Multi-scenario simulation method based on future land utilization

Abstract

The invention relates to a multi-scenario simulation method based on future land utilization, and aims to provide a scientific basis for land utilization planning management. The method comprises the following steps: S1, data collection and processing: collecting and processing related data, such as administrative vector data, DEM, climate, road, population and GDP data, and ensuring data unification and consistency; s2, driving force factor analysis: evaluating the influence of each driving force through a quantitative analysis method and a spatial analysis method, such as land utilization change analysis and transfer matrix analysis, and displaying the contribution of each driving force through methods such as a geographic detector and geographic weighted regression. And S3, future land utilization simulation: based on the Markov model and the PLUS model, simulating land utilization data under different scenes in the future, including natural development, ecological protection and economic development scenes. And S4, land utilization space-time evolution analysis: analyzing time and space evolution of land utilization, providing sustainable development suggestions, providing decision support for land utilization planning, and promoting optimal configuration of land resources.

Description

Technical Field

[0001] This invention relates to the field of land spatial layout optimization technology, specifically a method based on multi-scenario simulation of future land use. Background Technology

[0002] Land use change is a crucial factor affecting the ecological environment, economic development, and social stability. With urbanization, industrialization, and population growth, the utilization and management of land resources face enormous challenges. Rational planning and utilization of land resources are essential for achieving sustainable development goals. However, land use change is influenced by multiple driving forces, including both natural and anthropogenic factors, making the prediction and simulation of future land use changes complex and difficult.

[0003] Traditional land use research methods rely heavily on static data and simple trend analysis, failing to fully consider the dynamic and diverse nature of land use change. In recent years, with the development of Geographic Information Systems (GIS) and remote sensing technologies, land use change simulation and prediction techniques have significantly improved. Multi-scenario simulation methods, by constructing land use change models under different scenarios, can more comprehensively analyze and predict future land use change trends.

[0004] While existing multi-scenario land use simulation methods have achieved some degree of prediction of future land use change, they still have the following shortcomings: Data processing and integration are complex, making it difficult to guarantee data consistency and accuracy. Insufficient model accuracy validation leads to low reliability of simulation results. The lack of systematic analysis of land use change under multiple scenarios makes it difficult to comprehensively assess the impact of different scenarios.

[0005] To address the aforementioned issues, this invention proposes a method based on multi-scenario simulation of future land use. Through systematic data collection and processing, analysis of driving factors, multi-scenario simulation, and spatiotemporal evolution analysis, it provides a comprehensive, accurate, and reliable method for predicting land use changes, offering scientific basis and decision support for land use planning and management. Summary of the Invention

[0006] A method based on multi-scenario simulation of future land use includes: S1. Data Collection and Processing: A comprehensive collection and organization of literature, published research findings, policy documents, and statistical reports related to land use change was conducted. These documents were meticulously reviewed to understand the research progress on land use change. Analysis of these documents facilitated a qualitative assessment of the factors influencing land use change. Simultaneously, relevant data were collected, including administrative vector data, digital elevation model data, climate data, road data, population data, and GDP data. This stage laid the foundation for identifying key variables affecting land use dynamics within the region. The collected data were processed, including data merging, data cropping, projection transformation, resampling, and reclassification. Road vector data was converted to raster data using Euclidean distance, and climate NetCDF data was also converted to raster data. Through these data processing steps, it was ensured that all data had consistent projection, row and column numbers, cell size, and format.

[0007] S2. Driving Force Analysis: This section analyzes land use change using quantitative and spatial methods, including land use area change analysis, land use dynamic change analysis, and land use transfer matrix analysis, to understand the overall land use situation. Based on this information, the contribution of each driving force can be identified. Through methods such as geographic detector analysis and geographic weighted regression analysis, the influence of each driving force can be comprehensively and intuitively displayed using charts and graphs.

[0008] S3. Multi-Scenario Simulation of Future Land Use: First, the accuracy of the model and data needs to be validated. For example, land use data from 2000 and 2010, along with data on various driving forces, are used to obtain data on land use expansion from 2000 to 2010. Data on the development potential, change weights, and transition matrices of each land type—cultivated land, forest, shrubland, grassland, water, sound / snow / ice, wasteland, impermeable land, and wetland—are obtained. The PLUS model is used to simulate land use data for 2020. The simulated 2020 land use data is then compared with the actual 2020 land use data to obtain the Kappa coefficient. A Kappa coefficient greater than 80% indicates good progress in land use simulation. If the Kappa coefficient is low, the driving factors and other relevant coefficients need to be readjusted until the accuracy meets the requirements.

