Mine ecological restoration carbon sequestration rate prediction method

Abstract

The invention aims to provide a mine ecological restoration carbon sequestration rate prediction method for solving the problems of an existing ecological restoration carbon sequestration rate prediction method. The method comprises the following steps: step 1, collecting multiple parameter multi-source data including vegetation growth, earth surface change and soil change of an ecological restoration area; 2, integrating the data, and constructing a dynamic prediction model for the change of the mine ecological restoration carbon sequestration rate; 3, verifying and optimizing the prediction model by using historical data and field observation data; and 4, adopting a machine learning technical means for the optimized prediction model, and establishing a quantitative relationship between the carbon sequestration rate change and ecological restoration means of land utilization, vegetation coverage and soil attributes. The method overcomes the influence of extreme climate events on the carbon sequestration rate of an ecological system, can accurately predict the change trend of the carbon sequestration rate under different ecological restoration strategies in the future, and provides a powerful scientific basis for ecological restoration of a mining area.

Description

Technical Field

[0001] The present invention belongs to the technical field of carbon fixation in ecological environment restoration, and specifically relates to a method for predicting carbon fixation rate in mine ecological restoration. Background Art

[0002] With the development of mining, the ecological and environmental problems that follow are becoming more serious. For example, mining has caused serious damage to mountains and vegetation, damaged the natural habitats of wild animals and plants, and disasters such as landslides, mountain torrents, and collapse accidents have occurred from time to time. Therefore, it is necessary to repair the ecological environment of mines, and one of the results of mine ecological restoration is carbon neutrality. In order to quantify the results of mine ecological restoration, it is necessary to predict the changes in the carbon fixation rate after mine ecological restoration, so as to provide a scientific basis for mine ecological restoration, achieve long-term stability of ecological restoration in mining areas and continuous improvement of the environment, and ensure the realization of dual carbon goals. The carbon fixation rate refers to the rate at which plants absorb carbon dioxide per unit area and per unit time. In an ecosystem, the carbon fixation rate is affected by many factors, including climate, soil conditions, and plant species.

[0003] Although there have been studies on the prediction methods of carbon fixation rate in mine ecological restoration in the prior art, the existing methods still have certain defects.

[0004] First, the existing prediction methods do not have a sufficient understanding of the nonlinear impact of extreme climate events such as temperature change and precipitation change on vegetation structure changes and carbon cycle processes, and cannot accurately diagnose the response of carbon sinks in mining ecosystems to extreme climate events, especially the vulnerability under extreme climate events. For example, climate warming and extreme rainfall have a nonlinear superposition effect on vegetation structure, which weakens the carbon flux of multiple processes in the carbon cycle of mining ecosystems and causes the carbon cycle to show a slowdown phenomenon as a whole, resulting in relatively weak stability of carbon sinks in mining ecosystems, thus affecting the prediction accuracy of changes in carbon fixation rate in mining ecological restoration; secondly, there is a lack of systematic research on the impact of differences in biological composition of different regions and ecosystems on carbon fixation rate. Since different regions and ecosystems have different vegetation composition and structure, their carbon fixation rates will also be different. Finally, it is difficult for existing models to fully capture the complex and changeable natural factors that affect the carbon fixation rate, and the generated prediction results often lack sufficient diversity and accuracy. Since the existing data processing methods and technical means fail to fully support the precise quantification of this process, it is impossible to fully understand the dynamics of mining ecological cycles and it is difficult to provide a strong basis for changes in carbon fixation rate in mining ecological restoration. Summary of the invention

[0005] The purpose of the present invention is to provide a method for predicting the carbon fixation rate of ecological restoration in mines, in response to the problems existing in the existing ecological restoration carbon fixation rate prediction methods. The method is through data collection → model building → model verification → carbon fixation rate prediction → result evaluation. This method overcomes the impact of extreme climate events on the carbon fixation rate of the ecosystem and the disadvantage that the existing model is not accurate enough in predicting the carbon fixation rate. It can accurately predict the trend of carbon fixation rate changes under different ecological restoration strategies in the future, calculate the difference in carbon fixation before and after restoration, evaluate the restoration effect, and provide a strong scientific basis for ecological restoration in mining areas;

