Method and system for evaluating influence of Yangtze river basin upstream farmland-to-forest returning project on carbon reserves based on PLUS-INVEST model

Abstract

The invention discloses a PLUS-INVEST model-based method and system for evaluating the influence of a Yangtze river basin upstream farmland-to-forest returning project on carbon reserves, and the method comprises the steps: collecting the land utilization data and driving factor data of the Yangtze river upstream region in 2000-2020, simulating the land utilization change through a PLUS model, and calculating the carbon reserve change in combination with an InVEST model. Research finds that during 2000-2020, cultivated land, forest land and grassland are mainly used in the upstream region of the Yangtze River, and the total carbon reserve shows an oscillation change trend. By setting scenes of natural development, mild, moderate and severe farmland-to-forest returning, land utilization and carbon reserve changes under different scenes in 2040 are predicted. Results show that the policy of returning farmland to forests can significantly increase regional carbon reserves and inhibit carbon loss. According to the method, the limitation of a single model is overcome, and the land utilization change and carbon reserve dynamic comprehensive evaluation is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of ecological protection and land use management, and particularly relates to an evaluation method and system for the impact of the conversion of farmland to forest project in the upper reaches of the Yangtze River Basin on carbon storage based on the PLUS-INVEST model. Background Technique

[0002] With the increasingly severe problems of global climate change and environmental degradation, the carbon storage of ecosystems, as a key parameter for regulating the climate, has received extensive attention. The change in carbon storage directly affects the global carbon cycle and the stability of the climate system, and land use change is one of the main driving factors leading to changes in carbon storage. For example, changes in land use patterns such as the conversion of farmland to forest and the expansion of construction land will significantly affect the dynamic changes of ecosystem carbon pools such as vegetation cover and soil organic carbon storage. Therefore, studying the impact of land use change on carbon storage has become one of the core issues for understanding climate change and formulating carbon emission reduction policies.

[0003] In China, the policy of returning farmland to forest and grassland, as an important land use adjustment measure, has been proven to have a positive effect on increasing carbon storage. Since its launch in 1999, the conversion of farmland to forest project has covered tens of millions of hectares of land, greatly improving the ecological environment and enhancing the carbon sequestration capacity of ecosystems. However, the current research on the comprehensive benefits of the conversion of farmland to forest policy for ecosystems is not deep enough. Especially, the systematic evaluation of the impact of the conversion of farmland to forest on carbon storage under different implementation levels is still insufficient. In addition, existing research mostly focuses on the analysis of single regions or short-term effects, lacking a comprehensive evaluation of large-scale regions and long-term dynamic changes.

[0004] The upper reaches of the Yangtze River are an important ecological barrier and water conservation area in China, and its ecological function is crucial for the ecological security of the entire Yangtze River Basin and even the whole country. In recent years, with the proposal of the "carbon peak" and "carbon neutrality" goals by the Chinese government, the research enthusiasm of all sectors of society for reducing carbon emissions and increasing carbon storage has been further improved. In this context, how to improve regional carbon storage through scientific land use management and ecological restoration projects has become an urgent problem to be solved.

[0005] In the prior art, although there have been studies using ecosystem service models (such as the InVEST model) and land use change models (such as the PLUS model) to evaluate and predict carbon storage changes, these studies mostly focus on the application of single models and lack a comprehensive assessment of land use changes and carbon storage dynamics under different scenarios. For example, the InVEST model is mainly used for the quantitative assessment of ecosystem services, but it has a strong dependence on the quality of input data and is difficult to capture the dynamic process of land use changes; while the PLUS model can simulate land use changes, but its ability to evaluate the impact on ecosystem services is limited. In addition, when dealing with complex terrains and diverse ecosystems, the applicability and accuracy of existing models still face challenges.

