Method for predicting carbon reserve change of terrestrial ecosystem based on multi-land utilization scene

Abstract

The invention discloses a land ecosystem carbon reserve change prediction method based on a multi-land utilization scene, and relates to the technical field of ecosystem carbon reserve prediction. The method comprises the following steps: setting a spatial resolution of land utilization, obtaining historical land utilization data of a to-be-evaluated region, carrying out reclassification, forming a first-level land type of the to-be-evaluated region, obtaining a high-definition land utilization type graph of the to-be-evaluated region, constructing a driving factor influencing land utilization change, and obtaining a high-definition land utilization type graph of the to-be-evaluated region. Historical data of the driving factors are obtained to serve as land utilization driving samples, land utilization changes of the to-be-evaluated area are simulated through a Flus model, the land utilization scene type of the area is set according to a development planning strategy of the to-be-evaluated area, and according to the simulation result of the Flus model, the land utilization scene type of the to-be-evaluated area is set. And obtaining prediction results of the number of typical land utilization and the spatial distribution condition of the to-be-evaluated region under each scene type, and respectively calculating the total carbon reserve and carbon density of the to-be-evaluated region under each prediction result by using the InVEST model.

Description

Technical Field

[0001] The present invention relates to the technical field of ecosystem carbon storage prediction, and in particular to a method for predicting changes in terrestrial ecosystem carbon storage based on multiple land use scenarios. Background Art

[0002] The carbon storage of terrestrial ecosystems plays a very important role in the global carbon cycle and climate change, and is an important indicator for measuring the service function of regional ecosystems. Land use and cover change is one of the most important factors affecting carbon storage changes. By affecting the carbon storage of vegetation and soil in the ecosystem and thus affecting the carbon storage changes in the entire region, it can change the structure and function of the ecosystem to affect the ecosystem carbon cycle process.

[0003] In the existing technology, there is already a certain foundation for the research on linking future urban land changes and carbon storage at different spatial scales. For example, at the watershed scale, Yang Jie et al. studied the spatiotemporal changes of carbon storage in the Yellow River Basin based on InVEST and CA-Markov models; at the urban agglomeration scale, Xie Tianqi et al. estimated and predicted the carbon storage in the Huaihai Economic Zone based on land use; at the city scale, He Haishan et al. used the FLUS model to simulate the spatial distribution characteristics of land use types in Shenzhen under two scenarios: carbon sink maximization scenario and carbon emission minimization scenario; at a specific regional scale, Shi Mingjie et al. carried out the spatiotemporal evolution and prediction of carbon storage in the Yili Valley based on MCE-CA-Markov and InVEST models.

[0004] From the perspective of the future development prospects of different regions, under the condition of meeting the quantitative indicators and restricting the development red line required by the regional development planning policy, proposing a reasonable and effective zoning management model for future urban development and low-carbon city construction can provide a scientific basis and theoretical foundation for the future development and land space planning of the region. However, at present, there are few reports on how to combine future urban land use changes with carbon storage to carry out regional refined zoning management. Therefore, what kind of effective way to conduct multi-scenario simulation analysis of land use changes and carbon storage in the region is an important issue that needs to be solved urgently. To this end, we propose a prediction method for terrestrial ecosystem carbon storage changes based on multiple land use scenarios. Summary of the invention

[0005] The main purpose of the present invention is to provide a method for predicting changes in carbon storage in terrestrial ecosystems based on multiple land use scenarios, which can effectively solve the problems in the background technology.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is:

[0007] The prediction method of carbon storage change in terrestrial ecosystems under multiple land use scenarios includes:

[0008] The spatial resolution of land use is set to λm×λm according to the regional scale of the area to be evaluated, the historical land use data of the area to be evaluated is obtained, the land use data is preliminarily divided into several secondary types, the primary land types of the area to be evaluated are formed by reclassification, and a high-definition land use type map of the area to be evaluated is obtained; the primary land types include six types: cultivated land, forest land, grassland, water area, construction land, and future land;

[0009] The spatial resolution λ of the land use and the regional scale of the area to be evaluated satisfy the following relationship: ; In the formula, S represents the area of ​​the area to be evaluated, in m 2 ; ks is a constant coefficient, and ks∈[0.5×10 -2 , 1×10 -2 ];

[0010] Construct driving factors that affect land use change, obtain historical data of the driving factors as land use driving samples, adjust the resolution of the land use driving samples to λm×λm, and use the Flus land use prediction model to simulate the land use change in the area to be evaluated;

[0011] The driving factors include natural factors and social factors. The natural factors include DEM, elevation, normalized difference vegetation index, annual average precipitation, and annual average temperature. The social factors include distance from the city center, transportation network GDP, and population density.