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A method for predicting carbon fixation rate in mine ecological restoration, comprising the following steps:

[0008] Step 1: Establish a database by collecting multi-source data on multiple parameters in the ecological restoration area, including vegetation growth (normalized difference vegetation index NDVI, canopy structure, precipitation, temperature, light, extreme climate event data including the frequency and intensity of extreme events), surface changes (land use change data, digital elevation data DEM, etc.), and soil changes (soil moisture, soil organic matter content, etc.); the sources of multi-source data include historical data on the ecological restoration area and environmental and ecological data collected by observation equipment;

[0009] The historical data of the ecological restoration area include climate, environmental parameters (soil, terrain parameters), species attribute data (lifespan, shade tolerance and drought tolerance), ground observation data (tree species composition), multispectral remote sensing data (tree species distribution area), and lidar data (tree height and density);

[0010] The environmental and ecological data collected by the observation equipment include climate change, structural changes in vegetation, grassland, land and wetland ecosystems, carbon sink data flows of various ecosystems, and the growth status, health status and ecological functions of vegetation;

[0011] Through the deployment of JD-NQ14 climate observation instruments, LAINet, SpecNet, PhotoNet and LiDAR sensor equipment, different types of ecological data are collected. Through various monitoring equipment such as JD-NQ14 climate observation instruments, LAINet, SpecNet, PhotoNet and LiDAR sensors, different ecological parameters of regional mining ecosystems are observed. An ecosystem carbon sink comprehensive observation system is established to monitor the structural changes of vegetation, grassland, land and wetlands in real time, provide a continuous carbon sink data stream, and timely understand the growth status, health status and ecological function of mining vegetation; based on the collected environmental data and ecological data, a database of multiple parameters including vegetation growth, surface changes and soil changes is established;

[0012] Step 2: Process the data from step 1 using the InVEST model and the LANDIS PRO model, and then build a prediction model for changes in carbon fixation rates in mine ecological restoration;

[0013] The specific steps are:

[0014] Step 2.1: Clean the data collected in step 1 and input them into the LANDIS PRO model. Extract data related to carbon storage changes from the output data of the LANDIS PRO model, including changes in the area of ​​different tree species, grasslands, and land in the forest system and the growth rate of vegetation, and changes in the wetland area and the growth rate of vegetation in the wetland system. LANDIS PRO is a spatially intuitive forest landscape model that can simulate the spatiotemporal changes in tree species composition and landscape pattern at the landscape level. It is the basis for building a mine ecological restoration simulation platform, clarifying the current vegetation and environmental distribution pattern of reclaimed land, simulating the recent vegetation succession and carbon storage dynamics of each mine, and outputting key forest stand information data such as biomass and carbon storage from the model, and then extracting relevant information such as carbon storage and carbon sink.

[0015] Step 2.2: Use the output data of the LANDIS PRO model and the soil carbon density data, land use and land cover change data in the database of step 1 as input data to run the carbon storage module of the InVEST model. The model will output the spatial distribution and time series data of carbon storage to form a complete data set of carbon storage changes during mine ecological restoration. Then, the spatial and temporal data of carbon storage obtained by the InVEST model are statistically analyzed and data mined (processed using SPSS and IBM SPSS Modeler software) to identify the key factors affecting carbon storage changes as natural factors and human factors. Natural factors include vegetation types, forest age of vegetation, climate conditions (temperature, precipitation and carbon dioxide concentration), slope and aspect and other geographical factors in the ecosystem. Human factors include artificial afforestation and land use change (vegetation reclamation of bare land).

