Land utilization optimization method based on ecological system service supply and demand conflict balance

Abstract

The invention discloses a land utilization optimization method based on ecological system service supply and demand conflict balance. The method comprises the following steps: step 1, measuring and calculating supply and demand of each type of ES; step 2, ES supply and demand conflict area identification under the optimal analysis granularity; step 3, ES supply and demand dominant conflict area type identification; 4, performing multi-scene optimization on the land utilization quantity structure under the ES supply and demand conflict balance; step five, land utilization space pattern multi-scene optimization under ES supply and demand conflict balance; and 6, comparing land utilization optimization results under different scenes. According to the method, by means of multi-element models such as InVEST, RUSLE, linear distribution, ESDR, CESDR, GMLP and PLUS, land utilization optimization under multiple scenes is comprehensively carried out by taking optimization of a land utilization pattern and alleviation of ES supply and demand conflicts as targets, land utilization as a link, land utilization development probability as a link and ES supply and demand conflict balancing as a means.

Description

Technical Field

[0001] The present invention belongs to the fields of land resource management and national land space planning, and in particular relates to a land use optimization method based on the trade-off between supply and demand of ecosystem services. Background Art

[0002] Ecosystem services (ES) are the total benefits humans derive from ecosystems. ES supply and demand are a crucial link between human society and the natural environment, crucially impacting human well-being and sustainable development goals. With the rapid development of urbanization and industrialization, coupled with the disorganization of regional multidimensional development goals, competition and conflicts over land resource use have become increasingly prominent, leading to dramatic shifts in land use types and structures. This, in turn, significantly alters ecosystem structure, function, and processes. This exacerbates the imbalance between ES supply and demand, leading to ES supply and demand conflicts and posing a serious threat to ecological security and the sustainable development of human society.

[0003] ES supply-demand conflict refers to spatial imbalances, mismatches, and incoordination between supply and demand, manifesting as a deficit in absolute terms. ES supply-demand conflict is inherently resistant to resolution and can only be mitigated through specific measures. ES supply-demand conflict trade-offs, which employ game theory to identify solutions that minimize ES supply-demand conflicts, provide an important means of mitigating ES supply-demand conflicts and are of great significance for improving ES supply-demand relationships, enhancing ecological and environmental quality, and enhancing human well-being. Numerous studies have shown that ES supply-demand conflicts severely disrupt ecological balance, leading to ecological deficits and environmental injustice. However, irrational land development and a deteriorating ecological environment are exacerbating ES supply-demand conflicts, making them a key constraint on regional sustainable development. Studies on ES supply-demand conflict rarely address ES supply-demand conflict trade-offs, and a mature research framework for ES supply-demand conflict trade-off techniques and methods is lacking. Furthermore, ES supply-demand conflicts are complex and diverse in nature. Some scholars recognize that while ES supply-demand conflicts are difficult to resolve directly, they can be mitigated indirectly through measures such as ecological zoning, landscape planning, and sustainable land management. However, existing ES supply and demand conflict mitigation methods have not yet considered complex ES supply and demand conflict types and conflict trade-off methods, nor have they incorporated them into the spatial management framework of ES supply and demand conflicts. The ES supply and demand imbalance phenomenon has not been effectively improved.

[0004] Land is a vital vehicle for ecosystems. Adjusting and optimizing land use structure and pattern can ensure that different land uses meet corresponding ecological thresholds and maximize land use benefits, making it a crucial means of achieving sustainable ecological, economic, and social development. In recent years, some studies have emphasized incorporating ES supply and demand relationships into land use development and management decisions, offering new approaches for alleviating ES supply and demand conflicts. Other studies have also suggested that increasing ES supply capacity through land use optimization is a necessary strategy for alleviating ES supply and demand conflicts. Therefore, multi-scenario optimization of land use quantitative structure and spatial pattern from the perspective of ES supply and demand trade-offs can not only alleviate ES supply and demand conflicts and improve the ES supply and demand balance, but also achieve a balance between ecological, economic, and social benefits, providing a new perspective for making more informed land use decisions. However, existing technical methods for land use optimization have yet to further explore the connection between ES supply and demand conflicts, land use, and socioeconomic systems. Furthermore, they have not addressed the trade-offs between ES supply and demand, the adjustment of land use structure within conflict zones, and the revision of development probabilities. Consequently, a technical framework and method for alleviating ES supply and demand conflicts through multi-scenario land use optimization is lacking. There is still a large gap between the land use optimization plan and the actual needs of regional planning, and its guiding significance for national land space planning and ecological environment management is limited. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a land use optimization method based on the trade-off between supply and demand of ecosystem services in response to the shortcomings of the background technology. The method aims to alleviate the effective ES supply and demand conflict through land use optimization, and is used to solve the current problems of insufficient consideration of ES supply and demand conflicts in land use optimization, immature ES supply and demand conflict trade-off technology, and mismatch between land use optimization schemes and the actual multi-dimensional development needs of the region.

[0006] The present invention adopts the following technical solutions to solve the above technical problems: A land use optimization method based on the trade-off between supply and demand of ecosystem services includes the following steps: Step 1: Calculate the supply and demand of various types of ES. Use InVEST, RUSLE, and linear allocation models to calculate the supply and demand of important ES types in the study area. Perform corresponding local parameterization corrections in the process to reveal the spatial pattern of supply and demand of various types of ES. Step 2: Identify ES supply and demand conflict areas at the optimal analysis granularity. Based on the land use type and spatial resolution of the current year, set the initial granularity and granularity increment step for identifying ES supply and demand conflicts. Calculate the landscape pattern index that can characterize landscape connectivity at different granularities. Determine the optimal analysis granularity for identifying ES supply and demand conflict areas based on the changes in the landscape pattern index. Calculate the ESDR and CESDR values ​​of each type of ES in each evaluation unit using the ESDR and CESDR models based on the optimal analysis granularity. Identify evaluation units with CESDR values ​​< 0 as ES supply and demand conflict areas. Step 3: Identify the types of ES supply and demand-dominated conflict zones. Based on the ESDR size relationships of various ES types calculated in Step 2 and the identified ES supply and demand conflict zones, the constructed 2-fold principle is used to identify the types of ES supply and demand-dominated conflict zones to which each evaluation unit in the conflict zone belongs. This is the key to balancing ES supply and demand conflicts and optimizing land use. Step 4: Multi-scenario optimization of land use quantity structure under ES supply-demand conflict trade-offs. The regression relationship between the CESDR within the conflicting unit and the area ratio of different land use types is used to define the area thresholds of different land use types under ES supply-demand balance. The CESDR objective function (one of the ecological benefit objective functions) is constructed based on the regression relationship between the area of ​​different land use types and the CESDR mean of each type of land use. Taking the area threshold of each land use type under ES supply-demand balance as the primary constraint and considering future land development needs, various land area constraints and development goals under different scenarios (including ecological benefit, economic benefit, social benefit, and comprehensive benefit) are set. The GMLP model is then used to comprehensively conduct multi-scenario optimization of land use quantity structure under conflict trade-offs. Step 5: Multi-scenario optimization of land use spatial pattern under the trade-off between ES supply and demand conflicts; based on the land use data of the base year and the current year, combined with the driving factors of regional land use spatiotemporal evolution, a PLUS model for optimizing land use is constructed, model training and accuracy evaluation are performed, and the initial development probability of each land use type is obtained; based on the type characteristics of the ES supply and demand dominant conflict zone identified in step 3, land use conversion rules and land use development probability correction principles for the ES conflict zone are set to obtain the corrected land use development probability for the ES supply and demand conflict zone; based on the multi-scenario optimization results of the land use quantitative structure under the ES conflict trade-off in step 4, combined with the land use conversion rules and corrected land use development probability of the ES supply and demand conflict zone and non-conflict zone, the corrected land use development probability layers of the study area under different scenarios are obtained, and the PLUS model is used to carry out multi-scenario optimization of land use pattern under the trade-off between ES supply and demand conflicts.

[0007] Step 6: Compare the land use optimization results under different scenarios. Based on the land use optimization results of different scenarios obtained in step 5, analyze the conflict mitigation effect and landscape ecological pattern of the ES supply and demand conflict area, and reveal the advantages of the multi-scenario land use optimization method under the trade-off of ES supply and demand conflict.

[0008] Furthermore, in step one, various types of ES refer to important regional ES types, such as habitat quality (HQ), carbon sequestration (CS), soil conservation (SC), water conservation (WC), food production (FP) and other services; ES supply and demand is a general term for ES supply and demand. ES supply refers to the various services provided by the ecosystem of a specific region to human society within a specific period of time, and ES demand refers to the services obtained by human society from the ecosystem for survival and development; ES supply and demand are calculated using models such as the InVEST model, the linear allocation model, and the revised universal soil loss equation (RUSLE). In this process, field observation data and industry statistical data are combined to make local corrections to the corresponding parameters.

[0009] Furthermore, in step 2, land use types can be divided according to specific research objectives, referring to the land use classification system of the Chinese Academy of Sciences or combining regional realities, and their spatial resolution can be determined comprehensively based on regional realities; considering the attributes of decision variables in the land use quantitative structure optimization model (GMLP model), each land use type is divided into x i To express ( x i Indicates the iThe optimal analysis granularity refers to the optimal evaluation unit size for identifying ES supply and demand conflicts (i.e., the grid size when the overall landscape connectivity of the region is maximized after the land use raster data is resampled, i.e., the granularity). In determining the optimal analysis granularity, the landscape connectivity indicators involved may include the number of patches (NP), patch density (PD), connectivity (CONNECT), spread (CONTAG), aggregation (AI), etc., and the initial granularity and granularity increase step can be determined according to the actual situation of the region to identify the optimal analysis granularity for ES supply and demand conflicts. The analysis granularity is ultimately determined as the granularity that maximizes the overall landscape connectivity of the region. The ecological supply-demand ratio (ESDR) of different types of ES can characterize the supply-demand relationship of each type of ES, and is expressed as the proportional relationship between the difference between the supply and demand of different types of ES (i.e., the supply-demand difference) and the average of the maximum ES supply and maximum ES demand (i.e., the mean of the maximum supply and demand). The comprehensive supply-demand ratio (CESDR) is represented by the arithmetic mean of the ESDR of different types of ES. If CESDR>0, it means that the overall ES supply in the region is greater than the demand, that is, the overall ES supply and demand is surplus. If CESDR = 0, it means that the overall ES supply and demand is balanced. If CESDR<0, it means that the overall ES supply and demand is deficit. The evaluation unit with CESDR<0 is identified as an ES supply-demand conflict area, and the smaller the CESDR value, the greater the degree of ES supply-demand conflict.