[0009] Provided the model accuracy meets requirements, multiple land use scenarios for the future of the study area can be simulated. These scenarios can include natural development, ecological protection and development, and economic development scenarios. Using land use data from two baseline years, and based on a Markov model, the quantities of arable land, forest, shrubland, grassland, water, sound / snow cover, wasteland, impermeable land, and wetland can be predicted for specific future years. Transition matrices and domain weights are established for the natural development, ecological protection, and economic development scenarios. The PLUS model is used to obtain simulated land use data for the natural development, ecological protection, and economic development scenarios for specific future years in the study area.

[0010] S4. Analysis of the Spatiotemporal Evolution of Future Land Use: Utilizing land use simulation data under specific future year scenarios (natural development, ecological protection, and economic development), this study analyzes the temporal and spatial evolution and characteristics of land use. Based on this analysis, recommendations for the sustainable development of future land use are proposed, providing policy guidance for government land use planning.

[0011] Furthermore, the collection of driving factors includes, but is not limited to, government vector data, digital elevation model data, climate data, road data, population data, and GDP data.

[0012] Further data processing includes, but is not limited to, data merging, data cropping, projection transformation, resampling, reclassification, converting road vector data to raster data using Euclidean distance, and converting climate NetCDF data to raster data. It is essential to ensure that all data have consistent projection, row and column numbers, cell size, and format.

[0013] Furthermore, the analysis of driving factors uses quantitative and spatial analysis methods, including land use area change analysis, land use dynamic change analysis, and land use transfer matrix analysis, to understand the contribution of each driving factor. The influence of each driving factor is also displayed intuitively through methods such as geographic detector analysis and geographic weighted regression analysis.

[0014] Furthermore, when validating the accuracy of the model and data, two periods of land use data, such as land use data from 2000 and 2010, can be used to simulate the land use data for 2020 using the PLUS model. Then, the simulation data is compared and analyzed with the actual land use data for 2020, and the calculated Kappa coefficient is used to evaluate the accuracy of the simulation.

[0015] Furthermore, if the Kappa coefficient is greater than 80%, it indicates that the simulation accuracy is good; otherwise, it is necessary to adjust the driving force factors and correlation coefficients until the accuracy meets the requirements.

[0016] Furthermore, multiple scenario modes are set up, including but not limited to natural development scenarios, ecological protection and development scenarios, and economic development scenarios.

[0017] Furthermore, land use types include, but are not limited to, arable land, forest, shrubland, grassland, water, sound / snow, wasteland, impermeable land, and wetland.

[0018] Furthermore, the Markov model can be used to predict the quantity of each land type in specific future years.

[0019] Furthermore, it is necessary to set up transition matrices and domain weights for natural development scenarios, ecological protection scenarios, and economic development scenarios.

[0020] Furthermore, the PLUS model was used to obtain land use simulation data for specific future years under natural development scenarios, ecological protection scenarios, and economic development scenarios.

[0021] Furthermore, by analyzing simulation data of future land use under natural development scenarios, ecological protection scenarios, and economic development scenarios, we can analyze the temporal and spatial evolution characteristics of land use.

[0022] Furthermore, based on the analysis of the spatiotemporal evolution of land use, suggestions are proposed for the sustainable development of future land use.

[0023] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention comprehensively collects and organizes various literature, research results, and policy documents related to land use change during the data collection and processing phase. Simultaneously, the collected data undergoes meticulous processing, including data merging, cropping, projection transformation, resampling, and reclassification, ensuring the consistency and accuracy of all data. This makes the model input data more comprehensive and accurate, improving the reliability of the simulation results.

[0024] 2. In the multi-scenario simulation phase of future land use, this invention uses the Kappa coefficient as an indicator to verify model accuracy. When the Kappa coefficient is greater than 80%, it indicates that the land use simulation is progressing well; otherwise, adjustments to the model and data are needed. This verification method is more scientific and accurate, improving the reliability of the simulation results.

