Land use configuration optimization based on ecosystem service enhancement in Xi'an City

Ecosystem services play a crucial role in sustaining human life, providing numerous benefits that are indispensable for our well-being. However, these vital functions are increasingly compromised by land use changes that have been instigated by human activities. This study aims to evaluate the spatiotemporal variability of ecosystem service value (ESV) within the urban agglomeration located on the northern slope of the Tianshan Mountains over a historical period stretching from 1990 to 2020, utilizing land use data to conduct a thorough analysis. Subsequently, the Future Land Use Simulation (FLUS) model was employed to forecast ESV in 2030 under three developmental pathways: Ecological Protection Scenario (EPS), Cultivated Land Protection Scenario (CLPS), and Natural Development Scenario (NDS). The evaluation incorporated six primary land classes: cultivated land, forest land, grassland, water bodies, construction land, and unused land. The FLUS model was validated with strong accuracy (overall accuracy = 0.97, Kappa = 0.94). ESV was estimated using the value coefficient method based on equivalent factors, adjusted with a local economic coefficient for crop production. All values are expressed in constant 2020 CNY without further price normalization. Our results show that between 1990 and 2020, cultivated land expanded by 27.18% (17,721 to 22,538 km2) and construction land increased by 75.91% (1926 to 3388 km2), while grassland decreased from 63,502 to 59,027 km2 and unused land declined from 106,292 to 104,690 km2. Minor changes occurred in forest land and water bodies. Total ESV decreased from 679.06 × 108 CNY in 1990 to 657.67 × 108 CNY in 2020, a decline of 3.15%. Regulating, supporting, and cultural services all decreased, while provisioning services increased. Spatially, vegetated areas functioned as ESV hot spots, whereas construction-degraded areas were identified as cold spots. Scenario projections for 2030 show that under the CLPS and NDS, ESV would further decline by 11.49 × 108 CNY (−1.75%) and 10.18 × 108 CNY (−1.55%), respectively. In contrast, the EPS is projected to increase ESV by 4.53 × 108 CNY (+0.69%), reaching 662.20 × 108 CNY.

Land use change has a significant impact on the structure and function of ecosystems, and the transformation of ecosystems affects the mode and efficiency of land use, which reflects a mutual interaction relationship. The prediction and simulation of future land use change can enhance the foresight of land use planning, which is of great significance to regional sustainable development. In this study, future land use changes are characterized under an ecological optimization scenario based on the grey prediction (1,1) model (GM) and a future land use simulation (FLUS) model. In addition, the ecosystem service value (ESV) of Anhui Province from 1995 to 2030 were estimated based on the revised estimation model. The results indicate the following details: (1) the FLUS model was used to simulate the land use layout of Anhui Province in 2018, where the overall accuracy of the simulation results is high, indicating that the FLUS model is applicable for simulating future land use change; (2) the spatial layout of land use types in Anhui Province is stable and the cultivated land has the highest proportion. The most significant characteristic of future land use change is that the area of cultivated land continues to decrease while the area of built-up land continues to expand; and (3) the ESV of Anhui Province is predicted to increase in the future. The regulating service is the largest ESV contributor, and water area is the land use type with the highest proportion of ESV. These findings provide reference for the formulation of sustainable development policies of the regional ecological environment.

Predicting future urban waterlogging-prone areas by coupling the maximum entropy and FLUS model

Optimization of cultivated land pattern for achieving cultivated land system security: A case study in Heilongjiang Province, China