[0016] Step 2.3: Calculation of carbon fixation rate of mine ecological restoration

[0017] The change in carbon storage per unit area of ​​vegetation per unit time is expressed as the carbon fixation rate, which is calculated using the following formula:

[0018]

[0019] Where: ΔC r is the carbon fixation rate of vegetation, in tons / hectare·year; C t2 and C t1 are the organic carbon density at time t1 and t2, respectively, in tons / hectare;

[0020] Substitute the carbon storage data at different times and spaces obtained by the InVEST model in step 2.2 into formula (1) to calculate the carbon fixation rate and the change in carbon fixation rate;

[0021] Step 3: Use historical data and field observation data to verify and optimize the prediction model;

[0022] Use historical data and field observation data to verify the data obtained by the model in step 2, and then optimize and adjust the prediction model based on the verification results to improve the prediction accuracy and stability of the model;

[0023] Step 4: Use machine learning techniques to build a quantitative relationship between the change in carbon fixation rate and ecological restoration measures such as land use, vegetation cover, and soil properties for the prediction model optimized in step 3; for example, land use is cultivated land, grassland, forest land, etc.; vegetation cover is the proportion of covered area to total area; soil properties are soil organic matter content, nutrient content, etc.;

[0024] By inputting the parameters of the ecological restoration method used in the planned ecological restoration method into the model, the model can deduce the carbon storage at different times and spaces, and then use formula (1) to predict the change of regional carbon fixation rate in the future under this ecological restoration method.

[0025] Furthermore, the above prediction method also includes step 5, which evaluates the carbon fixation effect of the mine restoration area by calculating the difference in carbon fixation before and after the restoration of the planned ecological restoration method. If the carbon fixation amount increases significantly, it indicates that the restoration measures have achieved significant results. If the carbon fixation amount decreases, the restoration measures are re-formulated, such as using biological slope protection, soil improvement, vegetation reconstruction, etc., and then re-execute steps 4 and 5.

[0026] Furthermore, in the above prediction method, in step 1, the observation device includes:

[0027] JD-NQ14 climate observation instrument is used to measure soil moisture, temperature, light intensity, CO2 concentration, air temperature, air pressure, wind speed, wind direction and other parameters. It can evaluate the changes and laws of the ecological environment in the mine in real time. It can also monitor the changes in plant carbon flux under extreme weather conditions and diagnose the response of ecosystem carbon sinks to extreme climate events.

[0028] LAINet fully automatic vegetation leaf area index monitor is used for real-time monitoring of vegetation transmitted radiation and obtaining vegetation canopy structure information, such as leaf area index, average leaf inclination, aggregation index and canopy coverage;

[0029] SpecNet is a new type of intelligent hyperspectral networked spectrometer that can automatically observe the spectral reflectance of ground objects in a long-term series, and is used to record the reflectance characteristics of vegetation during dynamic growth and change.

[0030] PhotoNet is a phenological camera used to extract vegetation health, growth stage, and biomass;

[0031] The LiDAR sensor is a vegetation ecology multi-parameter laser radar measuring instrument used to obtain forest profile data and extract tree height and forest canopy structure parameters.

[0032] Furthermore, in the above prediction method, in step 3, the method for verifying the data obtained by the model in step 2 using historical data and field observation data is as follows:

[0033] Step 3.1: Obtain measured data through large-scale field surveys and establish a database of actual measurement parameters and biomass;

[0034] The field survey includes establishing a carbon sequestration accounting system based on canopy monitoring data and ground survey data obtained by LiDAR sensors; the areas are divided according to the ecological restoration types of each area of ​​the mine, including woodland, grassland, land and wetland, and the calculation methods for woodland carbon sequestration, grassland carbon sequestration, land carbon sequestration and wetland carbon sequestration are established respectively; the carbon content of the ecosystem is estimated by measuring the average carbon density of each type of vegetation and then multiplying the carbon density of each type of vegetation by the area;