[0025] 3. This invention sets up multiple scenario modes, including natural development scenario, ecological protection scenario, and economic development scenario. When simulating future land use changes, different transition matrices and weights can be set according to different scenarios, thereby obtaining land use simulation data under different scenarios. This makes the simulation results more comprehensive and diverse, helping policymakers to conduct more in-depth analysis and evaluation of future land use.

[0026] 4. This invention, through the analysis of the spatiotemporal evolution of future land use, can propose suggestions for the sustainable development of future land use. Based on the simulation results and analysis, it can provide specific policy recommendations and decision support for government departments' land use planning, contributing to the sustainable use and optimal allocation of land resources. Attached Figure Description

[0027] Figure 1 This is a flowchart illustrating the structural framework of the present invention; Detailed Implementation

[0028] The embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and should not be construed as limiting the scope of the invention.

[0029] like Figure 1 As shown, the present invention provides a method for multi-scenario simulation of future land use, comprising: S1. Data Collection and Processing: A comprehensive collection and organization of literature, published research findings, policy documents, and statistical reports related to land use change was conducted. These documents were meticulously reviewed to understand the research progress on land use change. Analysis of these documents facilitated a qualitative assessment of the factors influencing land use change. Simultaneously, relevant data were collected, including administrative vector data, digital elevation model data, climate data, road data, population data, and GDP data. This stage laid the foundation for identifying key variables affecting land use dynamics within the region. The collected data were processed, including data merging, data cropping, projection transformation, resampling, and reclassification. Road vector data was converted to raster data using Euclidean distance, and climate NetCDF data was also converted to raster data. Through these data processing steps, it was ensured that all data had consistent projection, row and column numbers, cell size, and format.

[0030] S2. Driving Force Analysis: This section analyzes land use change using quantitative and spatial methods, including land use area change analysis, land use dynamic change analysis, and land use transfer matrix analysis, to understand the overall land use situation. Based on this information, the contribution of each driving force can be identified. Through methods such as geographic detector analysis and geographic weighted regression analysis, the influence of each driving force can be comprehensively and intuitively displayed using charts and graphs.

[0031] S3. Multi-Scenario Simulation of Future Land Use: First, the accuracy of the model and data needs to be validated. For example, land use data from 2000 and 2010, along with data on various driving forces, are used to obtain data on land use expansion from 2000 to 2010. Data on the development potential, change weights, and transition matrices of each land type—cultivated land, forest, shrubland, grassland, water, sound / snow / ice, wasteland, impermeable land, and wetland—are obtained. The PLUS model is used to simulate land use data for 2020. The simulated 2020 land use data is then compared with the actual 2020 land use data to obtain the Kappa coefficient. A Kappa coefficient greater than 80% indicates good progress in land use simulation. If the Kappa coefficient is low, the driving factors and other relevant coefficients need to be readjusted until the accuracy meets the requirements.

[0032] Provided the model accuracy meets requirements, multiple land use scenarios for the future of the study area can be simulated. These scenarios can include natural development, ecological protection and development, and economic development scenarios. Using land use data from two baseline years, and based on a Markov model, the quantities of arable land, forest, shrubland, grassland, water, sound / snow cover, wasteland, impermeable land, and wetland can be predicted for specific future years. Transition matrices and domain weights are established for the natural development, ecological protection, and economic development scenarios. The PLUS model is used to obtain simulated land use data for the natural development, ecological protection, and economic development scenarios for specific future years in the study area.

[0033] S4. Analysis of the Spatiotemporal Evolution of Future Land Use: Utilizing land use simulation data under specific future year scenarios (natural development, ecological protection, and economic development), this study analyzes the temporal and spatial evolution and characteristics of land use. Based on this analysis, recommendations for the sustainable development of future land use are proposed, providing policy guidance for government land use planning.

[0034] As one embodiment of the present invention, the driving factors collected include, but are not limited to, political vector data, digital elevation model data, climate data, road data, population data, and GDP data.

[0035] As one embodiment of the present invention, data processing includes, but is not limited to, data merging, data cropping, projection transformation, resampling, reclassification, conversion of road vector data to raster data via Euclidean distance, and conversion of climate NetCDF data to raster data. It ensures that all data have consistent projection, row and column numbers, cell size, and format.

