Territorial space optimization method for cross-scale planning target and ecological toughness feedback

Abstract

The invention relates to a territorial space optimization method for cross-scale planning target and ecological toughness feedback, and the method comprises the steps: firstly carrying out the long-time-sequence territorial space function classification through a random forest algorithm based on a big data platform GEE, and analyzing a driving force factor; planning texts of multiple administrative levels are collected, a planning target is analyzed, a target parameter group is obtained by utilizing an equal interval method, and a future territorial space structure is modeled and simulated in combination with a'double evaluation 'result. On the basis, the vegetation structure is analyzed, and long-time-sequence toughness indexes of global and local grids are simulated based on the biophysical process. And finally, analyzing the change trend of all the toughness indexes, if trend change exists, feeding back and iterating the parameters until all the toughness indexes are kept stable or tend to a good trend, and ending circulation and outputting. According to the method, the feedback relationship between planning targets of different levels and the ecosystem toughness is quantified for the first time, effective integration of sustainability and toughness is successfully realized, and the scientificity of territorial space structure optimization and space decision is improved.

Description

Technical Field

[0001] The present invention relates to a land space optimization method for cross-scale planning objectives and ecological resilience feedback under the support of geographic information big data, and in particular to a land space scenario optimization simulation method for measuring ecological resilience changes under global climate change and feeding back to planning objective adjustments. Background Art

[0002] The national land space structure is a complex structure under the interaction of human-land relations. It refers to the spatial organization of the area, proportional relationship and interaction of different functions of national land space development and protection, including spatial agglomeration, spatial differentiation, topological structure, near- and long-range interactions, etc. It can be understood as a complex structure that is an organic compound of the natural ecosystem structure and the human social spatial organization structure. In the context of global climate change and the advocacy of sustainable development, how to optimize the sustainability and resilience of the integrated national land space structure is a hot and difficult problem in the field of national land space planning and natural ecological management.

[0003] The optimization of national land space structure consists of three basic elements: objective function, decision variables and constraints. In the past, the optimization of national land space structure usually took the natural system as a static constraint, and only considered the sustainability and efficiency of economic, social or ecological dimensions at the target level. For example, in 2023, Yang Xuedi et al. wrote an article entitled "Optimization of Land Use Function Layout of Lanxi Urban Agglomeration Integrating Ecological Security" in Volume 43, Issue 7 of "Acta Ecologica Sinica". By simulating the functions and structures of national land space under different socio-economic planning goals, the optimization goal of occupying the least ecological land was achieved; Zhai Chenyang et al. wrote an article entitled "Evolution and Scenario Simulation of Multidimensional Well-being Spatiotemporal Differences in Poyang Lake Area Based on System Dynamics" in Volume 41, Issue 8 of "Acta Ecologica Sinica". By simulating the evolution of national land space functions under different development modes, the scenario optimization scheme with the highest human well-being in the study area was explored. As climate change is increasingly confirmed and attracts widespread attention, ecosystem resilience has been proven to be a key factor in maintaining ecosystem sustainability, incorporated into natural resource and ecosystem management, and applied to the optimization of national land space pattern. As a result, the objective function, constraints and decision variables of national land space structure optimization have undergone fundamental changes. For example, previous objective functions have changed from focusing on a single dimension of sustainability, such as economic efficiency, social welfare, or ecosystem service functions at a single scale, to now considering economic and social sustainability goals and ecosystem resilience at multiple scales at the same time.

[0004] At present, substantial progress has been made in measuring ecosystem resilience. For example, with the help of remote sensing or monitoring data of long-term observations, the variance, skewness, peak and other change characteristics of data such as leaf area index and NPP in the time series are used to measure the resilience of ecosystems such as forests, grasslands, and lakes. Recently, some methods and models have also emerged to simulate the changes in ecosystems after disturbance from the perspective of disturbance response, and measure the overall resilience level of ecosystems through the change domain of system state variables, system recovery time, and residual impact on system steady state. However, there are currently no technical methods and models that incorporate ecosystem resilience into the optimization of national spatial structure by establishing a feedback relationship between the evolution of national spatial structure driven by planning goals and ecosystem resilience, especially for future predictions. The key difficulty lies in how to integrate socio-economic sustainability goals and ecosystem resilience across scales, that is, to maintain global and local ecosystem resilience while achieving future regional socio-economic sustainability goals.

[0005] In response to the above difficult problems, the present invention proposes a national land space optimization method with cross-scale planning objectives and ecological resilience feedback, so as to maximize the realization of socio-economic planning objectives at multiple levels while maintaining global and local ecosystem resilience. This technical method establishes an adaptive feedback relationship between cross-scale planning objectives, national land space structure evolution, and ecosystem resilience, simulates the evolution of national land space structure under different planning objectives, measures the feedback of future long-term simulation of national land space structure on ecosystem resilience under the background of climate change, and achieves a trade-off between the sustainability and resilience of national land space structure. This technology is suitable for national land space structure optimization and national land space planning, especially the trade-off and management of ecosystem resilience and socio-economic sustainability objectives at different administrative levels under climate change and human interference. Summary of the invention

[0006] The technical problem to be solved by the present invention is: in view of the fact that previous land space structure optimization simulation methods cannot quantify the feedback of different planning objectives on ecosystem resilience under climate change, and cannot incorporate them into the future land space structure optimization, a land space optimization method with cross-scale planning objectives and ecological resilience feedback is proposed, so as to simultaneously achieve the socio-economic sustainability goals of the future land space structure and the resilience of the ecosystem.

