Carbon reserve distribution prediction and evaluation method based on conflict detection and multi-objective optimization

Abstract

The invention relates to a carbon reserve distribution prediction and evaluation method based on conflict detection and multi-objective optimization, and belongs to the technical field of geographic information. Comprising the following steps: firstly, constructing a logistic regression model, performing logistic regression analysis to obtain a corresponding regression coefficient, and then sequentially calculating an occurrence probability, a local advantage index and a conflict strength index of each land type; then, multi-target constraint condition setting is carried out, coordination of land utilization conflicts is carried out based on multi-target constraints, and an optimized land utilization distribution pattern is obtained; and finally, inputting the land utilization data and the carbon library data before and after coordination into the InVEST model to obtain corresponding carbon reserve distribution conditions before and after optimization. According to the method, a framework combining a land utilization conflict identification and coordination method and an InVEST model is designed, an existing land utilization conflict identification and coordination method and a carbon sink distribution optimization method are improved, and the influence of land conflicts on regional carbon reserves is revealed.

Description

Technical Field

[0001] The invention relates to a carbon reserve distribution prediction and evaluation method based on conflict detection and multi-objective optimization, and belongs to the field of geographic information technology. Background Art

[0002] Carbon storage refers to the carbon captured and stored by terrestrial ecosystems in the form of soil organic matter, dead vegetation and living vegetation biomass. It is a widely used indicator in ecosystem service evaluation and is considered one of the most critical measurement parameters for assessing the responsiveness of terrestrial ecosystem production capacity and ecological resilience to climate change. Land use change is an important indicator reflecting the impact of human activities on the earth's surface system. It can cause changes in terrestrial carbon storage by changing the structure and function of the ecosystem, affecting vegetation cover, and causing changes in soil carbon storage by changing the soil environment. It is the main factor affecting changes in carbon storage and carbon cycle in terrestrial ecosystems.Land use conflict can be defined as the phenomenon of spatial competition and interest conflict between people and land caused by land use patterns and structures in the process of land use. Changes in the spatial pattern of land use will not only change the dynamics of land comprehensive use, but also change the nature of aboveground vegetation, destroy the connectivity of ecological landscapes, reduce regional vegetation coverage and primary ecosystem productivity, thereby changing regional vegetation carbon storage and carbon balance, and further causing the decline of ecosystem functions and the decline of ecosystem service value. Existing studies on land use conflict can be roughly divided into three categories. The first category focuses on case studies in developed countries; the second category focuses on urban land use conflicts in developing countries; and the third category focuses on studying the types of conflicts in urban and suburban areas in developed countries. For example, the literature "Urban land use efficiency in Ethiopia: An assessment of urban land uses sustainability in Addis Ababa" found that the land conflicts caused by the expansion of urban buildings in Ethiopia have destroyed the efficiency of urban land use. There are serious problems related to low land use efficiency in almost all expansion boundaries. The literature "Spatial and temporal analyses of potential land use conflict under the constraints of water resources in the middle reaches of the Heihe The "Heihe River" constructed a multi-index evaluation system for land use competitiveness for the middle reaches of the Heihe River, and obtained the spatiotemporal pattern of potential land use conflicts. The literature "How to reconcile land use conflicts in mega urban agglomeration? Ascenario-based study in the Beijing-Tianjin-Hebei region, China" took the Beijing-Tianjin-Hebei urban agglomeration as an example and constructed a spatial comprehensive conflict index (SCCI) to identify and evaluate land use conflicts. However, existing studies rarely link urban land conflicts, land cover changes and carbon stocks. By identifying and coordinating land conflicts and combining multiple sustainable development goal scenarios to simulate land use changes, conflicts and their impacts on terrestrial carbon pools, the above are the problems that the method proposed in the present invention hopes to solve and the direction of improvement.

[0003] In summary, identifying and resolving land use conflicts, optimizing urban land use, and reducing the negative impact of drastic changes in land use are the keys to rationally utilizing land resources, increasing regional carbon sinks, and achieving urban sustainability. Studying land use conflicts and optimizing them to restore ecosystem carbon storage levels, maintain carbon storage balance, and improve ecological benefits is an urgent task and is of great significance to sustainable development. Summary of the invention

[0004] The purpose of the present invention is to provide a method for optimizing land use patterns and regional carbon stock distribution, aiming to solve the technical problem of linking urban land conflicts, land cover changes and carbon stocks, simulating land use changes, conflicts and their impacts on terrestrial carbon pools by identifying and coordinating land conflicts and combining multiple sustainable development goal scenarios.