[0036] As one embodiment of the present invention, the driving force factor analysis uses quantitative analysis and spatial analysis methods, including land use area change analysis, land use dynamic change analysis, land use transfer matrix analysis, etc., to understand the contribution of each driving force, and uses methods such as geographic detector analysis and geographic weighted regression analysis to intuitively display the influence of each driving force factor.

[0037] As one embodiment of the present invention, when verifying the accuracy of the model and data, two periods of land use data, such as land use data from 2000 and 2010, can be used to simulate the land use data for 2020 using the PLUS model. Then, the simulation data is compared and analyzed with the actual land use data for 2020, and the calculated Kappa coefficient is used to evaluate the simulation accuracy.

[0038] As one embodiment of the present invention, if the Kappa coefficient is greater than 80%, it indicates that the simulation accuracy is good; otherwise, it is necessary to adjust the driving force factors and correlation coefficients until the accuracy meets the requirements.

[0039] As one embodiment of the present invention, multiple scenario modes are set, including but not limited to natural development scenario, ecological protection and development scenario, and economic development scenario.

[0040] As one embodiment of the present invention, land use types include, but are not limited to, arable land, forest, shrubland, grassland, water, sound / snow, wasteland, impermeable land, and wetland.

[0041] As one embodiment of the present invention, the Markov model can be used to predict the quantity of each land type in a specific future year.

[0042] As one embodiment of the present invention, it is necessary to set up transition matrices and domain weights for natural development scenarios, ecological protection scenarios, and economic development scenarios.

[0043] As one embodiment of the present invention, the PLUS model is used to obtain land use simulation data for natural development scenarios, land use simulation data for ecological protection scenarios, and land use simulation data for economic development scenarios for specific future years.

[0044] As one embodiment of the present invention, by analyzing future land use simulation data under natural development scenarios, ecological protection scenarios, and economic development scenarios, the temporal and spatial evolution characteristics of land use can be analyzed.

[0045] As one embodiment of the present invention, suggestions for the sustainable development of future land use are proposed based on the analysis of the spatiotemporal evolution of land use.

[0046] This invention provides a method for simulating future land use across multiple scenarios. First, by comprehensively collecting and organizing literature, research findings, policy documents, and statistical reports, and conducting detailed review and analysis, relevant data is collected and processed to lay the foundation for identifying key variables influencing land use dynamics. Second, through quantitative and spatial analysis, including analysis of land use area changes, dynamic changes, and transition matrices, the contribution of each driving force is understood. Then, by validating the model and data accuracy, multiple scenario models are set, including natural development, ecological protection, and economic development scenarios, to predict future land use. Finally, the simulation data is used to analyze the temporal and spatial evolution of land use and to propose recommendations for the sustainable development of future land use. This method helps provide policy guidance for government land use planning and has significant application prospects and social benefits.

[0047] The embodiments of the present invention are given for the purposes of illustration and description. Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for future land use multi-scenario simulation, characterized in that: Comprise: S1. Data collection and processing: A comprehensive collection and collation of literature, published research, policy documents and statistical reports related to land use change was conducted. A detailed review of these documents was conducted to understand the progress of research on land use change. Through the analysis of these documents, the qualitative assessment of the factors driving land use change was facilitated. At the same time, relevant data was collected, including administrative vector data, digital elevation model data, climate data, road data, population data and GDP data. This stage laid the foundation for determining the key variables affecting land use dynamics in the region. The data collected was processed, including data merging, data cropping, projection conversion, resampling, reclassification, road vector data converted to raster data through Euclidean distance, and climate NetCDF data converted to raster data. Through the above data processing, the projection, row number, pixel size and format of all data were unified. S2. Analysis of driving factors: Through quantitative and spatial analysis methods, including land use area change analysis, land use dynamic change analysis, land use transfer matrix analysis, etc., land use change was analyzed to understand the situation of land use change. Based on the land use change, the contribution of each driving force can be understood, and the influence degree of each driving force factor can be comprehensively and intuitively displayed through methods such as geographic detector analysis and geographic weighted regression analysis, and through charts and other means. S3. Future land use multi-scenario simulation: First, the model and data accuracy need to be verified, for example, using 2000 and 2010 land use data and various driving force data to obtain the land use expansion from 2000 to 2010. Obtain the development potential data of each land type such as cultivated land, forest, shrub, grassland, water, sound wave / snow, wasteland, impervious, wetland, change weight data, transfer matrix data, etc. Simulate the 2020 land use data through the PLUS model, then compare the simulated 2020 land use data with the actual 2020 land use data to obtain the Kappa coefficient. When the Kappa coefficient is greater than 80%, it means that the land use simulation is good. If the Kappa coefficient is low, the driving force factors and other related coefficients need to be adjusted until the accuracy meets the requirements. Under the condition that the model accuracy meets the requirements, the future land use multi-scenario of the study area can be simulated. Multiple scenario modes can be set, such as natural development scenario, ecological protection development scenario, and economic development scenario. The land quantity of each land type such as cultivated land, forest, shrub, grassland, water, sound wave / snow, wasteland, impervious, and wetland in future specific years can be predicted based on Markov model and two-period benchmark land use data. Based on different scenarios, set the transfer matrix and field weight of natural development scenario, ecological protection scenario, and economic development scenario. Use the PLUS model to obtain the natural development scenario land use simulation data, ecological protection scenario land use simulation data, and economic development scenario land use simulation data of the study area in future specific years. S4. Analysis of future land use spatio-temporal evolution: Use the natural development scenario land use simulation data, ecological protection scenario land use simulation data, and economic development scenario land use simulation data in future specific years to analyze the time and space evolution and characteristics of land use. Based on the above analysis, suggestions for future sustainable development of land use are put forward.