[0007] In order to solve the above technical problems, the present invention proposes a land space optimization method for cross-scale planning objectives and ecological resilience feedback, which includes the following steps:

[0008] Step 1: Classify the land space functions within the set time range in a long time series and analyze and summarize the factors affecting the evolution of land space structure;

[0009] Step 2: Obtain the planning objectives of the study area at each administrative level and construct a planning objective database to determine the functional types of the study area and the realization priority of the functional types. The planning objective database at least includes the planning objective value, objective change rate, current value, and evolution trend;

[0010] Step 3: Set the initial planning target parameter group and optimization step size;

[0011] Based on the planning target database obtained in step 2, based on the current supply values ​​of the parameters of each functional type in the study area, according to the target change rates in the planning of different administrative levels, respectively according to the target change rates that evolve within a specific planning period, calculate the potential target parameter values ​​that each functional type will reach at a specific time node under the planning requirements of the government at different administrative levels, combine the potential target parameter values ​​of different administrative levels at the same time node, and for the same functional type, take the union of the potential target parameter values ​​of all administrative levels to determine the target parameter optimization interval of the functional type; according to the importance of the functional type in the study area, select the optimization step length of the functional type within the range of 2%-20% of the corresponding target parameter optimization interval, divide the target parameter optimization interval by using the equal interval segmentation method based on the optimization step length to obtain a number of endpoint values, combine the endpoints of different functional types to obtain a target parameter group, and combine the realization priority of the functional types obtained in step 2, and sort the target parameter group from high to low based on the endpoint values;

[0012] Step 4: Simulation of future evolution of national land spatial structure;

[0013] By setting the target parameter group that the future national land space needs to meet, the constraints existing in the region, and the driving factors for the evolution of the regional national land space structure, the modeling method is used to simulate the evolution of the national land space function and structure, which specifically includes the following steps:

[0014] 4.1. Target parameter setting: among all the target parameter groups obtained in step 3, select the target parameter groups in order according to the sorting, take the target parameter group of the target year as the end point, and the data of the starting year corresponding to the target parameter group as the starting point, interpolate the target parameter values ​​at the specified time step, obtain the target parameter values ​​of each time node, generate the time series of the target parameter group, and use it as the target parameter input model in the simulation process; at the same time, with the help of the current land space structure data and the demand satisfaction spatial pattern data, use the spatial analysis method to calculate the average value of each land space utilization type for each target satisfaction ability, and use it as the target parameter satisfaction ability input model;

[0015] 4.2. Setting of constraints: With the help of the resource and environmental carrying capacity of the national land space planning and the results of the national land space development suitability evaluation, two types of spatial constraints are set: the upper limit of development and the lower limit of protection. The upper limit of development is obtained by removing the most suitable areas for agricultural production, animal husbandry production and ecological protection from the areas with the highest suitability for urban development, and the lower limit of protection is obtained by taking the union of the areas with the highest suitability for agricultural production and the areas with the highest importance for ecological protection;

[0016] 4.3. Driving force factor setting: using ROC test, analyze the correlation between the historical evolution of the spatial structure of the territory and geographical elements in the study area, select geographical elements with AUC>0.85 as the driving force factors of the evolution of the spatial function and structure of the territory, and use multivariate linear regression, combined with the spatial structure of the territory in the starting year, to analyze the quantitative relationship between the evolution of the spatial function and structure of the territory and the driving force factors;

[0017] 4.4. Complex system modeling and simulation: Using the driving force factors analyzed in step 4.3 and the quantitative relationship between the evolution of the functional structure of the national space and the driving force factors, based on the spatial atlas data of the driving force factors, the driving force weights of different driving factors are combined with the current national space functional structure and the ability of the national space utilization type to meet the functional type, and the weighted summation is obtained to obtain the probability atlas of the evolution of the functional structure of the national space. The calculation formula is as follows:

[0018]

[0019] Among them, Prob a，t is the probability of evolution of land space functional structure for land space utilization type a at time node t, o i is the implementation priority of function type i, Supply i，t Demand is the demand parameter that can satisfy function type i at time node t. i，t is the target parameter of function type i at time node t, w j is the quantitative weight of driving factor j, D j is the specific value of the driving force factor j, n k，t is the number of pixels of land space utilization type k at time node t, cap i，k is the supply capacity of a single land type k to functional type i, m, n and N are the number of functional types, the number of driving factors and the number of land space use types respectively;

[0020] In each grid unit of the land space functional structure, the land space utilization type with the highest evolution probability is selected as the evolution result; according to the land space structure of the current simulation result and the satisfaction capacity of the land space utilization type for the functional type, the demand parameter Supply that the land space structure functional type can satisfy is calculated.i :

[0021] Supply i =∑n j *cap i，j

[0022] Among them, Supply i is the required parameter that can meet the function type i, n j is the number of pixels of land class j, cap i，j is the supply capacity of a single land type j to functional type i, i = 1, 2, ..., N, N represents the number of functional types; the demand parameter is compared with the target parameter of the current time node. When the ratio of the maximum demand difference of a single functional type to the demand parameter is less than 1%, and the ratio of the demand difference of all functional types to the sum of the demand parameters of all functional types is less than 0.5%, the national land space structure simulation of the current time node is completed and the national land space structure simulation of the next time node is carried out, otherwise the current step is repeated until the requirements of the current time node are met;