[0005] To achieve the above purpose, the technical solution of the present invention is: a carbon storage distribution prediction and evaluation method based on conflict detection and multi-objective optimization, which can effectively identify land use conflict areas, and then coordinate under multi-objective constraints, and further combine with carbon sinks to radiate the optimization results of land use patterns to regional carbon storage allocation optimization; improve the recognition accuracy of land use conflict areas and major carbon sink areas; improve the method of land use conflict coordination. It includes the following steps:

[0006] S1: Construct the target variable, characteristic variables and covariates required for logistic regression analysis;

[0007] S2: Perform logistic regression analysis and obtain the corresponding regression coefficients, calculate the corresponding probability of occurrence of each land type based on the regression coefficients, then calculate the corresponding local advantage index of each land type based on the probability of occurrence, and finally calculate the corresponding conflict intensity index of each land type based on the local advantage index;

[0008] S3: Based on the local advantage index and the recognized livable line and warning line of land development intensity, multi-objective constraints are set to ensure the basic land use standards for food security and ecological security;

[0009] S4: Coordinate land use conflicts based on multi-objective constraints to obtain optimized land use data;

[0010] S5: Input the land use data before coordination and the optimized land use data and carbon pool data into the InVEST carbon storage assessment model to obtain the corresponding carbon storage distribution data before and after coordination, and realize the optimized carbon storage distribution prediction.

[0011] The step S1 specifically includes:

[0012] S1.1: Extract historical land use changes from land use data as target variables, reclassify the land space types in the land use data into three categories, namely building space, agricultural space, and ecological space, and represent them with different grid values. Then, generate three target variables based on the similarities and differences of grid values ​​in different periods, namely building space change, agricultural space change, and ecological space change, and mark whether there is a change;

[0013] S1.2: The characteristic variables consist of factors that affect land use transformation, including temperature, precipitation, population, GDP, DEM, night lights, main road distribution, slope, and aspect. The raster data of the above factors are downloaded and then resampled and reprojected to the same resolution and coordinate system as the land use data.

[0014] S1.3: Introduce three corresponding categorical covariates according to the category of the target variable to identify the land space type.

[0015] The step S2 specifically includes:

[0016] S2.1: Use linear logistic regression model to perform logistic regression modeling and obtain the regression coefficients of the three target variables for the characteristic variables and categorical covariates respectively;

[0017] S2.2: Based on the regression coefficient of logistic regression, the occurrence probability of the three land spaces is calculated respectively. The calculation formula is as follows:

[0018]

[0019] Where g represents the grid position, subscripts c, a, and e represent building space, agricultural space, and ecological space, respectively. cg , P ag , P eg They represent the probability of occurrence of building space, agricultural space and ecological space on grid g respectively; α is the regression coefficient of the corresponding influencing factor; α c , α a , α e is the regression coefficient of the influencing factors corresponding to the regression of building, agriculture and ecological space; X ig represents the grid value of the influencing factor of the i land space on grid g, X cg , X ag , X eg are the grid values ​​of the influencing factors in the regression process of construction, agriculture, and ecology respectively; DV c , DV a , DV e Represents the categorical covariates of building, agriculture, and ecological space respectively; β c , β a , β e DVc , DV a , DV e The regression coefficient; W is the stable ecological space, C is the unchanged stable building area; Neighborhood is the neighborhood of each land use type on each grid, and its specific calculation method is:

[0020]

[0021] Neighborhood h,k represents the degree of neighborhood aggregation of land use space h, k represents the kth h-th spatial patch in the neighborhood of a grid, n is the total number of patches in the neighborhood, S h,k represents the area of ​​the kth h spatial patch in the neighborhood, and writes the occurrence probability values ​​of the three land types into a three-channel raster image, where each channel represents the occurrence probability value of one land type;

[0022] S2.3: Calculate the local dominance index for each land type based on the probability of occurrence, using the following formula:

[0023]