[0010] Furthermore, in step 3, the ES supply and demand conflict cannot be completely resolved. Identifying the ES supply and demand dominant conflict area can clarify the key ES types that cause the supply and demand deficit in the conflict unit, and achieve the purpose of alleviating the ES supply and demand conflict in a focused manner. The process of identifying the type of ES supply and demand dominant conflict area is as follows: First, clarify the ES types with supply and demand deficit (i.e. ESDR is less than 0) in each conflict unit (assuming that the ES types with supply and demand deficit are m Class), secondly, calculate m Class ES i The absolute value of the ES-like ecological supply and demand ratio (ESDR Ai ) and this m The absolute value mean of the ecological supply-demand ratio of ES types ( MESDR A ), and finally, compare ESDR Ai and MESDR i The size relationship of each conflict unit is determined by the 2-fold principle. The 2-fold principle mainly uses the ESDR mean of each type of ES in the conflict unit and the 2-fold relationship between the ESDRs of each type of ES to identify the type of ES supply and demand dominated conflict zone. The 2-fold principle is explained as follows: If there is only a certain type of ES in a conflict unit, ESDR A (i =1 ) is greater than MESDR A , then the grid unit is determined to be a single dominant ES supply and demand conflict. For example, if in a conflict unit, there is only HQ ESDR A Greater than MESDR A , it indicates that HQ plays a leading role in the formation of ES supply and demand conflict, and the conflict unit is HQ-dominated conflict. If there are two types of ES in a certain conflict grid unit, ESDR A （i=2） Both greater than MESDR A , we further compare the two types of ES ESDR A Size, determine the largest ESDR A Is it greater than 2 times the second largest ESDR A , if so, then the conflict unit is composed of ESDR A The ES with the maximum value dominates (i.e., it is still a single dominant ES supply and demand conflict), otherwise the conflict unit is dominated by both ES types mentioned above (i.e., a combined dominant ES supply and demand conflict). For example, if in a conflict unit, the HQ and CS ESDR A Both greater than MESDR A , and HQ's ESDR A More than 2 times CS ESDR A , indicating that HQ plays a leading role in the formation of ES supply and demand conflict, so the conflict unit is still HQ-dominated conflict; if the HQ and carbon CS ESDR A Are greater than MESDR A , and HQ's ESDR A Less than 2 times CS ESDR A , indicating that HQ and CS play a leading role in forming ES supply and demand conflict, then the conflict unit is identified as HQ-CS dominant conflict. If there are three or more types of ESDR A Greater than MESDR A The dominant conflict zone types are still determined in sequence using the above method, and ultimately the ES supply and demand dominant conflict zone types corresponding to all conflict units can be determined.

[0011] Furthermore, in step 4, the trade-off between ES supply and demand in the process of optimizing the land use quantity structure mainly includes two aspects: first, defining the area threshold of each land use type under the ES supply and demand balance; second, setting the CESDR maximization target, so as to solve the optimal land use quantity structure that is conducive to improving the regional ES supply capacity and expanding the positive difference between ES supply and demand (i.e., the supply and demand difference is positive), thereby improving the regional ES supply and demand balance and alleviating the ES supply and demand conflict; defining the area threshold of each land use type under the ES supply and demand balance aims to solve the land use area threshold that can make the ESDR of each type of ES ≥ 0 based on the regression relationship between the ESDR of each type of ES and the area ratio of different land use types, so as to achieve the goal of improving the regional ES supply and demand balance and alleviating the ES supply and demand conflict. The least squares (OLS) regression model is mainly used to characterize the regression relationship between the ESDR of each type of ES and the area ratio of different land use types in all evaluation units. Among them, only the model with a high fit (R²) and passing the significance test (P value < 0.001) is retained to solve the area threshold of each land use type under the ES supply and demand balance; the goal of maximizing the comprehensive ecological supply and demand ratio is to construct an objective function through the relationship between the CESDR mean of each land use type and the area of ​​each type of land use to achieve the regional CESDR (that is, to minimize the degree of ES supply and demand conflict), so as to achieve the goal of improving the ES supply and demand balance of the region and alleviating the ES supply and demand conflict. This objective function is also one of the ecological benefit objective functions in the regional multidimensional development goals; the regional multi-objective function mainly includes four objective functions: ecological benefit, economic benefit, social benefit and comprehensive benefit. Among them, the ecological benefit objective function is composed of the CESDR objective function and the ecosystem service value objective function. The ecosystem service value objective function is represented by the product of the unit area ecosystem service value of each type and its area. The economic benefit objective function is represented by the product of the unit area economic benefit coefficient of each type and its area. The social benefit objective is represented by the weighted sum of per capita cultivated land area, per capita garden area, per capita green space area, per capita water area and per capita construction land area data; the multiple scenarios involved in land use optimization include natural development scenario (NDS), ecological protection scenario (ESD), and ecological protection scenario (ESD). The NDS (Natural Development Scenario), Socio-Economic Development Scenario (SDS), and Comprehensive Development Scenario (CDS) all involve different regional development objective functions. The NDS does not involve an objective function (continuing the historical development trend of the current land use development), the EPS involves the CESDR maximization objective function and the ecosystem service value objective function, the SDS involves the social benefit objective function and the economic benefit objective function, and the CDS involves the CESDR maximization objective function, the ecosystem service value objective function, the social benefit objective function, and the economic benefit objective function. The land use area constraint is determined by the land area threshold under the ES supply and demand balance and the land area threshold corresponding to future land use development demand. The land area threshold corresponding to future land use development demand is determined by combining the current land use area, the future population predicted by the GM (1,1) model, and relevant regional superior planning documents. The multi-scenario optimization of the land use quantity structure under the ES supply and demand trade-off is to use the GMLP model to solve the optimal land use quantity structure by combining the land use area threshold under the ES supply and demand balance, the land area threshold corresponding to future land use development demand, and the regional development goals under different scenarios.

[0012] Furthermore, in step five, regional land use data and the drivers of spatiotemporal land use evolution provide a crucial foundation for constructing the PLUS model. These drivers include elevation, slope, aspect, distance to rivers, soil type, temperature, precipitation, GDP, population density, distance to roads, distance to residential areas, ecological protection red lines, and permanent basic farmland. PLUS model simulations determine the importance of different drivers to spatiotemporal land use evolution and the probability of initial land use development. PLUS model simulation accuracy is comprehensively assessed using overall accuracy, Kappa coefficient, and FoM coefficient. Higher values ​​for these indicators indicate higher PLUS model simulation accuracy. The ES supply-demand conflict trade-off in the process of optimizing the spatial pattern of land use mainly includes two aspects: one is the setting of land use conversion rules based on the types of ES supply-demand dominant conflict zones; the other is the correction of land use development probabilities based on the types of ES supply-demand dominant conflict zones. The purpose is to set the rules and conversion probabilities (i.e., land use development probabilities) of land use conversion in ES supply-demand conflict zones so that each land use type can be converted to a land use type with high ES (ES here refers to the key ES type identified in step 3 that causes supply-demand conflicts in the unit) supply capacity, thereby achieving the goal of improving the ES supply-demand balance and alleviating ES supply-demand conflicts. Land-use conversion rules are the basis for determining the probability of land-use development. With respect to regional land-use conversion, with the exception of urban construction land and water areas, which are difficult to convert to other land uses, conversions between other land types must follow the basic rules for land-use conversion under different scenarios (see the description of land-use conversion rules in the Specific Implementation Methods regarding the differences in attributes between scenarios for details). This is the basic conversion rule that must be followed for land-use conversion within both ES supply-demand conflict zones and non-conflict zones. Furthermore, within ES supply-demand conflict zones, in addition to meeting the basic rules for land-use conversion under different scenarios, land types within each conflicting unit are more likely to convert to land types with higher ES supply capacity to alleviate ES supply-demand conflicts. Specifically, the higher the ES supply capacity of a particular land type, the higher the probability that other land types within the conflict zone will convert to that land type (i.e., the land-use development probability). This is the most important rule for land-use conversion within ES supply-demand conflict zones. As long as the basic conversion rules between land types are met, land types within ES supply-demand conflict zones can convert to each other, but the conversion probabilities vary.The land-use development probability of each land use based on the ES supply-demand dominant conflict zone type (i.e., the revised land-use development probability) is directly related to the initial development probability of each land use type, the dominant conflict zone type to which the conflict unit belongs, and the ES supply capacity of each land use type. The portion of the probability determined by the dominant conflict zone type and the ES supply capacity of each land use type is called the revised probability (expressed as the ratio of the mean ES supply of each land use type under the key ES determined above to the sum of the mean ES supply of all land use types). Therefore, the revised development probability of each land use type is considered to be the development probability of each land use type obtained by increasing the revised probability unit based on the initial probability of each land use type. The land-use development probability calculation method based on the ES supply-demand dominant conflict zone type can be specifically described as follows: If the ES supply-demand conflict type is the single dominant conflict type determined in step 3, such as an HQ-dominated conflict, then HQ is the key ES type causing the conflict. In land use optimization, it is necessary to focus on improving HQ supply capacity to alleviate this conflict. Within this conflict zone, the revised probability of each land use type is calculated as the ratio of the mean HQ supply of that land use type to the sum of the mean HQ supply of all land use types (. ) indicates that the revised development probability of each land type is regarded as the initial development probability of the land, and the revised probability under HQ is increased ( ) unit volume; if the ES supply-demand conflict type is the combination-dominated conflict determined in step 4, such as the HQ-CS-dominated conflict, then HQ and CS are the key ES types causing the conflict. In land use optimization, it is necessary to focus on improving the supply capacity of HQ and CS to alleviate this type of conflict. In the conflict area, the modified probability of each land type is determined by the modified probability under HQ ( ) and the modified probability under CS ( ) are composed of two parts, and the revised development probability of each land type is regarded as the initial development probability of the land, respectively increasing the revised probability under HQ ( ) and CS correction probability ( ) unit volume; for other ES supply-demand conflict zone types, the calculation method for the revised land use development probability of each land use in the ES supply-demand conflict zone is the same as above; while the land use development probability in non-ES supply-demand conflict zones is based on the land use conversion rules under different scenarios. In the initial development probability layer of a certain land use type, the land use development probability of areas that cannot be converted to that type of land use is set to 0, and the initial development probability is still used for other areas. The multi-scenario optimization of the spatial pattern of land use under the ES supply-demand conflict trade-off uses the optimal land use quantity structure under the ES supply-demand conflict trade-off determined in step 4 as the future area demand for each type of land use. At the same time, the land use type conversion rules and the revised land use development probabilities are simultaneously incorporated into the PLUS model to finally obtain the land use spatial pattern optimization results under different scenarios.

[0013] Furthermore, in step six, the ES supply-demand conflict cannot be completely resolved. The effectiveness of alleviating the ES supply-demand conflict is characterized by the CESDR value within the conflict zone. The larger the CESDR value within the conflict zone, the higher the conflict mitigation effect, and vice versa. The landscape ecological pattern is reflected by the landscape pattern index that characterizes landscape connectivity as described above. The advantages of land use optimization methods under different scenarios of ES supply-demand conflict trade-off scenarios are analyzed through the conflict mitigation effect of the ES supply-demand conflict zone and the regional landscape connectivity.

