

Land Use Optimization and Carbon Storage Estimation in the Yellow River Basin, China

by
Furui Xi
1,2,†,
Gang Lin
3,4,5,†,
Yanan Zhao
3,
Xiang Li
6,*,
Zhiyu Chen
7,* and
Chenglong Cao
6
1
China Institute of Geo-Environment Monitoring, Beijing 100081, China
2
Key Laboratory of Mine Ecological Effects and Systematic Restoration, Ministry of Natural Resources, Beijing 100081, China
3
Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
4
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
5
Key Laboratory of Carrying Capacity Assessment for Resource and Environment, Ministry of Natural Resources, Beijing 100101, China
6
College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
7
Research Institute of Highway Ministry of Transport, Beijing 100088, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Sustainability 2023, 15(14), 11278; https://doi.org/10.3390/su151411278
Submission received: 18 June 2023 / Revised: 12 July 2023 / Accepted: 17 July 2023 / Published: 20 July 2023
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Abstract

:
Urban development and coal extraction have caused conflicts regarding production, living, and ecological lands in the Yellow River basin. Here, a coupled genetic algorithm–patch generating land use simulation InVEST model was constructed to optimize land use/land cover (LULC) and simulate carbon storage changes. This study shows that the LULC changed dramatically from 2010 to 2020 in an area that accounts for 31.96% of the area of the Yellow River basin. Inappropriate land use conversion and encroachment have reduced carbon storage by 2.92 × 106 t, destroying the stability of the ecosystem. The development of cities has encroached on cultivated land, which may have affected the region’s food security. Following LULC optimization, ecological and cultivated lands are gradually being restored, and the transition between the different lands tends to be orderly, resulting in an increase of 24.84 × 106 t in carbon storage. The relationship between LULC and carbon storage shows that the high carbon intensity of woodland, grassland, and cultivated land is crucial to ensuring regional carbon balance. For the Yellow River basin, necessary environmental protection measures are the key to achieving high-quality economic development. This study can provide guidance for decision-makers in formulating ecosystem restoration plans.