[0035] Step 3.2: Use the database in step 3.1 to optimize and adjust the LANDIS PRO model and InVEST model.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] 1. The present invention combines a variety of remote sensing and ground observation data to establish a multi-parameter database including vegetation growth, surface changes and soil changes, and uses the InVEST model and the LANDIS PRO model for model integration. It dynamically predicts the changes in carbon reserves in the process of mine ecological restoration from multiple dimensions such as tree composition, age structure, and growth rate, identifies the key factors and trends affecting the changes in carbon reserves, and then constructs a prediction model for changes in carbon fixation rate in mine ecological restoration; through verification of independent historical data and field observation data, the model structure and parameter settings are continuously optimized to improve prediction accuracy and stability; combined with machine learning technology, the model can accurately predict the trend of changes in carbon fixation rate under different restoration strategies in the future, and calculate the difference in carbon fixation before and after restoration, evaluate the restoration effect, and provide a strong scientific basis for ecological restoration in mining areas, ensure the recovery and reconstruction of mine ecosystems, achieve long-term stability of ecological restoration in mining areas and continuous improvement of the environment, and help achieve the dual carbon goals.

[0038] 2. The JD-NQ14 climate observation instrument was used to observe and quantify the resistance and resilience of ecosystem productivity to extreme rainfall events. The vegetation structure regulates the response of ecosystem functions to extreme climate change. By monitoring the carbon flux of multiple processes in the carbon cycle of mining ecosystems under extreme weather, the response of ecosystem carbon sinks to extreme climate events was diagnosed, which helps to improve the response of ecosystems to extreme climate events in the natural coupled ecosystem process model, provide a scientific basis for the strategies of mine ecological restoration to cope with climate change, and establish carbon cycle parameters and databases for different ecosystems in mines under extreme weather to improve the prediction accuracy of changes in carbon fixation rates in mine ecological restoration.

[0039] 3. By calculating wetland carbon sequestration, we were able to estimate the carbon stock of wetland vegetation during the period without ground observation and quantify the impact of key water conditions changes on the carbon sequestration rate of wetland vegetation. This enabled us to monitor the temporal changes in carbon storage in mine restoration ecosystems, thereby scientifically evaluating the carbon sink capacity of spatial ecological restoration.

[0040] 4. Using the integrated model of InVEST and LANDIS PRO, combined with measured data and satellite remote sensing data, the carbon sequestration rates of different regions and ecosystems can be calculated. By calculating the carbon sequestration rates of different regions and ecosystems, we can have a more comprehensive understanding of the dynamics of the ecological cycle of mines, and solve the problem in the existing technology that there is no comprehensive and accurate carbon indicator calculation model, which makes it difficult to provide a strong basis for the prediction of changes in carbon sequestration rates in mine ecological restoration. In addition, the integrated model of InVEST and LANDISPRO is used to simulate the changes in the physical quantity and value of the ecological service system under different land cover scenarios. In the process of governance, we can adapt to local conditions and climate change, restore the original land type or improve the land type as much as possible, enhance the carbon sink capacity from land use, and achieve long-term stability of ecological restoration in mining areas and continuous improvement of the environment.

[0041] 5. The LANDIS PRO model is used to clarify the changing patterns of tree species distribution in the future, quantify the relative importance of population dynamics, climate change and interactions, and important biological attributes that affect changes in tree species distribution, incorporate the multi-scale processes that affect tree species distribution into the prediction of climate change responses, improve the theoretical model of tree species distribution prediction, and enhance the authenticity of the prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a schematic diagram of the flow chart of the prediction method of the present invention;

[0043] Figure 2 It is a schematic diagram of the comprehensive observation system for ecosystem carbon sinks in the present invention;

[0044] Figure 3 This is a schematic diagram of LAINet data output in the present invention;

[0045] Figure 4 This is a schematic diagram of SpecNet data output in the present invention;

[0046] Figure 5 It is a schematic diagram of PhotoNet data output in the present invention;

[0047] Figure 6 This is a schematic diagram of LiDAR sensor data output in the present invention;

[0048] Figure 7 This is a vegetation distribution map of a mining area. DETAILED DESCRIPTION

[0049] Example

[0050] A method for predicting carbon fixation rate in mine ecological restoration, see Figure 1 to Figure 7 , including the following steps:

