A method for constructing a preset resolution land system by fusing multi-source land data

Abstract

This invention discloses a method for constructing a land system with a preset resolution by integrating multi-source land data. The method involves acquiring two types of land data—high-resolution and low-resolution—as well as environmental and socio-economic driving factor data. The high-resolution data is upscaled to the target resolution to construct multiple types of land system unit data. A mapping relationship between the two types of data for land categories is established, calibration coefficients are calculated, and calibration is performed. Based on the calibrated data, the total land category demand for the target year is extrapolated and converted into the land system service demand. This invention relies on a mature multi-functional land simulation model, integrates multi-source data, sets model simulation constraints, directly calls the model for calculations, and inputs various types of data into the model. It combines environmental driving factors to determine the suitability of grid evolution, completes spatial supply and demand matching and land category spatial allocation, iteratively optimizes the grid land category distribution, and generates a spatial pattern of the land system with a preset resolution.

Description

Technical Field

[0001] This invention belongs to the field of land use data fusion and land space simulation technology, specifically relating to a method for constructing a land system with a preset resolution by fusing multi-source land data. Background Technology

[0002] With the continuous advancement of territorial spatial planning and refined management of land resources, the simulation and construction of spatial patterns of the land system has become an important technical means for resource allocation and ecological pattern optimization. Currently, most mainstream construction methods rely solely on low-resolution macroscopic statistical data for quantitative forecasting or solely on high-resolution remote sensing spatial data to complete pattern delineation. The integration of these two types of data is low, resulting in problems such as a disconnect between quantitative forecast trends and spatial layout details, significant differences in statistical standards among multiple data sources, and poor consistency of basic data.

[0003] Meanwhile, most existing conventional simulation methods directly apply general simulation tools for calculations, lacking a systematic data calibration process, making it difficult to eliminate area statistical biases between land data from different sources. Furthermore, they generally fail to incorporate land use classification methods to construct refined land units that couple type and density, resulting in insufficient spatial precision in the simulation results. In addition, traditional simulation methods do not adequately integrate national land space management constraints, rarely strictly enforcing control rules such as restrictions on urban construction land expansion. Ultimately, the simulation results have a low degree of alignment with actual national land management needs, easily leading to conflicts in land use layouts and fragmented spatial patterns, failing to meet the practical needs of constructing a high-precision, compliant land system. Summary of the Invention

[0004] This invention provides a method for constructing a land system with a preset resolution by integrating multi-source land data, comprising: acquiring raw data corresponding to a first land data system and a second land data system, as well as environmental and socio-economic driving factor data; wherein, the first land data system is a low-resolution land data system with a resolution lower than the preset resolution, capable of predicting land use quantity, and focusing on macro-level quantitative statistics; the second land data system is a high-resolution land data system with a resolution higher than the preset resolution, representing the true spatial pattern of land cover, and possessing detailed land type information; upscaling the raw data corresponding to the second land data system to the preset resolution, determining the dominant land type based on the proportion of land types within the grid, classifying density levels, and constructing a multi-type land system with type and density coupling. Metadata; Establish land category mapping relationships between the original data of the first land data system and the original data of the second land data system; Calculate calibration coefficients based on the area deviation of the benchmark year; Fit the original data of the second land data system to a specific scope; Extract scenario change trends based on the original data of the first land data system; Extrapolate the total land category demand for the target year based on the calibrated original data of the second land data system; Convert the total land category demand into the service demand of multiple land systems; Input the land system unit data, the land system service demand, and the environmental and socio-economic driving factor data into a pre-constructed multi-functional land simulation model; Perform spatial supply and demand matching calculations; Output the land system spatial pattern at a preset resolution.

[0005] According to one embodiment of the present invention, the environmental and socio-economic driving factor data includes raster statistical driving data of topography, meteorology, and socio-economic dimensions, which are used to drive the evolution of land spatial patterns.

[0006] According to one embodiment of the present invention, the step of upscaling the original data corresponding to the second land data system to a preset resolution further includes: aggregating the high-resolution original data to an intermediate resolution, and then resampling it to grid cells of the preset resolution; determining the dominant land type of each grid based on the area proportion of different land cover types in each grid cell; and dividing the density level of the preset level using the natural breakpoint method based on the coverage ratio of the dominant land type in each grid cell.

[0007] According to one embodiment of the present invention, the step of determining the dominant land cover type based on the land cover proportion within the grid, dividing density levels, and constructing multi-type land system unit data coupled with type and density further includes: statistically analyzing the area proportion of each land cover type within a grid unit of a preset resolution; selecting the land cover type with the largest area proportion as the dominant land cover type for each grid; dividing density levels of a preset level based on the area proportion of the dominant land cover type using the natural breakpoint method; and coupling the dominant land cover type with the corresponding density level to generate multi-type land system unit data.

[0008] According to one embodiment of the present invention, the step of calculating the calibration coefficient by combining the area deviation of the reference year and fitting the original data of the second land data system with a caliber further includes: using the reference year as a reference, obtaining the reference area of ​​each land type in the original data of the first land data system; obtaining the reference area of ​​the corresponding land type in the second land data system; calculating the ratio of the two reference areas to obtain the calibration coefficient of each land type; and calibrating the original data of the second land data system with a caliber based on the calibration coefficient.