[0023] Step 5: Measure ecosystem resilience indicators under climate change. The specific method is as follows:

[0024] According to the simulation results of the land space structure at each time node obtained in step 4, the area proportion of each land space utilization type at the grid scale is calculated. Combined with the vegetation structure proportion calculated by field survey, the long-term time series data of the vegetation type proportion of each grid unit is obtained. The calculation formula of the vegetation type proportion in the grid unit is as follows:

[0025]

[0026] Among them, PFT i，t is the proportion of vegetation type i in grid g at time node t, Cov j，g，t is the area proportion of land space utilization type j in grid g at time node t, Prop i，j is the proportion of vegetation type i in land use type j;

[0027] Based on the long-term climate change data of temperature, precipitation, sunshine days, evapotranspiration, and surface accumulated temperature, and the regional resource data of soil organic carbon, soil sandy loam, soil carbon and nitrogen content, soil pH, and soil plant cell deposition, the long-term vegetation type proportion data of all grid units in the study area were used as the ecosystem vegetation structure, and the long-term climate change data and regional resource data were used as the nutrients for biological competition. The biophysical process model LPJ-GUESS (Lund-Potsdam-Jena General Ecosystem Simulator), simulate the evolution of ecosystem status during the evolution of land space in the study area, generate the long-term evolution process of future ecosystem status and vegetation structure, obtain the long-term ecosystem parameter results of NPP, LAI, river runoff, ecosystem carbon flux, vegetation carbon sink, vegetation biomass quality, and vegetation organic carbon at the grid scale as local scale resilience indicators, and summarize all long-term ecosystem parameter results to the entire study area as global scale resilience indicators;

[0028] Step 6: Determine the conditions for triggering the loop feedback;

[0029] Based on the long-term ecological parameter results on the global and local grids in step 5, the sustainability and resilience of the system are judged according to the following steps:

[0030] 6.1. Global and local scale ecological status evaluation: Use the following algorithm to determine the evolution trend of ecological status parameter results at the global scale and the ecological status parameter results at the local scale:

[0031] ①Set the minimum trend length to t limit , select a moving window of time length, which moves backward from the starting point of the time series, where the value range of time length, is 20%-40% of the time span from the starting year to the target year;

[0032] ② Take the maximum and minimum values ​​of the ecological state parameter results in the moving window a i with a j , the corresponding time nodes are i and j respectively; if ij ≥ t limit , it is considered that within the time series T = {j, j+1, ..., i}, the time series has an upward trend; if ji ≥ t limit , it is considered that within the time series T = {i, i + 1, ..., j}, the time series has a downward trend; otherwise, there is no evolution trend within the moving window;

[0033] ③ Take the later of time nodes i and j as the starting point of the moving window and repeat step ② until the time series iteration is completed;

[0034] If any ecological state parameter time series data has both an upward and downward trend, select the next target parameter group and repeat step 4; if there are no two opposite trends, output the current result as the optimization result of the national land space function and pattern;

[0035] Step 7: Summarize and visualize the optimization results.

[0036] The method of the present invention adopts a big data processing method to achieve the optimization of the long-term national spatial structure. By constructing a space-time cube and segmentation technology, combined with the simulation of different functional land parameters based on forests and the spatial simulation method based on adaptive cellular automata, it can quickly and accurately simulate the evolution of the long-term national spatial functional structure. This method is suitable for the optimization of regional functional spatial structure with long time series, high density and large range, and is especially suitable for the simulation analysis and sustainable regulation of national spatial functions and structures in different scenarios of future national spatial planning.

[0037] This paper uses the random forest algorithm to achieve long-term national land space functional classification and driving factor analysis based on multi-source big data; according to the planning goals of multiple administrative levels, and through the coupling of the CLUMondo multivariate linear optimization model and the LPJ-GUESS biophysical process model, it can effectively simulate the long-term functional structure evolution and ecosystem state changes of national land space under different planning goals, and accurately identify potential ecosystem unsustainability and resilience risks under planning goals. This method quantified the feedback relationship between planning goals at different levels and ecosystem resilience for the first time, successfully achieved the effective integration of regional-scale sustainability goals and resilience indicators, and improved the scientific nature of national land space structure optimization and spatial decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The present invention will be further described below in conjunction with the accompanying drawings.

[0039] Figure 1 It is an overview of the study area of ​​the embodiment of the present invention.

[0040] Figure 2 It is a technical flow chart of the method of the present invention.

[0041] Figure 3 This is a diagram of the land space structure simulation results of an embodiment of the present invention.

[0042] Figure 4 This is a simulation result diagram of the proportion of functional land in national land space according to an embodiment of the present invention.

[0043] Figure 5 This is a diagram of the global system simulation results of the ecosystem state parameters according to an embodiment of the present invention.

[0044] Figure 6 This is a diagram of the local system simulation results of the ecosystem state parameters according to an embodiment of the present invention.

[0045] Figure 7 This is the final summary visualization result diagram of the embodiment of the present invention. DETAILED DESCRIPTION

[0046] The experimental data used in this experiment include: Landsat series images for 32 years from 1987 to 2019, surface reflectance products, 30m resolution; VIIRS night light data (Nighttime Day / Night Band Composites Version 1), 500m resolution; ChinaClim long-term temperature and precipitation monitoring and forecasting data, 1000m resolution; WorldPop long-term population data, 100m resolution; MODIS satellite NPP remote sensing inversion products, 500m resolution; digital elevation data (SRTM), 30m resolution; regional transportation network data in 2020; administrative division data of towns and administrative villages in 2020.