[0014] Compared with the prior art, the present invention adopts the above technical solution and has the following technical effects: The present invention proposes an identification method for ES supply and demand dominant conflict areas based on the supply and demand relationship of various types of ES and combined with the 2-fold principle. There are many types of ES supply and demand conflicts, and different types of conflicts are interconnected and influence each other; and ES supply and demand conflicts are hierarchical, and different regions will be dominated by different conflict types. The types of ES supply and demand dominant conflict areas can better reflect the conflict characteristics of the region. Compared with traditional methods for identifying ES supply and demand conflict areas, the ES supply and demand dominant conflict area identification method proposed in the present invention can take into account the quantitative relationship between ESDRs of various types of ES, objectively define the key ES types that cause ES supply and demand deficits within the ES supply and demand conflict area, and more scientifically identify the types of ES supply and demand dominant conflict areas to which each evaluation unit belongs; and the key ES types defined above can be directly linked to the ES supply capacity of various types of land use, building a bridge for alleviating ES supply and demand conflicts through ES supply and demand conflict trade-offs and land use optimization, which is more feasible and practical for alleviating ES supply and demand conflicts; The present invention closely links the ES supply and demand relationship, the types of ES supply and demand dominant conflict zones, and the ES supply capacity of each type of land, and constructs an ES supply and demand conflict trade-off technology that includes the definition of land use area thresholds under ES supply and demand balance, the setting of CESDR maximization objective functions, and the setting of land use conversion rules based on the types of ES supply and demand dominant conflict zones and the correction of land use development probabilities. Traditional ES supply and demand conflict research rarely involves ES supply and demand conflict trade-off technology, and mostly focuses on research based on ES supply capacity improvement strategies. The ES supply and demand conflict trade-off technology proposed in the present invention is beneficial for improving the overall ES supply and demand relationship in the region from a macro perspective, and is also beneficial for focusing on improving the supply capacity of key ES types within the ES supply and demand conflict zone from a micro perspective, thereby increasing the positive difference between the supply and demand of key ES types (i.e., reducing the degree of ES supply and demand deficit in the conflict zone). This provides technical support for the scientific alleviation of ES supply and demand conflicts, and the alleviation of conflicts is more targeted and operational. 3. The present invention seeks the intersection of land use and ES supply and demand conflicts, incorporates the ES supply and demand conflict trade-off into the entire process of land use quantitative structure and spatial pattern optimization, and proposes a technical method to improve the ES supply and demand relationship and alleviate ES supply and demand conflicts through multi-objective optimization of land use. Compared with traditional land use optimization, the multi-scenario optimization method of land use based on ES supply and demand conflict trade-off proposed in the present invention aims to alleviate ES supply and demand conflicts, takes land use as the basic carrier, and takes the land area threshold under ES supply and demand balance as the primary constraint of the land use quantitative structure. It optimizes the land use quantitative structure under different scenarios in combination with the multi-dimensional development goals of regional land use and future land development needs, and uses the land use conversion rules based on the dominant type of ES supply and demand and the revised land use development probability as the data basis for land use spatial pattern optimization to comprehensively carry out multi-scenario optimization of land use. This is more conducive to improving the ES supply and demand relationship and alleviating ES supply and demand conflicts in the process of land use optimization, while promoting the realization of multi-dimensional development goals coordinated with regional ecological environmental protection and social and economic development. 4. The key points and protection points of the present invention are to organically link the ES supply and demand relationship, the ES supply capacity of various types of regions, the ES supply and demand conflicts and the dominant conflict types, the regional multi-dimensional development goals, and the multi-scenario optimization of land use. From the perspective of ES supply and demand conflicts, a land use optimization method based on ES supply and demand conflict trade-offs is constructed, which achieves the goal of alleviating ES supply and demand conflicts in the process of optimizing the quantitative structure of land use and optimizing the spatial pattern. It provides a new perspective and technical method support for improving the regional ES supply and demand relationship and coordinating the contradictions between regional ecological environmental protection and social and economic development, and provides a decision-making basis for the county to carry out sustainable ecosystem management and land space planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A technical roadmap for land use optimization based on ES supply-demand trade-offs; Figure 2 the spatial pattern of supply and demand of different types of ES; Figure 3 The spatial pattern of land use in the starting year (2005) and the current year (2020) Figure 4 is the landscape pattern index at different granularity levels; Figure 5 is the spatial pattern of each type of ESDR at the optimal granularity level; Figure 6 It is the result of comprehensive ecological supply and demand ratio and ES supply and demand conflict zone division; Figure 7 The results of the classification of ES supply and demand-dominated conflict zone types; Figure 8The regression relationship between ESDR of each type of ES and the area proportion of each land use type (Part 1); Figure 9 The regression relationship between the ESDR of each type of ES and the area proportion of each land use type (Part 2); Figure 10 is the initial development probability of each land use type; Figure 11 The revised development probability of each land use in the ES supply and demand conflict zone under different scenarios (only key land use types are shown); Figure 12 The revised development probability of each land use in the study area under different scenarios (only key land use types are shown); Figure 13 These are the multi-scenario optimization results of land use under the trade-off between ES supply and demand. DETAILED DESCRIPTION

[0016] The technical solution of the present invention is further described in detail below with reference to the accompanying drawings: The present invention discloses a land use optimization method based on the trade-off between supply and demand of ecosystem services, such as Figure 1 As shown, the following steps are included: Step 1: Calculation of supply and demand of various types of ES: Using models such as InVEST, RUSLE, and linear allocation, the supply and demand of important ES in the study area are calculated respectively, and corresponding local parameterization corrections are made in the process to reveal the spatial pattern of supply and demand of various types of ES; Specifically, combining field sampling data and industry statistical data, the InVEST model, linear distribution model, RUSLE model, etc. were used to calculate and revise the supply and demand of habitat quality (HQ), carbon sequestration (CS), soil conservation (SC), water conservation (WC), and food production (FP) in the study area, and spatially visualized. ES supply refers to the amount of services provided by the ecosystem in a specific area to human society within a specific time, including the supply of important ES such as HQ, CS, SC, WC, and FP. ES demand refers to the amount of services obtained by human society from the ecosystem for survival and development, including the demand for important ES such as HQ, CS, SC, WC, and FP. The specific calculation formulas for the supply and demand of each type of ES are as follows:

[0017]

[0018] Step 2: Identification of ES supply and demand conflict areas at the optimal analysis granularity: Based on the land use type and spatial resolution of the current year, set the initial granularity for identifying ES supply and demand conflicts and the step size for increasing the granularity, calculate the landscape pattern index that can characterize landscape connectivity at different granularities, and determine the optimal analysis granularity for identifying ES supply and demand conflict areas based on the changes in the landscape pattern index; based on the optimal analysis granularity, use the ESDR model and CESDR model to calculate the ESDR value and CESDR value of each type of ES in each evaluation unit, and identify the evaluation unit with a CESDR value <0 as an ES supply and demand conflict area.

[0019] Specifically, referring to the land use classification system of the Chinese Academy of Sciences and combining with the actual situation of the study area, the land use types of the study area are divided into cultivated land (including dry land ( x 1) and paddy fields ( x 2)), Garden (Orchard ( x 3) Tea Garden x 4) Rubber x 5)), forest land (including eucalyptus ( x 6) Simao pine ( x 7) Shrubs x 8) and other woodlands ( x 9)), Grassland ( x 10 ), water areas ( x 11 ), construction land (including urban construction land ( x 12 )、Rural construction land( x 13 ) and industrial and mining construction land ( x 14 )) and unused land ( x 15 ) There are 7 first-level land types and 15 second-level land types. x 1 , x 2 , x 3 , x 4 , x 5 , x 6 , x 7 , x 8 , x 9 , x 10 , x 11 , x 12 , x 13 , x 14 , x 15are the decision variables corresponding to each category in the land use structure optimization model (GMLP model); based on the land use data of the current year and its resolution, the initial granularity is set to 100 m and the granularity increase step is 100 m. m, the number of patches (NP), patch density (PD), connectivity (CONNECT) and other landscape indices that can characterize the landscape connectivity of the study area were selected to calculate the above-mentioned landscape indices at different granularities, and the change diagram of each landscape index with increasing granularity was drawn. Finally, the optimal analysis granularity for identifying the conflict between ES supply and demand structure was determined to be the granularity that can maximize the landscape connectivity of the entire region. Based on the determined optimal analysis granularity, the ecological supply and demand ratio (ESDR) model was used to calculate the ESDR values ​​of different types of ES. This index characterizes the supply and demand relationship of each type of ES by the proportional relationship between the difference between the supply and demand of different types of ES (i.e., the supply and demand difference) and the average value of the maximum supply and maximum demand of ES (i.e., the mean value of the maximum supply and demand). CESDR is represented by the arithmetic mean of ESDR of different types of ES. If CESDR>0, it means that the overall ES supply and demand of the region is in surplus. If CESDR = 0 indicates that the overall ES supply and demand is balanced. If CESDR is less than 0, it indicates an overall ES supply and demand deficit. The evaluation unit with CESDR less than 0 is identified as an ES supply and demand conflict area, and the smaller the CESDR value, the greater the degree of ES supply and demand conflict.

[0020] The calculation formulas for NP, PD, and CONNECT are as follows:

[0021] The calculation formulas for the ecological supply and demand ratio (ESDR) and the comprehensive ecological supply and demand ratio (CESDR) are as follows:

[0022] Where, ESDR in For the The first evaluation unit (i.e. the optimal granularity) n The ecological supply and demand ratio of ES, ESS in For the Evaluation unit No. n The supply of ES i The sum of the supply of the nth ES of all pixels in the evaluation unit), ESD in For the Evaluation unit No. n The demand for the first ES The sum of the demand for the nth ES of all pixels in the evaluation unit), for The maximum value of for The maximum value of For the i The comprehensive ecological supply and demand ratio of each evaluation unit.

[0023] Step three: Identify the types of ES supply and demand-dominated conflict zones. Based on the ESDR size relationship of each type of ES calculated in step two and the identified range of the ES supply and demand conflict zone, the constructed 2-fold principle is used to identify the types of ES supply and demand-dominated conflict zones to which each evaluation unit in the conflict zone belongs. This is the key to carrying out ES supply and demand conflict trade-offs and land use optimization.

[0024] Specifically, first, we need to identify the ES types with supply and demand deficit (i.e. ESDR is less than 0) in each conflict unit (assuming that the ES types with supply and demand deficit are m Class); secondly, calculate m Class ES i The absolute value of the ES-like ecological supply and demand ratio (ESDR Ai ) and this m The absolute value of the ecological supply-demand ratio of ES ( MESDR A Finally, compare ESDR Ai and MESDR A The size relationship of each conflict unit is determined by the 2-fold principle. The 2-fold principle mainly uses the ESDR mean of each type of ES in the conflict unit and the 2-fold relationship between the ESDRs of each type of ES to identify the type of ES supply and demand dominated conflict zone. The 2-fold principle is specifically described as follows: If there is only a certain type of ES in a conflict unit, ESDR A ( i=1 ) is greater than MESDR A , then the grid unit is determined to be a single dominant ES supply and demand conflict. For example, if in a conflict unit, there is only HQ ESDR A Greater than MESDR A , it indicates that HQ plays a leading role in forming the ES supply and demand conflict, and the conflict unit is identified as HQ-dominated conflict. If there are two types of ES in a certain conflict grid unit, ESDR A （i=2） Both greater than MESDR A , we further compare the two types of ES ESDR A Size, determine the largest ESDR A Is it greater than 2 times the second largest ESDRA , if so, then the conflict unit is composed of ESDR A The ES with the maximum value dominates (i.e., it is still a single dominant ES supply and demand conflict), otherwise the conflict unit is dominated by the above two types of ES (i.e., a combined dominant ES supply and demand conflict). For example, if in a certain conflict unit, the HQ and CS ESDR A Both greater than MESDR A , and HQ's ESDR A More than 2 times CS ESDR A , indicating that HQ plays a leading role in forming the ES supply and demand conflict, then the conflict unit is still identified as HQ-dominated conflict; if HQ and CS ESDR A Are greater than MESDR A , and HQ's ESDR A Less than 2 times CS ESDR A , indicating that HQ and CS play a leading role in forming ES supply and demand conflict, then the conflict unit is identified as HQ-CS dominant conflict. If there are three or more types of ESDR A Greater than MESDR A The dominant conflict zone types are still determined in sequence using the above method, and ultimately the ES supply and demand dominant conflict zone types corresponding to all conflict units can be determined.

