Land resource dynamic model generation management system based on climate change

Abstract

The invention discloses a land resource dynamic model generation management system based on climate change, and relates to the technical field of land resource management systems. The system comprises a climate data integration module which uses an existing climate database and a remote sensing monitoring method to regularly update climate data and collect historical records and prediction data of extreme weather events; the land resource current situation evaluation module is used for carrying out detailed investigation on land resources in the to-be-researched area through a high-resolution remote sensing image and a GIS system; the dynamic model building module is based on the climate data collected by the climate data integration module and the land resource current situation collected by the land resource current situation evaluation module; and the simulation and prediction module is used for evaluating the influence degree and range of climate change on the land resources by utilizing the constructed land resource dynamic change model. According to the land resource dynamic model constructed by the method, the scientificity of land resource management and the accuracy of prediction data are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of land resource management systems, and in particular to a land resource dynamic model generation and management system based on climate change. Background Art

[0002] The land resource dynamic model is a model that reveals the magnitude, speed, and spatial distribution of land use changes based on the analysis of land use change. It analyzes the driving factors of land use and their interrelationships, explores the mechanism of land use change, and predicts the trend of future land use change and its impact on the ecological environment and socioeconomic development. Traditional land resource management models often ignore the impact of climate change, resulting in unscientific and unreasonable allocation and utilization of land resources. In addition, the prediction accuracy of existing land resource dynamic models is generally low, resulting in poor use results. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a land resource dynamic model generation and management system which can improve the scientificity of land resource management and accurately predict climate change.

[0004] In order to solve the above technical problems, the technical solution adopted by the present invention is: a land resource dynamic model generation and management system based on climate change, comprising:

[0005] Climate data integration module: Use existing climate databases and remote sensing monitoring methods to regularly update climate data and collect historical records and forecast data of extreme weather events;

[0006] Land resource status assessment module: It is used to conduct a detailed survey of land resources in the study area through high-resolution remote sensing images and GIS systems, obtain information on land use types, vegetation coverage, and soil types, and use spatial analysis methods to extract key characteristic parameters of land resources;

[0007] Dynamic model building module: Based on the climate data collected by the climate data integration module and the land resource status collected by the land resource status assessment module, the system dynamics and cellular automation modeling methods are used to build a land resource dynamic change model;

[0008] Simulation and prediction module: Use the constructed land resource dynamic change model to simulate the evolution trend of land resources under different climate scenarios. By adjusting the model parameters and input data, analyze the simulation results under different scenarios and evaluate the degree and scope of the impact of climate change on land resources.

[0009] Decision support module: used to provide decision support for the optimal allocation and sustainable use of land resources based on the simulation prediction results, and to formulate land use plans that adapt to climate change.

[0010] The beneficial effects of adopting the above technical solution are: the system described in this application improves the scientific nature of land resource management by integrating climate data and the current status of land resources to build a dynamic model of land resources. It can simulate and predict the evolution trend of land resources under different climate scenarios, making it possible to formulate response measures in advance. It provides effective decision-making support for the optimal allocation and sustainable use of land resources, which helps to improve the comprehensive benefits of land resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0012] Figure 1 It is a principle block diagram of the system described in the embodiment of the present invention. DETAILED DESCRIPTION

[0013] The following is a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0014] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0015] like Figure 1 As shown, the embodiment of the present invention discloses a land resource dynamic model generation and management system based on climate change, including:

[0016] Climate Data Integration Module 101: Use existing climate databases and remote sensing monitoring methods to regularly update climate data, including global and regional key indicators such as temperature and precipitation; at the same time, collect historical records and forecast data of extreme weather events to provide a comprehensive climate background for model construction;

[0017] Land resource status assessment module 102: used to conduct a detailed investigation of land resources in the study area through high-resolution remote sensing images and GIS systems, obtain information on land use types, vegetation coverage and soil types, and use spatial analysis methods to extract key characteristic parameters of land resources.

[0018] Dynamic model building module 103: Based on the climate data collected by the climate data integration module and the land resource status collected by the land resource status assessment module, a land resource dynamic change model is constructed using system dynamics and cellular automation modeling methods; the model should fully consider the impact mechanism of climate change on land resources, such as the impact of temperature on vegetation productivity, the effect of precipitation on soil erosion, etc.