[0012] The land use change simulation process includes the following steps:

[0013] According to the land use type map and the driving factors, the suitability probability estimation module of the Flus land use prediction model is used to fit the land use type of the base period with multiple spatial driving factors, and calculate the suitability probability of various land use types within the area to be evaluated within the research scope;

[0014] The neighborhood factor parameters are fine-tuned to obtain the neighborhood weights of each land type under the first scenario type, the second scenario type, ..., and the kth scenario type, respectively, where: In the formula, θ qk It is expressed as the neighborhood weight of the qth land type under the kth scenario type;

[0015] The adaptive inertial competition mechanism based on roulette selection in the Flus land use prediction model is used to solve the uncertainty and complexity of the mutual conversion of various land use types under the influence of natural effects and human activities, and the comprehensive rules are calculated accordingly:

[0016]

[0017] In the formula, I k t is the inertia t coefficient with the iteration number k; D t-1 is the difference in the number of pixels between a certain land type predicted at time t-1 and the actual land type in the land use type map;

[0018] Obtain historical land use change information data for the area to be evaluated, and compare the simulated land use with the actual land use;

[0019] The Kappa coefficient is used to test the accuracy of land pattern evolution prediction. The land types in previous years in the historical land use change law information data are used as training sets to predict the land type distribution at future moments; where Kappa coefficient = (P0-P c ) / (P p -P c ), where P0 is the correct simulation ratio; P c is the correct prediction ratio under random conditions of the model; P p It is the proportion of correct predictions under ideal conditions; when the Kappa coefficient is greater than 0.8, it means that the simulation effect is extremely good, the result is highly reliable, and the consistency is extremely good; when 0.6<Kappa coefficient≤0.8, it means that the simulation effect is good and the consistency is good; when 0.4<Kappa coefficient≤0.6, it means that the simulation effect is effective and the consistency is medium; when 0.2<Kappa coefficient≤0.4, it means that the simulation effect is poor; when the Kappa coefficient≤0.2, it means that the simulation effect is extremely poor.

[0020] According to the development planning strategy of the area to be evaluated, the land use scenario type of the area is set, including a first scenario type, a second scenario type, ..., and a kth scenario type; according to the simulation result of the Flus land use prediction model, a first prediction result of the typical quantity and spatial distribution of land use in the area to be evaluated under the first scenario type, a second prediction result under the second scenario type, ..., and a kth prediction result under the kth scenario type are obtained;

[0021] According to the obtained prediction results, using the InVEST model, respectively calculate the overall carbon storage and carbon density of the area to be assessed under the first prediction result, the second prediction result, ..., and the kth prediction result;

[0022] The calculation formula of overall carbon storage is as follows: C total =C above +C below +C soil +Cdead ; In the formula, in the formula: C total Represents the total carbon stock; C above Represents the aboveground carbon storage of vegetation; C below Represents the underground carbon storage of vegetation; C soil represents soil carbon storage; C dead represents the carbon stock of dead organic matter;

[0023] The carbon density calculation formula is as follows:

[0024]

[0025] In the formula, C SP is the soil carbon density (Mg / ha) obtained according to the annual precipitation in the area to be assessed; C BP , C BT are the biomass carbon density (Mg / ha) obtained according to the annual precipitation and annual average temperature of the area to be evaluated; P is the annual average precipitation (mm) of the area to be evaluated, and T is the annual average temperature (℃) of the area to be evaluated.

[0026] Since the carbon density value varies with soil properties and land use, it needs to be corrected. The annual average temperature and annual precipitation of the area to be evaluated and the local area are substituted into the above formula respectively. The ratio of the two is the correction coefficient. The product of the carbon density data of the local area and the correction coefficient is the carbon density data of the area to be evaluated, where:

[0027]

[0028] In the formula, K BP , K BT are the precipitation factor and temperature factor correction coefficients of biomass carbon density, C BP ' and C BP ” are the biomass carbon density data obtained based on annual precipitation in the area to be assessed and the region where the assessment is located; C BT ' and C BT " are the biomass carbon density data of the area to be assessed and the region based on the annual average temperature; C SP ' and C SP ” are the soil carbon density data of the area to be assessed and the region based on the annual average temperature; K B and K S They are biomass carbon density correction factor and soil carbon density correction factor, respectively.