[0025] ESDR Ai and MESDR A The calculation formula is as follows:

[0026] Where, ESDR Ai For those with a supply-demand deficit m Class ES i The absolute value of ESDR of ES class, m is the total number of ES types with supply and demand deficit, MESDR A For this m The mean of the absolute values ​​of ESDR for ES-like conditions.

[0027] Step 4: Multi-scenario optimization of land use quantity structure under ES supply-demand conflict trade-off; define the area threshold of different land use types under ES supply-demand balance based on the regression relationship between CESDR and the area ratio of different land use types within the conflict evaluation unit; construct the CESDR objective function (one of the ecological benefit objective functions) based on the regression relationship between the area of ​​different land use types and the CESDR mean of each type of land use; take the area threshold of each land use type under ES supply-demand balance as the primary constraint, and consider future land use development needs, set various land use area constraints and development goals under different scenarios (including ecological benefit goals, economic benefit goals, social benefit goals and comprehensive benefit goals), and use the GMLP model to comprehensively carry out multi-scenario optimization of land use quantity structure under conflict trade-off.

[0028] Specifically, the ES supply and demand conflict trade-off in the process of land use quantity structure optimization mainly includes two aspects: one is to define the area threshold of each land use type under the ES supply and demand balance; the other is to set the CESDR maximization target, so as to comprehensively solve the optimal land use quantity structure that is conducive to improving the regional ES supply capacity and expanding the positive difference between ES supply and demand (that is, the supply and demand difference is positive), thereby improving the regional ES supply and demand balance and alleviating ES supply and demand conflicts.

[0029] First, the area threshold of each land use type under ES supply and demand balance is defined based on the regression relationship between the ESDR of each type of ES and the area proportion of different land use types, so as to solve the land use area threshold that can make the ESDR of each type of ES ≥ 0, so as to achieve the goal of improving the ES supply and demand balance in the region and alleviating the ES supply and demand conflict; the least squares (OLS) regression model is used to characterize the regression relationship between the ESDR of each type of ES and the area proportion of different land use types in all evaluation units, among which only the regression curves with a high fit (R²) and passing the significance test (P value < 0.001) are retained to solve the area threshold of each land use type under ES supply and demand balance.

[0030] Furthermore, a CESDR maximization objective is set, aiming to construct an objective function through the relationship between the CESDR mean of each land use type and the area of ​​each type of land use to maximize the regional CESDR (i.e., minimize the degree of ES supply and demand conflict), thereby achieving the goal of improving the regional ES supply and demand balance and alleviating the ES supply and demand conflict. This objective function is also one of the ecological benefit objective functions in the regional multidimensional development goals. The CESDR objective function is constructed as follows:

[0031] Where, is the mean CESDR value of each land use type; is the area of ​​each land use type.

[0032] Furthermore, a multi-objective function for regional development is constructed to provide an important data basis for carrying out multi-scenario optimization of land use. The regional multi-objective function mainly includes four objective functions: ecological benefit, economic benefit, social benefit and comprehensive benefit. Among them, the ecological benefit objective function is composed of the CESDR objective function and the ecosystem service value objective function. The ecosystem service value objective function is represented by the product of the unit area ecosystem service value of each type of land and its area; the economic benefit objective function is represented by the product of the unit area economic benefit coefficient of each type of land and its area; the social benefit objective is represented by the weighted sum of per capita cultivated land area, per capita garden area, per capita green area, per capita water area and per capita construction land area data. The weight of each indicator is calculated using the coefficient of variation method. The calculation formula of the multi-objective function for regional development is as follows: ①CESDR objective function:

[0033] Where, is the mean CESDR value of each land use type; is the area of ​​each land use type.

[0034] ②ESV objective function:

[0035] Where, is the ESV coefficient of each land use type; is the area of ​​each land use type.

[0036] ③Economic benefits( EconB )Objective function:

[0037] Where, is the economic benefit coefficient of each land use type; is the area of ​​each land use type.

[0038] ④Social benefits ( Society )Objective function:

[0039] Where, is the social benefit weight coefficient of each land use type; is the area of ​​each land use type.

[0040] ⑤ Comprehensive benefit (CB) objective function: It is composed of four objective functions: CESDR, ESV, EconB and ScoiB.

[0041]

[0042] Furthermore, multiple scenarios for land use optimization are set up, the objective functions and land use constraints for different scenarios under the trade-off between ES supply and demand are clarified, and comprehensive land use optimization is carried out. The multiple scenarios involved in land use optimization include the Natural Development Scenario (NDS), the Ecological Protection Scenario (EPS), the Socioeconomic Development Scenario (SDS), and the Comprehensive Development Scenario (CDS). Among them, NDS refers to a land use development scenario based on existing development trends, which does not involve objective functions, does not consider the restrictions of planning policies on land use changes, and does not consider the correction of land use development probability and land use structure adjustment under conflict trade-offs. It continues the historical inertia of land use evolution and represents the future development trend of current land use; EPS scenario emphasizes the maintenance of regional ecological security, and its objective function involves the CESDR maximization objective function and the ES value objective function, striving to maximize the ecological benefits from both the physical quantity and value of ES; SDS emphasizes the coordinated development of social economy, involving the social benefit objective function and the economic benefit objective function, striving to maximize the social and economic benefits; CDS scenario emphasizes the coordinated development of ecological protection and social economy, and its objective function involves the CESDR maximization objective function, the ES value objective function, the social benefit objective function and the economic benefit objective function, striving to maximize the comprehensive benefits. The land use area constraint is determined by the land use area threshold under the ES supply and demand balance and the land use area threshold corresponding to future land use development needs. Among them, the land use threshold corresponding to future land use development needs is mainly determined by combining the current land use area, the future population predicted by the GM (1,1) model, and the relevant regional superior planning documents; the multi-scenario optimization of land use quantity structure under the ES supply and demand conflict trade-off is to combine the land use area threshold under the ES supply and demand balance, the land use area threshold corresponding to future land use development needs, and the regional development goals under different scenarios, and use the GMLP model to comprehensively solve the optimal regional land use quantity structure.

[0043] In the multi-scenario optimization process of land use quantity structure under the trade-off between ES supply and demand, the differences in the attributes of each scenario are as follows:

[0044] Note: In the table, CESDR is the objective function for maximizing the comprehensive ecological supply-demand ratio; ESV is the objective function for ecosystem service value; EcoB is the ecological benefit objective function; ESB is the social and economic benefit objective function, EconB is the economic benefit objective function; Society is the social benefit objective function; CompB is the comprehensive benefit objective function.

[0045] The principle of the grey multi-objective linear programming (GMLP) model used in the optimization of land use quantitative structure is as follows: The GMLP model is a combination of the grey linear programming model and the multi-objective programming model. Compared with other linear programming models, this model can examine the grey, multi-objective, and complex characteristics of regional land use structure. It can not only take into account the uncertainty of the objective function and constraints, but also respond to various elements in the national land use system, reflecting the complexity and evolution of the national land use system, and ultimately obtain effective national land use area optimization results. Its expression is as follows:

[0046] Where, f(x) It is a comprehensive objective function, which is mainly set according to different optimization objectives. c j is the benefit coefficient, which can also be expressed by the benefit contribution weights of various types of land use. x j are the various decision variables, i.e., the various land use types in the study area; a ij The constraint function of each decision variable is mainly obtained by using the GM (1, 1) model prediction or defined according to relevant policy planning documents. b i In the present invention, the constraints under different scenarios are determined by the land area threshold under the ES supply and demand balance and the land area threshold corresponding to the future land use development demand.

[0047] Step 5: Multi-scenario optimization of land use spatial pattern under the trade-off between ES supply and demand conflicts. Based on the land use data of the base year (i.e., the starting year) and the current year (i.e., the latest year for which land use data were obtained), combined with the driving factors of regional land use spatiotemporal evolution, a PLUS model for optimizing land use was constructed, model training and accuracy evaluation were performed, and the initial development probability of each land use type was obtained. Based on the type characteristics of the ES supply and demand-dominated conflict areas identified in step 3, land use conversion rules and land use development probability correction principles for the ES conflict areas were set to obtain the corrected land use development probability for the ES conflict areas. Based on the multi-scenario optimization results of the land use quantitative structure under the ES conflict trade-off in step 4, combined with the land use conversion rules and corrected land use development probability for the ES supply and demand conflict areas and non-conflict areas, the corrected land use development probability layers for the study area under different scenarios were obtained, and the PLUS model was used to carry out multi-scenario optimization of land use pattern under the trade-off between ES supply and demand conflicts.

[0048] Specifically, first, the main drivers of regional land use temporal and spatial variation were selected. Regional land use data and the drivers of land use temporal and spatial evolution provide a crucial data foundation for constructing the PLUS model. These drivers include elevation, slope, aspect, distance to rivers, soil type, temperature, precipitation, GDP, population density, distance to roads, distance to residential areas, ecological protection red lines, and permanent basic farmland. Elevation, slope, temperature, precipitation, GDP, and population density are all continuous variables (data is continuous), while aspect, soil type, ecological protection red lines, and permanent basic farmland are all discrete variables (i.e., categorical variables). Distance to rivers, roads, and residential areas can be continuous (e.g., calculating Euclidean distance) or discrete (e.g., buffer zone distance).

[0049] Furthermore, the land use data of the starting year and the current year as well as the driving factors of land use change are jointly input into the PLUS model, and the LEAS module of the PLUS model is used to calculate the initial development probability of land use for various types of land use (that is, the land use suitability probability calculated from the land use data of different periods and the driving factors of land use spatiotemporal evolution, that is, the probability of other land use types being converted to a certain land use type, which is also called land use suitability probability in other land use optimization models); the land use data of the starting year and the initial development probability of land use are input into the PLUS model, and the relevant parameters are set to simulate the land use pattern of the current year. The simulated land use pattern data and the actual land use data are compared, and the overall accuracy, Kappa coefficient and FoM coefficient are used to comprehensively evaluate the simulation accuracy of the constructed PLUS model; the larger the values ​​of the above three accuracy evaluation indicators are, the higher the simulation accuracy of the PLUS model.