2. The method for future land use multi-scenario simulation according to claim 1, wherein, In the S1 step, the collected driving force factors include but are not limited to political vector data, digital elevation model data, climate data, road data, population data, and GDP data.

3. The method of claim 1, wherein the future land use is simulated based on a plurality of scenarios. In the S1 step, data processing includes but is not limited to data merging, data cropping, projection conversion, resampling, reclassification, converting road vector data to raster data through Euclidean distance, converting climate NetCDF data to raster data, etc. Ensure that all data are unified in projection, row and column number, pixel size, and format.

4. The method of claim 1, wherein the future land use is simulated based on a plurality of scenarios. In the S2 step, the driving force factor analysis is performed by quantitative analysis and spatial analysis, including land use area change analysis, land use dynamic change analysis, land use transfer matrix analysis, etc., to understand the contribution of each driving force, and the influence degree of each driving force factor is intuitively displayed by geographic detector analysis and geographic weighted regression analysis.

5. The method for future land use multi-scenario simulation according to claim 1, characterized in that, In the S3 step, when verifying the model and data accuracy, two periods of land use data, such as 2000 and 2010, can be used to simulate the land use data in 2020 by the PLUS model, and then compared with the actual land use data in 2020 for analysis, and the Kappa coefficient obtained is used to evaluate the simulation accuracy.

6. The method for future land use multi-scenario simulation according to claim 1, wherein, In the S3 step, if the Kappa coefficient is greater than 80%, it means that the simulation accuracy is good, otherwise the driving force factors and related coefficients need to be adjusted until the accuracy meets the requirements.

7. The method of claim 1, wherein, In the S3 step, multiple scenario modes are set, including but not limited to natural development scenario, ecological protection development scenario, and economic development scenario.

8. The method of claim 1, wherein, In the S3 step, the land use types include but are not limited to cultivated land, forest, shrub, grassland, water, sound wave / snow, wasteland, impervious, and wetland.

9. The method of claim 1, wherein, In the S3 step, the Markov model can be used to predict the number of each land type in the future specific year.

10. The method of claim 1, wherein, In the S3 step, the transfer matrix and field weight of the natural development scenario, ecological protection scenario, and economic development scenario need to be set.

11. A method for future land use multi-scenario simulation as claimed in claim 1, wherein, In the S3 step, the PLUS model is used to obtain the natural development scenario land use simulation data, ecological protection scenario land use simulation data, and economic development scenario land use simulation data in the future specific year.

12. The method of claim 1, wherein, In the S4 step, by analyzing the future land use simulation data under the natural development scenario, ecological protection scenario, and economic development scenario, the time and space evolution characteristics of land use are analyzed.

13. The method of claim 1, wherein, In the S4 step, based on the analysis of the time and space evolution of land use, suggestions for sustainable development of future land use are put forward.

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