[0047] The study area is the Huangshui River Basin in Qinghai Province, located at 36°02′~37°28′N, 100°42′~103°04′E, with a basin area of ​​about 16,200km 2 It is located in the transition zone between the Qinghai-Tibet Plateau and the Loess Plateau and is an important tributary and main water source of the upper Yellow River (such as Figure 1 ). It is composed of the Huangshui mainstream area and the tributary Beichuan River Basin, involving three prefecture-level administrative regions: Xining City, Haidong City and Haibei Prefecture. The altitude is between 1606 and 4871m, with an average altitude of 2300m. The average annual precipitation is about 300-500mm, droughts occur frequently, and the ecology is fragile and heterogeneous. Therefore, the Huangshui River Basin has some characteristics of plateau basins and semi-arid areas, and is a typical ecologically fragile basin. At the same time, the Huangshui River Basin is the most densely populated and economically concentrated area in the entire Qinghai-Tibet Plateau. It has an area of ​​less than 1% of the Qinghai-Tibet Plateau and has a population and economic total of about 25%. In the past 30 years, climate change and human expansion have had a significant impact on the Huangshui River Basin, which is manifested in a slight increase in the average annual precipitation and the continuous networking of irrigation water diversion projects. Population, economy, etc. have further gathered in the Huangshui Valley and upstream, and urban construction land has expanded by 3.6 times, which has become increasingly contradictory to agricultural and animal husbandry production and ecological protection.

[0048] This technical method completes the classification of long-term images on the geographic information big data platform Google Earth Engine (GEE), and uses JavaScript language to implement spatiotemporal consistency correction based on spatiotemporal cubes, spatiotemporal cube segmentation and filling, and prediction of different functional land use parameters based on integrated forests. The spatial simulation of national land space functions is achieved with the help of the CLUMondo model, and the ecosystem resilience measurement is achieved with the help of the LPJ-GUESS model. Other operations are processed locally with Python, version 3.6.

[0049] The embodiment of the present invention provides a method for simulating the function and structure of a national territory space in a long time series, comprising the following steps: Figure 2 As shown:

[0050] Step 1: Conduct a long-term classification of the national land space functions over the past twenty years and analyze and summarize the factors affecting the evolution of the national land space structure.

[0051] With the help of Google Earth Engine big data cloud platform, using Landsat series long-term satellite image data, population data, VIIRS night light data, MODIS NPP inversion products, Landsat NDVI inversion products, and Landsat NDWI inversion products, the regional functions of the Huangshui River Basin were classified from the perspective of regional functions, and the regional functions of the land space were finally classified from 1989 to 2020. Based on the etiology analysis of regional functions, the influencing factors were selected as independent variables from the three dimensions of natural carrying capacity, socio-economic spatial organization, and regional interaction. The partial correlation analysis method was used to preliminarily select 15 influencing factors that affect the evolution of the national land space structure, specifically: altitude, slope, NPP, temperature, precipitation, soil sand, soil clay, soil organic carbon, soil water content, distance from irrigation canals, road accessibility, railway accessibility, urban accessibility, cost distance from Xining, and GDP.

[0052] Step 2: Obtain the planning objectives of the study area at each administrative level and build a planning objective database to determine the functional types of the study area and the realization priority of the functional types. The planning objective database at least includes the planning objective value, objective change rate, current value, and evolution trend.

[0053] In this step, the planning text data related to the Huangshui River Basin were collected through methods such as public document collection and interviews with government departments. Finally, several planning texts with different focuses were collected, including the basin water resources planning, Lanxi urban agglomeration development planning, and provincial and municipal land space planning. Meta-analysis, map interpretation, semantic extraction and other methods were used to analyze the sustainable positioning and planning goals of the Huangshui River Basin at different administrative levels from the three levels of the country, Qinghai Province, Xining City, Haidong City and its subordinate districts and counties, and generate a planning target database to determine a series of planning target ranges such as the Huangshui River Basin’s population urbanization rate of 72%-85% and the livestock carrying capacity growth of -0.55%-1.15% before 2050. The Huangshui River Basin’s leading function of ecological protection in the country and the leading function of urban development in prefecture-level cities were compared, and it was clarified that the main sustainable functions of the Huangshui River Basin are population accommodation, food production, livestock carrying capacity and ecological services, and the high priority of ecological protection and the low priority of agricultural development were determined.

[0054] Step 3: Set the initial planning target parameter group and optimization step size.

[0055] Based on the planning target database obtained in step 2, based on the current supply values ​​of the parameters of various functional types in the Huangshui River Basin, according to the target change rates in 18 planning texts at four levels, namely, Lanxi Urban Agglomeration, Qinghai Province, Xining City, Haidong City, and Haibei Prefecture, the target parameters for 2050 and 2100 are calculated respectively according to the target change rates of short-term, medium-term, and long-term evolution, and the target parameters of all administrative levels are combined to determine the optimized interval of the target parameters of the functional type, and the target parameter groups such as the population carrying capacity of the basin in 2050 is calculated to be between 3.5403 million and 5.0810 million, and the grain production is between 22.1307 million tons and 28.4962 million tons. According to the importance of the functional type of the research area, the optimization step size is selected as 10%, and the optimization interval of the target parameter is divided into several endpoint values ​​based on the optimization step size using the equal interval segmentation method, and the endpoints of different functional types are combined to obtain the target parameter group, and the target parameter group is sorted from high to low based on the endpoint values ​​in combination with the realization priority of the functional type obtained in step 2;

[0056] Step 4: Simulate the future evolution of the national land spatial structure.