Regional habitat quality is a proxy of biodiversity. Simulating changes in land use and habitat quality in urban agglomerations is the scientific basis for promoting the optimal allocation of land resources and building ecological civilizations in urban agglomerations. Therefore, we established a research framework mainly consisting of the Future Land Use Simulation (FLUS) model with the Integrated Valuation of Environmental Services and Tradeoffs (InVEST) model to predict the spatial and temporal distribution of habitat quality. In addition, we set three scenarios which were a natural development scenario, a cultivated land protection scenario, and an ecological protection scenario to analyze the changes of habitat quality in the Guanzhong Plain urban agglomeration in 2035. The results showed that: (1) the FLUS model had an excellent effect on the simulation of land-use change in the Guanzhong Plain urban agglomeration, with an overall accuracy of 0.952 and a kappa coefficient of 0.924. (2) From 2000 to 2035, the cultivated land area of the study area, which was mainly transferred into construction land and grassland, shrank due to the process of urbanization. (3) The habitat quality score of this region gradually decreased from 2000 to 2020, and it continued to decrease to 0.6921 in 2035 under the natural development scenario, while it increased under the other two scenarios. The low-value areas of habitat quality were mainly located in the middle of this region with Xi’an as the core, whereas the high-value areas were mainly distributed in the southern Qinling Mountains and the northern Loess Plateau. (4) Of the different scenarios, the ecological protection scenario had the highest habitat quality, while the natural development scenario had the lowest. Besides this, we also found that the cultivated protection scenario had high habitat quality, which was mainly because the rate of occupation of ecological land was controlled. The results are expected to provide a scientific basis for optimizing the spatial allocation of land resources and promoting the sustainable use of land space in other ecologically fragile urban agglomerations.

Rapid economic development and interference by human activities in rapid urbanization regions have caused great land use/land cover change (LUCC), which significantly affects ecosystem functions and services. It is crucial to assess the spatiotemporal evolution of ecosystem service value (ESV) in such regions, especially for the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) in China. In this study, we investigated and predicted the effect of LUCC on the ESV in the GBA from 1990 to 2030 using the latest annual 30 m LUCC database of China, the future land use simulation (FLUS) model, and ecosystem service evaluation approaches. The study period was divided into the historical period (1990–2015) and the forecast period (2015–2030). The results showed that forest and cropland were the dominant land-use types (>77% of the GBA), and the expansion of built-up land (3822.4 km2) was the clearest process during 1990–2015. The reduction of cropland and forest contributed the most to the decrease in the total ESV. Moreover, the results confirmed that the FLUS model is effective at predicting future LUCC in the GBA. The ESV was predicted to decrease to 4962.23 × 100 million yuan in the 2030s under the current development mode if regional forest and waterbody reductions are not constrained. This study provides a reference for promoting the rational use of land resources and ecological construction in the GBA and can help to promote ecological planning and environmental protection.

Land use optimization research based on FLUS model and ecosystem services–setting Jinan City as an example

The response and simulation of ecosystem services value to land use/land cover in an oasis, Northwest China

Multi-scenario simulation and prediction of land use can provide guidance for the optimization of land use patterns. Combining the GMOP model with the PLUS model can better evaluate the influence of different land use strategies on the comprehensive benefits of land use and improve the scientificity of the simulation results. This study takes Haikou City as the research area. As the political, economic, and cultural center of Hainan Province, it is the highest urbanization area in Hainan Province and also the vane of the urban development of Hainan Province. Its development experience and model play an important leading role in the surrounding cities. The land use data of 2010, 2015, and 2020 were selected, and the spatiotemporal pattern of land use under the 2035 Business As Usual scenario (BAU), Economic Development scenario (ED), and Economic and Ecological Balanced Development scenario (EEB) was simulated based on the GMOP-PLUS model. The results show that: (1) The prediction results generally show the trend of the decrease in cultivated land and forest land and the increase in construction land, among which the expansion capacity of construction land is the strongest, and the forest land is more occupied, but the increase and decrease in land use types are different under different scenarios. (2) The three simulation scenarios all show the trend of economic benefit improvement and ecological benefit decline, which indicates that the primary objective of Haikou City’s future development remains focused on economic construction, with the potential compromise of ecological functions to accommodate urban expansion. (3) The comprehensive benefits of the region in the EEB scenario are significantly higher than those in the BAU and ED scenarios. The optimized land use structure is more balanced, the scale of urban expansion is limited, and the loss of important ecological land is reduced to a minimum, which is more in line with the current concept of sustainable development. The study can serve as a reference for the coordinated development of urban planning, land use management, and ecological environment in Haikou.