[0009] According to one embodiment of the present invention, the step of extracting the scenario change trend based on the original data of the first land data system and extrapolating the total land demand for the target year by combining the original data of the calibrated second land data system further includes: determining the calibration area of ​​each land type in the benchmark year based on the original data of the second land data system after calibration in the benchmark year; extracting the average annual change range of each land type from the benchmark year to the target year from the original data of the first land data system; extrapolating the predicted area of ​​each land type in the target year based on the calibration area of ​​each land type in the benchmark year, combined with the average annual change range and the time span; taking the predicted total area of ​​each land type in the target year as the total land demand for the target year; and setting an irreversible constraint on urban construction land: confirming that the predicted area of ​​urban construction land in the target year is less than the corresponding calibration area in the benchmark year, and maintaining the calibration area in the benchmark year unchanged.

[0010] According to one embodiment of the present invention, the step of converting the total demand for land types into demand for multiple types of land system services further includes: establishing a correlation mapping relationship between land types and demand for land system services; converting the total demand for land types corresponding to each land type into demand for individual land system services according to a preset conversion rule; and summing up the demand for each individual land system service to obtain demand for multiple types of land system services.

[0011] According to one embodiment of the present invention, the step of performing spatial supply and demand matching calculations and outputting a land system spatial pattern at a preset resolution further includes: inputting the land system unit data, the service demand of the various land systems, and the environmental and socio-economic driving factor data into the multifunctional land simulation model; determining the suitability of grid evolution based on the multifunctional land simulation model and the environmental and socio-economic driving factors, and completing spatial supply and demand matching and land type spatial allocation according to the land system service demand; and iteratively optimizing the grid land type distribution under the preset resolution constraint to deduce the land system spatial pattern at the preset resolution.

[0012] According to one embodiment of the present invention, the density levels of the preset level are divided using the natural breakpoint method, and the corresponding land system unit data are constructed based on the division results.

[0013] According to one embodiment of the present invention, the step of fitting the original data of the second land data system with a calibrated range further includes: performing data calibration operation by using a segmented calibration method, calculating calibration coefficients according to different land use types, and calibrating the original data of the second land data system with a calibrated range.

[0014] This invention integrates the advantages of multi-source land data, achieving an organic unity between low-resolution quantitative prediction and high-resolution spatial detail, significantly improving the precision and data consistency of land system construction. By calibrating the data source, it eliminates biases between different data sources, ensuring the reliability of the simulation's foundational data. The introduction of irreversible constraints on urban construction land ensures that the simulation results strictly conform to the requirements of national land spatial management, enhancing its scientific rigor and compliance. Relying on a mature, multi-functional land simulation model and conducting constrained spatial supply and demand matching, it effectively solves problems such as land category conflicts and spatial fragmentation inherent in traditional methods, significantly improving simulation accuracy and practicality. This invention features a standardized process and clearly defined constraints, supporting applications such as national land spatial planning, optimal allocation of land resources, and optimization of ecological protection patterns. It has a wide range of applications and high potential for widespread adoption. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0016] Figure 1 This is a flowchart illustrating the method for constructing a land system with a preset resolution by fusing multi-source land data provided by the present invention.

[0017] Figure 2 This is a schematic diagram of the process provided by the present invention for upscaling the original data corresponding to the second land data system to a preset resolution.

[0018] Figure 3 This invention provides a flowchart illustrating the process of determining the dominant land type based on the land type proportion within a grid, classifying density levels based on the land type proportion, and constructing multi-type land system unit data that couples type and density.

[0019] Figure 4 This is a schematic diagram of the process provided by the present invention for calculating calibration coefficients by combining the area deviation of the reference year and fitting the original data of the second land data system with a caliber.

[0020] Figure 5 This is a flowchart illustrating the extrapolation of the total land use demand for the target year provided by the present invention.

[0021] Figure 6This is a schematic diagram of the process provided by the present invention for converting the total demand for land types into the demand for services of multiple land systems.

[0022] Figure 7 This is a schematic diagram of the process provided by the present invention for performing spatial supply and demand matching calculations and outputting the spatial pattern of the land system at a preset resolution.

[0023] Figure 8 This is a schematic diagram of the supply and demand matching of land system services provided by the present invention, used to illustrate the many-to-many supply relationship between LUH2 service demand data and different types of land system units.

[0024] Figure 9 This is a schematic diagram of the dominant land use type and density level classification provided by the present invention. It is used to illustrate the process of scaling up Globeland30 high-resolution data through a sliding window, determining the dominant land use type, and then dividing different density levels through the natural breakpoint method. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0026] The following is combined with Figures 1 to 9 The present invention describes a method for constructing a land system with a preset resolution by fusing multi-source land data.