[0057] By setting the target parameter group that the future national land space needs to meet, the constraints existing in the region, and the driving factors for the evolution of the regional national land space structure, the modeling method is used to simulate the evolution of the national land space function and structure, which specifically includes the following steps:

[0058] 4.1 Target parameter setting; Among all the target parameter groups, the target parameter groups are selected in order according to the sorting, and the logarithmic function fitting method is used to interpolate the scenario parameter time series with a time step of 5 years from the starting year data of 2020, the target parameter groups of 2050 and 2100, which are used as the target parameter input model in the simulation process; at the same time, with the help of the 2020 land space structure data and the demand satisfaction spatial pattern data, we resampled the demand and supply raster data to 90m, and spatially matched it with the land space utilization data raster, and used the table-based zoning statistics tool in ArcGIS to count the average values ​​of the demand and supply raster in different types of space according to different types of land space utilization. This method is used to calculate the average unit pixel satisfaction of four types of demands, namely population carrying capacity, food production, animal husbandry, and ecological services, for different utilization types, as the target parameter satisfaction capacity input model.

[0059] 4.2 Constraint setting: With the help of the resource and environmental carrying capacity of the national land space planning and the evaluation results of the suitability of national land space development, two types of spatial constraints are set: the upper limit of development and the lower limit of protection. The upper limit of development is obtained by removing the most suitable areas for agricultural production, animal husbandry production and ecological protection from the areas with the highest suitability for urban development. The lower limit of protection is obtained by taking the union of the areas with the highest suitability for agricultural production and the areas with the highest importance for ecological protection.

[0060] 4.3 Driving force factor setting: Using ROC test, the correlation between the evolution of land space structure in Huangshui River Basin from 1990 to 2020 and 15 geographical factors including altitude, slope, NPP, temperature, precipitation, soil sand, soil clay, soil organic carbon, soil water content, distance from irrigation canal, road accessibility, railway accessibility, urban accessibility, cost distance from Xining, and GDP was analyzed. Geographical factors with AUC>0.85 were selected as driving force factors for the evolution of land space function structure, and the driving effects of DEM, slope, temperature, precipitation, NPP, soil sand, soil clay, and distance from irrigation canal on cultivated land, and NPP, precipitation, DEM, soil organic carbon, and soil water content on forests were determined. Using multiple linear regression, combined with the land space structure of the starting year, the quantitative relationship between land space function and structural evolution and driving force factors was analyzed, and the correlation between land use generation probability and different driving factors was determined as the driving force weight of the driving factor, and the correlation polynomial between the two was obtained.

[0061] 4.4 Complex system modeling and simulation;

[0062] Using the driving force factors analyzed in step 4.3 and the quantitative relationship between the evolution of the functional structure of the national space and the driving force factors, based on the spatial atlas data of the driving force factors, the driving force weights of different driving factors are combined with the current functional structure of the national space and the satisfaction capacity of the functional type of the national space utilization type, and the weighted summation is obtained to obtain the probability atlas of the evolution of the functional structure of the national space. The calculation formula is as follows:

[0063]

[0064] Among them, Prob a，t is the probability of evolution of land space functional structure for land space utilization type a at time node t, o i is the implementation priority of function type i, Supply i，t Demand is the demand parameter that can satisfy function type i at time node t. i，t is the target parameter of function type i at time node t, w j is the quantitative weight of driving factor j, D j is the specific value of the driving force factor j, n k，tis the number of pixels of land space utilization type k at time node t, cap i，k is the supply capacity of a single land type k to functional type i, m, n and N are the number of functional types, the number of driving factors and the number of land space use types respectively;

[0065] In each grid unit of the land space functional structure, the land space utilization type with the highest evolution probability is selected as the evolution result; according to the land space structure of the current simulation result and the satisfaction capacity of the land space utilization type for the functional type, the demand parameter Supply that the land space structure functional type can satisfy is calculated. i :

[0066] Supply i =∑n j *cap i，j

[0067] Among them, Supply i is the required parameter that can meet the function type i, n j is the number of pixels of land class j, cap i，j is the supply capacity of a single land type j to functional type i, i = 1, 2, ..., N, N represents the number of functional types; the demand parameter is compared with the target parameter of the current time node. When the ratio of the maximum demand difference of a single functional type to the demand parameter is less than 1%, and the ratio of the demand difference of all functional types to the sum of the demand parameters of all functional types is less than 0.5%, the national land space structure simulation of the current time node is completed and the national land space structure simulation of the next time node is carried out, otherwise the current step is repeated until the requirements of the current time node are met;

[0068] According to the output scenario finally obtained by this patent, in terms of land space function and structure, the function realization is more concentrated and efficient, the concentration and contiguousness of the spatial structure is more significant and more consistent with the land function suitability pattern ( Figure 4-Figure 5 ). High-density settlements have become the main type of population accommodation, with the area share increasing from 21.80% in 2020 to 41.27% in 2100; cultivated land has gradually gathered in the middle reaches, and the proportion of cultivated land in the upper reaches has decreased by 52.04%, and high-yield cultivated land has become the dominant type, with the proportion increasing from 47.90% to 53.85%; high-NPP grasslands have always carried more than half of the livestock volume; the area of ​​natural land in areas with higher ecological vulnerability has increased by 17.32%, among which the two types of highly evolved vegetation communities, woodland and high-cover grassland, have increased by 254.08km 2 .