[0029] The method further comprises:

[0030] Divide the area to be assessed into unit grids of εm×εm, where ε≤λ, and obtain the carbon storage data value corresponding to the area where each unit grid is located;

[0031] A spatial autocorrelation analysis is performed on the obtained overall carbon stock of the area to be assessed, and whether the carbon stock distribution of each unit grid in the area to be assessed is measured based on the analysis results.

[0032] The spatial autocorrelation analysis includes a global spatial autocorrelation analysis method, which uses the global Moran's index MI as an evaluation standard for the spatial autocorrelation of carbon reserves in the area to be evaluated, wherein the calculation formula of the global Moran's index MI is:

[0033]

[0034] In the formula, Represented as a spatial weight matrix; x i It is represented by the carbon storage of the i-th unit grid in the area to be assessed; x j It is represented as the carbon storage of the jth unit grid in the area to be assessed; and i≠j; It is represented by the mean carbon storage of the unit grids in the area to be assessed; n is represented by the number of unit grids in the area to be assessed; and MI∈[-1,1]; when MI<0, it indicates negative correlation; when MI>0, it indicates positive correlation; when MI=0, it indicates that the carbon storage of each unit grid in the area to be assessed is independent of each other; the closer MI is to 1, the more significant the agglomeration effect between the carbon storage of each unit grid in the area to be assessed; the closer MI is to -1, the more significant the divergence between the carbon storage of each unit grid in the area to be assessed.

[0035] The spatial autocorrelation analysis also includes a local spatial autocorrelation analysis method, wherein the local spatial autocorrelation analysis method uses a local Moran index MI i As an evaluation criterion for the spatial autocorrelation of carbon storage in the area to be evaluated, the local Moran index MI of the carbon storage of the i-th unit grid in the area to be evaluated is i is defined as:

[0036]

[0037] Among them, MI i ∈[-1,1]; when MI i >0, it means that the i-th unit grid is the spatial accumulation of similar values ​​around it, the high observation value area is surrounded by high values, and the low observation value area is surrounded by low values; when MI i When MI is less than 0, it means that the i-th unit grid is a spatial accumulation of dissimilarity, where the low-value area is surrounded by high-value areas or the high-value area is surrounded by low-value areas. i When =0, it means that the i-th unit grid has no spatial correlation with the adjacent unit grids.

[0038] The present invention has the following beneficial effects:

[0039] Compared with the existing technology, the spatial resolution of land use is set according to the regional scale of the area to be evaluated, the historical land use data of the area to be evaluated is obtained, the land use data is preliminarily divided into several secondary types, the primary land type of the area to be evaluated is formed by reclassification, a high-definition land use type map of the area to be evaluated is obtained, the driving factors affecting land use change are constructed, the historical data of the driving factors are obtained as land use driving samples, the resolution of the land use driving samples is adjusted to λm×λm, the land use change of the area to be evaluated is simulated by using the Flus land use prediction model, the land use scenario type of the area to be evaluated is set according to the development planning strategy of the area to be evaluated, and the simulation results of the Flus land use prediction model are used to predict the land use change of the area to be evaluated. The prediction results of the typical quantity and spatial distribution of land use in the area to be evaluated under each scenario type are obtained. According to the obtained prediction results, the InVEST model is used to calculate the overall carbon storage and carbon density of the area to be evaluated under each prediction result. The area to be evaluated is divided into unit grids of εm×εm, and the carbon storage data value corresponding to the area where each unit grid is located is obtained. The spatial autocorrelation analysis of the overall carbon storage of the area to be evaluated is carried out. According to the analysis results, whether the carbon storage distribution of each unit grid in the area to be evaluated is measured. By proposing a reasonable and effective zoning management model for future urban development and low-carbon city construction, a scientific basis and theoretical foundation can be provided for the future development and land space planning of the region. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 The present invention is a flow chart of a method for predicting changes in carbon storage in terrestrial ecosystems under multiple land use scenarios. DETAILED DESCRIPTION

[0041] The present invention will be further described below in conjunction with specific implementation methods, wherein the accompanying drawings are only used for exemplary descriptions and represent only schematic diagrams rather than actual drawings, and should not be understood as limiting the present invention. In order to better illustrate the specific implementation methods of the present invention, some parts of the accompanying drawings may be omitted, enlarged or reduced, and do not represent the size of the actual product.