[0050] The PLUS model is mainly used for multi-scenario optimization of land use spatial pattern. Its basic principles are as follows: The PLUS model is a land-use change simulation model based on multi-period land-use data. By combining the Land Expansion Analysis Strategy (LEAS) module and the CA model (CARS) module based on multi-type random patch seeds, it can simulate the formation and evolution of patches of different land types. It also assesses the importance of various land-use drivers to the evolution of national spatial patterns, thereby deeply exploring the mechanisms of land-use evolution. The LEAS module combines the advantages of both transformation and pattern analysis strategies, using a random forest algorithm to sample land-use expansion, mine information on expansion factors, and calculate the development probability of each land type and the contribution of driving factors to land-use expansion. The CARS module, combining random seeds with a threshold-decreasing mechanism, uses a roulette wheel method to determine the national spatial land use status during the iterative process, subject to the constraints of development probability. This allows for simulation and prediction of future landscape patterns, resulting in national spatial land use structure and spatial layout under different scenarios.

[0051] Furthermore, ES supply and demand conflict trade-offs are carried out during the optimization of the spatial land use pattern. The ES supply and demand conflict trade-offs during the optimization of the spatial land use pattern mainly include two aspects: the first is the setting of land use conversion rules based on the types of ES supply and demand dominant conflict zones, and the second is the correction of land use development probabilities based on the types of ES supply and demand dominant conflict zones. Both aspects aim to enable each land use type to convert to a land use type with high ES (ES here refers to the key ES type that causes supply and demand conflicts in the unit identified in step 4) by setting the rules and conversion probabilities (i.e., land use development probabilities) of land use conversion in ES supply and demand conflict zones, thereby achieving the goal of improving the ES supply and demand balance and alleviating ES supply and demand conflicts. On the one hand, land-use conversion rules are the basis for determining the probability of land-use development. With respect to regional land-use conversion, with the exception of urban construction land and water areas, which are difficult to convert to other land uses, conversions between other land types must follow the basic rules for land-use conversion under different scenarios (see the table below for a description of land-use conversion rules based on the differences in attribute values ​​across scenarios). This is the basic conversion rule that must be followed for land-use conversion within both ES supply-demand conflict zones and non-conflict zones. Furthermore, within ES supply-demand conflict zones, in addition to meeting the basic rules for land-use conversion under different scenarios, land types within each conflicting unit are more likely to convert to land types with higher ES supply capacity to alleviate ES supply-demand conflicts. Specifically, the higher the ES supply capacity of a particular land type, the higher the probability that other land types within the conflict zone will convert to that land type (i.e., the probability of land-use development). This is the most important rule for land-use conversion within ES supply-demand conflict zones. Specifically, as long as the basic conversion rules between land types are met, land types within ES supply-demand conflict zones can convert to each other, but the conversion probabilities vary. On the other hand, the development probability of each land use based on the type of the dominant conflict zone of ES supply and demand (i.e., the revised development probability of each land use) is directly related to the initial development probability of each land type, the dominant conflict zone type to which the conflict unit belongs, and the ES supply capacity of each land type. In the present invention, the part of the probability determined based on the dominant conflict zone type and the ES supply capacity of each land type is called the revised probability, which is expressed by the proportion of the average ES supply of each land type under the key ES type determined above to the sum of the average ES supply of all land types; therefore, the revised development probability of each type of land use is regarded as the development probability of each land use type obtained by increasing the revised probability unit on the basis of the initial probability of each land use.

[0052] The land use development probability calculation method based on the ES supply and demand dominant conflict zone type can be specifically described as follows: If the ES supply and demand conflict type is the single dominant conflict determined in step 4, such as the HQ dominant conflict, then HQ is the key ES type causing the conflict. In land use optimization, it is necessary to focus on improving the HQ supply capacity to alleviate this type of conflict. In this conflict zone, the correction probability of each land type is calculated by the ratio of the mean HQ supply of this land type to the sum of the mean HQ supply of all land types ( ) indicates that the revised development probability of each land type is regarded as the initial development probability of the land, and the revised probability under HQ is increased ( ) unit volume; if the ES supply-demand conflict type is the combination-dominated conflict determined in step 4, such as the HQ-CS-dominated conflict, then HQ and CS are the key ES types causing the conflict. In land use optimization, it is necessary to focus on improving the supply capacity of HQ and CS to alleviate this type of conflict. In the conflict area, the modified probability of each land type is determined by the modified probability under HQ ( ) and the modified probability under CS ( ) are composed of two parts, and the revised development probability of each land type is regarded as the initial development probability of the land, respectively increasing the revised probability under HQ ( ) and CS correction probability ( ) The land development probability obtained per unit volume; for other ES supply-demand dominant conflict zone types, the calculation method of the revised land development probability in the ES supply-demand conflict zone is the same as above; and the land development probability in non-ES supply-demand conflict zones is based on the land use conversion rules under different scenarios. In the initial development probability layer of a certain land use type, the land development probability of the area that cannot be converted to this type of land is set to 0, and other areas still follow the initial development probability.

[0053] The example formulas for the land use development probability correction method based on ES supply and demand-dominated conflict zone types (taking habitat quality (HQ)-dominated conflicts and habitat quality-carbon sequestration (HQ-CS)-dominated conflicts as examples) are as follows:

[0054] Where, The first in the conflict zone The development probability of the land use class after correction, the value range is [0,1]; For the conflict zone Initial development probability of land use type; In the service of habitat quality Corrected probability of land use type; In the service of carbon sequestration Class land use correction probability; nis the number of land use types ( n =15); MHQS j For the Average habitat quality supply of each land use type; MCSS j For the Average carbon sequestration supply for each land use type.

[0055] Furthermore, the optimal land use quantity structure under different scenarios of ES supply-demand conflict trade-offs determined in step 4 is used as the area demand for various types of land use in the future. At the same time, the land use type conversion rules under ES supply-demand conflict trade-offs and the revised land use development probabilities are simultaneously input into the PLUS model (the natural development scenario does not involve the setting of land use conversion rules and development probability correction under conflict trade-offs), and finally the optimization results of land use spatial patterns under different scenarios are obtained.

[0056] In the multi-scenario optimization process of land use spatial pattern under the trade-off between ES supply and demand, the attributes of each scenario are shown in the following table:

[0057] Step 6: Compare the land use optimization results under different scenarios. Based on the land use optimization results of different scenarios obtained in step 5, analyze the conflict mitigation effect and landscape ecological pattern of the ES supply and demand conflict area, and reveal the advantages of the multi-scenario land use optimization method under the trade-off of ES supply and demand conflict.

[0058] Specifically, ES supply and demand conflicts cannot be completely resolved. The effectiveness of alleviating ES supply and demand conflicts is characterized by the CESDR value within the conflict zone. The larger the CESDR value within the conflict zone, the higher the conflict mitigation effectiveness, and vice versa. The landscape ecological pattern is reflected by the landscape pattern index that characterizes landscape connectivity as described above. The advantages of land use optimization methods under different scenarios of ES supply and demand conflict trade-off scenarios are analyzed by analyzing the conflict mitigation effectiveness and regional landscape connectivity in the ES supply and demand conflict zone.

[0059] The key points and protection points of the present invention are to organically link the ES supply and demand relationship, the ES supply capacity of each type of land, the ES supply and demand conflict and the dominant conflict type, the regional multi-dimensional development goals, and the multi-scenario optimization of land use. From the perspective of ES supply and demand conflict, a multi-scenario optimization method for land use based on ES supply and demand conflict trade-off is constructed, which achieves the goal of alleviating ES supply and demand conflicts in the process of land use quantity structure optimization and spatial pattern optimization at the same time. It provides a new perspective and technical method support for improving regional ES supply and demand relationship and coordinating the contradictions between regional ecological environmental protection and social and economic development, and provides a decision-making basis for counties to carry out sustainable ecosystem management and national land space planning.

[0060] In addition to the above technical solutions, the ES types involved in step one of the present invention, in addition to the habitat quality, carbon sequestration, soil conservation, water conservation, and food supply involved in the present invention, can also be combined with regional actual conditions to select other more representative and more critical ES types, such as flood regulation, gas regulation, cultural entertainment, nutrient maintenance and other services, to carry out multi-scenario optimization of land use under ES supply and demand balance.

[0061] Secondly, in addition to the landscape connectivity indicators such as number of patches (NP), patch density (PD), connectivity (CONNECT), spread (CONTAG), and aggregation (AI) selected in the present invention, other landscape aggregation indicators can also be selected, such as the dispersion and juxtaposition index (IJI), landscape segmentation (DIVISION), separation index (SPLIT), effective particle size (MESH), and patch cohesion (COHESION).

[0062] Secondly, in addition to the landscape connectivity indicators such as number of patches (NP), patch density (PD), connectivity (CONNECT), spread (CONTAG), and aggregation (AI) selected in the present invention, other landscape aggregation indicators can also be selected, such as the dispersion and juxtaposition index (IJI), landscape segmentation (DIVISION), separation index (SPLIT), effective particle size (MESH), and patch cohesion (COHESION).

[0063] Again, in addition to the elevation, slope, aspect, distance to rivers, soil type, temperature, precipitation, GDP, population density, distance to roads, distance to settlements, ecological protection red lines, and permanent basic farmland selected in the present invention, the driving factors of spatiotemporal evolution of land use involved in step five can also be selected based on regional actual conditions to select driving factors that are more in line with regional characteristics to define the initial development probability of different land use types.

[0064] Finally, in addition to the natural development scenario, ecological protection scenario, socio-economic development scenario and comprehensive development scenario set in the present invention, the multiple scenarios for land use optimization involved in steps four and five can also set development scenarios with more regional characteristics based on regional development realities. For example, for important carbon emission areas, a carbon neutrality scenario can be set; for important agricultural product production areas in the country, a farmland protection scenario can be set, etc.

[0065] The following, combined with accompanying figures and specific examples, uses Lancang Lahu Autonomous County (Lancang County) in southwestern Yunnan as a case study to explore a multi-scenario land use optimization plan for 2035, balancing ES supply and demand. This example is intended only to illustrate the present invention and should not be construed as limiting its scope, either spatially or temporally.

[0066] like Figure 1 As shown in Figure 2, the multi-scenario optimization method for land use based on the trade-off between supply and demand of ecosystem services includes the following steps 1 to 6: Step 1: Calculate the supply and demand of each type of ES.

[0067] Combining field sampling data and industry statistics, the InVEST model, linear distribution model, RUSLE model, etc. are used to calculate and revise the supply and demand of important ES types such as regional habitat quality, carbon sequestration, soil conservation, water conservation, and food production, and perform spatial visualization ( Figure 2 ).

[0068] Step 2: Identify ES supply and demand conflict areas at the optimal analysis granularity.