[0019] Simulation and prediction module 104: using the constructed land resource dynamic change model to simulate the evolution trend of land resources under different climate scenarios, by adjusting model parameters and input data, analyzing the simulation results under different scenarios, and evaluating the degree and scope of the impact of climate change on land resources;

[0020] Decision support module 105: used to provide decision support for the optimal allocation and sustainable use of land resources based on the simulated prediction results, formulate land use plans that adapt to climate change, and propose ecological protection measures to ensure the long-term safety and sustainable use of land resources.

[0021] The climate data integration module 101 collects climate data by the following method:

[0022] 1) Determine the data source:

[0023] The climate data integration module first identifies the sources of data, which include:

[0024] Ground meteorological observation station data: Ground meteorological observation stations measure and record various meteorological parameters in real time by deploying various meteorological observation equipment (such as thermometers, hygrometers, barometers, anemometers, etc.); these observation stations are spread all over the world and provide basic data for climate data integration.

[0025] Satellite remote sensing data: Satellite remote sensing is used to obtain all-weather, high-resolution meteorological data over a wide area. These data are of great significance for monitoring global climate change, extreme weather events, etc.

[0026] Ocean buoy and ship observation data: Obtain ocean meteorological parameter data through ocean buoy and ship observations;

[0027] Climate model output data: By running climate models, simulating and predicting future climate change trends, climate model output data is obtained.

[0028] 2) Data collection:

[0029] Automatic collection: Meteorological observation stations and satellite remote sensing systems transmit real-time observation data to data centers or cloud platforms for subsequent processing and analysis.

[0030] Manual collection and entry: Traditional observation stations and special data sources are entered into the climate data integration module through manual collection and entry, which may still require manual observation and data entry. Although this method is less efficient, it still plays an irreplaceable role in some cases.

[0031] Data exchange and sharing: Obtain meteorological observation data from other countries and regions through the data exchange platform;

[0032] 3) Data processing and integration:

[0033] Data quality control: Identify the collected data and remove outliers and missing values, and correct and standardize the data;

[0034] Data format conversion: convert the data format for subsequent unified processing and analysis;

[0035] Data integration: Integrate data that have undergone quality control and format conversion according to certain rules and logic; this includes spatial integration of data (such as integrating observation data from different locations into the same geographic spatial framework) and temporal integration (such as integrating observation data from different time points into time series data).

[0036] Data storage and management: The integrated climate data is stored in a dedicated database and effectively managed and maintained. This includes security measures such as data backup, recovery, and access permission control.

[0037] Furthermore, the land resource status assessment module 102 obtains the land resource status by the following method:

[0038] 1) Information collection:

[0039] Through field visits and surveys, we obtain information on land quality, topography, vegetation coverage and soil type; use satellite platforms to obtain remote sensing images to understand the land resource conditions in specific areas; use low-altitude remote sensing from aircraft to obtain higher-resolution image data and further refine land use status information; and use various sensors (such as soil moisture sensors, meteorological sensors, etc.) to monitor key parameters of land resources, such as soil moisture, temperature, light intensity, etc.

[0040] 2) Data processing and analysis:

[0041] The various types of data collected are sorted, classified and counted to form a systematic land resource status data set; spatial analysis is performed using the Geographic Information System (GIS) to determine land use types and land development potential data; the characteristics and patterns of land resources are revealed through mean, standard deviation or correlation analysis.

[0042] Furthermore, the method for constructing a land resource dynamic change model by the dynamic model construction module includes the following steps:

[0043] 1) System dynamics model construction:

[0044] Clarify the scope and objectives of the model and define the boundaries of the land resource dynamics system; select socioeconomic variables (such as population, GDP, urbanization rate, etc.), policy variables (such as land policy, environmental policy, etc.), climate variables (such as temperature, precipitation, etc.) and land resource variables (such as cultivated land, forest land, construction land, etc.); define the specific meaning and measurement units of each variable;

[0045] Analyze the cause-effect relationship between variables and construct the cause-effect diagram of the system; determine the key feedback loops and identify the main dynamic characteristics of the system;