[0051] Step 1: Establish a database by collecting multi-source data on multiple parameters in the ecological restoration area, including vegetation growth (normalized difference vegetation index NDVI, canopy structure, precipitation, temperature, light, extreme climate event data including the frequency and intensity of extreme events), surface changes (land use change data, digital elevation data DEM, etc.), and soil changes (soil moisture, soil organic matter content, etc.); the sources of multi-source data include historical data on the ecological restoration area and environmental and ecological data collected by observation equipment;

[0052] The historical data of the ecological restoration area include climate, environmental parameters (soil, terrain parameters), species attribute data (lifespan, shade tolerance and drought tolerance), ground observation data (tree species composition), multispectral remote sensing data (tree species distribution area), and lidar data (tree height and density);

[0053] The environmental and ecological data collected by the observation equipment include climate change, structural changes in vegetation, grassland, land and wetland ecosystems, carbon sink data flows of various ecosystems, and the growth status, health status and ecological functions of vegetation;

[0054] Through the deployment of JD-NQ14 climate observation instruments, LAINet, SpecNet, PhotoNet and LiDAR sensor equipment, different types of ecological data are collected. Through various monitoring equipment such as JD-NQ14 climate observation instruments, LAINet, SpecNet, PhotoNet and LiDAR sensors, different ecological parameters of regional mining ecosystems are observed. An ecosystem carbon sink comprehensive observation system is established to monitor the structural changes of vegetation, grassland, land and wetlands in real time, provide a continuous carbon sink data stream, and timely understand the growth status, health status and ecological function of mining vegetation; based on the collected environmental data and ecological data, a database of multiple parameters including vegetation growth, surface changes and soil changes is established;

[0055] In step 1, the observation equipment includes:

[0056] JD-NQ14 climate observation instrument is used to measure soil moisture, temperature, light intensity, CO2 concentration, air temperature, air pressure, wind speed, wind direction and other parameters. It can evaluate the changes and laws of the ecological environment in the mine in real time. It can also monitor the changes in plant carbon flux under extreme weather conditions and diagnose the response of ecosystem carbon sinks to extreme climate events.

[0057] LAINet fully automatic vegetation leaf area index monitor is used for real-time monitoring of vegetation transmitted radiation and obtaining vegetation canopy structure information, such as leaf area index, average leaf inclination, aggregation index and canopy coverage;

[0058] SpecNet is a new type of intelligent hyperspectral networked spectrometer that can automatically observe the spectral reflectance of ground objects in a long-term series, and is used to record the reflectance characteristics of vegetation during dynamic growth and change.

[0059] PhotoNet is a phenological camera used to extract vegetation health, growth stage, and biomass;

[0060] The LiDAR sensor is a vegetation ecology multi-parameter laser radar measuring instrument used to obtain forest profile data and extract tree heights and forest canopy structure parameters;

[0061] Step 2: Integrate the data from step 1 through the InVEST model and the LANDIS PRO model, and then build a prediction model for changes in carbon fixation rate in mine ecological restoration;

[0062] The specific steps are:

[0063] Step 2.1: Clean the data collected in step 1 and input them into the LANDIS PRO model. Extract data related to carbon storage changes from the output data of the LANDIS PRO model, including changes in the area of ​​different tree species, grasslands, and land in the forest system and the growth rate of vegetation, and changes in the wetland area and the growth rate of vegetation in the wetland system. LANDIS PRO is a spatially intuitive forest landscape model that can simulate the spatiotemporal changes in tree species composition and landscape pattern at the landscape level. It is the basis for building a mine ecological restoration simulation platform, clarifying the current vegetation and environmental distribution pattern of reclaimed land, simulating the recent vegetation succession and carbon storage dynamics of each mine, and outputting key forest stand information data such as biomass and carbon storage from the model, and then extracting relevant information such as carbon storage and carbon sink.

