A method and system for evaluating dynamic changes of vegetation carbon sink in high-altitude mining areas

Abstract

This invention relates to the field of carbon sequestration assessment technology, specifically a method and system for assessing the dynamic changes of vegetation carbon sequestration in high-altitude mining areas. The invention obtains multi-source data from the target area of ​​a high-altitude mining area through multi-source data collection, and then analyzes it to obtain four core permafrost parameters: aerodynamic roughness, water and heat flux contribution rate, heterogeneity attenuation coefficient, and insulation efficiency attenuation coefficient. Based on these four core parameters, a comprehensive quantitative assessment is performed to obtain permafrost assessment parameters, addressing the shortcomings of existing technologies that do not couple permafrost dynamics and eliminating the systematic bias in carbon sequestration simulation caused by static soil parameters. Furthermore, by comprehensively analyzing the permafrost assessment parameters and vegetation carbon sequestration parameters, a comprehensive carbon sequestration index for each pixel is obtained, and a carbon sequestration report for the target area is output. This quantifies the impact of mining disturbance and ecological restoration on vegetation carbon sequestration, providing a quantitative basis for optimizing mining area restoration plans.

Description

Technical Field

[0001] This invention relates to the field of carbon sequestration assessment technology, specifically to a method and system for assessing the dynamic changes in vegetation carbon sequestration in high-altitude mining areas. Background Technology

[0002] Assessing the dynamic changes in vegetation carbon sequestration in high-altitude mining areas is a core technological support for bridging alpine ecological protection and mining area ecological restoration. High-altitude regions are often core ecological security barriers in my country, where alpine vegetation ecosystems are fragile and have weak resistance to disturbance. Mining activities can easily cause irreversible carbon sequestration losses, and existing carbon sequestration accounting systems are severely inadequate for these special regions. The development of this assessment technology can accurately quantify the dual impacts of mining disturbance and ecological restoration on vegetation carbon sequestration, filling the research gap on the dynamic patterns of carbon sequestration in alpine mining areas and improving the terrestrial ecosystem carbon sequestration accounting system. Simultaneously, it can provide quantitative evidence for optimizing mining area restoration plans, verifying carbon sequestration projects, and implementing ecological compensation mechanisms, achieving synergistic effects between mining area ecological governance and carbon sequestration enhancement, and strengthening regional ecological security barriers.

[0003] Permafrost is widely distributed in high-altitude mining areas. Changes in the thickness of its active layer can significantly alter the moisture, temperature, and nutrient status of images, thereby strongly affecting the key determinants of vegetation carbon sequestration. Existing methods for assessing dynamic changes in carbon sequestration generally treat soil as a static or simple dynamic parameter, failing to couple permafrost dynamics as a core driving variable for modeling, resulting in systematic biases in carbon flux simulation. Summary of the Invention

[0004] This invention provides a method and system for assessing the dynamic changes of vegetation carbon sinks in high-altitude mining areas, in order to solve the aforementioned technical problems.

[0005] The first aspect of this invention provides a method for assessing the dynamic changes of vegetation carbon sinks in high-altitude mining areas, including... A1: Multi-source data of the mining area is obtained by collecting data from multiple sources in the target area of ​​the high-altitude mining area.

[0006] As a further improvement of the present invention A2: Permafrost assessment parameters for dynamic quantitative analysis of permafrost based on multi-source data from mining areas.