[0027] Figure 1 This is a flowchart illustrating the method for constructing a land system with a preset resolution by fusing multi-source land data, as provided by the present invention. Figure 1 As shown, the method for constructing a land system with a preset resolution by fusing multi-source land data provided by the present invention includes: In step S100, the raw data corresponding to the first land data system and the second land data system, as well as environmental and socio-economic driving factor data, are acquired. The first land data system is a low-resolution land data system with a resolution lower than the preset resolution, capable of predicting land use quantity, and focusing on macro-level quantitative statistics. The second land data system is a high-resolution land data system with a resolution higher than the preset resolution, representing the real land cover spatial pattern, and possessing detailed land use information. In step S200, the original data corresponding to the second land data system is upscaled to a preset resolution, the dominant land type is determined according to the land type proportion in the grid, density levels are divided, and multi-type land system unit data coupled with type and density is constructed. In step S300, a land category mapping relationship is established between the original data of the first land data system and the original data of the second land data system. The calibration coefficient is calculated in combination with the area deviation of the benchmark year, and the original data of the second land data system is fitted with a caliber. In step S400, the scenario change trend is extracted based on the original data of the first land data system, and combined with the original data of the calibrated second land data system, the total land demand for the target year is extrapolated, and the total land demand is converted into the service demand of multiple land systems. In step S500, land system unit data, land system service demand, and environmental and socio-economic driving factor data are input into a pre-constructed multifunctional land simulation model to perform spatial supply and demand matching calculations and output the land system spatial pattern at a preset resolution.

[0028] Specifically, in step S100, data acquisition is performed. Two types of land data with different characteristics, as well as environmental and socio-economic driving factor data, are collected. One type is a low-resolution land data system, with a spatial resolution lower than the preset resolution, possessing land use quantity prediction capabilities and focusing on macro-level quantitative statistics; LUH2 land use data is used as an example. The other type is a high-resolution land data system, with a spatial resolution higher than the preset resolution, representing the true spatial pattern of land cover and possessing detailed land cover information; Globeland30 land cover data is used as an example. Simultaneously, environmental driving factor data is collected. This data covers topography, meteorology, and socio-economic dimensions, and is raster statistical data used to drive the evolution of land spatial patterns in subsequent mature simulation models.

[0029] In step S200, land system unit data is constructed. The high-resolution land data is scaled, aggregated to an intermediate resolution, and then resampled to grid cells of a preset resolution. The area proportion of each land cover type within each grid cell is calculated, and the type with the largest area proportion is selected as the dominant land type for that grid. The natural breakpoint method is used to classify density levels based on the coverage proportion of the dominant land type, and the dominant land type is coupled with its corresponding density level to generate land system unit data with type-density coupling.

[0030] In step S300, data calibration is performed. A land use mapping relationship is established between the low-resolution land data and the high-resolution original land data. Using the base year as a reference, the base area for each land use type in both sets of data is obtained. A segmented calibration method is used to independently calculate the ratio of the two base areas according to different land use types, obtaining calibration coefficients. These calibration coefficients are then used to calibrate the upscaled high-resolution land system unit data, eliminating statistical biases between data.

[0031] In step S400, demand extrapolation and transformation are performed. Based on the calibrated high-resolution land system unit data, the calibration area for the base year is determined by combining the calibration coefficient. The average annual change in each land type is extracted from the low-resolution land data, and the predicted area for each land type in the target year is calculated as the total land type demand. An irreversible constraint on urban construction land is set; if the predicted area is less than the base area, the calibration area for urban construction land in the base year remains unchanged. The correlation between land types and land system services is established, and the data is converted into individual service demand data according to preset conversion rules. The total demand for multiple land system services is then obtained.

[0032] In step S500, the spatial pattern of the land system is output. Land system unit data, land system service demand, and environmental and socio-economic driving factor data are input into a pre-constructed mature multifunctional land simulation model (Land-N2N model). The Land-N2N model is a refined land system simulation model based on the CLUMOND model, which is oriented towards the multifunctionality of the land system, integrates random forest regression and competitive advantage mechanisms, and is an improvement on the CLUMOND model. The model combines environmental driving factors to determine the suitability of grid evolution, completes spatial supply and demand matching and land use spatial allocation, iteratively optimizes the grid land use distribution under a preset resolution constraint, and outputs the spatial pattern of the land system at a preset resolution.

[0033] According to an embodiment of the present invention, environmental and socio-economic driving factor data, including raster statistical driving data of topography, meteorology, and socio-economic dimensions, are used to drive the evolution of land spatial patterns.

[0034] Specifically, topographic data includes raster information such as elevation, slope, and aspect, reflecting the regional topographic relief characteristics and influencing the suitability and utilization direction of land development and construction. Meteorological data includes raster statistics such as average annual temperature, annual precipitation, and sunshine duration, reflecting regional climatic differences, determining the basic conditions for vegetation growth, and thus affecting the distribution patterns of land use types such as farmland and forest land. Socioeconomic data includes raster statistics such as population density, road network density, urban-rural distance, and GDP distribution, characterizing the intensity of human activities and the level of economic development, directly driving dynamic processes of land use such as urban expansion and changes in arable land. By resampling and reprojecting the above multi-dimensional data into a completely consistent raster format, a comprehensive characterization of the combined impact of natural conditions and human activities on the evolution of land spatial patterns can be achieved, providing reliable and standardized driving data for subsequent mature simulation model development.

[0035] Figure 2 This is a schematic diagram illustrating the process of upscaling the original data corresponding to the second land data system to a preset resolution, as provided by the present invention. Figure 2As shown, according to an embodiment of the present invention, upscaling the original data corresponding to the second land data system to a preset resolution further includes: In step S211, the high-resolution raw data is aggregated to an intermediate resolution and then resampled to a grid cell of a preset resolution. In step S212, the dominant land type of each grid is determined based on the area proportion of different land cover types within each grid cell; In step S213, density levels are divided based on the coverage ratio of the dominant land type in each grid.