[0006] Therefore, the present invention aims to combine the advantages of the PLUS model and the InVEST model, simulate and evaluate the land use changes in the upper reaches of the Yangtze River region from 2000 to 2020 and their impacts on carbon storage, and predict the land use and carbon storage changes under different scenarios of returning farmland to forests in 2040, so as to provide a scientific basis for regional land use optimization and the improvement of ecosystem service functions. By comprehensively applying the two models, the present invention overcomes the limitations of single models, can more accurately evaluate the impact of land use changes on carbon storage, and provides strong support for policy making. Summary of the Invention

[0007] To solve the above technical problems, the present invention proposes an evaluation method and system for the impact of the project of returning farmland to forests in the upper reaches of the Yangtze River Basin based on the PLUS-INVEST model, which can improve the regional carbon sink capacity while realizing the coordinated development of society and economy.

[0008] On the one hand, to achieve the above object, the present invention provides an evaluation method for the impact of the project of returning farmland to forests in the upper reaches of the Yangtze River Basin based on the PLUS-INVEST model, including:

[0009] Collect land use data and driving factor data in the upper reaches of the Yangtze River Basin;

[0010] Use the PLUS model to simulate land use changes from 2000 to 2020 and predict land use changes under different scenarios of returning farmland to forests in 2040;

[0011] Combine the InVEST model to calculate carbon storage changes under different land use scenarios;

[0012] Analyze the impact of land use changes on carbon storage and provide a scientific basis for regional land use optimization and the improvement of ecosystem service functions.

[0013] Optionally, the land use data includes the distribution and area information of cultivated land, forest land, grassland, water area, unused land and construction land.

[0014] Optionally, the driving factor data includes socio-economic factors and climate environmental factors, where the socio-economic factors include the distance from GDP, population, railways, highways, county-level residential areas, and rivers, and the climate environmental factors include elevation, slope, aspect, soil organic matter content, soil pH value, soil sand content, temperature, and precipitation.

[0015] Optionally, the PLUS model dynamically simulates land use change and generates the development potential of each land use type by combining the Markov prediction module, rule extraction system, LEAS model, and CA model.

[0016] Optionally, the InVEST model quantifies the change in carbon storage under different land use scenarios by calculating the aboveground biomass carbon density, underground biomass carbon density, dead organic matter carbon density, and soil organic matter carbon pool carbon density.

[0017] Optionally, the different scenarios of returning farmland to forest in 2040 include natural development scenario, mild scenario of returning farmland to forest, moderate scenario of returning farmland to forest, and severe scenario of returning farmland to forest, corresponding to different transfer probabilities of cultivated land to forest and grassland respectively.

[0018] Optionally, the analysis of the change in carbon storage includes the spatio-temporal analysis of the carbon storage in the upper reaches of the Yangtze River Basin from 2000 to 2020, and the predictive analysis of the carbon storage under different scenarios in 2040.

[0019] Optionally, the method further includes verifying the accuracy of the model by comparing the simulated land use data in 2020 with the actual data to verify the accuracy of the PLUS model.

[0020] On the other hand, to achieve the above object, the present invention also provides an evaluation system for the impact of the project of returning farmland to forest in the upper reaches of the Yangtze River Basin based on the PLUS-INVEST model, including:

[0021] A data collection module, a land use simulation module, a carbon storage calculation module, and an analysis module;

[0022] The data collection module is used to collect land use data and driving factor data of the upper reaches of the Yangtze River Basin;

[0023] The land use simulation module is used to simulate land use change by using the PLUS model;

[0024] The carbon storage calculation module is used to calculate the change in carbon storage by combining the InVEST model;

[0025] The analysis module is used to analyze the impact of land use change on carbon storage.

[0026] A computer-readable storage medium stores a computer program thereon, and when the computer program is executed by a processor, the evaluation method for the impact of the conversion of farmland to forest project in the upper reaches of the Yangtze River Basin based on the PLUS-INVEST model is implemented.