[0007] As a further improvement to the present invention, the specific analysis content of the dynamic quantitative analysis of frozen soil based on multi-source data from the mining area is as follows: S1: Identify multi-source data from the mining area to obtain surface cover parameters, meteorological monitoring data, in-situ monitoring data of permafrost hydrothermal data, soil profile physicochemical parameters, snow cover remote sensing parameters, and disturbance type boundary data. S2: Acquire basic spatial data, meteorological monitoring data, and disturbance type boundary data of the mining area, and analyze them using a multi-scale geometric optical inversion algorithm to obtain aerodynamic roughness; S3: Acquire meteorological monitoring data, permafrost hydrothermal in-situ monitoring data, soil profile physicochemical parameters and aerodynamic roughness, and analyze the hydrothermal flux contribution rate through a phase change interface tracking hydrothermal coupled finite volume algorithm. S4: Acquire basic spatial data of the mining area, remote sensing parameters of snow cover, and boundary data of disturbance types, and use the GWR semi-variogram coupling algorithm to analyze and obtain the heterogeneity attenuation coefficient; S5: Obtain in-situ monitoring data of hydrothermal activity in frozen soil, physical and chemical parameters of soil profile, and boundary data of disturbance type. Then, use the conjugate gradient method to solve the transient heat conduction inverse problem algorithm to analyze and obtain the thermal insulation performance attenuation coefficient. S6: The permafrost assessment parameters are obtained by comprehensively and quantitatively evaluating aerodynamic roughness, water and heat flux contribution rate, heterogeneity attenuation coefficient, and insulation performance attenuation coefficient. Specifically: The four core parameters of permafrost were normalized and their values ​​were taken, that is, the aerodynamic roughness, water and heat flux contribution rate, heterogeneity attenuation coefficient, and insulation performance attenuation coefficient were normalized to the interval [0, 1]. A comprehensive weighting factor was assigned to each core parameter of permafrost. The TOPSIS method was used to calculate and output the permafrost assessment parameters for the four core parameters and the comprehensive weighting factor. The specific parameters are as follows: Construct a weighted standardized decision matrix:

[0008] Optimal solution output:

[0009] Output of worst solution:

[0010] Calculate the Euclidean distance between each pixel and the optimal and worst solutions:

[0011] Input the optimal and worst solutions into the permafrost assessment quantification formula. Calculated frozen soil assessment parameters The value range is [0, 1].

[0012] Where a is the preset parameter number corresponding to each core parameter of permafrost, a=1, 2, 3, 4; Let a be the core parameter of the permafrost in the j-th pixel; This is the weighted standardized value of the a-th permafrost core parameter of the j-th pixel; The comprehensive weighting factor for the a-th core parameter of permafrost; These are the optimal solution and the worst solution, respectively. Let be the Euclidean distance between the j-th pixel and the optimal and worst solutions.

[0013] Furthermore, the specific analysis of S2 is as follows: The target mining area is divided into multiple regular cell grids according to the preset standard cell area. Based on the spatial basic data of the mining area, and taking the average cell elevation as the benchmark, features that exceed the preset feature definition are marked as an independent rough element. The total number of rough elements in each cell and the average height of each rough element are statistically obtained. The horizontal width of each rough element perpendicular to the annual prevailing wind direction is obtained based on meteorological monitoring data.

[0014] Based on the annual prevailing wind direction from meteorological monitoring data, the windward projected area of ​​each roughness element is calculated. Then, the windward area ratio of each pixel is calculated using the formula for windward area ratio. Calculate the output pixel windward area ratio in, These are the average height of the rough element, the horizontal width perpendicular to the prevailing annual wind direction, and the area of ​​the standard pixel, respectively. Let be the windward projected area of ​​the i-th rough element; This represents the total number of rough elements within each pixel.

[0015] Obtain the pixel slope of each pixel and the regional average slope of the target acquisition area in the database, and calculate it using the terrain correction formula. The terrain correction factor is calculated. ;in, The data includes pixel slope and regional average slope; the surface cover type of each rough element is obtained based on the spatial basic data of the mining area; a rough element drag coefficient is set for each type of surface cover; the regional type of each pixel is obtained based on the disturbance type boundary data, and a mining disturbance type coefficient is set for each regional type.

[0016] Input the pixel windward area ratio, terrain correction factor, roughness element drag coefficient, and mining disturbance type coefficient into the set geometric optical inversion formula. Calculate and output aerodynamic roughness ;in, This is the reference aerodynamic roughness for the pre-defined unperturbed control group. These are the rough element drag coefficient and the mining disturbance type coefficient, respectively. The preset von Kármán constant is fixed at 0.41. This is a preset momentum stability correction function.