[0036] Specifically, in step S211, the second land data system uses Globeland30 land cover data with a resolution of 30 meters, and the preset resolution is set to 1 kilometer (example value, which can be adjusted flexibly in practice). To avoid spatial information distortion caused by direct downscaling, the original 30-meter resolution data is first aggregated to an intermediate resolution of 990 meters (990 meters is an example intermediate resolution, which can be adjusted as needed in practice). The continuity of land use distribution is preserved through neighborhood aggregation. Then, a resampling method is used to convert the 990-meter data into standard 1-kilometer grid cells to complete the scale unification.

[0037] In step S212, for each 1-kilometer grid cell, the area of ​​each land cover type under the Globeland 30 classification system within the cell is statistically analyzed and its proportion is calculated. The type with the largest area proportion is selected as the dominant land type of the grid. For example, if arable land accounts for 65%, forest land accounts for 20%, grassland accounts for 10%, and urban construction land accounts for 5% in a certain grid, then arable land is the dominant land type of that grid; similarly, if urban construction land accounts for 75%, arable land accounts for 15%, and other land types account for 10% in another grid, then urban construction land is the dominant land type of that grid.

[0038] In step S213, based on the coverage ratio of the dominant land type in the grid, the natural breakpoint method is used to divide the density into three levels: high, medium, and low. For example, cultivated land with a proportion higher than 70% is considered high-density cultivated land, 30% to 70% is medium-density cultivated land, and less than 30% is low-density cultivated land; forest land with a proportion higher than 80% is high-density forest land, 40% to 80% is medium-density forest land, and less than 40% is low-density forest land; urban construction land with a proportion higher than 60% is high-density urban land, 20% to 60% is medium-density urban land, and less than 20% is low-density urban land. This distinguishes the differences in coverage density under the same dominant land type, and provides refined grid units for subsequent land system unit construction and model simulation.

[0039] Figure 3 This invention provides a flowchart illustrating the process of determining the dominant land type based on the land type proportion within a grid, classifying density levels based on the land type proportion, and constructing multi-type land system unit data that couples type and density. Figure 9 The diagram shows the dominant land use types and density levels. (See attached diagram.) Figure 3 As shown, according to an embodiment of the present invention, the dominant land type is determined based on the proportion of land types within the grid, density levels are divided, and multi-type land system unit data coupled with type and density is constructed, further including: In step S221, the area proportion of each land cover type within the preset resolution grid cell is calculated; In step S222, the land cover type with the largest area is selected as the dominant land type for each grid; In step S223, density levels are determined based on the area proportion of the dominant land type; In step S224, the dominant land type is coupled with the corresponding density level to generate multi-type land system unit data.

[0040] Specifically, in step S221, the preset resolution is 1 kilometer. Based on the Globeland30 data after scale conversion, the actual area of ​​10 standard land cover types, including cultivated land, forest land, grassland, shrubland, wetland, water body, artificial surface, bare land, snow and ice, and tundra, is uniformly counted for each 1-kilometer grid cell. Then, the percentage of each type of area in the total area of ​​the grid is calculated to obtain the area proportion data of each type, which provides a quantitative basis for subsequent determination of dominant type and division density.

[0041] In step S222, the area proportions of the 10 land cover types within the same grid are compared horizontally, and the type with the largest proportion is selected as the dominant type of the grid unit, thus representing the main land cover characteristics of the grid. For example, in a certain grid, cultivated land accounts for 68%, forest land 18%, grassland 9%, artificial surface 5%, and other types 0%, then cultivated land is the dominant type; in another grid, artificial surface accounts for 72%, cultivated land 15%, water bodies 8%, and other types 5%, then artificial surface is the dominant type.

[0042] In step S223, for the identified dominant grid types, based on their area proportion within the grid, they are uniformly divided into three density levels: low, medium, and high using the natural breakpoint method. Standard Globeland30 includes 10 basic land cover types: cultivated land, forest land, grassland, shrubland, wetland, water bodies, artificial surfaces, bare land, snow and ice, and tundra. Each type is matched with 3 density levels, theoretically resulting in 10 × 3 = 30 "type-density" combinations. After removing types that did not appear in the actual study area, 27 effective combinations were ultimately formed. The density grading thresholds corresponding to each type are detailed in Table 1.

[0043] Table 1 shows the density grading thresholds for the remaining 9 effective classes after removing non-existent types from the theoretical 30 classes. It includes three columns: minimum value, natural break 1, and natural break 2. Minimum value: The lowest threshold for determining the proportion of the dominant type within a grid; if the proportion is below this value, it is not considered the dominant type of the grid. Natural breakpoint 1: The boundary between low density and medium density; Natural breakpoint 2: The boundary between medium density and high density.

[0044] The grading rules are as follows: Low density: minimum value ≤ percentage < natural breakpoint 1; Medium density: Natural break point 1 ≤ proportion < natural break point 2; High density: Natural breakpoints 2 ≤ percentage ≤ 100%.