[0064] Step 2.2: Use the output data of the LANDIS PRO model and the soil carbon density data, land use and land cover change data in the database of step 1 as input data to run the carbon storage module of the InVEST model. The model will output the spatial distribution and time series data of carbon storage to form a complete data set of carbon storage changes during mine ecological restoration. Then, the spatial and temporal data of carbon storage obtained by the InVEST model are statistically analyzed and data mined (processed using SPSS and IBM SPSS Modeler software) to identify the key factors affecting carbon storage changes as natural factors and human factors. Natural factors include vegetation types, forest age of vegetation, climate conditions (temperature, precipitation and carbon dioxide concentration), slope and aspect and other geographical factors in the ecosystem. Human factors include artificial afforestation and land use change (vegetation reclamation of bare land).

[0065] Step 2.3: Calculation of carbon fixation rate of mine ecological restoration

[0066] The change in carbon storage per unit area of ​​vegetation per unit time is expressed as the carbon fixation rate, which is calculated using the following formula:

[0067]

[0068] Where: ΔC r is the carbon fixation rate of vegetation, in tons / hectare / year; C t2 and C t1 are the organic carbon density at time t1 and t2, respectively, in tons / hectare;

[0069] Substitute the carbon storage data at different times and spaces obtained by the InVEST model in step 2.2 into formula (1) to calculate the carbon fixation rate and the change in carbon fixation rate;

[0070] Step 3: Use historical data and field observation data to verify and optimize the prediction model;

[0071] Use historical data and field observation data to verify the data obtained by the model in step 2, and then optimize and adjust the prediction model based on the verification results to improve the prediction accuracy and stability of the model;

[0072] Step 3.1: Obtain measured data through large-scale field surveys and establish a database of actual measurement parameters and biomass;

[0073] The field survey includes establishing a carbon sequestration accounting system based on canopy monitoring data and ground survey data obtained by the LiDAR sensor; wherein the regions are divided according to the ecological restoration types of various regions of the mine, including woodland, grassland, land and wetland, and a forest land carbon sequestration calculation method, a grassland carbon sequestration calculation method, a land carbon sequestration calculation method and a wetland carbon sequestration calculation method are established respectively; the carbon content of the ecosystem is estimated by measuring the average carbon density of each vegetation, and then multiplying the carbon density of each vegetation by the area; in this embodiment, 74 sample plots (arbor forests and shrub forests) are surveyed in the mining area to investigate the parameters of the reclaimed vegetation in the sample plots, and then the average carbon density of the vegetation is obtained;

[0074] The carbon sequestration in forest land is calculated by the biomass method:

[0075] The main methods for calculating grassland carbon sequestration are: cover the grassland within a unit area in a closed measurement room, and the change of CO2 concentration over time is the CO2 flux, through CO2 flux × grassland area = grassland carbon sequestration; the main methods for calculating land carbon sequestration are: soil carbon density = soil volume × soil density × soil organic matter content ÷ 1.724; soil reserve = area of ​​each soil subtype × average soil thickness × average soil bulk density × conversion coefficient;

[0076] The main methods for calculating wetland carbon sequestration are: calculating vegetation carbon storage, soil carbon storage, and water carbon storage in wetlands;

[0077] Calculation of vegetation carbon storage: Calculate the carbon storage of vegetation by measuring parameters such as vegetation biomass, biomass density and carbon content;

[0078] Calculation of soil carbon storage: Calculate soil carbon storage by measuring soil organic carbon content, total carbon content and other parameters;

[0079] Calculation of water carbon storage: Calculate the carbon storage of water by measuring parameters such as dissolved organic carbon content and total organic carbon content in water;

[0080] Step 3.2, optimize and adjust the LANDIS PRO model and InVEST model using the database in step 3.1;

[0081] Step 4: Use machine learning techniques to build a quantitative relationship between the change in carbon fixation rate and ecological restoration measures such as land use, vegetation cover, and soil properties for the prediction model optimized in step 3; for example, land use is cultivated land, grassland, forest land, etc.; vegetation cover is the proportion of covered area to total area; soil properties are soil organic matter content, nutrient content, etc.;

[0082] The parameters of the ecological restoration method used in the planned ecological restoration method are input into the model, and the model can deduce the carbon storage at different times and spaces, and then use formula (1) to predict the change of regional carbon fixation rate in the future under this ecological restoration method.