[0017] Furthermore, the specific analysis of S3 is as follows: A hydrothermal coupling control equation for permafrost considering phase change was constructed. Based on in-situ monitoring data of permafrost hydrothermal activity layer at the target soil depth in the target collection area, a fully coupled hydrothermal control equation was constructed, which includes a moisture control equation and a heat control equation. The above equations were spatially discretized and temporally discretized in a fully implicit format. The 0℃ isotherm was used as the freeze-thaw front, and a phase change iteration interval with a preset temperature interval was set. Then, phase change interface tracking was used to solve for the freeze-thaw phase change latent heat flux, soil heat conduction flux, and soil moisture convection heat flux of the active layer.

[0018] The water control equation is ; The heat control equation is ; Among them, soil volumetric water content was extracted based on in-situ hydrothermal monitoring data of frozen soil. and soil temperature Extracting unsaturated hydraulic conductivity of soil based on soil profile physicochemical parameters Soil matrix potential and soil effective thermal conductivity ; The set model processing time step is matched with the time step of multi-source data acquisition; Target soil depth; The effective volumetric heat capacity of the soil, of which, The heat capacity of unfrozen soil was extracted based on the physicochemical parameters of the soil profile. The values ​​are the density of standard water, latent heat of phase change, and soil ice content, all extracted from in-situ monitoring data of frozen soil hydrothermal activity. The upper boundary condition of the equation is the preset surface heat flux, the lower boundary condition is the soil temperature and humidity at the deepest point of the target soil depth, and the initial condition is the soil temperature and humidity profile at the start of the simulation.

[0019] The latent heat flux of freeze-thaw phase change, soil heat conduction flux, and soil moisture convection heat flux are input into the predefined formula for calculating the contribution of water and heat flux. Calculate and output the contribution rate of water and heat flux. The value range is 0 to 1; among which, These are the latent heat flux of freeze-thaw phase change, soil heat conduction flux, and soil moisture convection heat flux, respectively.

[0020] Furthermore, the specific analysis of S4 is as follows: The snow cover degree of each pixel is obtained based on remote sensing parameters of snow cover, and the spatial coordinates, elevation, slope and aspect parameters of each pixel are obtained based on the spatial basic data of the mining area. Then, a GWR model is constructed. Based on this model, the snow distribution residuals of each pixel are extracted. ;in, Let the snow cover of the j-th pixel be . Let J be the spatial coordinates of the j-th pixel; For the defined regression cutoff term, All are preset regression coefficients of independent variables; These are the elevation, slope, and aspect parameters of the j-th pixel, respectively. Let be the regression residual of the j-th pixel, and label it as the snow distribution residual.

[0021] Spatial structure analysis of snow cover distribution residuals in each pixel is performed using a semivariogram to calculate the semivariogram value. Based on this semivariogram, the variation law of the semivariogram value with a preset lag distance is extracted. Based on this variation law, core heterogeneity parameters are extracted, including nugget value, sill value, and snow cover range. This is achieved through the formula... The output hysteresis is calculated as follows: semivariogram value ;in, The lag distance is The corresponding number of pixel pairs, The spatial lag distance preset for a pixel can be set to multiple scales; This represents the residual of snow cover distribution.

[0022] The heterogeneity attenuation coefficient is obtained by calculating the heterogeneity core parameters using the heterogeneity attenuation calculation formula. Calculate and output the heterogeneity attenuation coefficient ;in, These are the nugget value, sill value, and snow cover range of the heterogeneity core parameters, respectively. The nugget coefficient; The standard snow cover range is preset for the undisturbed control area.

[0023] Furthermore, the specific analysis of S5 is as follows: Based on the disturbance type boundary data, the area at a predetermined surface depth in the target acquisition area is marked as the organic matter layer. A forward problem model of transient heat conduction in the organic matter layer is constructed based on in-situ hydrothermal monitoring data of frozen soil, soil profile physicochemical parameters, and disturbance type boundary data. This includes soil dry bulk density extracted based on soil profile physicochemical parameters. Specific heat capacity of soil mass ; Soil temperature; The effective thermal conductivity of the organic matter layer is denoted as , and is the parameter to be inverted. The upper boundary of the model equations is set as the surface at the preset surface starting line, and the lower boundary is the soil temperature at the corresponding location at the surface depth. The initial conditions are the distance from the surface starting line to the surface depth at the start of the simulation, and the depth of the organic matter layer. Soil temperature profile.