[0024] Among them, P c,i , P a,i , P e,i Represent the average occurrence probability of construction, agriculture, and ecological space within the neighborhood of grid i, LDI j It represents the ratio of the occurrence probability of j space in its neighborhood to the total occurrence probability, which is the local advantage index. j The values ​​are written into a three-channel raster image, with each channel representing the local dominance index of a land type;

[0025] S2.4: Calculate the conflict intensity index based on the local advantage index. The calculation formula is as follows:

[0026]

[0027] WCI op Represents the weighted conflict intensity between o space and p space. o and p can be any of the architectural, agricultural, and ecological spaces but cannot have the same value at the same time. LDI o and LDI p They represent the local dominance index of o space and p space in their neighborhoods respectively, and the total conflict intensity TWCI on the grid is:

[0028] TWCI=WCI ce +WCI ca +WCI ae

[0029] Among them WCIce 、WCI ca 、WCI ae The WCI is the conflict intensity index between building space and ecological space, building space and agricultural space, and agricultural space and ecological space, which is used to measure the spatial distribution and intensity of land conflicts. ce 、WCI ca 、WCI ae The values ​​of TWCI and TWCI are written into a four-channel raster image. The first three channels represent the conflict intensity index of a land type respectively, and the last channel is the total conflict intensity.

[0030] The step S3 specifically includes:

[0031] S3.1: Calculate the habitable area and warning area of ​​the construction land in the study area based on the internationally recognized habitable area and warning area of ​​land development intensity;

[0032] S3.2: Obtain the ecological protection red line area and cultivated land protection task area in the study area;

[0033] S3.3: Set up multiple simulation scenarios;

[0034] S3.4: Arrange the three local advantage indices in descending order according to the size of their respective grid values, and count the number of grids corresponding to each value.

[0035] The step S4 specifically includes:

[0036] S4.1: Conduct multi-objective conflict coordination based on different scenarios. First, according to the local advantage index of agricultural space and the local advantage index of ecological space, the number of grids of agricultural and ecological space land for basic land use to ensure food security and ecological security is selected from large to small until the total area of ​​the grid reaches the constraint value. The selected grids are used as the basic agricultural and ecological space for all scenarios, and the selected grids will no longer participate in subsequent coordination. Set the mark column to mark them respectively.

[0037] S4.2: Select the basic building space grid according to the size of the local advantage index of the building space, select the habitable line or warning line of the building space according to the scenario, and select the basic building land grid of each scenario from large to small according to the value of the local advantage index of the building space, and set the mark column to mark it;

[0038] S4.3: For the remaining grids, compare the local advantage index of agricultural space and the local advantage index of ecological space, convert the larger value into its corresponding land type, and for the scenarios where the importance of agricultural or ecological space needs to be highlighted, convert all of them into the corresponding agricultural space or ecological space;

[0039] S4.4: Assign corresponding land grids to each scenario, set corresponding scenario columns, and select corresponding grids of basic land for food security, basic land for ecological security, and basic building land for each scenario in combination with the tag columns, and then allocate the remaining grids according to S4.3;

[0040] S4.5: Read the values ​​of the scenario column data and write them into three-channel raster images respectively, where each channel in each image represents a land type, and the number of images is the number of corresponding simulation scenarios.

[0041] The step S5 specifically includes:

[0042] S5.1: Obtain carbon pool data and classify carbon pool types into four types that conform to the InVEST model, including aboveground biomass carbon pool, underground biomass carbon pool, soil carbon pool, and dead organic matter carbon pool. The calculation method of the carbon storage of the InVEST model is as follows:

[0043] C i =C i,above +C i,below +C i,soil +C i,dead

[0044]

[0045] i is the i-th land use type; C i is the carbon density of the i-th land type; C i,above , C i,below , C i,soil , C i,dead are the aboveground biomass carbon density, belowground biomass carbon density, soil organic matter carbon density and dead organic matter carbon density of the i-th land type; C total is the total carbon storage in the entire region; A i is the total area of ​​the i-th land use type; q is the number of land type classifications;

[0046] S5.2: Input the real land use spatial data and carbon pool data before coordination into the InVEST model to obtain the real carbon storage distribution raster data. Input the land use spatial data and carbon pool data after optimization based on multi-objective constraints into the InVEST model to obtain the carbon storage distribution prediction raster data under multiple scenarios after coordination. Compare and analyze the land use distribution and the corresponding carbon storage distribution before and after coordination.