[0083] Step 5: Evaluate the carbon sequestration effect of the mine restoration area by calculating the difference in carbon sequestration before and after the planned ecological restoration method. If the carbon sequestration increases significantly, it means that the restoration measures have achieved significant results. If the carbon sequestration decreases, re-formulate restoration measures, such as using biological slope protection, soil improvement, vegetation reconstruction, etc., and then re-execute steps 4 and 5.

[0084] Take the reclamation of a mining area as an example:

[0085] The parameters of the reclaimed vegetation in 74 sample plots (tree forests and shrub forests) were investigated in the mining area. The total area of ​​the reclaimed area was 1527 hectares. The total carbon storage of the reclaimed land was 5.30×10 4 Tons, the carbon storage per unit area is 34.68 tons / hectare, of which arbor forest is the main source of carbon storage, about 5.24×10 4 t, accounting for more than 95% of the total carbon storage;

[0086] The carbon density of aboveground vegetation in the reclamation areas of each mine ranged from 23.22 to 45.50 t·hm -2 The average carbon density is 34.68t·hm -2 For different vegetation types, the carbon density of arbor forest is higher, ranging from 23.87 to 49.71 t·hm -2 The carbon density of shrub forest is 0.32~7.51t·hm -2 , grassland carbon density is 2.59~2.78t·hm -2 The carbon density of cultivated land is 3.77t·hm -2 ;

[0087] The method of the present invention predicts:

[0088] In 2027, the total vegetation carbon storage in the reclamation area will reach 7.19×10 4 Tons, vegetation carbon density reached 47.07 tons / hectare, the carbon sequestration of vegetation in the reclaimed land in the mining area will reach 18913.70 tons in the past five years, and the carbon sequestration rate of vegetation in the reclaimed land of the mine is 2.48t·hm -2 ·a -1 .

Claims

1. A method for predicting carbon fixation rate in mine ecological restoration, characterized in that: The following steps are involved: Step 1: Establish a database by collecting multi-source, multi-parameter data including vegetation growth, surface changes, and soil changes in the ecological restoration area; the multi-source data sources include historical data on the ecological restoration area and environmental and ecological data collected by observation equipment; Step 2: The data from step 1 are processed by the InVEST model and the LANDIS PRO model, and then a dynamic prediction model for changes in carbon fixation rate of mine ecological restoration is constructed; Step 2.1: Clean the data collected in step 1 and input them into the LANDIS PRO model. Extract data related to carbon storage changes from the output data of the LANDIS PRO model, including the area changes and vegetation growth rates of different tree species, grasslands and land in the forest system, and the changes in wetland area and vegetation growth rates in the wetland system. Step 2.2: Use the output data of the LANDIS PRO model and the soil carbon density data, land use and land cover change data in the database of step 1 as input data to run the carbon storage module of the InVEST model. The model will output the spatial distribution and time series data of carbon storage to form a complete data set of carbon storage changes during the process of mine ecological restoration. Then, perform statistical analysis and data mining on the carbon storage space and time data obtained by the InVEST model to identify the key factors affecting carbon storage changes as natural factors and human factors. Step 2.3: Calculation of carbon fixation rate of mine ecological restoration The change in carbon storage per unit area of ​​vegetation per unit time is expressed as the carbon fixation rate, which is calculated using the following formula: Where: ΔC r is the carbon fixation rate of vegetation, in tons / hectare·year; C t2 and C t1 are the organic carbon density at time t1 and t2, respectively, in tons / hectare; Substitute the carbon storage data at different times and spaces obtained by the InVEST model in step 2.2 into formula (1) to calculate the carbon fixation rate and the change in carbon fixation rate; Step 3: Use historical data and field observation data to verify and optimize the prediction model; Use historical data and field observation data to verify the data obtained by the model in step 2, and then optimize and adjust the prediction model based on the verification results to improve the prediction accuracy and stability of the model; Step 4: Use machine learning techniques to build a quantitative relationship between changes in carbon sequestration rate and ecological restoration measures of land use, vegetation cover, and soil properties using the prediction model optimized in step 3; By inputting the parameters of the ecological restoration method used in the planned ecological restoration method into the model, the model can deduce the carbon storage at different times and spaces, and then use formula (1) to predict the change of regional carbon fixation rate in the future under this ecological restoration method.