[0027] Technical effects of the present invention: The present invention discloses an evaluation method and system for the impact of the conversion of farmland to forest project in the upper reaches of the Yangtze River Basin based on the PLUS-InVEST model. The PLUS model can dynamically simulate land use changes, while the InVEST model can accurately quantify ecosystem services, especially changes in carbon storage. The combination of the two overcomes the limitations of a single model in ecosystem service evaluation and land use change simulation, and realizes a comprehensive evaluation of land use change and carbon storage dynamics. The present invention predicts the land use and carbon storage changes in different scenarios in 2040 by setting multiple scenarios of converting farmland to forest, providing a scientific basis for policy making. Scenario analysis shows that the implementation of the policy of converting farmland to forest can significantly increase the regional carbon storage and reduce carbon loss caused by land use changes. Through high-precision land use data and driving factor analysis, the present invention improves the simulation accuracy of the model and is successfully applied to the upper reaches of the Yangtze River with complex terrain and diverse ecosystems. At the same time, the model verification results show that the present invention has high accuracy and reliability in predicting land use changes and carbon storage. The present invention provides a scientific basis for land use optimization and ecosystem service function improvement in the upper reaches of the Yangtze River, helps to promote regional ecological restoration and carbon sink capacity improvement, and provides strong support for achieving the goals of "carbon peak" and "carbon neutrality". In summary, the present invention has significant technical advantages and application value in the field of land use change and carbon storage evaluation, and can provide strong support for regional ecological management and policy making. Description of the Drawings

[0028] The drawings forming a part of this application are used to provide a further understanding of this application. The schematic embodiments and descriptions thereof of this application are used to explain this application and do not constitute an improper limitation of this application. In the drawings:

[0029] Figure 1 It is a schematic flowchart of the evaluation method for the impact of the conversion of farmland to forest project in the upper reaches of the Yangtze River Basin based on the PLUS-INVEST model according to an embodiment of the present invention;

[0030] Figure 2 It is a schematic structural diagram of the evaluation system for the impact of the conversion of farmland to forest project in the upper reaches of the Yangtze River Basin based on the PLUS-INVEST model according to an embodiment of the present invention. Detailed Embodiments

[0031] It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. The following will describe the present application in detail with reference to the accompanying drawings and in combination with the embodiments.

[0032] It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0033] As Figure 1 shown, in this embodiment, an evaluation method for the impact of the conversion of cropland to forest project in the upper reaches of the Yangtze River Basin based on the PLUS-INVEST model is provided, including:

[0034] Collect land use data and driving factor data in the upper reaches of the Yangtze River Basin;

[0035] Use the PLUS model to simulate land use changes from 2000 to 2020 and predict land use changes under different cropland-to-forest scenarios in 2040;

[0036] Combine the InVEST model to calculate the change in carbon storage under different land use scenarios;

[0037] Analyze the impact of land use changes on carbon storage and provide a scientific basis for regional land use optimization and the improvement of ecosystem service functions.

[0038] Further, the land use data includes the distribution and area information of cultivated land, forest land, grassland, water area, unused land and construction land.

[0039] Further, the driving factor data includes social and economic factors and climate and environmental factors, where the social and economic factors include the distance from GDP, population, railways, highways, county-level residential areas and rivers, and the climate and environmental factors include elevation, slope, aspect, soil organic matter content, soil pH value, soil sediment content, temperature and precipitation.

[0040] Further, the PLUS model dynamically simulates land use changes by combining the Markov prediction module, rule extraction system, LEAS model and CA model, and generates the development potential of each land use type.

[0041] Further, the InVEST model quantifies the change in carbon storage under different land use scenarios by calculating the aboveground biomass carbon density, belowground biomass carbon density, dead organic matter carbon density and soil organic matter carbon pool carbon density.

[0042] Furthermore, the different scenarios of returning farmland to forest in 2040 include the natural development scenario, the mild scenario of returning farmland to forest, the moderate scenario of returning farmland to forest, and the severe scenario of returning farmland to forest, which respectively correspond to different transfer probabilities of cultivated land to forest land and grassland.

[0043] Furthermore, the analysis of carbon storage change includes the spatio-temporal change analysis of carbon storage in the upper reaches of the Yangtze River Basin from 2000 to 2020, and the predictive analysis of carbon storage under different scenarios in 2040.

[0044] Furthermore, the method also includes the verification of the model accuracy by comparing the simulated land use data in 2020 with the actual data to verify the accuracy of the PLUS model.