[0069] Step 5: Measure ecosystem resilience indicators under climate change.

[0070] According to the simulation results of the land space structure at each time node obtained in step 4, we used 0.5°*0.5° grid units to count the area proportion of each land space utilization type at the grid scale, and combined with the vegetation structure proportion counted by field surveys to obtain long-term data on the proportion of vegetation types in each grid unit; based on the long-term climate change data of temperature, precipitation, sunshine days, and surface accumulated temperature, and the regional resource data of soil organic carbon, soil sandy loam, soil carbon and nitrogen content, soil pH, and soil plant cell deposition, the biophysical process model LPJ-GUESS was used to obtain the long-term ecosystem parameter results of NPP, LAI, and river runoff in 9 grids in the Huangshui River Basin as local-scale resilience indicators; and the sum of NPP, river runoff, and the mean of LAI were counted, and all long-term ecosystem parameter results were summarized to the entire Huangshui River Basin as global-scale resilience indicators.

[0071] After the optimization simulation of this patent, the final ecological state of the output scenario maintains an upward trend of sustainability and resilience ( Figure 6-Figure 7 ). On a global scale, the NPP and LAI of the Huangshui River Basin increased by 0.84% ​​and 0.92% in 2100, respectively, while the river runoff has been declining for more than 30 years, and the absolute value of the linear fitting coefficient is less than 0.25; at the same time, on a local scale, the NPP and LAI increased by at least 0.60% and 0.67%, respectively, while the river runoff maintained a relatively stable trend after 2030, with most grids showing a downward trend, and the average annual change rate of all grids remaining between -0.40% and 0.15%.

[0072] Step 6: Determine the conditions for triggering the loop feedback.

[0073] Based on the long-term ecological parameter results on the global and local grids in step 5, the sustainability and resilience of the system are judged according to the following steps:

[0074] 6.1. Global and local scale ecological status evaluation: Use the following algorithm to determine the evolution trend of ecological status parameter results at the global scale and the ecological status parameter results at the local scale:

[0075] ① Set the minimum trend length to 10 years and select a moving window with a time length of 20 years, which moves backward from the starting point of the time series;

[0076] ② Take the maximum and minimum values ​​of the ecological state parameter results in the moving window a i with a i , the corresponding time nodes are i and j respectively; if ij ≥ t limit , it is considered that within the time series T = {j, j+1, ..., i}, the time series has an upward trend; if ji ≥ t limit, it is considered that within the time series T = {i, i + 1, ..., j}, the time series has a downward trend; otherwise, there is no evolution trend within the moving window;

[0077] ③ Take the later of time nodes i and j as the starting point of the moving window and repeat step ② until the time series iteration is completed;

[0078] If any ecological state parameter time series data has both an upward and downward trend, then select the next target parameter group and repeat step 4; if there are no two opposite trends, then output the current result as the optimization result of the national land space function and pattern; in this embodiment, the Huangshui River Basin shows the consistency of the ecological parameter evolution trend at both the global and local scales ( Figure 6-Figure 7 ). On a global scale, the evolution of NPP and LAI in the Huangshui River Basin only shows an upward trend, and the time stages are 2020 to 2070 and 2020 to 2060, respectively, while the river runoff only shows a downward trend; on a local scale, the NPP and LAI of all grid units also only show an upward trend after 2020, while the river runoff shows a downward trend in 6 grids, and the remaining 3 grids do not show a significant evolution trend; both indicate that the optimization of the basin will enhance its biodiversity protection and be subject to a smaller risk of soil and water loss.

[0079] Step 7: Summarize and visualize the optimization results to identify key areas for sustainability and resilience management. The output results include the adjusted planning target parameter set and the future trend line of the ecological resilience parameters associated with the national land spatial pattern. Areas where ecological state parameters are prone to rising or falling during the adaptation feedback process are identified as key areas for sustainability and resilience management, and the results are visualized on a single map. In this case, local grids 2 and 3 are identified as agricultural areas for soil erosion renewal, grid 5 is identified as an urbanized area at risk of ecological diversity loss, and grid 7 is identified as an agricultural-pastoral transition zone where ecosystem function and quality are declining (such as Figure 7 shown).

[0080] Step 8: Scenario comparison and optimization results analysis

[0081] In order to verify the optimization effectiveness of this method, three scenarios, including the initial scenario (BAU) and the intermediate optimization process (SUS) in the optimization scenario parameter group, were selected for comparison with the final optimization output result (SUS-RES). The optimization effects of the national land space function and structure between the scenarios were measured through the evolution of NPP, LAI, river runoff, ecosystem carbon flux, vegetation carbon sink, vegetation biomass quality, vegetation organic carbon ecosystem state parameters, land use intensity, and total functional land area. The scenarios were compared to analyze the advantages of the final optimization output results over other scenarios.

[0082] Compared with the BAU scenario, the SUS-RES scenario has an optimization effect on the state of ecological parameters at the global scale. In this scenario, NPP and LAI continue to rise rapidly, while the growth of NPP and LAI decreases significantly after 2030, and after 2060, NPP continues to decline at a rate of 0.15%; compared with the SUS scenario, the SUS-RES scenario better optimizes the state of the ecosystem in the local system. The growth rates of NPP and LAI of all grid units are always higher than 0.60%, while the changes in NPP and LAI of grids 2 and 3 in the SUS scenario are always lower than 0.10% after 2060; compared with the above two comparison scenarios, the SUS-RES scenario maintains the fastest possible social development under the premise of maintaining the continuous enhancement of the ecological state of the Huangshui River Basin system, and the sustainability and resilience of the system are optimized and enhanced.