[0024] Using soil temperature profiles as observed values ​​and the effective thermal conductivity of the organic matter layer as the parameter to be inverted, a conjugate gradient method is used to construct an inverse problem solution model. The objective function is: The effective thermal conductivity of the organic matter layer is obtained by inversion using this model. The iteration termination condition is set as either the rate of change of the objective function being less than a preset upper limit for the objective function change, or the number of iterations being less than a preset upper limit for the number of iterations. The effective thermal conductivity of the organic matter layer for each pixel is then calculated and output. The objective function is defined as the model that minimizes the objective. The soil temperature output by the transient heat conduction problem model of the organic matter layer is... Soil temperature extracted from in-situ hydrothermal monitoring data of frozen soil; The preset total number of simulation time steps is matched with the time series length of multi-source data acquisition.

[0025] The effective thermal conductivity of the organic layer is input into the preset formula for quantifying the thermal insulation performance degradation. The thermal insulation performance attenuation coefficient was calculated. ;in, The standard reference thermal conductivity is set to the pre-defined standard organic layer.

[0026] A3: Extract vegetation carbon sink parameters from multi-source data of the mining area.

[0027] A4: Perform a comprehensive analysis of permafrost assessment parameters and vegetation carbon sink parameters to output a carbon sink report for the target collection area.

[0028] The second aspect of the present invention provides a dynamic change assessment system for vegetation carbon sinks in high-altitude mining areas, including a multi-source data acquisition module, a permafrost quantitative assessment module, a vegetation carbon sink extraction module, and a comprehensive assessment module for carbon sinks in mining areas.

[0029] The multi-source data acquisition module is used to acquire multi-source data of the target acquisition area in the high-altitude mining area by installing the multi-source data acquisition group.

[0030] The frozen soil quantitative assessment module is used to perform dynamic quantitative analysis of frozen soil parameters based on multi-source data from mining areas.

[0031] The vegetation carbon sequestration extraction module is used to extract vegetation carbon sequestration parameters from multi-source data of the mining area.

[0032] The comprehensive assessment module for carbon sequestration in mining areas is used to comprehensively analyze permafrost assessment parameters and vegetation carbon sequestration parameters and output a carbon sequestration report for the target collection area.

[0033] As a further improvement of the present invention The vegetation carbon sequestration parameters are matched with each pixel in the target acquisition area to output the vegetation carbon sequestration assessment value for each pixel; the permafrost assessment parameters and the corresponding vegetation carbon sequestration assessment values ​​for each pixel are input into the permafrost disturbance correction formula. The perturbation-corrected carbon sink assessment value of the j-th pixel is calculated. ;in, The preset permafrost disturbance correction coefficient is based on experimental calibration. The perturbation-corrected carbon sink assessment value is combined with the corresponding permafrost assessment parameters to obtain the comprehensive carbon sink index for each pixel, i.e., through the comprehensive quantification formula. Calculate and output the carbon sink comprehensive index of pixel j. Its value range is [0, 1]; based on the carbon sink comprehensive index, the carbon sink report of the target collection area is output; among which, These are the preset standard reference vegetation carbon sink assessment values ​​and standard reference permafrost assessment parameters, respectively.

[0034] The beneficial effects of the technical solution provided by this invention compared with the prior art are as follows: 1. This invention obtains four core parameters of frozen soil through analysis: aerodynamic roughness, water and heat flux contribution rate, heterogeneity attenuation coefficient, and thermal insulation performance attenuation coefficient. Based on these four core parameters, a comprehensive quantitative evaluation is conducted to obtain frozen soil evaluation parameters. This solves the problem of existing technologies not coupling frozen soil dynamics and eliminates the systematic bias in carbon sink simulation caused by static soil parameters.