[0045] Taking cultivated land as an example: the minimum value is 19.8%, the natural breakpoint 1 is 65.8%, and the natural breakpoint 2 is 87.6%, corresponding to low-density cultivated land (19.8%~65.8%), medium-density cultivated land (65.8%~87.6%), and high-density cultivated land (87.6%~100%); the remaining 8 categories are classified according to the thresholds in Table 1.

[0046] Table 1. Explanation of Land Cover Type Density Classification Thresholds In step S224, the dominant type of each grid cell is coupled with the corresponding density level to form a standardized combination of type and density identifier, such as low-density grassland, medium-density wetland, and high-density artificial surface. Finally, 27 types of land system unit data that cover the entire area, are uniformly classified, and are quantifiable are generated, providing standardized basic units for subsequent data calibration, demand extrapolation, and mature simulation model derivation.

[0047] Figure 4 This is a schematic diagram illustrating the process provided by the present invention for calculating calibration coefficients based on the area deviation of the reference year and fitting the original data of the second land data system with a specific scope. For example... Figure 4 As shown, according to an embodiment of the present invention, the calibration coefficient is calculated by combining the area deviation of the reference year, and the original data of the second land data system is fitted with a caliber, which further includes: In step S310, the base area of ​​each land type in the original data of the first land data system is obtained with reference to the base year; In step S320, the baseline area of ​​the corresponding land type in the second land data system is obtained; In step S330, the ratio of the two reference areas is calculated to obtain the calibration coefficient for each land cover. In step S340, the original data of the second land data system is calibrated based on the calibration coefficient.

[0048] Specifically, in step S310, a unified base year is selected (2020 is used as an example, but it can be flexibly adjusted according to research needs), and the total statistical area of ​​each land type in the first land data system (LUH2) under that base year is extracted. LUH2 is a global coarse-resolution data, covering major land types such as cultivated land, forest land, grassland, and artificial land surfaces. Its base area is a macro-statistical total, representing the statistical results of land use quantity at a global scale.

[0049] In step S320, the total area of ​​the corresponding land type in the land system unit data (derived from Globeland30) after scale transformation under the same reference year is extracted simultaneously. This data is a high-resolution, refined result at 1 km, based on real land cover statistics, with more detailed land type classification and area statistics that are more in line with reality, serving as a reference benchmark for the real area of ​​the reference year.

[0050] In step S330, a segmented calibration method is used to calculate independently by land type, and the ratio of the areas of the two sets of data benchmarks is calculated for each land type to obtain the calibration coefficient for the corresponding land type. (Refer to...) Figure 8 The diagram illustrates the one-to-one correspondence between land use categories in the two datasets, establishing a mapping relationship between LUH2 and Globeland30 to provide a foundation for subsequent caliber calibration. First, the correspondence between land use categories in the two datasets is established: in LUH2, all types of crops uniformly correspond to cultivated land; primary and secondary forests uniformly correspond to forest land; pastures and natural pastures uniformly correspond to grassland; urban construction land corresponds to artificial land surfaces; and primary and secondary non-forest vegetation corresponds to shrubland. For each land use category (cultivated land, forest land, grassland, artificial land surfaces, etc.), the ratio is calculated to obtain the calibration coefficient, which is expressed as: ; In the formula, For the corresponding land category calibration coefficient, The area of ​​LUH2 land category is the base year. The area of ​​land by type is based on the base year Globeland30.

[0051] In step S340, the calibration coefficients for each land type are multiplied by the upscaled high-resolution land system unit data to complete the caliber calibration. After calibration, the high-resolution refined data aligns with the low-resolution macroscopic statistical caliber, eliminating biases caused by differences in classification systems, resolutions, and statistical calibers. This provides a unified and reliable foundation of data for subsequent target year demand extrapolation and mature simulation model development. The caliber-caliber-caliber benchmark area is expressed as: ; In the formula, For the calibrated reference area, The original reference area, This is the calibration coefficient for land use categories.

[0052] Figure 5 This is a flowchart illustrating the extrapolation of the total land use demand for a target year, provided by this invention. For example... Figure 5 As shown, according to an embodiment of the present invention, extrapolating the total land use demand for a target year based on the original data of a calibrated second land data system further includes: In step S411, the calibration area of ​​each land type in the base year is determined based on the original data of the second land data system after calibration in the base year. In step S412, the average annual change of each land type from the baseline year to the target year is extracted from the raw data of the first land data system; In step S413, the predicted area of ​​each land type in the target year is extrapolated based on the calibration area of ​​each land type in the base year, combined with the annual average change range and time span. In step S414, the predicted total area of ​​each land type in the target year is used as the total demand for land types in the target year; In step S415, an irreversible constraint on urban construction land is set: confirming that the predicted area of ​​urban construction land in the target year is less than the corresponding calibration area in the benchmark year, and maintaining the calibration area in the benchmark year unchanged.

[0053] Specifically, in step S411, a unified base year (e.g., 2020) is selected. First, the actual statistical area of ​​each land type in the land system unit data (derived from Globeland) for that base year is obtained. Then, the area of ​​each land type is multiplied by the corresponding calibration coefficient obtained in step S330 to convert it into a base year calibration area adapted to the first land data system (LUH2) caliber. This step eliminates the caliber differences between the two sets of data, ensuring that the base area is consistent with the subsequent extrapolation caliber.