[0042] The specific implementation process of the technical solution of the present invention includes the following steps:

[0043] Step 1: Set the spatial resolution of land use to λm×λm according to the regional scale of the area to be evaluated, where the spatial resolution λ of land use and the regional scale of the area to be evaluated satisfy the following relationship: In the formula, S represents the area of ​​the area to be evaluated, in m 2 ; ks is a constant coefficient, and ks∈[0.5×10 -2, 1×10 -2 ];

[0044] Step 2: Obtain historical land use data of the area to be evaluated, preliminarily divide the land use data into several secondary types, form the primary land types of the area to be evaluated through reclassification, and obtain a high-definition land use type map of the area to be evaluated; among them, the primary land types include six types: cultivated land, forest land, grassland, water area, construction land, and future land;

[0045] Step 3: Construct the driving factors that affect land use change, obtain the historical data of the driving factors as land use driving samples, adjust the resolution of the land use driving samples to λm×λm, and use the Flus land use prediction model to simulate the land use change in the area to be evaluated;

[0046] Among them, the driving factors include two categories: natural factors and social factors. Natural factors include DEM, elevation, normalized difference vegetation index, annual average precipitation, and annual average temperature; social factors include distance from the city center, transportation network GDP, and population density;

[0047] The land use change simulation process includes the following steps:

[0048] Step 31: Based on the land use type map and driving factors, the suitability probability estimation module of the Flus land use prediction model is used to fit the base period land use type with multiple spatial driving factors to calculate the suitability probability of various land use types within the study area in the area to be evaluated;

[0049] Step 32: fine-tune the neighborhood factor parameters to obtain the neighborhood weights of each land type under the first scenario type, the second scenario type, ..., and the kth scenario type, respectively, where: In the formula, θ qk It is expressed as the neighborhood weight of the qth land type under the kth scenario type;

[0050] Step 33: Use the adaptive inertial competition mechanism based on roulette wheel selection in the Flus land use prediction model to solve the uncertainty and complexity of the mutual conversion of various land use types under the combined influence of natural effects and human activities, and calculate the comprehensive rules:

[0051]

[0052] In the formula, I k t is the inertia t coefficient with the iteration number k; D t-1 is the difference in the number of pixels between a certain land type predicted at time t-1 and the actual land type in the land use type map;

[0053] Step 34: Obtain historical land use change law information data of the area to be evaluated, and compare the simulated land use situation with the actual land use situation;

[0054] Step 35: Use the Kappa coefficient to test the accuracy of land pattern evolution prediction, use the land types of previous years in the historical land use change law information data as the training set, and predict the land type distribution at the future moment; where Kappa coefficient = (P0-P c ) / (P p -P c ), where P0 is the correct simulation ratio; P c is the correct prediction ratio under random conditions of the model; P p It is the proportion of correct predictions under ideal conditions; when the Kappa coefficient is greater than 0.8, it means that the simulation effect is extremely good, the result is highly reliable, and the consistency is extremely good; when 0.6<Kappa coefficient≤0.8, it means that the simulation effect is good and the consistency is good; when 0.4<Kappa coefficient≤0.6, it means that the simulation effect is effective and the consistency is medium; when 0.2<Kappa coefficient≤0.4, it means that the simulation effect is poor; when the Kappa coefficient≤0.2, it means that the simulation effect is extremely poor.

[0055] Step 4: According to the development planning strategy of the area to be evaluated, the land use scenario type of the area is set, including the first scenario type, the second scenario type, ..., and the kth scenario type. According to the simulation results of the Flus land use prediction model, the typical quantity and spatial distribution of land use in the area to be evaluated are obtained, including the first prediction result under the first scenario type, the second prediction result under the second scenario type, ..., and the kth prediction result under the kth scenario type;

[0056] Step 5: Based on the obtained prediction results, the InVEST model is used to calculate the overall carbon storage and carbon density of the area to be assessed under the first prediction result, the second prediction result, ..., and the kth prediction result;

[0057] The calculation formula of overall carbon storage is as follows: C total =C above +C below +C soil +C dead ; In the formula, in the formula: C total Represents the total carbon stock; C above Represents the aboveground carbon storage of vegetation; C below Represents the underground carbon storage of vegetation; C soil represents soil carbon storage; C dead represents the carbon stock of dead organic matter;

[0058] The carbon density calculation formula is as follows:

[0059]

[0060] In the formula, C SP is the soil carbon density (Mg / ha) obtained according to the annual precipitation in the area to be assessed; C BP , C BT are the biomass carbon density (Mg / ha) obtained according to the annual precipitation and annual average temperature of the area to be evaluated; P is the annual average precipitation (mm) of the area to be evaluated, and T is the annual average temperature (℃) of the area to be evaluated.