Accurate prediction of land use and land cover (LULC) change is essential for sustainable development and climate change adaptation planning. This study projects LULC changes across 17 administrative regions of South Korea from 2020 to 2050 using the Future Land Use Simulation (FLUS) model under four integrated SSP-RCP scenarios: SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. The model was calibrated with land cover data for 2000–2010 and validated against observations for 2010–2020 using socioeconomic variables together with CMIP6 climate projections. In practical terms, FLUS produces scenario-based maps of future land patterns that inform land regulation, infrastructure planning, and climate adaptation. Across all scenarios, urban areas expanded by 488,000–585,000 ha, mainly through the conversion of agricultural land, which accounted for 10–24% of transitions in high-growth regions. Agricultural land decreased by 124,000–174,000 ha, and forests declined by 473,000–572,000 ha. Transformation intensity peaked around 2030 and then slowed in later decades. Urban expansion was greatest under SSP5-8.5, followed by SSP3-7.0, SSP1-2.6, and SSP2-4.5. Gyeonggi Province exhibited the most pronounced spatial change, whereas Seoul showed limited additional growth consistent with its already saturated urban structure. Validation results indicated an overall accuracy range of 57–83% with metropolitan areas generally outperforming provincial regions. These findings reveal spatial and temporal hotspots of land cover change and provide region-specific information that can guide urban development, land and ecosystem management, climate adaptation policy, and progress toward carbon neutrality.

The urbanization process has intensified land use changes and ecological pressure, and landscape ecological vulnerability assessment has become a key scientific issue. Taking Fuzhou City as an example, this study constructs a GA-PLUS coupled model based on land use data in 2000, 2010 and 2020, and sets three scenarios of natural development (ND), economic development (ED) and ecological protection (EP) to simulate the spatial pattern of land use in 2030. Unlike traditional approaches that optimize land use areas directly, this study innovatively targets transition probability matrices, enabling more effective scenario differentiation and deeper revelation of policy-driven land conversion mechanisms. The landscape pattern index and landscape vulnerability index were applied to analyze the spatial and temporal evolution characteristics of landscape ecological vulnerability. The results show that: (1) from 2000 to 2020, land use in Fuzhou City has changed significantly, with an increase of 474.68 km2 in construction land and a decrease of 506 km2 in forest land, presenting a spatial pattern of “arable land as the main body, forest land scattered, and construction land clustered”. (2) The simulation of the three scenarios shows that the ED scenario shows the most significant expansion of construction land (change rate of 8.14%), the EP scenario shows the most increase of ecological land, and the ND scenario maintains a relatively balanced development. (3) Landscape pattern changes showed increased fragmentation, but patch cohesion maintained a high level (> 99.95%), and the EP scenario had the highest landscape diversity (SHDI = 0.9164). (4) The landscape ecological vulnerability showed a spatial pattern of “high vulnerability in the southeast and low vulnerability in the northwest”, and the EP scenario could improve the vulnerability situation most effectively, and the area of severely vulnerable area was reduced to 5.70%. The land use change and landscape ecological vulnerability analysis of Fuzhou City were effectively simulated, which provided an important scientific basis for the sustainable development of Fuzhou City.

Protective forests are vital to ecological security in arid desert regions, but their spatial distribution is often inefficient. This study aims to optimize the spatial distribution of protective forests in Alaer City using a combination of the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and the Future Land Use Simulation (FLUS) model. The optimization focuses on three objectives: economic benefits, ecological benefits, and food security. A neural network model is applied to analyze forest distribution suitability based on spatial factors. The results show that the optimized distribution significantly enhances GDP, carbon sequestration, water yield, and food production, while reducing soil erosion. The forest area is mainly concentrated along rivers, agricultural fields, and desert edges, with increased coverage at the Taklamakan Desert’s periphery improving wind and sand resistance. The FLUS model is validated with high accuracy (90.73%). This study provides a theoretical foundation for the sustainable development of protective forests, balancing ecological and economic goals in Alaer City.