[0035] 2. This invention obtains the comprehensive carbon sink index of each pixel by comprehensively analyzing the permafrost assessment parameters and vegetation carbon sink parameters, and then outputs a carbon sink report of the target collection area, quantifying the impact of mining disturbance and ecological restoration on vegetation carbon sink, and providing a quantitative basis for optimizing mining area restoration plans. Attached Figure Description

[0036] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. The following drawings are not deliberately drawn to scale according to the actual size, but are intended to show the main idea of ​​this application.

[0037] Figure 1 This is a flowchart of a method for assessing the dynamic changes of vegetation carbon sinks in high-altitude mining areas according to the present invention. Figure 2 This is a flowchart of dynamic quantitative analysis of permafrost based on multi-source data from a mining area using a method for assessing dynamic changes in vegetation carbon sequestration in high-altitude mining areas, as described in this invention. Figure 3 This is a schematic diagram of the principle of a dynamic change assessment system for vegetation carbon sinks in high-altitude mining areas according to the present invention. Detailed Implementation

[0038] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0039] For ease of understanding, the specific process of the embodiments of the present invention is described below. Please refer to [link / reference]. Figure 1-2 In this embodiment of the invention, a method for assessing the dynamic changes of vegetation carbon sinks in high-altitude mining areas includes: A1. Multi-source data acquisition: Multi-source data is acquired from the target acquisition area in the high-altitude mining area.

[0040] Multi-source data acquisition includes obtaining basic spatial data of the mining area through remote sensing and aerial surveys of the target acquisition area; obtaining meteorological monitoring data through meteorological stations; obtaining in-situ hydrothermal monitoring data of frozen soil through permafrost monitoring boreholes; obtaining soil profile physicochemical parameters by collecting soil samples at a predetermined depth in the target acquisition area; acquiring snow cover remote sensing parameters based on remote sensing image data; and obtaining disturbance type boundary data based on the basic spatial data of the mining area, such as vector boundary data of the regional types of the target acquisition area, including vector boundary data of mining pit area, waste rock pile area, ecological restoration area and disturbance control area. The multi-source data for the mining area includes spatial basic data of the mining area, meteorological monitoring data, in-situ monitoring data of permafrost hydrothermal parameters, soil profile physicochemical parameters, snow cover remote sensing parameters, and disturbance type boundary data.

[0041] A2. Dynamic quantitative analysis of permafrost in high-altitude mining areas: Permafrost assessment parameters for dynamic quantitative analysis of permafrost based on multi-source data from mining areas.

[0042] The specific analysis content of the dynamic quantitative analysis of frozen soil based on multi-source data from the mining area is as follows: S1: Identify multi-source data from the mining area to obtain surface cover parameters, meteorological monitoring data, in-situ monitoring data of permafrost hydrothermal data, soil profile physicochemical parameters, snow cover remote sensing parameters, and disturbance type boundary data.

[0043] S2: Acquire spatial baseline data, meteorological monitoring data, and disturbance type boundary data of the mining area, and analyze them using a multi-scale geometric-optical inversion algorithm to obtain aerodynamic roughness, specifically: The target mining area is divided into multiple regular pixel grids according to the preset standard pixel area. Based on the spatial basic data of the mining area, and taking the average pixel elevation as the benchmark, features that exceed the preset feature definition are marked as an independent rough element. The total number of rough elements in each pixel and the average height of each rough element are calculated. The horizontal width of each rough element perpendicular to the annual prevailing wind direction is obtained based on meteorological monitoring data. For example, if the spatial basic data of the mining area is acquired by 10m resolution remote sensing imagery, and the target acquisition area is divided into a 10m×10m regular pixel grid, then the fixed area of ​​a single pixel is 100㎡. The feature definition setting defines continuous protrusions, features with an elevation difference exceeding 0.5m, and features with a horizontal projection area exceeding 0.25㎡ as an independent rough element.