[0061] Since the carbon density value varies with soil properties and land use, it needs to be corrected. The annual average temperature and annual precipitation of the area to be evaluated and the local area are substituted into the above formula respectively. The ratio of the two is the correction coefficient. The product of the carbon density data of the local area and the correction coefficient is the carbon density data of the area to be evaluated, where:

[0062]

[0063] In the formula, K BP , K BT are the precipitation factor and temperature factor correction coefficients of biomass carbon density, C BP ' and C BP ” are the biomass carbon density data obtained based on annual precipitation in the area to be assessed and the region where the assessment is located; C BT ' and C BT " are the biomass carbon density data of the area to be assessed and the region based on the annual average temperature; C SP ' and C SP ” are the soil carbon density data of the area to be assessed and the region based on the annual average temperature; K B and K S They are biomass carbon density correction factor and soil carbon density correction factor, respectively.

[0064] Step 7: Divide the area to be assessed into unit grids of εm×εm, where ε≤λ, and obtain the carbon storage data value corresponding to the area where each unit grid is located;

[0065] Step 8: Perform spatial autocorrelation analysis on the overall carbon stock of the area to be assessed, and measure whether the carbon stock distribution of each unit grid in the area to be assessed is clustered based on the analysis results.

[0066] Among them, spatial autocorrelation analysis includes global spatial autocorrelation analysis method, which uses the global Moran's index MI as the evaluation standard for the spatial autocorrelation of carbon storage in the area to be evaluated. The calculation formula of the global Moran's index MI is:

[0067]

[0068] In the formula, Represented as a spatial weight matrix; x i It is represented by the carbon storage of the i-th unit grid in the area to be assessed; x j It is represented as the carbon storage of the jth unit grid in the area to be assessed; and i≠j; It is represented by the mean carbon storage of the unit grids in the area to be assessed; n is represented by the number of unit grids in the area to be assessed; and MI∈[-1,1]; when MI<0, it indicates negative correlation; when MI>0, it indicates positive correlation; when MI=0, it indicates that the carbon storage of each unit grid in the area to be assessed is independent of each other; the closer MI is to 1, the more significant the agglomeration effect between the carbon storage of each unit grid in the area to be assessed; the closer MI is to -1, the more significant the divergence between the carbon storage of each unit grid in the area to be assessed.

[0069] Spatial autocorrelation analysis also includes local spatial autocorrelation analysis, which uses the local Moran index MI i As the evaluation criterion of spatial autocorrelation of carbon storage in the area to be evaluated, the local Moran index MI of carbon storage in the i-th unit grid of the area to be evaluated is: i is defined as:

[0070]

[0071] Among them, MI i ∈[-1,1]; when MI i >0, it means that the i-th unit grid is the spatial accumulation of similar values ​​around it, the high observation value area is surrounded by high values, and the low observation value area is surrounded by low values; when MI i When MI is less than 0, it means that the i-th unit grid is a spatial accumulation of dissimilarity, where the low-value area is surrounded by high-value areas or the high-value area is surrounded by low-value areas. i When =0, it means that the i-th unit grid has no spatial correlation with the adjacent unit grids.

[0072] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for predicting changes in carbon storage in terrestrial ecosystems under multiple land use scenarios, characterized in that: include: The spatial resolution of land use is set to λm×λm according to the regional scale of the area to be evaluated, the historical land use data of the area to be evaluated is obtained, the land use data is preliminarily divided into several secondary types, the primary land types of the area to be evaluated are formed by reclassification, and a high-definition land use type map of the area to be evaluated is obtained; Construct driving factors that affect land use change, obtain historical data of the driving factors as land use driving samples, adjust the resolution of the land use driving samples to λm×λm, and use the Flus land use prediction model to simulate the land use change in the area to be evaluated; According to the development planning strategy of the area to be evaluated, the land use scenario type of the area is set, including a first scenario type, a second scenario type, ..., and a kth scenario type; according to the simulation result of the Flus land use prediction model, a first prediction result of the typical quantity and spatial distribution of land use in the area to be evaluated under the first scenario type, a second prediction result under the second scenario type, ..., and a kth prediction result under the kth scenario type are obtained; According to the obtained prediction results, the InVEST model is used to calculate the overall carbon storage and carbon density of the area to be evaluated under the first prediction result, the second prediction result, ..., and the kth prediction result.