Graphical Abstract

1. Introduction

Changes in land use/land cover (LULC) have greatly changed the natural environment of humans [1,2,3,4,5,6]. Rapid urban development has brought enormous benefits to the economy and society; however, this has been accompanied by a large amount of unreasonable land expansion, leading to a series of problems such as increased carbon emissions, heavy metal soil pollution, ozone layer depletion, haze, etc., which have caused serious impacts on the environment and human health. There is a close relationship between terrestrial ecosystem carbon pool and LULC [4,5]. For example, vegetation and soil may generate carbon sources or sinks in the process of land use change, which directly affects global climate change [6]. Firstly, the biomass of different land types differs significantly, and a change from one land type to another inevitably leads to changes in biomass and, consequently, carbon storage [7,8]. Secondly, LULC changes can also affect plants, soil respiration, litter decomposition rates, and ecosystem carbon processes [9,10,11]. Finally, LULC changes can change the energy consumption structure and intensity of society and affect the carbon cycle [12,13]. With the increase in CO2 concentrations in the atmosphere, the impact of LULC changes on the carbon cycle has become a primary focus of researchers [6]. Enhancing terrestrial carbon sinks is considered one of the most established ways to mitigate climate change, a nature-based solution to offset anthropogenic CO2 emissions, and an important means to achieve carbon neutrality [14,15].
China is the largest carbon emitter [16,17]. Currently, the world emits approximately 4.01 × 1010 t of CO2 per year, of which 86% originates from fossil fuel use and 14% from LULC changes [18]. China is a key area for global carbon storage research because of its vast size, different climatic zones, and different ecosystems [19]. With urbanization, more and more cultivated land and woodland are being transformed, resulting in land conversion from carbon sinks to carbon sources as large amounts of carbon are released into the atmosphere from these disturbed terrestrial ecosystems [20]. At the 75th UN General Assembly, the Chinese government announced that “China’s CO2 emissions will peak around 2030, working towards carbon neutrality by 2060” (later simply referred to as “dual carbon goals”) [21]. Specifically, China has committed to increasing woodland storage and reducing carbon emissions [22]. Therefore, it is crucial to study the impact of carbon storage and LULC changes in China’s terrestrial ecosystems on carbon emission reduction in China [6,9,20].
Ecosystems are the major part of the carbon cycle, and China contributes 10–31% of the global terrestrial carbon sink with 6.5% of the world’s land area [23]. In recent years, studies on the changes in carbon storage in LUCC and terrestrial ecosystems have been carried out in large numbers [15,24,25]. For example, Yu et al. [26] stated that LULC change in the USA from 1980–2016 resulted in significant carbon emissions mainly due to the conversion of agricultural land into high–carbon–density natural ecosystems such as wetlands. However, analyses in China suggest that LULC changes may present a diametrically opposite situation, i.e., provide a significant carbon sink [27]. Similarly, Chang et al. [9] analyzed LULC changes across China from 2000 to 2018 and found they were characterized by a decrease in cultivated land and grassland, afforestation (increase in woodland), with an increase in the carbon storage of 1.32 Pg C caused by LULC change. However, Zhu et al. [6] found that the carbon storage of terrestrial ecosystems in arid areas in China decreased by 90.95 Tg between 1980 and 2015 mainly because of grassland degradation. In addition, Lin et al. [16] calculated the per capita LULC carbon emissions in China from 2006 to 2016 and found that construction land was the primary source of carbon emissions, accounting for more than 96.8% of carbon emissions, while woodlands were the main carbon sink. Most of the existing studies have explored carbon storage or spatial and temporal changes in ecosystems based on past LULC changes, and fewer studies have modeled carbon storage in the context of future land optimization, although it is recognized that LULC optimization is an effective way to mitigate carbon storage decline [28,29,30,31].
In addition, LULC also has a crucial impact on the environment and human health. In the context of the limited expansion of agricultural land, it is necessary to use land more intensively in order to achieve higher food production on a limited land area. This requires nitrogen application or irrigation, which, if not properly managed, may lead to soil degradation or have a negative impact on water quality [1]. People living in the vicinity of highly polluted areas may be polluted by heavy metals due to the long-term accumulation of toxic heavy metals, and eating plants grown on contaminated soil may cause serious harm to human health [2]. Similarly, there is a strong correlation between LULC and air pollutants. For example, an increase in cultivated land may lead to an increase in the concentrations of SO2, CO, etc., while an increase in woodland reduces the concentrations of SO2, CO, etc. An increase in water will reduce the concentrations of PM10 and PM2.5. Therefore, more reasonable land use management is one of the key measures to control concentrations of atmospheric pollutants and indirectly safeguard human health [3]. The Yellow River basin, the largest basin in China, has undergone dramatic LULC changes and is facing enormous pressure and severe challenges to its ecosystem functions and biodiversity [32]. It is currently experiencing land degradation and desertification in the upper reaches, soil erosion in the middle reaches, and river channel sedimentation in the lower reaches, affecting the ecosystem balance of the entire basin [33,34,35]. Since 2019, the Yellow River basin’s ecological protection and high-quality development have been elevated to a major national strategy, including the LULC changes that have become a hotspot of academic interest [36]. However, most existing studies have only focused on the provincial administrative areas through which the watershed flows or only a specific section of the basin was selected for study [37]. Given the vast geographical and developmental differences of the Yellow River basin, it is necessary to take the entire basin as the object of study and analyze its LULC structure, changes, and optimization [32,34,37]. To this end, this study constructed a coupled GA–PLUS–InVEST model with the objectives of (1) exploring the LULC change process and the resulting carbon storage changes in the Yellow River basin from 2010 to 2020, (2) predicting how soil carbon storage will change under future LULC optimization, and (3) making recommendations for LULC in the Yellow River basin. The achievement of the above objectives can guide policymakers and regulators in the Yellow River basin to protect the ecological environment and enhance carbon sequestration under climate change and LULC changes.

2. Materials

2.1. Study Area

The Yellow River basin (95°59′–118°58′ E, 31°56′–42°03′ N) is an important ecological barrier and carbon storage area. The Yellow River basin extends from the Bayankara Mountains in the west, the Bohai Sea in the east, the Qinling Mountains in the south, and the Yin Mountains in the north. It passes through nine provinces in China and covers a total area of approximately 795,000 km2, accounting for approximately 8.3% of the total area of China (Figure 1). In 2020, the total population in the Yellow River basin was 170 million, accounting for 11.5% of the total population in China; the gross domestic product (GDP) reached CHY 1.1 × 105 billion, accounting for 10.5% of China’s GDP [37]. In recent years, with the development of populations and economies and the implementation of national policies, such as the construction of ecological civilization and the “dual carbon goals”, the ecological protection and high-quality development of the Yellow River basin have become important research topics.
In 2019, Xi [36] pointed out that the Yellow River basin is a coal resource-rich area and ecological hub in China that plays an important role in economic development and ecological security. However, the Yellow River basin’s high-intensity, resource-dependent economic development model has led to unbalanced regional development, substantial carbon emissions, and an uneven spatial layout of land use. Xi et al. [38] showed that production and living spaces seriously encroach on ecological space, causing the prominent spatial conflict problem in the Yellow River basin. Therefore, as the Yellow River basin enters an important stage of high-quality development, problems of how to scientifically and reasonably lay out the land use space, balance the multi-dimensional factors, promote ecological restoration, enhance carbon storage and carbon sequestration rates, and reduce carbon emissions have become critical issues to be solved in the Yellow River basin.