[0044] Based on the annual prevailing wind direction from meteorological monitoring data, the windward projected area of ​​each roughness element is calculated. Then, the windward area ratio of each pixel is calculated using the formula for windward area ratio. Calculate the output pixel windward area ratio ;in, These are the average height of the rough element, the horizontal width perpendicular to the prevailing annual wind direction, and the area of ​​the standard pixel, respectively. Let be the windward projected area of ​​the i-th rough element; This represents the total number of rough elements within each pixel. Under a fixed prevailing wind direction, the obstruction effect of rough elements on airflow is determined solely by their windward projected area perpendicular to the wind direction. A larger projected area results in stronger interference with momentum exchange in the airflow, leading to greater surface roughness—a fundamental principle of atmospheric boundary layer dynamics. Using a simplified two-dimensional vertical projection, the windward projected area is directly calculated from the height and width perpendicular to the wind direction of the rough elements. The numerator of the pixel windward area ratio calculation formula is summed to obtain the total windward projected area of ​​all rough elements within a single pixel, reflecting the overall obstruction capability of the entire pixel. The denominator is the fixed area of ​​the pixel; through ratio calculations, the absolute area is converted into a dimensionless relative proportion, achieving standardized comparison between different pixels, which can be directly used for subsequent roughness inversion.

[0045] Obtain the pixel slope of each pixel and the regional average slope of the target acquisition area in the database, and calculate it using the terrain correction formula. The terrain correction factor is calculated. ;in, The system identifies pixel slope and regional average slope, respectively. Given the significant topographic undulations in mining areas, the cosine ratio of slope is used to correct for the acceleration and deceleration effects of topography on airflow. Based on the spatial baseline data of the mining area, the surface cover type of each rough element is obtained, including but not limited to waste rock piles, bare land, and vegetation cover. A rough element drag coefficient is assigned to each surface cover type, for example, 0.8 for waste rock piles, 0.4 for bare land, and 0.3 for vegetation cover. Based on the boundary data of disturbance types, the regional type of each pixel is obtained, and mining disturbance type coefficients are assigned to each regional type, for example, 0.9 for pit areas, 2.2 for waste rock pile areas, 1.0 for ecological restoration areas, and 1.0 for undisturbed control areas. This corrects for differences in underlying surface under different mining disturbance scenarios, quantifying the abrupt impact of extreme disturbances such as pits and waste rock piles on roughness.

[0046] Input the pixel windward area ratio, terrain correction factor, roughness element drag coefficient, and mining disturbance type coefficient into the set geometric optical inversion formula. Calculate and output aerodynamic roughness ;in, This is the reference aerodynamic roughness for the pre-defined unperturbed control group. These are the rough element drag coefficient and the mining disturbance type coefficient, respectively. The preset von Kármán constant is fixed at 0.41. The preset momentum stability correction function is determined experimentally, for example, through Monin-Obukhov similarity theory, and output from meteorological monitoring data. The classical momentum exchange theory based on a geometric optics model introduces the roughness element drag coefficient, von Kármán constant, and momentum stability correction function. A quantitative correlation is established between the windward area ratio of the roughness element and the roughness, while simultaneously correcting for the influence of atmospheric stability on momentum exchange, ensuring the physical rationality of the inversion results.

[0047] S3: Acquire meteorological monitoring data, in-situ hydrothermal monitoring data of frozen soil, soil profile physicochemical parameters, and aerodynamic roughness, and analyze the hydrothermal flux contribution rate using a phase change interface tracking hydrothermal coupled finite volume algorithm. Specifically: A hydrothermal coupling control equation for permafrost considering phase change was constructed. Based on in-situ monitoring data of permafrost hydrothermal activity layer at the target soil depth in the target collection area, a fully coupled hydrothermal control equation was constructed, which includes a moisture control equation and a heat control equation. The above equations were spatially discretized and temporally discretized in a fully implicit format. The 0℃ isotherm was used as the freeze-thaw front, and a phase change iteration interval with a preset temperature interval was set. Then, phase change interface tracking was used to solve for the freeze-thaw phase change latent heat flux, soil heat conduction flux, and soil moisture convection heat flux of the active layer.