[0069] With reference to the land use classification system of the Chinese Academy of Sciences and combined with the actual situation of the study area, the land use type data of Lancang County in the current year (2020) (resolution of 30 m × 30 m) were divided into cultivated land (including dry land ( x 1 ) and paddy fields ( x 2 ))、Garden(Orchard( x 3 ),tea garden( x 4 ),rubber( x 5 ), woodlands (including eucalyptus ( x 6 )、Simao pine( x 7 ),shrub( x 8 ) and other woodlands ( x 9 )),grassland( x 10 ), water areas ( x 11 ), construction land (including urban construction land ( x 12 )、Rural construction land( x 13 ) and industrial and mining construction land ( x 14 )) and unused land (x 15 ) There are 7 first-level land types and 15 second-level land types ( Figure 3 ); The initial granularity for identifying ES supply and demand conflicts was further set to 100 m and the granularity increment step was 100 m. The CONNECT, NP, and PD indices that can characterize landscape connectivity were calculated at different granularities. Figure 4 From the above, it can be seen that when the granularity is 1000 m, CONNECT in the study area reaches its maximum value, and the NP value and PD value decrease and tend to be stable. Therefore, the optimal analysis granularity (i.e., evaluation unit size) for identifying ES supply and demand conflicts is defined as 1000 m×1000 m. Based on the optimal analysis granularity, the ESDR model and CESDR model are used to calculate the ESDR value of each type of ES in each evaluation unit ( Figure 5 ) and CESDR values ​​( Figure 6 ), the evaluation units with CESDR values ​​< 0 are identified as ES supply and demand conflict areas, and the evaluation units with CESDR ≥ 0 are identified as non-conflict areas, and spatial visualization is performed.

[0070] Step 3: Identify the types of ES supply and demand-dominated conflict zones.

[0071] Based on the relationship between the supply and demand ratios of various ES types calculated in step 2 and the identified ES supply and demand conflict areas, the ES types with supply and demand deficits (i.e., ESDR less than 0) in each conflict unit are identified (assuming that there are ES types with supply and demand deficits). m Class); secondly, calculate m Class ES i The absolute value of the ES-like ecological supply and demand ratio (ESDR Ai ) and this m The absolute mean of the ecological supply-demand ratio of ES-like MESDR A Finally, compare ESDR Ai and MESDR A The size relationship is calculated, and the 2-fold principle is used to determine the ES supply and demand dominant conflict zone type of each conflict unit, further clarifying the key ES type that causes the ES supply and demand conflict unit.

[0072] The 2-fold principle is specifically described as follows: If within a conflict unit, there is only one type of ES ESDR A ( i=1 ) is greater than MESDR A , then the grid unit is determined to be a single dominant ES supply and demand structure conflict. For example, if in a conflict unit, there is only HQ ESDR A Greater than MESDRA , it indicates that HQ plays a leading role in forming the ES supply and demand conflict, and the conflict unit is identified as HQ-dominated conflict. If there are two types of ES in a certain conflict grid unit, ESDR A （i=2） Both greater than MESDR A , we further compare the two types of ES ESDR A Size, determine the largest ESDR A Is it greater than 2 times the second largest ESDR A , if so, then the conflict unit is composed of ESDR A The ES with the maximum value dominates (i.e., it is still a single dominant ES supply and demand conflict), otherwise the conflict unit is dominated by the above two types of ES (i.e., a combined dominant ES supply and demand conflict). For example, if in a certain conflict unit, the HQ and CS ESDR A Both greater than MESDR A , and HQ's ESDR A More than 2 times CS ESDR A , indicating that HQ plays a leading role in forming the ES supply and demand conflict, then the conflict unit is still identified as HQ-dominated conflict; if HQ and CS ESDR A Are greater than MESDR A , and HQ's ESDR A Less than 2 times CS ESDR A , indicating that HQ and CS play a leading role in forming ES supply and demand conflict, then the conflict unit is identified as HQ-CS dominant conflict. If there are three or more types of ESDR A Greater than MESDR A , the dominant conflict zone type is still determined in sequence using the above method.

[0073] Finally, the identification results of ES supply and demand-dominated conflict zone types in Lancang County are as follows: Figure 7 shown.

[0074] Step 4: Multi-scenario optimization of land use quantity structure under the trade-off between ES supply and demand.

[0075] First, the least squares (OLS) regression model was used to characterize the regression relationship between the ESDR of each type of ES and the area proportion of different land use types in all evaluation units ( Figure 8 and 9 ), the area thresholds of each land use type under the ES supply and demand balance are shown in the following table:

[0076] Secondly, the CESDR maximization objective function is constructed based on the regression relationship between the area of ​​different land use types and the CESDR mean of each type of land use, and is used as one of the ecological benefit objective functions. The CESDR maximization objective function is set as follows:

[0077] Furthermore, in combination with the multi-dimensional development goals of the region, a multi-objective function is set. NDS does not involve an objective function, EPS involves the objective functions of maximizing CESDR and ecosystem service value, SDS involves the objective functions of social benefits and economic benefits, and CDS involves the objective functions of maximizing CESDR, ecosystem service value, social benefits, and economic benefits. In the multi-scenario optimization process of land use quantity structure under the trade-off between ES supply and demand, the differences in the attributes of each scenario are shown in the following table:

[0078] In the table, CESDR The objective function for maximizing the comprehensive ecological supply-demand ratio is: EcoB is the ecological benefit objective function; ESB is the social and economic benefit objective function, EconB For economic benefits; Society For social benefits; CompB is the comprehensive benefit objective function.

[0079] The multi-objective function calculation formula in the present invention is as follows: ①CESDR objective function:

[0080] ②ESV objective function:

[0081] ③Economic benefits( EconB )Objective function:

[0082] ④Social benefits ( Society )Objective function:

[0083] ⑤ Comprehensive benefit (CB) objective function:

[0084] Furthermore, taking the land use area threshold under ES supply and demand balance as the primary constraint, the land use area constraint conditions in the land use optimization process are comprehensively set in combination with the current land use area, the future population predicted by the GM (1,1) model, and the relevant regional superior planning documents. The land use area constraint conditions thus set are shown in the following table:

[0085]

[0086] Finally, based on the land use area constraints under the ES supply and demand balance and the objective functions under different scenarios, the GMLP model was used to comprehensively solve the optimal land use quantitative structure under different scenarios. The land use quantitative structure in 2035 under the natural development scenario was directly predicted by the Markov-Chain model embedded in the PLUS model, without involving the objective function. The optimal land use quantitative structure under different scenarios under the ES supply and demand trade-off is shown in the following table:

[0087] Step 5: Multi-scenario optimization of land use spatial pattern under the trade-off between ES supply and demand.

[0088] First, we selected the main drivers of spatiotemporal land use change in the region. These drivers include elevation, slope, aspect, distance to rivers, soil type, temperature, precipitation, GDP, population density, distance to roads, distance to residential areas, ecological protection red lines, and permanent basic farmland. Elevation, slope, temperature, precipitation, GDP, and population density are continuous variables, while aspect, soil type, ecological protection red lines, permanent basic farmland, distance to rivers, distance to roads, and distance to residential areas are discrete variables.

[0089] Furthermore, the land use data of the starting year (2005) and the current year (2020) as well as the driving factors of land use change were input into the PLUS model, and the LEAS module was used to calculate the initial development probability of each type of land use ( Figure 10 The land use data and initial land use development probability data of 2005 were input into the PLUS model, and the relevant parameters were set to simulate the land use pattern in 2005. The simulated and actual land use data in 2020 were compared, and the overall accuracy of the PLUS model was 0.79, the Kappa coefficient was 0.73, and the FoM coefficient was 0.31, indicating that the simulation results of the model are highly consistent with the actual situation, and the simulation accuracy is credible. The model can be effectively applied to the simulation of the land use pattern in Lancang County in 2035.

[0090] Furthermore, ES supply and demand conflict trade-offs are carried out during the optimization of land use spatial pattern. The ES supply and demand conflict trade-offs during the optimization of land use spatial pattern mainly include two aspects: the first is the setting of land use conversion rules based on the types of ES supply and demand dominant conflict areas, and the second is the correction of land use development probability based on the types of ES supply and demand dominant conflict areas. On the one hand, with regard to regional land-use conversion, with the exception of urban construction land and water areas, which are generally difficult to convert to other land uses, conversions between other land types must follow the basic rules for land-use conversion under different scenarios (see the table below for a description of land-use conversion rules in the context of attribute differences between scenarios). This is the basic conversion rule that must be followed for land-use conversion within both ES supply-demand conflict zones and non-conflict zones. Furthermore, within ES supply-demand conflict zones, in addition to meeting the basic rules for land-use conversion under different scenarios, land types within each conflicting unit are more likely to convert to land types with high ES supply capacity to alleviate ES supply-demand conflicts. That is, the higher the ES supply capacity of a particular land type, the higher the probability that other land types within the conflict zone will convert to that land type (i.e., the land use development probability). This is the most important rule for land-use conversion within ES supply-demand conflict zones. That is, provided that the basic conversion rules between land types are met, land types within ES supply-demand conflict zones can convert to each other, but the conversion probabilities vary. On the other hand, the development probability of each land use based on the type of the dominant conflict zone of ES supply and demand (i.e., the revised development probability of each land use) is directly related to the initial development probability of each land use type, the dominant conflict zone type to which the conflict unit belongs, and the ES supply capacity of each land use type. The part of the probability determined based on the dominant conflict zone type and the ES supply capacity of each land use type is called the revised probability, which is expressed as the ratio of the mean ES supply of each land use type under the key ES type determined above to the sum of the mean ES supply of all land use types. Therefore, the revised development probability of each land use type in the conflict zone is regarded as the development probability of each land use type obtained by increasing the revised probability unit on the basis of the initial probability of each land use type ( Figure 11 The land use development probability in non-ES supply-demand conflict areas is based on the land use conversion rules under different scenarios. In the initial development probability layer of a certain land use type, the land use development probability of areas that cannot be converted to this type of land is set to 0, and the initial development probability is still used for other areas. In this way, the revised land use development probability layers of the study area under different scenarios of ES supply-demand conflict can be determined ( Figure 12 ).

[0091] Furthermore, the optimal land use quantity structure under different scenarios of ES supply-demand conflict trade-off determined in step 5 is used as the area demand of each type of land use in the future. At the same time, the land use type conversion rules under ES supply-demand conflict trade-off and the revised land use development probability are simultaneously input into the PLUS model (the natural development scenario does not involve the setting of land use conversion rules under conflict trade-off and the revision of development probability). After setting relevant parameters, the optimization results of land use spatial pattern under different scenarios are finally obtained ( Figure 13 ).

[0092] The differences in attributes for each scenario are shown in the following table:

[0093] Step 6: Comparison of land use optimization results under different scenarios.

[0094] Based on the land use optimization results for different scenarios obtained in Step 6, we analyzed the conflict mitigation effectiveness, key land use type conversion characteristics, and landscape ecological patterns in the ES supply-demand conflict zone, revealing the advantages of the multi-scenario land use optimization method under the ES supply-demand conflict trade-off. The conflict mitigation effectiveness in the ES supply-demand conflict zone and the landscape ecological patterns of the study area are shown in the table below.