[0054] In step S412, the changes in the area of ​​various land types over several consecutive years before and after the benchmark year are statistically analyzed from the long-term historical data of the first land data system. The average annual increase or decrease in area is calculated to obtain the average annual change in various land types such as cultivated land, forest land, grassland, and artificial land surface, reflecting the long-term trend of changes in various land types.

[0055] In step S413, using the calibrated area of ​​the base year as a base, and combining the annual average change range of each land type and the time span from the base year to the target year, the predicted area of ​​each land type in the target year is calculated year by year. For example, the calibrated cultivated land area in the base year is 10,000 km². 2Based on an average annual growth rate of 1% and a time span of 15 years, the predicted cultivated land area for the target year is calculated. The same method is used to calculate the predicted area for other land types in the target year. The calculation method for the predicted area of ​​each land type in the target year is as follows (the example is for 2035, but can be flexibly adjusted according to research needs): ; In the formula, For the target year Predicted area of ​​land type For the area after calibration, This represents the average annual change. The time span from the base year to the target year.

[0056] In step S414, the predicted area of ​​each land type in the target year obtained in step S413 is summarized and organized, and directly used as the total land type demand in the target year, which will be used to convert it into the land system service demand in the future.

[0057] In step S415, an irreversible constraint rule is set for urban construction land: Because urban construction land is difficult to restore after construction, when the predicted area of ​​urban construction land in the target year is less than the calibrated area in the base year, no area reduction is implemented, and the calibrated area of ​​the base year remains unchanged; if the predicted area is greater than or equal to the calibrated area of ​​the base year, the calculated result is used to ensure that urban construction land only increases and does not decrease, conforming to actual land use patterns. The calculation method for the target year area under the irreversible constraint of urban construction land is as follows: ; In the formula, The target annual urban construction land area, This represents the average annual change in urban construction land. The time span is from the base year to the target year.

[0058] This step leverages the long-term trend advantage of low-resolution data to perform quantitative extrapolation, while simultaneously controlling the evolution rules of construction land, providing compliant and controllable land use demand constraints for subsequent mature simulation model extrapolation.

[0059] Figure 6 This is a schematic diagram illustrating the process of converting total land use demand into multi-type land system service demand, as provided by the present invention. Figure 6 As shown, according to an embodiment of the present invention, the total demand for land types is converted into the demand for services of multiple land systems, further including: In step S421, a mapping relationship is established between land types and the demand for land system services; In step S422, the total land demand for each land type is converted into the demand for individual land system services according to the preset conversion rules. In step S423, the demand for each individual land system service is summarized to obtain the demand for multiple types of land system services.

[0060] Specifically, in step S421, a mapping relationship is established between land types and the demand for land system services. (Refer to...) Figure 8 This study establishes a many-to-many matching relationship between land systems and service demands. Land systems and services have a many-to-many supply-demand relationship; one type of land system can provide multiple services, and one type of service can be supplied by multiple types of land systems. For example, arable land mainly corresponds to food supply services, forest land to carbon sequestration and ecological conservation services, grassland to livestock supply and ecological regulation services, artificial land surfaces to residential and industrial carrying capacity services, wetlands and water bodies to hydrological regulation services, and bare land and permanent snow and ice to ecological maintenance services. This constructs a standardized many-to-many mapping relationship between land types and services, providing a clear basis for subsequent conversion.

[0061] In step S422, the total demand for each land type is converted into the demand for individual land system services according to preset conversion rules. Conversion ratios are pre-set based on land use intensity and service supply capacity per unit area. For example, 1 hectare of arable land is converted into 1 unit of food supply service, 1 hectare of forest land into 0.8 units of carbon sequestration service, and 1 hectare of artificial land surface into 1.2 units of residential and industrial services. These conversion ratios can be adjusted according to actual research needs. For other land types, the target year's land area demand is converted into the corresponding demand for individual services according to the corresponding conversion rules, achieving a quantitative transformation from "land type area" to "service demand."

[0062] In step S423, the demand for each individual land system service is summarized to obtain the demand for multiple types of land system services. The demand for various individual services, such as food supply, carbon sequestration, ecological conservation, livestock supply, residential industry, hydrological regulation, and ecological maintenance, is summarized and summed according to service type to form a complete and unified set of demand for multiple types of land system services. This set serves as the direct input to the mature Land-N2N model, supporting subsequent spatial supply and demand matching calculations.

[0063] Figure 7 This is a schematic diagram illustrating the process provided by the present invention for performing spatial supply and demand matching calculations and outputting a land system spatial pattern at a preset resolution. Figure 7 As shown, according to an embodiment of the present invention, spatial supply and demand matching calculations are performed to output a land system spatial pattern with a preset resolution, further including: In step S521, land system unit data, demand for various land system services, and environmental and socio-economic driving factor data are input into the multifunctional land simulation model; In step S522, relying on the multifunctional land simulation model, the suitability of grid evolution is determined by combining environmental and socio-economic driving factors, and spatial supply and demand matching and land type spatial allocation are completed based on the land system service demand. In step S523, under the constraint of a preset resolution, the distribution of land types in the grid is iteratively optimized to deduce the spatial pattern of the land system at the preset resolution.