[0045] Data collection and preprocessing:

[0046] Data source: The land use data of the upper reaches of the Yangtze River in 2000, 2005, 2010, 2015, and 2020, with a resolution of 300 meters × 300 meters, is from the Data Center for Resources and Environmental Sciences. Classification standard: According to the "Classification of Current Land Use" (GB / T 21010-2007) and the actual situation of the study area, it is merged into 6 categories: cultivated land, forest land, grassland, water area, unused land, and construction land.

[0047] Socio-economic data: GDP, population density, distance from railway / road / county residential area. The data is from the National Bureau of Statistics and OpenStreetMap (OSM), and the spatial distribution is calculated by the Euclidean distance tool of ArcGIS10.2.

[0048] Climate and environmental data:

[0049] Precipitation and temperature: The annual average precipitation (MAP) and annual average temperature (MAT) from 2000 to 2020 are obtained by using the Anusplin interpolation method.

[0050] Topographic data: Elevation (DEM), slope, and aspect are generated based on ASTER GDEMV3.

[0051] Soil data: Soil organic matter content, pH value, and sediment content are from the national soil dataset released by the Nanjing Institute of Soil Science, Chinese Academy of Sciences.

[0052] Data standardization: All raster data is unified into the WGS_1984 coordinate system, resampled to a resolution of 300 meters, and normalized by the Z-Score method to eliminate the dimension difference.

[0053] PLUS model parameter setting and scenario simulation:

[0054] Selection of driving factors and calculation of weights: 15 driving factors are selected: Natural factors: elevation, slope, aspect, annual average precipitation, annual average temperature, soil organic matter content, soil pH value, soil sand content. Socio-economic factors: GDP, population density, distance to railway, distance to highway, distance to county-level residential area, distance to river. Weight calculation: The random forest algorithm is used to train the model based on the land use change data from 2010 to 2015, extract the contribution degree of each factor to the change of land use type, and generate the weights of driving factors.

[0055] Simulation and verification of land use change: Historical data training: Input the land use data of 2010 and 2015, extract the land use expansion rules through the LEAS module of the PLUS model, and generate the simulation results of 2020 in combination with the CARS module. Accuracy verification: Use the confusion matrix to calculate the Kappa coefficient (≥0.85) and the overall accuracy (≥90%), and verify the reliability of the model.

[0056] Multi-scenario setting: Natural development scenario: Based on the land use change trend from 2000 to 2020, use the Markov chain to predict the land use demand in 2040. Returning farmland to forest scenario: Mild returning farmland to forest / grassland scenario: Increase the transfer probabilities of cultivated land to forest land and grassland by 20% and 10% respectively. Moderate returning farmland to forest and grassland scenario: Increase the transfer probabilities of cultivated land to forest land and grassland by 35% and 20% respectively. Severe returning farmland to forest and grassland scenario: Increase the transfer probabilities of cultivated land to forest land and grassland by 50% and 30% respectively.

[0057] Calculation of carbon storage in the InVEST model:

[0058] Calibration of carbon density value: Benchmark carbon density: Refer to the carbon density data of different land use types in the upper reaches of the Yangtze River in the literature by Xie Xianli, Li Keren, etc.

[0059] Climate correction:

[0060] Calibration of soil carbon density:

[0061]

[0062] Among them, C a is the benchmark soil carbon density, K S is the soil type correction coefficient (the value ranges from 0.8 to 1.2), and MAP is the annual average precipitation in the study area (826.0 mm).

[0063] Calibration of biomass carbon density:

[0064]

[0065] Among them, C bThe reference biomass carbon density, K B is the vegetation type correction coefficient (ranging from 0.9 to 1.1), and MAT is the average annual temperature in the study area (7.5°C).

[0066] Carbon storage calculation:

[0067]

[0068] Among them, are the aboveground biomass, underground biomass, soil organic matter, and dead organic matter carbon density of the i-th land use type (t / hm 2 ), respectively, and A i is the area of the i-th land use type.

[0069] Spatio-temporal change analysis: Based on the land use transfer matrix, calculate the changes in carbon storage under each scenario in 2000 - 2020 and 2040, and focus on analyzing the contribution rate of the conversion from cultivated land to forest land to carbon storage.