2.2. Materials

In this study, the data used mainly include LULC data, restricted development areas and driving factor data required for the GA–PLUS model, and carbon density data required for the InVEST model.
The quantitative and structural optimization target constraints required for the GA–PLUS model were derived from the Yellow River Basin Comprehensive Plan (2012–2030) (http://yrcc.gov.cn/zwzc/lygh/201303/t20130321_129411.html (accessed on 25 May 2023)), the Outline of the Yellow River Basin’s Ecological Protection and High-quality Development Plan (http://www.gov.cn/zhengce/2021-10/08/content_5641438.htm (accessed on 25 May 2023)), and the medium- and long-term plans of the provinces in the Yellow River basin. The LULC data were obtained from the Resource and Environment Science and Data Centre of the Chinese Academy of Sciences (RESDC, http://www.resdc.cn/ (accessed on 25 May 2023)). The spatial resolution of LULC was 1 km × 1 km and the time range was from 2010 to 2020. The restricted development areas were derived from the ecological protection red line delineation range maps published by provinces and cities in the Yellow River basin, and the data were artificially vectorized and concatenated. The digital elevation model (DE), population density, GDP, and normalized difference vegetation index (NDVI) were obtained from RESDC, and the spatial resolution was 1 km × 1 km. Temperature and precipitation data were obtained from the National Tibetan Plateau Science and Data Centre (http://data.tpdc.ac.cn/ (accessed on 25 May 2023)), and the spatial resolution was 0.00833° × 0.00833° (approximately 1 km), with a temporal interval of monthly. The slope was generated using the “Slope” tool of ArcGIS 10.7. Other data were generated using the “Euclidean Distance” tool of ArcGIS 10.7 based on “OpenStreetMap” road, water, and point of information data. The studies of De Reu et al. [39] and Li et al. [40] were referenced to calculate the topographic position index and landscape fragmentation index, respectively.
Carbon density is an important input parameter for the InVEST model to accurately assess carbon storage. In this study, carbon density data for different land types were obtained based on the “2010s Chinese terrestrial ecosystem carbon density dataset” [41] and the nationwide carbon density studies by Chen et al. [42] and Yang et al. [43] and modified using relevant literature on the Yellow River basin.

3. Methods

3.1. Research Procedures

The following steps were used to carry out LULC optimization and carbon storage estimation of the Yellow River basin in this study (Figure 2):
Step 1. Characterization of LULC evolution: Based on the grid data of LULC, the spatiotemporal evolution characteristics, laws, and existing spatial conflicts of land use in the Yellow River basin were analyzed.
Step 2. Optimization of future LULC spatial patterns: The GA–PLUS model was constructed to optimize the quantitative structure and spatial unit layout changes of LULC in the Yellow River basin.
Step 3. Carbon storage estimation: The InVEST model was used to estimate spatiotemporal changes in carbon storage and analyze the positive impact of LULC optimization on carbon storage in the Yellow River basin.
Step 4. Analysis of the relationship between carbon storage and LULC changes: By comparing and analyzing the differences in the impacts of different LULC types on carbon storage, directions and suggestions were put forward for safeguarding ecological security and promoting ecological restoration and high-quality development in the Yellow River basin in the future.

3.2. Genetic Algorithms (GA)

As a complex system optimization problem, the overall search strategy and optimization search method of GA provides a reliable solution for the optimization of LULC. GA performs evolutionary operations for the entire population during computation and is not easily disturbed by external conditions, thereby having stronger robustness in solving multi-objective optimization problems of land use.
The GA achieves various operations by setting decision variables and constructing objective functions and constraint conditions. For the constraint values of the decision variables, reference was made to the Yellow River Basin Comprehensive Plan (2012–2030), the Outline of the Yellow River Basin’s Ecological Protection and High-quality Development Plan, and the medium- and long-term plans of the provinces in the Yellow River basin, as well as the current situation of socioeconomic development and LULC in the Yellow River basin. For the objective function, this study set three objectives of economic development, food security, and ecological priority for optimizing the quantitative structure of land use [29]. The economic development objective was set as the maximum regional GDP, the food security objective was set as the maximum regional food production, and the ecological priority was set as the lowest regional carbon emission level. For the constraints, this study determined the expected area of each type of LULC in conjunction with the planning objectives.
The adaptation function was constructed as follows:
F s u i t a b l e = ∑ i = 1 6 Y i _ max ⁡ _ G D P + Y max ⁡ _ g o + ∑ i = 1 6 1 / Y i _ min ⁡ _ c a r b o n + 1 / | ( ∑ i = 1 6 x i − Y a r e a ) |
where Y i _ max ⁡ _ G D P is the maximum GDP, Y max ⁡ _ g o is the maximum food production, and Y i _ min ⁡ _ c a r b o n is the minimum carbon emissions.