[0064] Specifically, in step S521, the constructed land system unit data, the converted demand for various land system services, and the environmental driving factor data covering topography, meteorology, and social dimensions are uniformly input into the pre-constructed mature Land-N2N model. Before input, it is ensured that all data maintains a consistent spatial resolution and projected coordinate system to guarantee accurate spatial correspondence between data, providing a standardized and complete data input foundation for model calculation.

[0065] In step S522, based on the constructed Land-N2N model and combined with various environmental driving factor data, the suitability of each grid unit for evolution into different land system types is comprehensively analyzed, considering topographic conditions, climate characteristics, and the impact of social activities. Relying on a coarse-high resolution iterative soft quantity constraint mechanism, the supply-demand gap and conversion priority are dynamically adjusted to conduct spatial supply-demand matching, rationally allocating different service demands to suitable grids, completing the spatial layout allocation of each land type, and ensuring that the total demand and spatial supply capacity are coordinated.

[0066] In step S523, under the constraint of a preset resolution, the initial land use spatial allocation result is iteratively optimized multiple times. By continuously adjusting the grid land use distribution, spatial conflicts and unreasonable distributions are gradually eliminated, making the overall pattern more reasonable and stable. Finally, a land system spatial pattern with a preset resolution that meets supply and demand balance, satisfies the constraints, and has a continuous and coordinated spatial distribution is derived. The calculation method is as follows: ; In the formula, For the first The grid points to the first The comprehensive transfer potential of land-like system transformation For local suitability, Represents a grid From the initial land category To target land type Competitive advantage value at the time of conversion To convert resistance, This is a neighborhood effect.

[0067] This step relies on a mature model to complete automated inference calculations, achieving multi-source data coupling simulation without modifying the model's core algorithm, thus ensuring the stability and reproducibility of the simulation results. According to an embodiment of the present invention, the density levels of the preset levels are divided using the natural breakpoint method, and the corresponding land system unit data is constructed based on the division results.

[0068] Specifically, this invention employs the natural breakpoint method to complete threshold grading during the land use density classification process. This algorithm adaptively grades the data based on its own numerical distribution characteristics, maximizing the preservation of the data distribution patterns of land use proportions and reducing subjective errors caused by manual grading. First, the area proportion of the same dominant land use type within each grid unit across the entire region is extracted to form a single land use type proportion dataset. Second, the dataset is sorted and clustered to minimize the data variance within the same level interval and maximize the data variance between different intervals, automatically identifying data abrupt change critical points to determine the density grading breakpoints for each level. Finally, based on the solved grading breakpoints, all land use types are uniformly divided into three preset density levels: low, medium, and high.

[0069] Compared to conventional methods such as equidistant grading and manual threshold grading, the natural breakpoint method closely reflects the natural distribution characteristics of land cover aggregation, objectively distinguishing grid units with sparse, moderate, and dense cover, and avoiding grading distortion caused by artificially set thresholds. After defining the density of each land cover type using this grading method, the dominant land cover type in the grid is coupled with its corresponding density level to form refined and differentiated type-density composite units. This completes the standardized construction of land system unit data across the entire region, providing a hierarchical, reasonably bounded, and realistically surface-aggregated basic grid unit for subsequent mature simulation models.

[0070] According to an embodiment of the present invention, the method of fitting the raw data of the second land data system with different scopes further includes: The data calibration operation was completed using a segmented calibration method. Calibration coefficients were calculated according to different land use types to calibrate the original data of the second land data system.

[0071] Specifically, this invention employs a segmented calibration method in the data calibration process to achieve differentiated correction of multi-source land data, avoiding the problems of land use deviation offsetting and correction distortion caused by uniform calibration across the entire region. First, based on a pre-constructed land use mapping relationship, the land cover system is divided into independent land use types such as cultivated land, forest land, grassland, and artificial surfaces, and processed independently according to land use type. Second, based on the statistical area of ​​the baseline year, the area of ​​each land use type in the two data sources is obtained separately, and a dedicated calibration coefficient is calculated for each type to eliminate mutual interference from differences in the area of ​​different land use types. Finally, using the calibration coefficients of each land use type, the high-resolution land data is corrected to achieve differentiated data caliber fitting based on type.

[0072] Compared to a single calibration method covering the entire region, the segmented calibration method fully considers the differences in statistical deviations among land types, adapts to the error characteristics of different land types, and accurately eliminates systematic deviations caused by differences in resolution, classification systems, and statistical calibers, ensuring that the calibration benchmarks for each land type are independent and accurate. After calibration, the consistency and uniformity of multi-source data are significantly improved, providing standardized input data with consistent caliber, controllable errors, and accurate land type reproduction for the Land-N2N model simulation.

[0073] In summary, this invention integrates land data of different resolutions to construct land system units with both type and density characteristics, achieving unified calibration of data caliber and accurate conversion of supply and demand. It utilizes the mature Land-N2N model for constrained spatial matching and adds irreversible control constraints on urban construction land, effectively addressing technical shortcomings in traditional simulations such as inconsistent calibers, fragmented spatial patterns, and imbalances in land supply and demand. The overall methodology of this invention is clear, logically complete, and has well-defined constraint rules, enabling stable output of high-precision and highly consistent land system spatial patterns. This provides reliable technical support for land spatial planning, ecological protection and restoration, and climate change scenario simulation, and the method is highly versatile and widely applicable.