[0095]

[0096] Overall, the land use optimization results for Lancang County in 2035 under different scenarios provide multiple options for county-level land use planning. Compared with the other three scenarios, the NDS does not consider the trade-off between ES supply and demand, simply continuing the historical inertia of land use change in Lancang County. The NDS optimization results represent future trends in current land use. The conflict zone under the NDS has the lowest CESDR of -4.51, indicating the most severe ES supply and demand conflict. The landscape fragmentation is low, while the concentration and connectivity are high, facilitating species migration and exchange, resulting in the most disorderly land use expansion. The EPS emphasizes regional ecological and environmental protection, ignoring socioeconomic development and striving to maximize ecological benefits. The EPS has the highest CESDR of -2.93 in the ES supply and demand conflict zone, a 35.02% increase compared to the NDS CESDR, indicating that the EPS minimizes ES supply and demand conflict and achieves the best conflict mitigation. The landscape connectivity and concentration are both highest, making it most conducive to promoting species diversity and maintaining ecosystem sustainability. However, the EPS also exhibits high landscape fragmentation and heterogeneity. The CESDR of the ES supply-demand conflict zone under SDS was relatively low, at -3.44, an increase of 23.74% over the CESDR under NDS, but lower than that under EPS and CDS, indicating that the ES supply-demand conflict was still well alleviated, but the degree of alleviation was worse than that under EPS and CDS; the land landscape fragmentation was low, but the landscape connectivity and agglomeration were the lowest, which would have a certain inhibitory effect on species migration and exchange; the CESDR of the ES supply-demand conflict zone under CDS was relatively high, at -3.37, an increase of 25.21% over NDS, indicating that the ES supply-demand conflict was effectively alleviated, but the degree of alleviation was lower than that under EPS; the landscape agglomeration and connectivity were high, but the landscape fragmentation was also high, indicating that although the land landscape heterogeneity under CDS was the highest, it was still conducive to maintaining migration and information exchange among biological species as a whole.

[0097] In summary, compared to the NDS, which does not consider ES supply-demand trade-offs, the EPS, SDS, and CDS, while each focusing on different land use optimization objectives, all consider regional ES supply-demand relationships, ES supply-demand conflict types, and trade-offs, making them more conducive to improving ES supply-demand relationships and alleviating ES supply-demand conflicts. However, the land use patterns in the NDS remain valuable for future land use planning in avoiding population-land conflicts and ES supply-demand conflicts potentially caused by uncontrolled land expansion. Decision makers can select more appropriate optimization options based on regional realities to adjust and optimize local land use structure and spatial patterns.

[0098] All English abbreviations in this example are as follows: ①ES: Ecosystem Service / Ecosystem Service; ②HQ: Habitat Quality / Habitat Quality; ③CS: Carbon Sequestration / Carbon Sequestration; ④SC: Soil Conservation / Soil Conservation; ⑤WC: Water Conservation / Water Conservation; ⑥FP: Food Production / Food Production; ⑦ESDR: Ecological Supply-demand Ratio / Ecological Supply-demand Ratio; ⑧CESDR: Comprehensive Ecological Supply-demand Ratio / Comprehensive Ecological Supply-demand Ratio; ⑨MESDR: Mean Ecological Supply-demand Ratio / Mean Ecological Supply-demand Ratio; ⑩ESDRA: Absolute Value of Ecological Supply-demand Ratio / Absolute Value of Ecological Supply-demand Ratio; ⑪GMLP: Grey Multi-objective Linear Programming / Grey Multi-objective Linear Programming; ⑫PLUS: Patch-generating Land Use Simulation / Patch-generating Land Use Simulation; ⑬NDS: Natural Development Scenario / Natural Development Scenario; ⑭EPS: Ecological Protection Scenario / Ecological Protection Scenario; ⑮SDS: Socio-economic Development Scenario / Socio-economic Development Scenario; ⑯CDS: Comprehensive Development Scenario / Comprehensive Development Scenario.

[0099] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those skilled in the art in the art to which this invention belongs. It should also be understood that terms such as those defined in common dictionaries should be understood to have meanings consistent with their meanings in the context of the prior art and, unless defined as such, will not be interpreted in an idealized or overly formal sense.

[0100] The above embodiments are only for illustrating the technical concept of the present invention and are not intended to limit the scope of protection of the present invention. Any changes made on the basis of the technical solution in accordance with the technical concept proposed by the present invention shall fall within the scope of protection of the present invention. The above embodiments of the present invention are described in detail, but the present invention is not limited to the above embodiments. Various changes can be made within the knowledge of ordinary technicians in this field without departing from the purpose of the present invention.

Claims

1. A land use optimization method based on the trade-off between supply and demand of ecosystem services, characterized by: The steps include: Step 1: Calculation of supply and demand of various types of ES: The integrated assessment model of ecosystem services and trade-offs, the modified general soil loss equation model, and the linear allocation model were used to calculate the supply and demand of various types of ES in the study area. Corresponding local parameterization corrections were made in the process to reveal the spatial pattern of supply and demand of various types of ES. Step 2: Identification of ES supply-demand conflict areas at the optimal analysis granularity: Based on the land use type and spatial resolution of the current year, the initial granularity for identifying ES supply-demand conflicts and the step size for increasing the granularity are set. The landscape pattern index that can characterize landscape connectivity at different granularities is calculated. Based on the changes in the landscape pattern index, the optimal analysis granularity for identifying ES supply-demand conflict areas is determined. Based on the optimal analysis granularity, the ESDR and CESDR values ​​of each type of ES in each evaluation unit are calculated using the ESDR model and the CESDR model. Evaluation units with CESDR values ​​< 0 are identified as ES supply-demand conflict areas. Step 3: Identification of the type of ES supply-demand conflict zone: Based on the supply-demand ratio of each type of ES calculated in step 2 and the identified range of the ES supply-demand conflict zone, the constructed 2-fold principle is used to identify the type of ES supply-demand conflict zone to which each evaluation unit in the ES supply-demand conflict zone belongs; Step 4: Multi-scenario optimization of land use quantity structure under ES supply-demand conflict trade-offs: The regression relationship between the CESDR within the conflict assessment unit and the area ratio of different land use types is used to define the area thresholds of different land use types under ES supply-demand balance; the CESDR objective function is constructed based on the regression relationship between the area of ​​different land use types and the CESDR mean of each type of land use; with the area threshold of each land use type under ES supply-demand balance as the primary constraint, while considering future land development needs, various land area constraints and development goals under different scenarios are set, including ecological benefit goals, economic benefit goals, social benefit goals, and comprehensive benefit goals, and the GMLP model is used to comprehensively carry out multi-scenario optimization of land use quantity structure under conflict trade-offs; Step 5: Multi-scenario optimization of land use spatial pattern under the trade-off between ES supply and demand: Based on land use data from the base year and the current year, combined with the driving factors of regional land use spatiotemporal evolution, a PLUS model for optimizing land use was constructed. Model training and accuracy evaluation were performed, and the initial development probability of each land use type was obtained. Based on the type characteristics of the ES supply-demand conflict zone identified in step 3, set the land use conversion rules and land use development probability correction principles for the ES conflict zone to obtain the corrected land use development probability of the ES supply-demand conflict zone; Based on the multi-scenario optimization results of land use quantity structure under ES conflict trade-off in step 4, combined with the land use conversion rules and revised land use development probability of ES supply and demand conflict areas and non-conflict areas, the revised land use development probability layers of the study area under different scenarios were obtained, and the PLUS model was used to carry out multi-scenario optimization of land use pattern under ES supply and demand conflict trade-off. Step 6. Comparison of land use optimization results under different scenarios: Based on the land use optimization results of different scenarios obtained in step 5, the conflict mitigation effect and landscape ecological pattern of the ES supply and demand conflict area are analyzed to reveal the advantages of the multi-scenario land use optimization method under the trade-off of ES supply and demand conflict.

2. The land use optimization method based on ecosystem service supply and demand trade-off according to claim 1, characterized in that: In step 1, various types of ES refer to important ES types in the region, including habitat quality, carbon sequestration, soil conservation, water conservation, and food production; ES supply and demand is a general term for ES supply and demand. ES supply refers to the various services provided by the ecosystem of a specific region to human society within a specific period of time, and ES demand refers to the services obtained by human society from the ecosystem for survival and development. ES supply and demand are mainly calculated comprehensively using the ecosystem service assessment and trade-off model, the linear allocation model, and the modified general soil loss equation model. In this calculation process, field observation data and industry statistical data are combined to make local corrections to the corresponding parameters.

3. The land use optimization method based on ecosystem service supply and demand trade-off according to claim 1, characterized in that: In step 2, land use types can be divided according to the specific research objectives, referring to the land use classification system of the Chinese Academy of Sciences or combining with regional realities, and their spatial resolution is determined comprehensively according to regional realities; considering the attributes of decision variables in the land use quantity structure optimization model, each land use type is divided into x i To express, x i Indicates the i The decision variables corresponding to the land use types are as follows; the optimal analysis granularity refers to the optimal evaluation unit size for identifying ES supply and demand conflicts, that is, the grid size when the overall landscape connectivity of the region is maximized after the land use raster data is resampled, that is, the granularity size; in the process of determining the optimal analysis granularity, the landscape connectivity indicators involved may include the number of patches NP, patch density PD, connectivity CONNECT, spread CONTAG, and aggregation AI landscape pattern index, while the initial granularity and granularity increase step are determined according to the actual situation of the region. The optimal analysis granularity for identifying ES supply and demand structure conflicts is finally determined to be the granularity that can maximize the overall landscape connectivity of the region. The granularity corresponding to the maximum landscape connectivity of the region; the ecological supply-demand ratio (ESDR) of different types of ES can characterize the supply-demand relationship of each type of ES, mainly expressed by the difference between the supply and demand of different types of ES, that is, the proportional relationship between the supply-demand difference and the average of the maximum ES supply and demand, that is, the mean of the maximum supply and demand; the comprehensive supply-demand ratio (CESDR) reflects the overall situation of the supply-demand relationship of multiple types of ES, and is represented by the arithmetic mean of the ESDR of different types of ES. If CESDR>0, it means that the overall ES supply in the region is greater than the demand, that is, the overall ES supply and demand is surplus; if CESDR = 0, it means that the overall ES supply is equal to the demand, that is, the overall ES supply and demand is balanced; if CESDR<0, it means that the overall ES supply is less than the demand, that is, the overall ES supply and demand deficit; the evaluation unit with CESDR<0 is identified as an ES supply-demand conflict area, and the smaller the CESDR value, the greater the ES supply-demand conflict; the identified ES supply-demand conflict area provides a spatial carrier for conducting ES supply-demand conflict trade-offs.