3.3. Patch-Generating Land Use Simulation (PLUS) Model

3.3.1. Model Principles

The PLUS model had a more significant effect on revealing potential drivers of LULC changes and simulating the spatiotemporal evolution characteristics of multiple LULC patches [44]. The PLUS model consists of a land expansion analysis strategy (LEAS) and the CA model based on multi-class random patch seeding (CARS). The LEAS extracts the part of the expansion change of each type of LULC between two periods of LULC change, randomly extracts sampling points for analysis, and explores the internal mechanism of the change of each type of LULC and drivers. The CARS component combines random seed generation and a decreasing threshold mechanism to automatically simulate the patch-level changes of multiple LULC categories in a spatiotemporal dynamic manner, subject to the constraints of the development probability of each LULC category and the constraints of the domain weights and transfer matrix so that the total LULC can meet the future demand at a macro level.
In this study, the GA model was coupled with the PLUS model to optimize the quantitative structure of LULC in the Yellow River basin by setting multiple sustainable development objectives in the GA model. The final optimization results met the strategic objectives of the sustainable and high-quality development of the Yellow River basin and satisfied the evolutionary rules of LULC in terms of spatial layout.

3.3.2. Parameter Settings

(1)
Drive factor selection
The drivers are important factors that lead to changes in LULC patterns. The influence of various factors on LULC changes was analyzed based on the relevant literature, and a total of 14 drivers of LULC changes in the Yellow River basin were selected. The drivers of LULC change in the Yellow River basin were treated as driving variables, and the data sources for the drivers are described in Section 2.2.
(2)
LEAS parameter settings
Our study used random sampling; the default sampling rate was 0.01, the number of decision trees was 20, the number of features in training RF was 14, and the number of parallel threads was set to 4.
(3)
CARS parameter setting
The neighborhood weight parameters were calculated with reference to Luo et al. [45] and were set at 0.45235 for cultivated land, 0.0473 for woodland, 0.0428 for grassland, 0.00035 for water bodies, and 0.00035 for construction land. The transfer matrix parameters are set out in Table 1. The settings of other parameters in CARS were all default values.

3.4. The InVEST Model

The Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) is a model developed by the US Natural Capital Project Team to assess the functional capacity of ecosystem services and their economic value. The carbon module of the InVEST model divides an ecosystem’s carbon storage into four primary carbon pools, and the total carbon storage of the study area is obtained by adding them together:
CT = Ca + Cb + Cs + Cd
where Ca, Cb, Cs, and Cd represent the carbon storage of above-ground, below-ground, soil, and dead organic, respectively. CT represents the total carbon storage.

4. Results

4.1. Analysis of the Evolutionary Characteristics of LULC

Table 2 and Figure 3 show the changes in LULC in quantity and space in the Yellow River basin. Overall, the LULC of the Yellow River basin is dominated by grassland, with the proportion of grassland area reaching 47.31% and 47.98% in 2010 and 2020, respectively. Grasslands are mainly distributed in the southwest of Qinghai and the west of Inner Mongolia. The area of cultivated land is second only to grassland. The proportion of cultivated land reached 26.14% and 25.13% in 2010 and 2020, respectively. The decrease in cultivated land is mainly caused by the expansion of central cities in the middle and lower reaches of the Yellow River provinces. Cultivated land is concentrated mainly in the Yellow River basin’s lower and middle plain areas, including Shandong, Henan, Shanxi, and Shaanxi. Woodland accounted for 13.32% and 13.45% of the area in 2010 and 2020, respectively, and was mainly located in Henan, Shanxi, and Shaanxi in the middle reaches of the Yellow River basin, with scattered distribution in southwest Gansu and northern Qinghai in the upper reaches of the Yellow River basin. Construction land was concentrated in the central urban areas of the major cities along the nine provinces. From 2010 to 2020, the proportion of construction land increased from 2.43% to 3.63%. The Yellow River basin has a relatively small area of water, mainly the Yellow River, and its widely distributed tributaries at all levels within the area. It is worth noting that there are also large areas of unused land in the Yellow River basin, especially in the Inner Mongolia region, possibly because of the difficulty of exploiting the land conditions in these areas for localized economic development. However, with the implementation of policies related to the ecological protection and high-quality development of the Yellow River basin in recent years, the development and use of unused land have significantly reduced, from 9.06% in 2010 to 7.90% in 2020, and unused land has mainly been transformed into ecological land.
Figure 4 shows the Sankey diagram of the area change in LULC types in the Yellow River basin from 2010 to 2020. The intensity of transition in LULC types in the Yellow River basin from 2010 to 2020 was significant, with approximately 255.08 × 103 km2 of the area undergoing change. The transition of LULC types is mainly reflected in the net increase in construction land, grassland, water bodies, and woodland, as well as the net decrease in unused and cultivated land. Transitions from construction land were mainly to cultivated land, accounting for 1.07% of the total of the study area. However, the encroachment of construction land on cultivated land and grassland due to urban expansion resulted in a net increase in the overall area of construction land, which covered 1.77% and 0.60% of the total of the study area, respectively. Grasslands were mainly converted into cultivated land and woodland, accounting for 5.97% and 3.10% of the total of the study area, respectively. At the same time, 6.18% and 3.04% of the total of the study area were converted from cultivated and woodland areas to grassland, respectively, resulting in a relative balance between grassland and cultivated and woodland areas in terms of occupation and compensation. The net increase in water bodies was mainly due to the conversion of cultivated land, grassland, and unused land, which accounted for 0.37%, 0.36%, and 0.12% of the total of the study area, respectively. The woodland area continued to increase, mainly from cultivated land and grassland, but the increase was smaller-scale, mainly due to the “Three-North Shelter Forestation Project” and the “Grain for Green” project. The cultivated land area decreased more significantly, changing mainly to woodland, construction land, and grassland, accounting for 6.18%, 1.77%, and 1.28% of the total of the study area, respectively. This is related to the expansion of urban construction, the “Three-North Shelter Forestation Project”, and the implementation of “Grain for Green”, as mentioned above. Unused land was effectively exploited during the decade, with varying degrees of conversion to other land types.