[0046] Establish mathematical equations for each variable to describe its dynamic change process; use system dynamics software to simulate the model, set reasonable simulation parameters and initial conditions; run the model, observe the dynamic change process of the system, and analyze the simulation results under different scenarios;

[0047] 2) Cellular automaton model construction:

[0048] Divide the study area into several cells, each cell represents a certain land area; define the set of cell states, such as cultivated land, forest land, grassland, construction land, etc.; determine the neighborhood range of the cell, usually including the surrounding adjacent cells; formulate the transformation rules of the cell state based on climate data, land resource status and expert knowledge; these rules should reflect the interaction and spatial dependence between land use types; use land resource status data to initialize the state distribution of the cell space;

[0049] According to the transformation rules, simulate the changes of the cell state in each time step; repeat the iterative process until the predetermined simulation time is reached or the stop condition is met; output the simulation results, including the spatial distribution map of land use types and time series data;

[0050] 3) Model coupling and comprehensive evaluation:

[0051] The system dynamics model and the cellular automaton model are coupled to achieve an organic combination of macroscopic dynamic changes and microscopic spatial changes; the macroscopic dynamic change results output by the system dynamics model are used as part of the cellular automaton model input, and the spatial distribution results output by the cellular automaton model are fed back to the system dynamics model for verification and adjustment.

Claims

1. A land resource dynamic model generation and management system based on climate change, characterized in that include: Climate data integration module: Use existing climate databases and remote sensing monitoring methods to regularly update climate data and collect historical records and forecast data of extreme weather events; Land resource status assessment module: It is used to conduct a detailed investigation of land resources in the study area through high-resolution remote sensing images and GIS systems, obtain information on land use types, vegetation coverage, and soil types, and use spatial analysis methods to extract key characteristic parameters of land resources; Dynamic model building module: Based on the climate data collected by the climate data integration module and the land resource status collected by the land resource status assessment module, the system dynamics and cellular automation modeling methods are used to build a land resource dynamic change model; Simulation and prediction module: Use the constructed land resource dynamic change model to simulate the evolution trend of land resources under different climate scenarios. By adjusting the model parameters and input data, analyze the simulation results under different scenarios and evaluate the degree and scope of the impact of climate change on land resources. Decision support module: used to provide decision support for the optimal allocation and sustainable use of land resources based on the simulation prediction results, and to formulate land use plans that adapt to climate change.

2. A land resource dynamic model generation and management system based on climate change as claimed in claim 1, characterized in that: The method for constructing a land resource dynamic change model by a dynamic model construction module includes the following steps: 1) System dynamics model construction: Clarify the scope and objectives of the model and define the boundaries of the land resource dynamic change system; select socio-economic variables, policy variables, climate variables and land resources as variables, and define the specific meaning and measurement units of each variable; Analyze the cause-effect relationship between variables, construct the cause-effect diagram of the system, determine the key feedback loops, and identify the main dynamic characteristics of the system; Establish mathematical equations for each variable to describe its dynamic change process, use system dynamics software to simulate the model, set simulation parameters and initial conditions, run the model, observe the dynamic change process of the system, and analyze the simulation results under different scenarios; 2) Cellular automaton model construction: Divide the study area into several cells, each cell represents a certain land area; define the set of cell states; determine the neighborhood range of the cell, including the surrounding adjacent cells; formulate cell state conversion rules based on climate data, land resource status and expert knowledge; these rules reflect the interaction and spatial dependence between land use types; Use the land resource status data to initialize the state distribution of the cellular space; According to the transformation rules, simulate the changes of the cell state in each time step; repeat the iterative process until the predetermined simulation time is reached or the stop condition is met; output the simulation results, including the spatial distribution map of land use types and time series data; 3) Model coupling and comprehensive evaluation: The system dynamics model and the cellular automaton model are coupled to realize the combination of macroscopic dynamic changes and microscopic spatial changes; the macroscopic dynamic change results output by the system dynamics model are used as part of the input of the cellular automaton model, and the spatial distribution results output by the cellular automaton model are fed back to the system dynamics model for verification and adjustment.

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