4. The land use optimization method based on ecosystem service supply and demand trade-off according to claim 1, characterized in that: In step three, the ES supply and demand conflict cannot be completely resolved. Identifying the ES supply and demand dominant conflict area can clearly identify the key ES types that cause the ES supply and demand deficit in the conflict unit, that is, the evaluation unit with ES supply and demand conflict at the optimal granularity, so as to achieve the purpose of alleviating the ES supply and demand conflict in a focused manner; the identified ES supply and demand dominant conflict area also provides an important basis for the setting of conversion rules and development probability correction of various land uses under the ES supply and demand conflict trade-off, and provides a data basis for land use optimization under the conflict trade-off; the process of identifying the type of ES supply and demand dominant conflict area is as follows: First, clarify the ES type with supply and demand deficit, that is, ESDR less than 0 in each conflict unit. Assume that the ES types with supply and demand deficit are m Class; secondly, calculation m Class ES i The absolute value of the ES-like ecological supply and demand ratio ESDR Ai and this m The absolute mean of the ecological supply-demand ratio of ES-like MESDR A Finally, compare ESDR Ai and MESDR i The size relationship of each conflict unit is determined by the 2-fold principle, and the type of ES supply and demand dominant conflict zone to which each conflict unit belongs is determined by the 2-fold principle. The 2-fold principle is the core method for delineating the type of ES supply and demand dominant conflict zone, which mainly uses the ESDR mean of each type of ES in the conflict unit and the 2-fold relationship between the ESDRs of each type of ES to comprehensively identify the type of ES supply and demand dominant conflict zone. The 2-fold principle is specifically described as follows: If in a certain conflict unit, there is only a certain type of ES ESDR A ( i= 1) Greater than MESDR A , then the grid unit is determined to be a single dominant ES supply and demand conflict. For example, if in a conflict unit, there is only habitat quality service ESDR A Greater than MESDR A , it indicates that habitat quality services play a leading role in forming ES supply and demand conflicts, and the conflict unit is identified as habitat quality-dominated conflict. If two types of ES exist in a conflict grid unit at the same time, ESDR A ( i= 2) Both greater than MESDR A , we further compare the two types of ES ESDR A Size, determine the largest ESDR A Is it greater than 2 times the second largest ESDR A , if so, then the conflict unit is composed of ESDR A The ES with the maximum value is dominant, which is still a single dominant ES supply and demand conflict. Otherwise, the conflict unit is dominated by the above two ES types, which is a combined dominant ES supply and demand conflict. For example, if in a conflict unit, the habitat quality service and carbon sequestration CS service ESDR A Both greater than MESDR A , and the habitat quality services ESDR A More than 2 times the carbon sequestration service ESDR A , indicating that habitat quality services play a leading role in the formation of ES supply and demand conflicts, and the conflict unit is still identified as habitat quality-dominated conflict; if habitat quality services and carbon sequestration services ESDR A Are greater than MESDR A , and the habitat quality services ESDR A Less than 2 times the carbon sequestration service ESDR A , indicating that habitat quality services and carbon sequestration services play a dominant role in forming ES supply and demand conflicts, then the conflict unit is identified as habitat quality-carbon sequestration dominant conflict. If there are three or more types of conflicts in a conflict unit, ESDR A Greater than MESDR A The dominant conflict zone type is still determined in sequence using the above method, and the ES supply and demand dominant conflict zone type corresponding to all conflict units is finally determined.

5. The land use optimization method based on ecosystem service supply and demand trade-off according to claim 1, characterized in that: In step 4, the trade-off between ES supply and demand in the land use quantity structure optimization process primarily involves two aspects: first, defining the area thresholds for each land use type under ES supply and demand equilibrium; and second, setting a CESDR maximization objective. This approach comprehensively determines the optimal land use quantity structure that enhances regional ES supply capacity and expands the positive difference between ES supply and demand, i.e., a positive supply-demand gap. This, in turn, improves the regional ES supply and demand equilibrium and, to a certain extent, alleviates ES supply and demand conflicts. Defining the area thresholds for each land use type under ES supply and demand equilibrium aims to determine the land use area threshold that achieves an ESDR of ≥ 0, based on the regression relationship between the ESDR of each ES type and the area proportion of each land use type. This approach aims to improve regional ES supply and demand equilibrium and alleviate ES supply and demand conflicts. A least-squares OLS regression model is used to characterize the regression relationship between the ESDR of each ES type and the area proportion of each land use type within all evaluation units. The model has a high R² fit and passes the significance test with a P value less than 0.The regression curve of 001 is retained to solve the area threshold of each land use type under the ES supply and demand balance; the goal of maximizing the comprehensive ecological supply and demand ratio is set to construct an objective function through the relationship between the CESDR mean value of each land use type and the area of ​​each type of land use to achieve the maximum comprehensive ecological supply and demand ratio of the region, that is, to minimize the degree of ES supply and demand conflict, thereby achieving the goal of improving the ES supply and demand balance of the region and alleviating the ES supply and demand conflict. This objective function is also one of the ecological benefit objective functions in the regional multidimensional development goals; the regional multi-objective function mainly includes the ecological benefit objective function, the economic benefit objective function, and the social benefit objective function. Among them, the ecological benefit objective function is composed of the CESDR objective function and the ecosystem service value objective function. The ecosystem service value objective function is represented by the product of the unit area ecosystem service value of each type and its area. The economic benefit objective function is represented by the product of the economic benefit coefficient of each type and its area. The social benefit objective is represented by the weighted sum of per capita cultivated land area, per capita garden area, per capita green space area, per capita water area and per capita construction land area data; the multiple scenarios involved in land use optimization include natural development scenario, ecological protection scenario, The regional development objective functions differ across different scenarios. The natural development scenario involves no objective function and continues the historical trend of current land use development. The ecological protection scenario involves the CESDR maximization objective function and the ecosystem service value objective function. The socioeconomic development scenario involves the social benefit objective function and the economic benefit objective function. The comprehensive development scenario involves the CESDR maximization objective function, the ecosystem service value objective function, the social benefit objective function, and the economic benefit objective function. The land use area constraint is determined by the land area threshold under the ES supply and demand balance and the land area threshold corresponding to future land use development demand. The land area threshold corresponding to future land use development demand is determined primarily by combining the current land use area, the future population predicted by the GM (1,1) model, and relevant regional superior planning documents. The multi-scenario optimization of the land use quantity structure under the ES supply and demand trade-off is to use the GMLP model to solve the optimal land use quantity structure by combining the land use area threshold under the ES supply and demand balance, the land area threshold corresponding to future land use development demand, and the regional development goals under different scenarios.

6. A land use optimization method based on ecosystem service supply and demand trade-off according to claim 1: In step 5, the regional land use data and the driving factors of land use spatiotemporal evolution provide an important data basis for constructing the PLUS model, wherein: The driving factors of spatiotemporal evolution of land use include elevation, slope, aspect, distance to rivers, soil type, temperature, precipitation, GDP, population density, distance to roads, distance to settlements, ecological protection red line, and permanent basic farmland. Through PLUS model simulation, the importance of different driving factors to the spatiotemporal evolution of land use, also known as contribution, can be determined. The initial development probability of land use can also be determined, that is, the land use suitability probability calculated by land use data of different periods and the driving factors of spatiotemporal evolution of land use, that is, the probability of other land use types being converted to a specific land use type. The simulation accuracy of the PLUS model is comprehensively evaluated using the overall accuracy, Kappa coefficient and FoM coefficient. The above index values ​​are The larger it is, the higher the simulation accuracy of the PLUS model will be. The ES supply and demand conflict trade-off in the process of optimizing the spatial pattern of land use mainly includes two aspects: one is the setting of land use conversion rules based on the types of ES supply and demand dominant conflict zones; the other is the correction of land use development probability based on the types of ES supply and demand dominant conflict zones. The purpose is to set the rules and conversion probability of land use conversion in ES supply and demand conflict zones, that is, the land use development probability, so that each land use type can be transformed into a land use type with high ES. The ES here refers to the key ES type that causes the supply and demand conflict of the unit identified in step 3, and the land use type with supply capacity is transformed, thereby achieving the goal of improving the ES supply and demand balance and alleviating ES supply and demand conflicts. Land use conversion rules are the basis for determining the probability of land use development. For regional land use conversion, in general, except for urban construction land and water areas which are difficult to convert to other land uses, the conversion between other land types must follow the basic rules of land use conversion under different scenarios. This is the basic conversion rule that must be followed for land use conversion within ES supply and demand conflict areas and non-conflict areas. In addition, for ES supply and demand conflict areas, in addition to meeting the basic rules of land use conversion under different scenarios, the land types in each conflict unit are more inclined to convert to land types with high ES supply capacity to alleviate ES supply and demand conflicts. That is, the higher the ES supply capacity of a certain land type, the higher the probability that the other land types in the conflict area will convert to this land type, i.e., the land use development rate. The higher the development probability, the more land use is needed. This is the most important rule for land conversion in ES supply-demand conflict zones. That is, under the premise of meeting the basic conversion rules between land types, land types within the ES supply-demand conflict zone can be converted to each other, but there are differences in conversion probabilities. The land use development probability of each land type based on the type of the dominant conflict zone of ES supply and demand, that is, the revised land use development probability, is directly related to the initial development probability of each land type, the dominant conflict zone type to which the conflict unit belongs, and the ES supply capacity of each land type. The part of the probability determined by the dominant conflict zone type and the ES supply capacity of each land type is called the revised probability, which is expressed as the ratio of the mean ES supply of each land type under the key ES type determined above to the sum of the mean ES supply of all land types. Therefore, the revised development probability of each type of land use is regarded as the development probability of each land use type obtained by increasing the revised probability unit on the basis of the initial probability of each land use; the land use development probability calculation method based on the ES supply and demand dominant conflict zone type can be specifically described as follows: if the ES supply and demand conflict type is the single dominant conflict determined in step 3, such as the habitat quality HQ dominant conflict, then habitat quality is the key ES type causing the conflict. In land use optimization, it is necessary to focus on improving the habitat quality supply capacity to alleviate this type of conflict. In this conflict zone, the revision probability of each land type is The positive probability is expressed as the ratio of the mean habitat quality supply of the land type to the sum of the mean habitat quality supply of all land types, and the revised land use development probability of each land type is regarded as the land use development probability obtained by improving the revised probability unit under the habitat quality on the basis of the initial development probability of the land; if the ES supply and demand conflict type is the combination-dominated conflict determined in step 4, such as the habitat quality-carbon sequestration-dominated conflict, then habitat quality and carbon sequestration are the key ES types causing the conflict. In land use optimization, it is necessary to focus on improving the supply capacity of habitat quality and carbon sequestration to alleviate this type of conflict. For the remaining ES supply-demand-dominated conflict zones, the calculation method for the revised land use development probabilities of each land type is the same as above. However, the land use development probabilities in non-ES supply-demand conflict zones are based on the land use conversion rules under different scenarios. In the initial development probability layer of a certain land use type, the land use development probability of areas that cannot be converted to that type of land use is set to 0, while the initial development probability is still used for other areas. The multi-scenario optimization of the land use spatial pattern under the ES supply-demand conflict trade-off uses the optimal land use quantity structure under different scenarios under the ES supply-demand conflict trade-off determined in step 4 as the future area demand of each type of land use. At the same time, the land use type conversion rules and the revised land use development probabilities are simultaneously incorporated into the PLUS model to obtain the land use spatial pattern optimization results under different scenarios.

7. The land use optimization method based on ecosystem service supply and demand trade-off according to claim 1 is characterized by: In step 6, the ES supply-demand conflict cannot be completely resolved. The effectiveness of mitigating the ES supply-demand conflict is represented by the CESDR value within the conflict zone. The larger the CESDR value within the conflict zone, the higher the conflict mitigation effect, and vice versa. The landscape ecological pattern is reflected by the landscape pattern index that represents landscape connectivity as described above. The advantages of land use optimization methods under different scenarios of ES supply and demand conflict trade-off scenarios are analyzed through the conflict mitigation effectiveness and regional landscape connectivity in ES supply and demand conflict areas.

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