[0083] In addition to the above embodiments, the present invention may also have other implementation modes. Any technical solution formed by equivalent replacement or equivalent transformation falls within the protection scope required by the present invention.

Claims

1. A land space optimization method for cross-scale planning objectives and ecological resilience feedback, including the following steps: Step 1: Classify the land space functions within the set time range in a long time series and analyze and summarize the factors affecting the evolution of land space structure; Step 2: Obtain the planning objectives of the study area at each administrative level and construct a planning objective database to determine the functional types of the study area and the realization priority of the functional types. The planning objective database at least includes the planning objective value, objective change rate, current value, and evolution trend; Step 3: Set the initial planning target parameter group and optimization step size; Based on the planning target database obtained in step 2, based on the current supply values ​​of the parameters of each functional type in the study area, according to the target change rates in the planning of different administrative levels, respectively, according to the target change rates that evolve in a specific planning period, calculate the potential target parameter values ​​that each functional type will reach at a specific time node under the planning requirements of the government at different administrative levels, combine the potential target parameter values ​​of different administrative levels at the same time node, and for the same functional type, take the union of the potential target parameter values ​​of all administrative levels to determine the target parameter optimization interval of the functional type; According to the importance of the functional type of the study area, the optimization step of the functional type is selected within the range of 2%-20% of the corresponding target parameter optimization interval. The target parameter optimization interval is divided into several endpoint values ​​based on the optimization step using the equal interval segmentation method. The endpoints of different functional types are combined to obtain the target parameter group. Combined with the implementation priority of the functional type obtained in step 2, the target parameter group is sorted from high to low based on the endpoint values; Step 4: Simulation of future evolution of national land spatial structure; By setting the target parameter group that the future national land space needs to meet, the constraints existing in the region, and the driving factors for the evolution of the regional national land space structure, the modeling method is used to simulate the evolution of the national land space function and structure, which specifically includes the following steps: 4.

1. Target parameter setting: among all the target parameter groups obtained in step 3, select the target parameter groups in order according to the sorting, take the target parameter group of the target year as the end point, and the data of the starting year corresponding to the target parameter group as the starting point, interpolate the target parameter values ​​at the specified time step, obtain the target parameter values ​​of each time node, generate the time series of the target parameter group, and use it as the target parameter input model in the simulation process; at the same time, with the help of the current land space structure data and the demand satisfaction spatial pattern data, use the spatial analysis method to calculate the average value of each land space utilization type for each target satisfaction ability, and use it as the target parameter satisfaction ability input model; 4.

2. Setting of constraints: With the help of the resource and environmental carrying capacity of the national land space planning and the results of the national land space development suitability evaluation, two types of spatial constraints are set: the upper limit of development and the lower limit of protection. The upper limit of development is obtained by removing the most suitable areas for agricultural production, animal husbandry production and ecological protection from the areas with the highest suitability for urban development, and the lower limit of protection is obtained by taking the union of the areas with the highest suitability for agricultural production and the areas with the highest importance for ecological protection; 4.

3. Driving force factor setting: using ROC test, analyze the correlation between the historical evolution of the spatial structure of the territory and geographical elements in the study area, select geographical elements with AUC>0.85 as the driving force factors of the evolution of the spatial function and structure of the territory, and use multivariate linear regression, combined with the spatial structure of the territory in the starting year, to analyze the quantitative relationship between the evolution of the spatial function and structure of the territory and the driving force factors; 4.