[0047] The beneficial effects of the present invention are:

[0048] (1) The framework of this paper can reveal the characteristics of urban land use conflicts and their impacts on urban carbon dynamics;

[0049] (2) The optimization method for carbon storage distribution of the present invention can help identify the main carbon sink areas in the study area, providing a scientific basis for the effective management of regional carbon storage and carbon balance;

[0050] (3) The present invention simulates the impact of land use conflict and coordination on urban carbon dynamics, links land use conflict, land cover change and carbon sink of ecosystem services, provides new ideas for regional carbon sink research, and provides support for analyzing urban carbon sink potential.

[0051] The method and results of the present invention can also be used as a reference for other cities of the same type, providing a reference for regional green and low-carbon development, sustainable development, and the formulation of future land rational utilization policies. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a flow chart of the carbon reserve distribution prediction and evaluation method based on conflict detection and multi-objective optimization of the present invention;

[0053] Figure 2 It is an example of the probability map of occurrence of three land use spaces of the present invention;

[0054] Figure 3 It is an example of a local advantage index map of three land use spaces of the present invention;

[0055] Figure 4 It is an example of the three land use spaces and the overall conflict intensity map of the present invention;

[0056] Figure 5 It is a land use conflict coordination process diagram of the present invention;

[0057] Figure 6 It is an example of a land use map under multiple scenarios after land use conflict coordination of the present invention;

[0058] Figure 7 This is an example of the carbon storage distribution map before and after the land use conflict coordination of the present invention. Figure 7 (a) is the carbon storage distribution map under multiple scenarios after coordination. Figure 7 (b) is the uncoordinated carbon stock distribution map. DETAILED DESCRIPTION

[0059] The specific implementation of the present invention is further described below with reference to the accompanying drawings and specific examples:

[0060] like Figure 1 As shown in the figure, a carbon storage distribution prediction and evaluation method based on conflict detection and multi-objective optimization is presented.

[0061] The embodiment uses two phases of land use raster data from Esri Sentinel-2 Land Cover, reclassifies them into three land use types: building, agriculture, and ecological land in ArcGIS 10.2 (the raster value is 1 for building, 2 for agriculture, and 3 for ecology), resamples to 30m resolution, and sets the spatial reference to WGS_1984_UTM_zone_48N; the influencing factors include aspect, slope, DEM, population, GDP, average annual precipitation, average annual temperature, night lights, and main roads, which are also resampled and reprojected into raster data identical to the land use data; then the land use data and the influencing factor data raster values ​​are exported to an Excel table In the figure, each row represents a grid in the original image. Based on the similarities and differences in the column values ​​of the two-period land use data, three target variable columns are generated, namely, building space change, agricultural space change, and ecological space change columns, with values ​​of 0 or 1, 0 represents no change, and 1 represents change; three corresponding classified covariate columns are set based on the target variable, and then the influencing factor column is standardized if the value difference in the influencing factor column is too large or there are too many extreme values; then the LogisticRegression in the python machine learning package sklearn is used for logistic regression analysis to obtain the regression coefficients of the feature variable column for the three target variables.

[0062] Next, the occurrence probability of the three land spaces on each grid is calculated based on the regression coefficient of logistic regression. Taking the occurrence probability of building space as an example, the calculation method is as follows:

[0063]

[0064] Where g represents the grid position, subscript c represents the building space, P cg represents the probability of building space appearing on grid g; α is the regression coefficient of the corresponding influencing factor; α c is the regression coefficient of the influencing factors corresponding to the architectural space regression; X ig Represents the grid value of the influencing factor of the i-land space on the grid g. The value of i is building space, agricultural space, and ecological space. cg DV is the grid value of the influencing factors in the process of architectural space regression; c β represents the categorical covariate of building space; c For DV c The regression coefficient of ; W is a stable ecological space, such as reservoirs and large lakes, and C is a stable building area that has not changed; Neighborhood is the neighborhood of each land use type on each grid, and its specific calculation method is:

[0065]

[0066] Neighborhood c,k represents the degree of neighborhood aggregation of building space, k represents the kth building space patch in the neighborhood of a grid, n is the total number of patches in the neighborhood, S c,k Represents the area of ​​the kth construction space patch in the neighborhood; the specific calculation of Neighborhood is realized by using a sliding window algorithm on the land use raster data of the second phase. A 3×3 window is moved from top to bottom and from left to right to traverse the entire map. Each move reads the value of each land type in the window, counts the number, and calculates the neighborhood according to the formula. The result is written into an Excel table. The occurrence probability is calculated by reading the required values ​​in the Excel table according to the formula to calculate the occurrence probability of the three land types on each grid, and its value is written as a new grid. After visualization, it is as follows Figure 2 .