Land use change is an important factor in atmospheric carbon emissions. Most of the existing studies focus on modeling the land use pattern for a certain period of time in the future and calculating and analyzing carbon emissions. However, few studies have optimized the spatial pattern of land use from the perspective of the impact of carbon emission constraints on land use structure. Therefore, in this study, the effects of land use change on carbon emissions from 1990 to 2020 were modeled using a carbon flow model for Sanmenxia, Henan, China, as an example. Then, the land use carbon emission function under the low carbon target was constructed, and the differential evolution (DE) algorithm was used to obtain the optimized land use quantity structure. Finally, the PLUS model was used to predict the optimal spatial configuration of land use patterns to minimize carbon emissions. The study produced three major results. (1) From 1990 to 2020, the structural change of land use in Sanmenxia mainly occurred between cultivated land, forest land, grassland and construction land. During this period of land use change, the carbon emissions from construction land first increased and then decreased, but despite the decrease, carbon emissions still exceeded carbon sinks, and the carbon metabolism of land use was still far from equilibrium. (2) Between 2010 and 2020, the area of cultivated land began to decrease, and the area of forest land rapidly increased, and land-use-related carbon emissions showed negative growth. This showed that the structural adjustment of energy consumption in Sanmenxia during the period decreased carbon emissions in comparison with the previous period. (3) A comparison of predicted optimized land use patterns with land use patterns in an as-is development scenario showed a decrease in construction land area of 23.05 km2 in 2030 with a steady increase in forest land area and a decrease in total carbon emission of 20.43 t. The newly converted construction land in the optimized land use pattern was concentrated in the ribbon-clustered towns built during urban expansion along the Shaanling basin of the Yellow River and the Mianchi-Yima industrial development area.

Land use serves as a connecting link between human activities and the natural ecology of the surface; under the multi-objective background of national policies and dual-carbon tasks, land use transformation is studied and simulated in multiple scenarios, and carbon stock changes are analyzed based on future land use to explore the path for a region to achieve multi-objective coordination. Drawing upon land use data from 2000 to 2020 in Lintao County, Gansu Province, we conducted an in-depth analysis of the dynamics governing land use transformation. Subsequently, employing the FLUS (Future Land Use Simulation) model, we simulated the projected land use for Lintao County in 2035 under various scenarios. Furthermore, we utilized the InVEST (Integrated Valuation of Ecosystem Services and Trade-offs) model to assess the change in carbon stock within the study area under each scenario. These analyses aim to furnish a robust scientific foundation for future land use planning endeavors in Lintao County. The conclusions are as follows: (1) The land use transition in Lintao County from 2000 to 2020 showed the strongest motivation for construction land growth, with continued rapid growth in the scale of urban land and other construction land and relatively slow growth in the land for rural settlement areas, while cropland and water areas continued to decrease, forest land grew slowly, the magnitude of land use change exhibited a higher intensity in river townships compared with mountainous townships. (2) The simulation results of cropland protection scenario (CPS), ecological protection scenario (EPS), economic development scenario (EDS), and comprehensive development scenario (CDS) in 2035 are better. Among them, the CDS, which considers various types of higher-level strategic requirements and can compensate for the single-goal nature of the single-demand scenario, demonstrates a higher level of rationality in the land use pattern. (3) The total carbon stock in descending order is the EPS, CDS, EDS, and CPS. Among these, the CDS is at a higher level of total carbon stock, and the changes in carbon stock in each land use site are more balanced, which is an ideal carbon stock state and a scenario more in line with multi-objective coordination.

This study analyzes the outcomes of Cellular Automata (CA) with different neighborhood sizes and spatial resolution configurations on the performance of the Future Land Use Simulation (FLUS) model. The analysis is executed using three analogic images to extract the land use/land cover in Bogota, Colombia, for three years: 1998, 2004, and 2010. The FLUS model has an Artificial Neuronal Network model, which was used for calculating the relationships between the land uses and the associated drivers and to estimate the probability of occurrence of each land use. Whenever a CA is used to model and simulate, sensitivity analysis (SA) becomes a crucial step in CA modeling to understand better the influence of parameters’ changes in the simulation outcomes. Therefore, the SA is conducted by varying the neighborhood sizes between 3 × 3, 5 × 5, and 7 × 7 for 5 and 30 meters. In addition, cross-classification maps, Area Under the Curve (AUC) of the Total Operating Characteristic, landscape metrics, the figure of merit, Fuzzy Kappa, and disagreement metrics were calculated to assess how well the model performed. High AUC values and low disagreement results show that, in general, the model performed well, and the accuracy of the outputs improves with a 3 × 3 neighborhood size and 5 meters spatial resolution. This study provides a broad assessment approach to the different methods that must be considered to evaluate the sensitivity of CA models in the simulation of urban wetlands’ spatial-temporal evolution.