[0070] Result output and policy optimization: Report generation: Output the land use change map, carbon storage spatial distribution map, and change trend table. Policy recommendations: The increase in carbon storage is the largest under the scenario of intensive conversion of cultivated land to forest land (5.333×10 8 t). It is recommended to give priority to implementing the policy of intensive conversion of cultivated land to forest land. Designate the Jialing River Basin as an ecological protection red line to restrict the expansion of construction land. Establish a carbon sink compensation mechanism to provide economic incentives to farmers who convert cultivated land to forest land.

[0071] As Figure 2 shown, in this embodiment, an evaluation system for the impact of the conversion of cropland to forest project in the upper reaches of the Yangtze River Basin based on the PLUS-INVEST model is also provided, including:

[0072] A data collection module, a land use simulation module, a carbon storage calculation module, and an analysis module;

[0073] The data collection module is used to collect land use data and driving factor data in the upper reaches of the Yangtze River Basin;

[0074] The land use simulation module is used to simulate land use changes using the PLUS model;

[0075] The carbon storage calculation module is used to calculate the changes in carbon storage in combination with the InVEST model;

[0076] The analysis module is used to analyze the impact of land use changes on carbon storage.

[0077] A computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the evaluation method for the impact of the conversion of cropland to forest project in the upper reaches of the Yangtze River Basin based on the PLUS-INVEST model.

[0078] Taking the Jialing River Basin in the upper reaches of the Yangtze River as an example: Inputting the land use data from 2000 to 2020, it is simulated that in the natural development scenario, the cultivated land will decrease by 6824 km² in 2040 2 , and the forest land will increase by 12928 km² 2 . Adopting the scenario of intensive conversion of cropland to forest, the transfer probability from cultivated land to forest land is increased by 50%, and it is predicted that the forest land area will increase by 37232 km² in 2040 2 , and the carbon storage will increase by 2.535×10 8 t. Comparing the spatial distribution map of carbon storage shows that the carbon density in the southeastern mountainous area has a significant increase, verifying the optimization effect of the conversion of cropland to forest on high-carbon sink areas

[0079] The present invention uses the combined method of the PLUS model and the InVEST model to analyze the changes in land use types and the trends of carbon storage in the upper reaches of the Yangtze River from 2000 to 2020, and accurately predicts the land use types and carbon storage in the study area under four different scenarios in 2040

[0080] The combination of the ecosystem service assessment ability of the InVEST model and the spatial simulation and prediction ability of the PLUS model can achieve a comprehensive assessment of land use change and carbon storage dynamics under different policies and development paths in the upper reaches of the Yangtze River. This helps decision-makers understand the changes in ecosystem services under various development scenarios and provides scientific support for regional land management and ecological policy formulation. At the same time, the coupling of the two models can also make up for the deficiencies of a single model in predicting future land use type changes. However, there are also limitations when applying this model combination in the upper reaches of the Yangtze River. First, the InVEST model has a strong dependence on the quality of input data, especially the need for carbon density, land use, and socioeconomic data. It is difficult to obtain these data in the upper reaches of the Yangtze River with complex terrain, and it is difficult to ensure the accuracy and integrity of the data, which may lead to deviations in the model results. Second, the simulation results of the PLUS model largely depend on the setting of driving factors, and this setting has a certain degree of subjectivity. Especially in a diverse ecosystem like the upper reaches of the Yangtze River, the reasonable selection and parameter setting of driving factors have a significant impact on the simulation accuracy. In addition, the terrain in the upper reaches of the Yangtze River is complex and diverse, and the dynamic changes of different land use types are often affected by a combination of natural and human factors. It is difficult for traditional model combinations to fully capture these complexities. Therefore, future research can be improved from the following aspects: First, adopt more refined data collection methods, such as remote sensing technology and drone monitoring, to obtain higher-precision carbon density and land use data. Second, introduce machine learning and multi-objective optimization algorithms to improve the scientificity of driving factor setting and the simulation accuracy of the model. Finally, multi-scenario simulations can be used to increase the robustness of the model. Combining different socioeconomic development assumptions, explore the interactive effects of policies and environmental changes to cope with possible future changes.