[0067] Next, the local dominance index of each land type is calculated based on the probability of occurrence, and the calculation formula is as follows:

[0068]

[0069] Among them, P c,i , P a,i , P e,i Represent the average occurrence probability of construction, agriculture, and ecological space within the neighborhood of grid i, LDI c The local dominance index (LDI) represents the ratio of the occurrence probability of a building space in its neighborhood to the total occurrence probability. c The calculation of the sliding window algorithm is also applied to the occurrence probability grid image generated in the previous step to calculate the local dominance index of the three land types on each grid and write its value into a new raster image for visualization. Figure 3 ; Then the conflict intensity index is calculated based on the local advantage index. The calculation formula is as follows:

[0070]

[0071] WCI ce Represents the weighted conflict intensity between architectural space and ecological space, LDI e The ratio of the occurrence probability of an ecological space in its neighborhood to the total occurrence probability is the local advantage index, WCI ce The calculation of the local advantage index is also performed by applying the sliding window algorithm on the newly generated local advantage index grid image to calculate the conflict intensity index of the three land types on each grid and write its value into the new grid image for visualization. Figure 4 .

[0072] In the next step of setting up multi-objective constraints, the livable line area and warning line area of ​​the construction land in the study area are calculated based on the internationally recognized land development intensity of 20% as the livable line and 30% as the warning line; finally, four scenarios are set, namely natural development, food security, ecological protection, and highly urbanized scenarios.

[0073] Next, the conflict coordination process of multiple objectives is carried out in different scenarios. Figure 5 First, all four scenarios need to meet the two constraints of ensuring basic land for food security and ensuring basic land for ecological security. Therefore, according to the local advantage index of agricultural space and the local advantage index of ecological space, the number of grids of agricultural and ecological space land for ensuring basic land for food security and ecological security is taken out from large to small according to the value, until the total area of ​​the grid reaches the constraint value. The taken out grids are used as the basic agricultural and ecological space for all four scenarios, and the taken out grids will no longer participate in the subsequent coordination. Two marking columns are set in the Excel table to mark them as 1; then the basic building space grid is selected according to the size of the local advantage index of the building space. Except for the highly urbanized scenario, which uses the land development intensity warning line, the other three scenarios all take the livable line. Similarly, each scenario is selected from large to small according to the value of the local advantage index of the building space. The basic building land grid is set in the Excel table, and a marking column is set to mark it as 1; then for the remaining grids, the food security scenario converts them all into agricultural land, and the ecological security scenario converts them all into ecological land. In the natural development scenario and the highly urbanized scenario, the local advantage index of the agricultural space and the local advantage index of the ecological space are compared, and the larger value is converted into its corresponding land type; then the corresponding land grid is assigned to each scenario, and four columns are set in the Excel table corresponding to the four scenarios, and combined with the marking column, the corresponding grids of basic land for ensuring food security, basic land for ensuring ecological security, and basic building land are selected for each scenario, and then the remaining grids are assigned according to the rules; finally, the values ​​in the four scenario columns are written as new grid data, and the land use distribution data under the four scenarios after coordination are obtained as follows Figure 6 shown.

[0074] The last step is to obtain carbon pool data as shown in Table 1. According to the reclassified land types, the carbon density of these six land types is merged into the carbon density of construction, agriculture, and ecology. Specifically, construction land is classified as construction space, cultivated land is classified as agricultural space, and the other four types are classified as ecological space. Then, the second-phase land use raster data and carbon pool data are input into the InVEST model to obtain the real carbon storage distribution raster data. The optimized multi-scenario land use raster data and carbon pool data are input into the InVEST model to obtain the coordinated carbon storage distribution raster data under multiple scenarios. The results after visualization are as follows: Figure 7 ,Table 2 shows the comparison of carbon storage values ​​before and after coordination.