[0074] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for constructing a land system with a preset resolution by fusing multi-source land data, characterized in that, include: The process involves acquiring raw data from the first land data system and the second land data system, as well as environmental and socio-economic driving factor data. The first land data system is a low-resolution land data system with a resolution lower than the preset resolution, capable of predicting land use quantity, and focusing on macro-level quantitative statistics. The second land data system is a high-resolution land data system with a resolution higher than the preset resolution, representing the true spatial pattern of land cover, and possessing detailed land use information. The original data corresponding to the second land data system is upscaled to a preset resolution, the dominant land type is determined according to the proportion of land types in the grid, density levels are divided, and multi-type land system unit data coupled with type and density is constructed. Establish a land category mapping relationship between the original data of the first land data system and the original data of the second land data system, calculate the calibration coefficient based on the area deviation of the benchmark year, and perform caliber fitting on the original data of the second land data system. Based on the original data of the first land data system, the scenario change trend is extracted, and combined with the original data of the calibrated second land data system, the total land type demand for the target year is extrapolated, and the total land type demand is transformed into the service demand of multiple land system types. The land system unit data, the land system service demand, and the environmental and socio-economic driving factor data are input into a pre-constructed multifunctional land simulation model to perform spatial supply and demand matching calculations and output the land system spatial pattern at a preset resolution.

2. The method according to claim 1, characterized in that, The environmental and socioeconomic driving factor data, including raster statistical driving data of topography, meteorology, and socioeconomic dimensions, are used to drive the evolution of land spatial patterns.

3. The method according to claim 1, characterized in that, The step of upscaling the original data corresponding to the second land data system to a preset resolution further includes: High-resolution raw data is aggregated to an intermediate resolution and then resampled to grid cells of a preset resolution. The dominant land type in each grid is determined based on the area proportion of different land cover types within each grid unit. Density levels are assigned based on the coverage ratio of dominant land types within each grid.

4. The method according to claim 1, characterized in that, The process of determining the dominant land type based on the proportion of land types within the grid, classifying density levels, and constructing multi-type land system unit data coupled with type and density further includes: The area proportion of each land cover type within a grid cell of a preset resolution is statistically analyzed. The land cover type with the largest area ratio is selected as the dominant land type for each grid; Based on the area proportion of the dominant land type, density levels are divided into preset levels; By coupling the dominant land type with the corresponding density level, multiple types of land system unit data are generated.

5. The method according to claim 1, characterized in that, The step of calculating the calibration coefficient by combining the area deviation of the reference year and fitting the original data of the second land data system with a specific scope further includes: Using the base year as a reference, obtain the base area of ​​each land type in the original data of the first land data system; Obtain the baseline area of ​​the corresponding land category in the second land data system; Calculate the ratio of the two reference areas to obtain the calibration coefficient for each land cover type; Based on the calibration coefficient, the original data of the second land data system is calibrated.

6. The method according to claim 1, characterized in that, The step of extracting the scenario change trend based on the original data of the first land data system, and extrapolating the total land demand for the target year by combining the original data of the calibrated second land data system, further includes: Based on the original data of the second land data system after calibration for the base year, determine the calibrated area of ​​each land type for the base year; Extract the average annual change of each land type from the baseline year to the target year from the raw data of the first land data system; Using the calibrated area of ​​each land type in the base year as the base, and combining the annual average change range and time span, the predicted area of ​​each land type in the target year is extrapolated; The total predicted area of ​​each land type in the target year will be used as the total demand for each land type in the target year. Set an irreversible constraint on urban construction land: if the predicted area of ​​urban construction land in the target year is less than the corresponding calibration area in the benchmark year, maintain the calibration area in the benchmark year unchanged.

7. The method according to claim 1, characterized in that, The step of converting the total demand for land types into the demand for services across multiple land systems further includes: Establish a mapping relationship between land types and the demand for land system services; According to the preset conversion rules, the total demand for each land type is converted into the demand for a single land system service. The demand for each individual land system service is summarized to obtain the demand for multiple types of land system services.

8. The method according to claim 1, characterized in that, The step of performing spatial supply and demand matching calculations and outputting a land system spatial pattern at a preset resolution further includes: The land system unit data, the demand for various land system services, and the environmental and socio-economic driving factor data are input into the multifunctional land simulation model. Based on the aforementioned multifunctional land simulation model, the suitability of grid evolution is determined by combining the aforementioned environmental and socio-economic driving factors, and spatial supply and demand matching and land type spatial allocation are completed according to the aforementioned land system service demand. Under the constraint of preset resolution, the distribution of land types in the grid is iteratively optimized, and the spatial pattern of the land system at the preset resolution is deduced.

9. The method according to claim 3, characterized in that, The density levels of the preset levels are divided using the natural breakpoint method, and the corresponding land system unit data is constructed based on the division results.

10. The method according to claim 1, characterized in that, The method of fitting the original data of the second land data system with different standards also includes: The data calibration operation was completed using a segmented calibration method. Calibration coefficients were calculated according to different land use types to calibrate the original data of the second land data system.

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