4.2. Analysis of LULC Optimization Results

The GA–PLUS model was used to simulate the LULC conditions in 2020 and compare them with the actual situation. The results showed that the overall accuracy of the GA–PLUS model simulation was 92.94%, and the kappa coefficient was 0.8967. Generally, when the kappa coefficient is greater than 0.8, there is a high agreement between the two maps.
As can be observed from Figure 5 and Figure 6, the quantitative structure and spatial patterns of LULC in the optimized Yellow River basin are changed significantly compared to the current development status. The grassland and unused land areas have decreased. The grassland area decreased by approximately 8.3 × 103 km2, accounting for 11.74% of the total grassland region. This decrease was mainly due to the transition to woodland, cultivated land, and water bodies, especially in the topographically complex Qinghai, where the transition from grassland to woodland is clear. The largest change in unused land was a decrease of 95.4 × 103 km2 directly related to the increase in construction land resulting mainly from the large-scale expansion of construction on both sides of the Yellow River in northern Inner Mongolia, accounting for 34.87% of the total area of unused land. Construction land, cultivated land, woodland, and water bodies have increased. Driven by food security and ecological priority objectives, the areas of construction land and woodland increased by 2.81 × 103 km2 and 1.55 × 103 km2, respectively. The increase in cultivated land was mainly located in the middle and lower reaches of the Yellow River, and the disorderly expansion of construction land has been effectively controlled, gradually transforming the construction land into cultivated land. The increase in woodland was mainly observed in Gansu, Qinghai, Henan, and southern Shaanxi, consistent with the national “Three-North Shelter Forestation Project” in the medium- and long-term construction planning objectives.

4.3. Analysis of the Spatiotemporal Evolution of Carbon Storage

The carbon storage in the Yellow River basin in 2010 and 2020 were calculated using the InVEST model, and the results are shown in Table 3 and Figure 4. The carbon storage in the Yellow River basin in 2010 and 2020 were 4912.23 × 106 t and 4909.30 × 106 t, respectively, with an overall decrease in carbon storage of 2.92 × 106 t (Table 3). Regarding LULC types, the carbon storage of grassland, woodland, construction land, and water has increased, while those of cultivated land and unused land have decreased significantly, which is the main reason for the overall decrease in carbon storage in the Yellow River basin from 2010 to 2020. Because of the ecological restoration and the policy of “Grain for green”, grasslands and woodlands as the main carbon pools have compensated to some extent for the loss of carbon storage caused by the reduction in farmland in recent years.
The spatial distribution and changes in carbon storage in the study area from 2010 to 2020 show regular spatial differentiation characteristics (Figure 7). In line with the distribution characteristics of vegetation and LULC types, the high-value areas of carbon storage were mainly located in Qinghai and Gansu in the upper reaches of the study area, in the Qinling region, Fen River basin, and Luo River basin in the middle and lower reaches of the Yellow River basin, which are mostly woodlands and high-cover grasslands due to topographical and geomorphological factors. Areas with low carbon storage values were mainly unused lands such as bare and sandy lands, mainly located in the Kubuqi Desert, Ulan Buh Desert, and Mu Us Sandland in Inner Mongolia in the upper reaches of the Yellow River basin. In terms of spatial changes, the areas where carbon storage increased were mainly areas where woodland, grassland, and cultivated land had been added, while areas where carbon storage decreased were mainly areas where unused land had been developed, construction land had been expanded, and water bodies had been increased.
The results of further carbon storage calculations for the Yellow River basin after LULC optimization using the InVEST model are shown in Table 3 and Figure 8. From Table 3, it can be observed that the area of cultivated land, woodland, and construction land increased and promoted the stable recovery of carbon storage of the Yellow River after optimization, with the optimized carbon storage increasing by 24.84 × 106 t compared to 2020. The carbon storage of cultivated land, woodland, and construction land increased by 17.61 × 106 t, 14.93 × 106 t, and 4.17 × 106 t, respectively. Although the grassland carbon storage decreased by 5.53 × 106 t, it is known from the previous section that grassland transitions were mainly oriented to woodland and occurred in the specific complex geomorphic area of the study area. In contrast, the carbon density and ecosystem stability of woodlands were higher than those of grasslands; thus, the transformation process of grassland areas and carbon storage reduction in this study were benign and reflected the ecosystem restoration and regional stability of the study area. Spatially, the distribution of carbon storage in the Yellow River basin before and after optimization remained generally consistent. Because of the gradual restoration of the original arable land under the food security objective, the carbon storage in the middle and lower reaches of the Yellow River basin in the Shaanxi, Shanxi, Henan, and Shandong provinces increased significantly, and the sporadically distributed “point” distribution of low-value areas will gradually turn into “surface” distribution of high-value areas in the future. The sporadic distribution of low-value areas will gradually change to the “surface” distribution of high-value areas. The increase in forested land under the ecological priority goal will change the low carbon storage value to a high value in the Qinling Mountains.