[0075] Table 1 Carbon density of different land types (mg / hm -2 )

[0076]

[0077] Table 2 Comparison of carbon storage before and after coordination

[0078]

[0079] The specific implementation modes of the present invention are described in detail above in conjunction with the accompanying drawings, but the present invention is not limited to the above implementation modes, and various changes can be made within the knowledge scope of ordinary technicians in this field without departing from the purpose of the present invention.

Claims

1. A carbon stock distribution prediction and assessment method based on conflict detection and multi-objective optimization, characterized in that: S1: Construct the target variable, characteristic variables and covariates required for logistic regression analysis; S2: Perform logistic regression analysis and obtain the corresponding regression coefficients, calculate the corresponding probability of occurrence of each land type based on the regression coefficients, then calculate the corresponding local advantage index of each land type based on the probability of occurrence, and finally calculate the corresponding conflict intensity index of each land type based on the local advantage index; S3: Based on the local advantage index and the recognized livable line and warning line of land development intensity, multi-objective constraints are set to ensure the basic land use standards for food security and ecological security; S4: Coordinate land use conflicts based on multi-objective constraints to obtain optimized land use data; S5: Input the land use data before coordination and the optimized land use data and carbon pool data into the InVEST carbon storage assessment model to obtain the corresponding carbon storage distribution data before and after coordination, and realize the optimized carbon storage distribution prediction.

2. The carbon stock distribution prediction and evaluation method based on conflict detection and multi-objective optimization according to claim 1 is characterized in that: The step S1 specifically includes: S1.1: Extract historical land use changes from land use data as target variables, reclassify the land space types in the land use data into three categories, namely building space, agricultural space, and ecological space, and represent them with different grid values. Then, generate three target variables based on the similarities and differences of grid values ​​in different periods, namely building space change, agricultural space change, and ecological space change, and mark whether there is a change; S1.2: The characteristic variables consist of factors that affect land use transformation, including temperature, precipitation, population, GDP, DEM, night lights, main road distribution, slope, and aspect. The raster data of the above factors are downloaded and then resampled and reprojected to the same resolution and coordinate system as the land use data. S1.3: Introduce three corresponding categorical covariates according to the category of the target variable to identify the land space type.

3. The carbon stock distribution prediction and evaluation method based on conflict detection and multi-objective optimization according to claim 1 is characterized in that: The step S2 specifically includes: S2.1: Use linear logistic regression model to perform logistic regression modeling and obtain the regression coefficients of the three target variables for the characteristic variables and categorical covariates respectively; S2.2: Based on the regression coefficient of logistic regression, the occurrence probability of the three land spaces is calculated respectively. The calculation formula is as follows: Where g represents the grid position, subscripts c, a, and e represent building space, agricultural space, and ecological space, respectively. cg , P ag , P eg They represent the probability of occurrence of building space, agricultural space and ecological space on grid g respectively; α is the regression coefficient of the corresponding influencing factor; α c , α a , α e is the regression coefficient of the influencing factors corresponding to the regression of building, agriculture and ecological space; X ig represents the grid value of the influencing factor of the i land space on grid g, X cg , X ag , X eg are the grid values ​​of the influencing factors in the regression process of construction, agriculture, and ecology respectively; DV c , DV a , DV e Represents the categorical covariates of building, agriculture, and ecological space respectively; β c , β a , β e DV c , DV a , DV e The regression coefficient; W is the stable ecological space, C is the unchanged stable building area; Neighborhood is the neighborhood of each land use type on each grid, and its specific calculation method is: Neighborhood h,k represents the degree of neighborhood aggregation of land use space h, k represents the kth h-th spatial patch in the neighborhood of a grid, n is the total number of patches in the neighborhood, S h,k represents the area of ​​the kth h spatial patch in the neighborhood, and writes the occurrence probability values ​​of the three land types into a three-channel raster image, where each channel represents the occurrence probability value of one land type; S2.3: Calculate the local dominance index for each land type based on the probability of occurrence, using the following formula: Among them, P c,i , P a,i , P e,i Represent the average occurrence probability of construction, agriculture, and ecological space within the neighborhood of grid i, LDI j It represents the ratio of the occurrence probability of j space in its neighborhood to the total occurrence probability, which is the local advantage index. j The values ​​are written into a three-channel raster image, with each channel representing the local dominance index of a land type; S2.4: Calculate the conflict intensity index based on the local advantage index. The calculation formula is as follows: WCI op Represents the weighted conflict intensity between o space and p space. o and p can be any of the architectural, agricultural, and ecological spaces but cannot have the same value at the same time. LDI o and LDI p They represent the local dominance index of o space and p space in their neighborhoods respectively, and the total conflict intensity TWCI on the grid is: TWCI=WCI ce +WCI ca +WCI ae Among them WCI ce 、WCI ca 、WCI ae The conflict intensity indexes between building space and ecological space, building space and agricultural space, and agricultural space and ecological space are used to measure the spatial distribution and intensity of land conflicts. ce 、WCI ca 、WCI ae The values ​​of TWCI and TWCI are written into a four-channel raster image. The first three channels represent the conflict intensity index of a land type respectively, and the last channel is the total conflict intensity.