[0081] The above is only the preferred specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed in this application should be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

Claims

1. The evaluation method of the impact of the returning farmland to forest project in the upper reaches of the Yangtze River basin on carbon storage based on the PLUS-INVEST model is characterized by: include: Collect data on land use and drivers in the upper Yangtze River Basin; The PLUS model was used to simulate land use changes from 2000 to 2020, and to predict land use changes in 2040 under different scenarios of returning farmland to forest; Combined with the InVEST model, the carbon storage changes under different land use scenarios were calculated; Analyze the impact of land use changes on carbon storage and provide a scientific basis for regional land use optimization and ecosystem service function enhancement.

2. The method for evaluating the impact of the returning farmland to forest project in the upper reaches of the Yangtze River basin on carbon storage based on the PLUS-INVEST model as claimed in claim 1, characterized in that: The land use data include the distribution and area information of cultivated land, forest land, grassland, water area, unused land and construction land.

3. The method for evaluating the impact of the returning farmland to forest project in the upper reaches of the Yangtze River basin on carbon storage based on the PLUS-INVEST model as claimed in claim 1, characterized in that: The driving factor data include socioeconomic factors and climatic and environmental factors, among which socioeconomic factors include GDP, population, railways, highways, county-level residential areas and distances to rivers; climatic and environmental factors include elevation, slope, aspect, soil organic matter content, soil pH, soil sand content, temperature and precipitation.

4. The method for evaluating the impact of the returning farmland to forest project in the upper reaches of the Yangtze River basin on carbon storage based on the PLUS-INVEST model as claimed in claim 1, characterized in that: The PLUS model dynamically simulates land use changes and generates the development potential of each land use type by combining the Markov prediction module, rule extraction system, LEAS model and CA model.

5. The method for evaluating the impact of the returning farmland to forest project in the upper reaches of the Yangtze River basin on carbon storage based on the PLUS-INVEST model as claimed in claim 1, characterized in that: The InVEST model quantifies the changes in carbon storage under different land use scenarios by calculating the aboveground biomass carbon density, underground biomass carbon density, dead organic matter carbon density and soil organic matter carbon pool carbon density.

6. The method for evaluating the impact of the returning farmland to forest project in the upper reaches of the Yangtze River basin on carbon storage based on the PLUS-INVEST model as claimed in claim 1, characterized in that: The different scenarios of returning farmland to forest in 2040 include a natural development scenario, a mild returning farmland to forest scenario, a moderate returning farmland to forest scenario and a severe returning farmland to forest scenario, which correspond to different probabilities of transfer of cultivated land to forest land and grassland respectively.

7. The method for evaluating the impact of the returning farmland to forest project in the upper reaches of the Yangtze River basin on carbon storage based on the PLUS-INVEST model as claimed in claim 1, characterized in that: The carbon storage change analysis includes the analysis of the spatiotemporal changes in carbon storage in the upper reaches of the Yangtze River Basin from 2000 to 2020, as well as the forecast analysis of carbon storage under different scenarios in 2040.

8. The method for evaluating the impact of the project of returning farmland to forest in the upper reaches of the Yangtze River basin on carbon storage based on the PLUS-INVEST model as claimed in claim 1, characterized in that: The method also includes verification of model accuracy by comparing the simulated 2020 land use data with actual data to verify the accuracy of the PLUS model.

9. An assessment system for the impact of the returning farmland to forest project in the upper reaches of the Yangtze River basin on carbon storage based on the PLUS-INVEST model, characterized by: include: Data collection module, land use simulation module, carbon storage calculation module and analysis module; The data collection module is used to collect land use data and driving factor data in the upper reaches of the Yangtze River Basin; The land use simulation module is used to simulate land use changes using the PLUS model; The carbon storage calculation module is used to calculate the carbon storage change in combination with the InVEST model; The analysis module is used to analyze the impact of land use change on carbon storage.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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