5. Discussion

5.1. Discussion of the Main Results

This study comprehensively analyzed the characteristics of the spatiotemporal evolution of LULC in the Yellow River basin from 2010 to 2020. The results showed that the quantity and spatial characteristics of LULC in the Yellow River basin are clearly divergent, with LULC types dominated by grassland, cultivated land, and woodland, and the area of them reaching more than 85% of the total of the study area. The spatial differentiation of LULC types in the Yellow River basin was driven by topographical and geomorphological features, climatic conditions, and other factors. In addition, despite the unprecedented intensity of land development in the Yellow River basin due to economic development, there are still large sand and desert areas in Inner Mongolia that cannot be used effectively for the time being. Between 2010 and 2020, the land use types of the Yellow River basin underwent a significant transfer, mainly manifested as a net decrease in agricultural land, an increase in industrial and ecological land, and the development and use of unused land. It is important to note that ecological and agricultural lands have been seriously encroached upon by construction land during this decade under the influence of economic development. If urban–rural and economic–ecological relationships cannot be balanced in the future, this will further endanger the ecological environment and agricultural development.
The optimization results of the GA–PLUS model showed that LULC types in the Yellow River basin shifted in a beneficial direction, driven by the goals of economic development, food security, and ecological priority. The areas of construction land, cultivated land, and woodland increased, mainly because of the transformation of grassland to woodland in complex mountainous areas, which improved the stability of the ecosystem. The encroachment of agricultural land by urban expansion in the plains decreased, and the area of cultivated land gradually returned. Construction lands on both sides of the Yellow River safeguarded the goal of economic development through the rational development of unused land. Overall, through the rational layout and orderly expansion of the land space, the optimized results achieved the effective promotion of ecological restoration and high-quality development on the basis of safeguarding ecological security and food security in the Yellow River basin.
The InVEST model was used to accurately account for carbon storage, and the results showed that the overall carbon storage in the Yellow River basin declined by 2.92 × 106 t between 2010 and 2020 as a result of LULC shifts due to coal mining and urban expansion, as described in the previous section. The trajectory of this development could result in a vicious cycle in the future. However, the optimization of LULC effectively solves these problems. The optimization of land use leads to a steady rebound of carbon storage, which increased by 24.84 × 106 t. Spatially, the grasslands in the Qinling region are transformed into high-carbon-density woodlands, and the scattered “dotted” low-value areas are gradually transformed into “surface” high-value areas in the future in the middle and lower reaches of the study area.
In summary, we proposed a comprehensive framework that integrates the GA, PLUS, and InVEST models to help improve ecosystem quality and increase carbon storage in the Yellow River basin by optimizing LULC. The study results have important practical implications for the scientific and accurate assessment of carbon storage and for guiding the integrated spatial optimization of the Yellow River basin.