2. The method for predicting changes in carbon storage in terrestrial ecosystems based on multiple land use scenarios according to claim 1, characterized in that: The method further comprises: Divide the area to be assessed into unit grids of εm×εm, where ε≤λ, and obtain the carbon storage data value corresponding to the area where each unit grid is located; A spatial autocorrelation analysis is performed on the obtained overall carbon stock of the area to be assessed, and whether the carbon stock distribution of each unit grid in the area to be assessed is measured based on the analysis results.

3. The method for predicting changes in carbon storage in terrestrial ecosystems based on multiple land use scenarios according to claim 1, characterized in that: The driving factors include natural factors and social factors. The natural factors include DEM, elevation, normalized difference vegetation index, average annual precipitation, and average annual temperature; the social factors include distance from the city center, transportation network GDP, and population density.

4. The method for predicting changes in carbon storage in terrestrial ecosystems based on multiple land use scenarios according to claim 1, characterized in that: The first-level land types include six types: cultivated land, forest land, grassland, water area, construction land, and future land.

5. The method for predicting changes in carbon storage in terrestrial ecosystems based on multiple land use scenarios according to claim 1, characterized in that: The spatial autocorrelation analysis includes a global spatial autocorrelation analysis method, which uses the global Moran's index MI as an evaluation standard for the spatial autocorrelation of carbon reserves in the area to be evaluated, wherein the calculation formula of the global Moran's index MI is: In the formula, Expressed as a spatial weight matrix; x i It is represented by the carbon storage of the i-th unit grid in the area to be assessed; x j It is represented as the carbon storage of the j-th unit grid in the area to be assessed; and i≠j; It is represented by the mean carbon storage of the unit grids in the area to be assessed; n is represented by the number of unit grids in the area to be assessed; and MI∈[-1,1].

6. The method for predicting changes in carbon storage in terrestrial ecosystems based on multiple land use scenarios according to claim 1, characterized in that: The spatial autocorrelation analysis also includes a local spatial autocorrelation analysis method, wherein the local spatial autocorrelation analysis method uses a local Moran index MI i As an evaluation criterion for the spatial autocorrelation of carbon storage in the area to be evaluated, the local Moran index MI of the carbon storage of the i-th unit grid in the area to be evaluated is i is defined as: Among them, MI i ∈[-1,1].

7. The method for predicting changes in carbon storage in terrestrial ecosystems based on multiple land use scenarios according to claim 5, characterized in that: When using the global Moran index MI as the evaluation criterion for the spatial autocorrelation of carbon storage in the area to be assessed, When MI < 0, it indicates a negative correlation; When MI>0, it indicates a positive correlation; When MI = 0, it indicates that the carbon storage of each unit grid in the area to be assessed is independent of each other; The closer MI is to 1, the more significant the agglomeration effect between carbon stocks in each unit grid in the area to be assessed. The closer MI is to -1, the more significant the divergence between carbon stocks in each unit grid in the area to be assessed.

8. The method for predicting changes in carbon storage in terrestrial ecosystems based on multiple land use scenarios according to claim 6, characterized in that: Using the local Moran index MI i As an evaluation criterion for the spatial autocorrelation of carbon stocks in the region to be assessed, When MI i When >0, it means that the i-th unit grid is the spatial accumulation of similar values ​​around it, the high observation value area is surrounded by high values, and the low observation value area is surrounded by low values; When MI i When <0, it means that the i-th unit grid is a spatial accumulation of dissimilarity, where the low-value area is surrounded by high-value areas or the high-value area is surrounded by low-value areas; When MI i When =0, it means that the i-th unit grid has no spatial correlation with the adjacent unit grids.

9. The method for predicting changes in carbon storage in terrestrial ecosystems based on multiple land use scenarios according to claim 1, characterized in that: The spatial resolution λ of the land use and the regional scale of the area to be evaluated satisfy the following relationship: In the formula, S represents the area of ​​the area to be evaluated, in m 2 ; ks is a constant coefficient, and ks∈[0.5×10 -2 , 1×10 -2 ].

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