4. The carbon storage distribution prediction and evaluation method based on conflict detection and multi-objective optimization according to claim 1 is characterized in that: The step S3 specifically includes: S3.1: Calculate the habitable area and warning area of ​​the construction land in the study area based on the internationally recognized habitable area and warning area of ​​land development intensity; S3.2: Obtain the ecological protection red line area and cultivated land protection task area in the study area; S3.3: Set up multiple simulation scenarios; S3.4: Arrange the three local advantage indices in descending order according to the size of their respective grid values, and count the number of grids corresponding to each value.

5. The carbon storage distribution prediction and evaluation method based on conflict detection and multi-objective optimization according to claim 1 is characterized in that: The step S4 specifically includes: S4.1: Conduct multi-objective conflict coordination based on different scenarios. First, according to the local advantage index of agricultural space and the local advantage index of ecological space, the number of grids of agricultural and ecological space land for basic land use to ensure food security and ecological security is selected from large to small until the total area of ​​the grid reaches the constraint value. The selected grids are used as the basic agricultural and ecological space for all scenarios, and the selected grids will no longer participate in subsequent coordination. Set the mark column to mark them respectively. S4.2: Select the basic building space grid according to the size of the local advantage index of the building space, select the habitable line or warning line of the building space according to the scenario, and select the basic building land grid of each scenario from large to small according to the value of the local advantage index of the building space, and set the mark column to mark it; S4.3: For the remaining grids, compare the local advantage index of agricultural space and the local advantage index of ecological space, convert the larger value into its corresponding land type, and for the scenarios where the importance of agricultural or ecological space needs to be highlighted, convert all of them into the corresponding agricultural space or ecological space; S4.4: Assign corresponding land grids to each scenario, set corresponding scenario columns, and select corresponding grids of basic land for food security, basic land for ecological security, and basic building land for each scenario in combination with the tag columns, and then allocate the remaining grids according to S4.3; S4.5: Read the values ​​of the scenario column data and write them into three-channel raster images respectively, where each channel in each image represents a land type, and the number of images is the number of corresponding simulation scenarios.

6. The carbon storage distribution prediction and evaluation method based on conflict detection and multi-objective optimization according to claim 1 is characterized in that: The step S5 specifically includes: S5.1: Obtain carbon pool data and classify carbon pool types into four types that conform to the InVEST model, including aboveground biomass carbon pool, underground biomass carbon pool, soil carbon pool, and dead organic matter carbon pool. The calculation method of the carbon storage of the InVEST model is as follows: C i =C i,above +C i,below +C i,soil +C i,dead i is the i-th land use type; C i is the carbon density of the i-th land type; C i,above , C i,below , C i,soil , C i,dead are the aboveground biomass carbon density, belowground biomass carbon density, soil organic matter carbon density and dead organic matter carbon density of the i-th land type; C total is the total carbon storage in the entire region; A i is the total area of ​​the i-th land use type; q is the number of land type classifications; S5.2: Input the real land use spatial data and carbon pool data before coordination into the InVEST model to obtain the real carbon storage distribution raster data. Input the land use spatial data and carbon pool data after optimization based on multi-objective constraints into the InVEST model to obtain the carbon storage distribution prediction raster data under multiple scenarios after coordination. Compare and analyze the land use distribution and the corresponding carbon storage distribution before and after coordination.

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