5.2. The Relationship between Carbon Storage and LULC Changes

Our results found that changes in carbon storage and spatial patterns in the Yellow River basin were highly correlated with land use change and spatial distribution, and other research studies have also identified land use change as one of the most important factors contributing to global changes in terrestrial ecosystems’ carbon storage [46,47,48]. To further analyze the variability of the effects of differences in the land transfer area, soil, and vegetation carbon density on carbon storage among different land use types, this study calculated the corresponding carbon storage transition matrix based on the area transfer among land use types (Figure 9).
The decrease in total carbon storage from 2010 to 2020 was mainly reflected in the shift from cultivated land to construction land, from woodland to cropland and grassland, and from grassland to construction land and unused land (Figure 9a). The increase in carbon storage was mainly reflected in land use changes, primarily in transforming cultivated land to grassland and woodland, grassland to woodland, water bodies to cultivated land, and unused land to grassland. The total carbon storage in the Yellow River basin increased from 2020 to after the optimization of land use, and the change in land type corresponding to the increase in carbon storage was precisely the same as in the 2010–2020 period (Figure 9b). The decrease in carbon storage was mainly observed in the shift from woodland to grassland and from grassland to construction land and unused land. The changes in carbon storage caused by the above transitions between land use types were all greater than 20 × 106 t. Although the increase in carbon storage was smaller between 2020 and land use optimization than between 2010 and 2020, the constraint of the land use optimization target set in the GA–PLUS model resulted in an increase in carbon storage after optimization compared to 2020. For example, these constraints promoted conversions between land use types with reduced carbon storage, such as converting forest and grassland to cultivated land. Therefore, necessary ecological conservation measures can help promote regional carbon balance.
Comparing the carbon intensity values of the different land use types, it can be observed that the reduction in carbon storage caused by the conversion of woodland, grassland, and cultivated land to other land use types was mainly due to the fact that the carbon intensity of above-ground, below-ground, soil, and dead organic matter was much higher in these three land use types than in the others. Conversely, the shift from other LULC types to these types increased carbon storage for the same reason. In particular, woodland, the largest carbon reservoir in the ecosystem, was the barrier to ecosystem security and stability and the key to increasing the ecosystem’s carbon sink to achieve the regional “dual carbon goal”. Therefore, the future ecological restoration and high-quality development of the Yellow River basin should be a reasonable distribution of the spatial pattern of the land to ensure ecological security and promote regional carbon balance.

5.3. Limitations

To a great extent, the above discussions prove the reliability of the model and the results of this study and provide reliable technical support and policy recommendations for optimizing the Yellow River basin’s spatial layout and improving the ecosystem’s quality and carbon storage. Nevertheless, there are some shortcomings of our study that must be further explored and solved in the future.
The first limitation involves carbon density. The carbon density values in this study were based on previous research results and a synthesis of relevant research literature on the Yellow River basin, which helped improve the accuracy of the assessment results compared with previous studies that mostly used national-scale carbon density for regional studies. The study found that carbon density was influenced by environmental changes and human activities, such as increased temperature and precipitation, which increased the decomposition rate of soil organic carbon and reduced soil carbon density. However, this was not explored in greater depth in this study because of limited data and the length of the study. Subsequent studies will develop temperature and rainfall models to complement the carbon density data for the study area and improve their accuracy.
The second is the limitation of LULC optimization. Due to the limited planning for the future spatial development of the land that involves the entire Yellow River basin, the land use optimization targets were determined by integrating the individual plans of each province, thereby lacking a macro grasp of the overall development layout of the study area. A future model’s constraint targets should be revised in response to the latest development plans.
Finally, there are limitations to the InVEST model. In estimating carbon storage from large-scale land use changes, the InVEST model ignored key indicators of ecosystem carbon sequestration, such as vegetation photosynthesis and soil respiration. At the same time, differences in carbon sequestration capacity due to the internal structure of land use were not considered, which led to some errors in the estimated carbon storage. Future studies should consider the spatial heterogeneity within LULC and explore the impact of different plant species on carbon storage under the same LULC type.

6. Conclusions

In this study, we analyzed the LULC evolution patterns and characteristics of the Yellow River basin by establishing a coupled GA–PLUS–InVEST model to estimate LULC carbon storage and analyzed the effectiveness of LULC optimization on ecosystem restoration and quality and carbon enhancement. The expansion of urban and rural areas and the large-scale mining of coal resources have seriously damaged the ecological balance, encroaching on 6.78% of agricultural land, 3.73% of water bodies, and 1.28% of grassland, and reducing the area of cultivated land by 8.05 × 103 km2 from 2010 to 2020. Continuing this expansion would intensify the conflict between urban and rural development, break the ecological barrier of the river basin, and threaten regional food security. LULC optimization effectively solves the above problems and provides a direction for optimizing the Yellow River basin’s spatial layout and improving the ecosystem’s quality to increase carbon storage in the future. After optimization, the areas of construction land, cultivated land, and woodland all showed an increasing trend. These land transformations will promote urbanization and steady economic growth and safeguard cultivated and ecological land, regional carbon balance, ecosystem stability, and sustained food production output. These are also the main construction ideas and directions for the future planning of the Yellow River basin. This study provides optimal control paths and development suggestions for the spatial layout of LULC. It also provides technical support for ecosystem restoration and carbon enhancement, which is essential for enhancing and planning ecosystem carbon sink functions in other regions and for achieving China’s “dual carbon goal”.