4. Complex system modeling and simulation: Using the driving force factors analyzed in step 4.3 and the quantitative relationship between the evolution of the functional structure of the national space and the driving force factors, based on the spatial atlas data of the driving force factors, the driving force weights of different driving factors are combined with the current national space functional structure and the ability of the national space utilization type to meet the functional type, and the weighted summation is obtained to obtain the probability atlas of the evolution of the functional structure of the national space. The calculation formula is as follows: Among them, Prob a,t is the probability of evolution of land space functional structure for land space utilization type a at time node t, o i is the implementation priority of function type i, Supply i,t Demand is the demand parameter that can satisfy function type i at time node t. i,t is the target parameter of function type i at time node t, w j is the quantitative weight of driving factor j, D j is the specific value of the driving force factor j, n k,t is the number of pixels of land space utilization type k at time node t, cap i,k is the supply capacity of a single land type k to functional type i, m, n and N are the number of functional types, the number of driving factors and the number of land space use types respectively; In each grid unit of the land space functional structure, the land space utilization type with the highest evolution probability is selected as the evolution result; according to the land space structure of the current simulation result and the satisfaction capacity of the land space utilization type for the functional type, the demand parameter Supply that the land space structure functional type can satisfy is calculated. i : Supply i =∑n j *cap i,j Among them, Supply i is the required parameter that can meet the function type i, n j is the number of pixels of land class j, cap i,j is the supply capacity of a single land type j to functional type i, i = 1, 2, ..., N, N represents the number of functional types; the demand parameter is compared with the target parameter of the current time node. When the ratio of the maximum demand difference of a single functional type to the demand parameter is less than 1%, and the ratio of the demand difference of all functional types to the sum of the demand parameters of all functional types is less than 0.5%, the national land space structure simulation of the current time node is completed and the national land space structure simulation of the next time node is carried out, otherwise the current step is repeated until the requirements of the current time node are met; Step 5: Measure ecosystem resilience indicators under climate change. The specific method is as follows: According to the simulation results of the land space structure at each time node obtained in step 4, the area proportion of each land space utilization type at the grid scale is calculated. Combined with the vegetation structure proportion calculated by field survey, the long-term time series data of the vegetation type proportion of each grid unit is obtained. The calculation formula of the vegetation type proportion in the grid unit is as follows: Among them, PFT i,t is the proportion of vegetation type i in grid g at time node t, Cov j,g,t is the area proportion of land space utilization type j in grid g at time node t, Prop i,j is the proportion of vegetation type i in land use type j; Based on the long-term climate change data of temperature, precipitation, sunshine days, evapotranspiration, and surface accumulated temperature, and the regional resource data of soil organic carbon, soil sandy loam, soil carbon and nitrogen content, soil pH, and soil plant cell deposition, the long-term data of the proportion of vegetation types in all grid units in the study area are used as the ecosystem vegetation structure, and the long-term climate change data and regional resource data are used as nutrients for biological competition. The biophysical process model LPJ-GUESS, which simulates the material competition of ecosystem vegetation, is used to simulate the evolution of ecosystem state during the evolution of land space in the study area, generate the long-term evolution process of the state and vegetation structure of the future ecosystem, obtain the long-term ecosystem parameter results of NPP, LAI, river runoff, ecosystem carbon flux, vegetation carbon sink, vegetation biomass, and vegetation organic carbon at the grid scale, and use them as local-scale resilience indicators. All long-term ecosystem parameter results are summarized to the entire study area as global-scale resilience indicators. Step 6: Determine the conditions for triggering the loop feedback; Based on the long-term ecological parameter results on the global and local grids in step 5, the sustainability and resilience of the system are judged according to the following steps: 6.

1. Global and local scale ecological status evaluation: Use the following algorithm to determine the evolution trend of ecological status parameter results at the global scale and the ecological status parameter results at the local scale: ①Set the minimum trend length to t limit , select a moving window with a time length of l, which moves backward from the starting point of the time series, where the value range of the time length l is 20%-40% of the time span from the starting year to the target year; ② Take the maximum and minimum values ​​of the ecological state parameter results in the moving window a i with a j , the corresponding time nodes are i and j respectively; if ij ≥ t limit , it is considered that within the time series T = {j, j + 1, ..., i}, the time series has an upward trend; if ji ≥ t limit , it is considered that within the time series T = {i, i + 1, ..., j}, the time series has a downward trend; otherwise, there is no evolution trend in the moving window; ③ Take the later of time nodes i and j as the starting point of the moving window and repeat step ② until the time series iteration is completed; If any ecological state parameter time series data has both an upward and downward trend, select the next target parameter group and repeat step 4; if there are no two opposite trends, output the current result as the optimization result of the national land space function and pattern; Step 7: Summarize and visualize the optimization results.

2. The method for optimizing land space with cross-scale planning objectives and ecological resilience feedback according to claim 1 is characterized by: Step 1 The specific method is as follows: With the help of Google Earth Engine big data cloud platform, using long-term satellite image data, population data, VIIRS night light data, MODIS NPP inversion products, Landsat NDVI inversion products, and Landsat NDWI inversion products, the long-term classification of the regional functions of the study area's land space is carried out from the perspective of regional functions. Based on the etiology analysis of regional functions, influencing factors are selected as independent variables from the three dimensions of natural carrying capacity, socio-economic spatial organization, and regional interaction. The partial correlation analysis method is used to preliminarily select the influencing factors that affect the evolution of the land space structure.

3. The method for optimizing land space with cross-scale planning objectives and ecological resilience feedback according to claim 1 is characterized by: The specific method of step 2 is as follows: collect planning text data related to the study area through public document collection and interviews with government departments; use meta-analysis, map interpretation, and semantic extraction methods to analyze the sustainability positioning and planning goals of the study area at different administrative levels from the national, provincial, and local administrative levels, and generate a planning goal database at different administrative levels; compare the sustainability positioning of the study area at different administrative levels, determine the functional type of the study area, and analyze the implementation priority of the functional type of the study area.

4. The method for optimizing land space with cross-scale planning objectives and ecological resilience feedback according to claim 1 is characterized in that: The specific details of step 7 are as follows: the output results include the adjusted planning target parameter set, the future trend line of the ecological resilience parameters associated with the national land space pattern, and the areas where the ecological state parameters are prone to rising or falling during the adaptation feedback process are identified as key areas for sustainability and resilience management, and the results are visualized on a graph.

5. The method for optimizing land space with cross-scale planning objectives and ecological resilience feedback according to claim 1 is characterized in that: It also includes step 8, scenario comparison and optimization result analysis: The upper and lower limits of scenario parameters in the optimization scenario parameter group were selected for comparison with the final optimization results. The optimization effects of the functions and structures of the national land spaces between the scenarios were measured through the evolution of NPP, LAI, river runoff, ecosystem carbon flux, vegetation carbon sink, vegetation biomass, vegetation organic carbon ecosystem state parameters, land use intensity, and total area of ​​functional land. The scenarios were compared to analyze the advantages of the final optimization output results over other scenarios.

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