2. The method for predicting carbon fixation rate in mine ecological restoration according to claim 1, characterized in that: It also includes step 5, which evaluates the carbon sequestration effect of the mine restoration area by calculating the difference in carbon sequestration before and after restoration using the planned ecological restoration method. If the carbon sequestration amount increases significantly, it indicates that the restoration measures have achieved significant results. If the carbon sequestration amount decreases, the restoration measures are re-formulated and steps 4 and 5 are re-executed.

3. The method for dynamic prediction of carbon fixation rate in mine ecological restoration according to claim 1 is characterized in that: In step 1: The historical data of the ecological restoration area include climate, environmental parameters, species attribute data, ground observation data, multi-spectral remote sensing data, and lidar data; The environmental and ecological data collected by the observation equipment include climate change, structural changes in vegetation, grassland, land and wetland ecosystems, carbon sink data flows of various ecosystems, and the growth status, health status and ecological functions of vegetation; Through the deployment of JD-NQ14 climate observation instruments, LAINet, SpecNet, PhotoNet and LiDAR sensor equipment, different types of ecological data are collected. Through a variety of monitoring equipment: JD-NQ14 climate observation instruments, LAINet, SpecNet, PhotoNet and LiDAR sensors, different ecological parameters of regional mining ecosystems are observed. An ecosystem carbon sink comprehensive observation system is established to monitor the structural changes of vegetation, grassland, land and wetlands in real time, provide a continuous carbon sink data stream, and timely understand the growth status, health status and ecological function of mining vegetation; based on the collected environmental and ecological data, a database of multiple parameters including vegetation growth, surface changes and soil changes is established.

4. The method for predicting carbon fixation rate in mine ecological restoration according to claim 3 is characterized in that: In step 1, the observation equipment includes: JD-NQ14 climate observation instrument is used to measure soil moisture, temperature, light intensity, CO2 concentration, air temperature, air pressure, wind speed, and wind direction parameters. It can evaluate the changes and laws of the ecological environment in the mine in real time. It can also monitor the changes in plant carbon flux under extreme weather conditions and diagnose the response of ecosystem carbon sinks to extreme climate events. LAINet fully automatic vegetation leaf area index monitor, used for real-time monitoring of vegetation transmitted radiation and obtaining vegetation canopy structure information, such as leaf area index, average leaf inclination, aggregation index and canopy coverage; SpecNet is a new type of intelligent hyperspectral networked spectrometer that can automatically observe the spectral reflectance of ground objects in a long-term series, and is used to record the reflectance characteristics of vegetation during dynamic growth and change. PhotoNet is a phenological camera used to extract vegetation health, growth stage, and biomass; The LiDAR sensor is a vegetation ecology multi-parameter laser radar measuring instrument used to obtain forest profile data and extract tree height and forest canopy structure parameters.

5. The method for dynamic prediction of carbon fixation rate in mine ecological restoration according to claim 1 is characterized in that: In step 3, the method for verifying the data obtained by the model in step 2 using historical data and field observation data is as follows: Step 3.1: Obtain measured data through large-scale field surveys and establish a database of actual measurement parameters and biomass; The field survey includes establishing a carbon sequestration accounting system based on canopy monitoring data and ground survey data obtained by LiDAR sensors; the areas are divided according to the ecological restoration types of each area of ​​the mine, including woodland, grassland, land and wetland, and the calculation methods for woodland carbon sequestration, grassland carbon sequestration, land carbon sequestration and wetland carbon sequestration are established respectively; the carbon content of the ecosystem is estimated by measuring the average carbon density of each type of vegetation and then multiplying the carbon density of each type of vegetation by the area; Step 3.2: Use the database in step 3.1 to optimize and adjust the LANDIS PRO model and InVEST model.

Images