Author Contributions

Conceptualization, F.X. and G.L.; methodology, X.L. and Y.Z.; software, Y.Z.; validation, Z.C. and C.C.; formal analysis, X.L.; investigation, X.L.; resources, G.L.; data curation, F.X.; writing—original draft preparation, X.L. and Y.Z.; writing—review and editing, F.X. and G.L.; visualization, Z.C.; funding acquisition, F.X. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the China Geological Survey Project (grant no. DD20221726), Humanities and Social Sciences Project funded by the Ministry of Education (grant no. 20YJCZH087), Basic Scientific Research Funds of China University of Mining and Technology (Beijing)—Top Innovative Talents Cultivation Fund for Doctoral Postgraduates (grant no. BBJ2023020), and National Natural Science Foundation (grant no. 42202280).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

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

Acknowledgments

We would like to thank the anonymous reviewers for their helpful remarks.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Location of the Yellow River basin.
Figure 1. Location of the Yellow River basin.
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Figure 2. Research procedures map.
Figure 2. Research procedures map.
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Figure 3. The changes in LULC in the Yellow River basin: (a) 2010 and (b) 2020.
Figure 3. The changes in LULC in the Yellow River basin: (a) 2010 and (b) 2020.
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Figure 4. Sankey diagram of LULC transfer in the Yellow River basin from 2010 to 2020.
Figure 4. Sankey diagram of LULC transfer in the Yellow River basin from 2010 to 2020.
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Figure 5. The results of LULC optimization in the Yellow River basin.
Figure 5. The results of LULC optimization in the Yellow River basin.
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Figure 6. Sankey diagram of LULC transfer in the Yellow River basin from 2020 to the optimized model.
Figure 6. Sankey diagram of LULC transfer in the Yellow River basin from 2020 to the optimized model.
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Figure 7. Spatial pattern of carbon storage in the Yellow River basin: (a) 2010 and (b) 2020.
Figure 7. Spatial pattern of carbon storage in the Yellow River basin: (a) 2010 and (b) 2020.
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Figure 8. Spatial pattern distribution of carbon storage in the optimized Yellow River basin.
Figure 8. Spatial pattern distribution of carbon storage in the optimized Yellow River basin.
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Figure 9. Land use types’ carbon storage transition matrices: (a) from 2010 to 2020 and (b) from 2020 to post-optimization (Unit: 106 t).
Figure 9. Land use types’ carbon storage transition matrices: (a) from 2010 to 2020 and (b) from 2020 to post-optimization (Unit: 106 t).
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Table
Table 1. Optimal land use transfer matrix for the Yellow River basin.
Table 1. Optimal land use transfer matrix for the Yellow River basin.
Land Use TypeCultivated LandWoodlandGrasslandWater BodiesConstruction LandUnused Land
Cultivated Land100000
Woodland111010
Grassland111110
Water Bodies100100
Construction Land000010
Unused Land111111
Table
Table 2. The changes in LULC in quantity in the Yellow River basin, 2010–2020.
Table 2. The changes in LULC in quantity in the Yellow River basin, 2010–2020.
Land Use TypeArea of LULC in 2010
(103 km2)		The Proportion of the Area of LULC in 2010Area of LULC in 2020
(103 km2)		The Proportion of the Area of LULC in 2020Variable Area
(103 km2)
Cultivated Land208.6426.14%200.5925.13%−8.05
Woodland106.3513.32%107.3413.45%+1.00
Grassland377.6047.31%382.9647.98%+5.36
Water Bodies13.901.74%15.251.91%+1.35
Construction Land19.372.43%28.973.63%+9.60
Unused Land72.359.06%63.097.90%−9.26
Table
Table 3. Changes in the quantitative structure of carbon storage.
Table 3. Changes in the quantitative structure of carbon storage.
Land Use TypeCarbon Storage 2010
(106 t)		Proportion
(%)		Carbon Storage 2010
(106 t)		Proportion
(%)		Carbon Storage 2010
(106 t)		Proportion
(%)		2010–2020 Carbon Storage Changes
(106 t)		2020–Optimizing Carbon Storage Changes
(106 t)
Cultivated Land1308.5926.64%1258.1025.63%1275.7125.85%−50.5017.61
Woodland1023.2720.83%1032.8721.04%1047.7921.24%9.6014.93
Grassland2515.2251.20%2550.9151.96%2545.3951.59%35.70−5.53
Water Bodies0.130.00%0.140.00%0.150.00%0.010.01
Construction Land16.840.34%25.260.51%29.430.60%8.434.17
Unused Land48.180.98%42.020.86%35.660.72%−6.17−6.35
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Xi, F.; Lin, G.; Zhao, Y.; Li, X.; Chen, Z.; Cao, C. Land Use Optimization and Carbon Storage Estimation in the Yellow River Basin, China. Sustainability 2023, 15, 11278. https://doi.org/10.3390/su151411278

AMA Style

Xi F, Lin G, Zhao Y, Li X, Chen Z, Cao C. Land Use Optimization and Carbon Storage Estimation in the Yellow River Basin, China. Sustainability. 2023; 15(14):11278. https://doi.org/10.3390/su151411278

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Xi, Furui, Gang Lin, Yanan Zhao, Xiang Li, Zhiyu Chen, and Chenglong Cao. 2023. "Land Use Optimization and Carbon Storage Estimation in the Yellow River Basin, China" Sustainability 15, no. 14: 11278. https://doi.org/10.3390/su151411278

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