[0048] The water control equation is ; The heat control equation is ; Among them, soil volumetric water content was extracted based on in-situ hydrothermal monitoring data of frozen soil. and soil temperature Extracting unsaturated hydraulic conductivity of soil based on soil profile physicochemical parameters Soil matrix potential and soil effective thermal conductivity ; The set model processing time step is matched with the time step of multi-source data acquisition; Target soil depth; The effective volumetric heat capacity of the soil, of which, The heat capacity of unfrozen soil was extracted based on the physicochemical parameters of the soil profile. The values ​​are the density of standard water, latent heat of phase change, and soil ice content, all extracted from in-situ monitoring data of frozen soil hydrothermal activity. The upper boundary condition of the equation is the preset surface heat flux, the lower boundary condition is the soil temperature and humidity at the deepest point of the target soil depth, and the initial condition is the soil temperature and humidity profile at the start of the simulation.

[0049] The moisture control equation describes the transport of soil moisture within the active layer of permafrost under the influence of temperature and gravity gradients, providing dynamic parameters for the heat equation. The core mechanism of hydrothermal coupling in permafrost is the transport of moisture within the active layer, driven by both matrix potential and gravity, with hydraulic conductivity varying with soil moisture content and the freeze-thaw cycle. The heat control equation describes the spatiotemporal variation of soil temperature within the active layer, incorporating the latent heat of freeze-thaw phase change into the heat transfer process. Soil temperature changes are determined by spatial differences in heat conduction flux. During the freeze-thaw process, the phase change between water and ice absorbs or releases a significant amount of latent heat, altering the effective heat capacity of the soil.

[0050] The latent heat flux of freeze-thaw phase change, soil heat conduction flux, and soil moisture convection heat flux are input into the predefined formula for calculating the contribution of water and heat flux. Calculate and output the contribution rate of water and heat flux. The value ranges from 0 to 1, with a larger value indicating a stronger influence of the latent heat of phase change on permafrost thawing; among which, These are the latent heat flux of freeze-thaw phase change, soil heat conduction flux, and soil moisture convection heat flux, respectively. The formula for calculating the contribution of water and heat flux is used to quantify the proportion of the latent heat of freeze-thaw phase change in the total water and heat flux of the active layer of permafrost, reflecting the degree of influence of the phase change process on permafrost thawing. Among them, the thawing depth of the active layer of permafrost is determined by the total water and heat flux. The latent heat of phase change is the core characteristic that distinguishes permafrost from non-permafrost. The higher its proportion, the stronger the dominant role of the freeze-thaw process in the dynamics of permafrost, and the more obvious the impact of mining disturbance on permafrost.

[0051] S4: Acquire spatial baseline data of the mining area, remote sensing parameters of snow cover, and boundary data of disturbance types. Then, use the GWR semi-variogram coupling algorithm to analyze and obtain the heterogeneity attenuation coefficient, specifically: The snow cover degree of each pixel is obtained based on remote sensing parameters of snow cover, and the spatial coordinates, elevation, slope and aspect parameters of each pixel are obtained based on the spatial basic data of the mining area. Then, a GWR model is constructed. Based on this model, the snow distribution residuals of each pixel are extracted. ;in, Let the snow cover of the j-th pixel be . Let J be the spatial coordinates of the j-th pixel; For the defined regression cutoff term, All are preset regression coefficients of independent variables; These are the elevation, slope, and aspect parameters of the j-th pixel, respectively. Let be the regression residual of the j-th pixel, and label it as the snow cover distribution residual. The spatial distribution of snow cover is affected by both natural factors and human disturbances. Conventional global regression models cannot capture spatial non-stationarity. By introducing spatial coordinates, the GWR model is established to achieve local regression at the pixel scale and separate the contribution of natural factors.

[0052] Spatial structure analysis of snow cover distribution residuals in each pixel is performed using a semivariogram to calculate the semivariogram value. Based on this semivariogram, the variation law of the semivariogram value with a preset lag distance is extracted. Based on this variation law, core heterogeneity parameters are extracted, including nugget value, sill value, and snow cover range. This is achieved through the formula... The output hysteresis is calculated as follows: semivariogram value ;in, The lag distance is The corresponding number of pixel pairs, The spatial lag distance preset for a pixel can be set to multiple scales, such as 10m, 50m, and 100m. This represents the residual of snow cover distribution. Pixels that are spatially closer have more similar attribute values. The semivariogram quantifies the degree of variation and autocorrelation range of spatial data by calculating the attribute variance of pixel pairs at different lag times.

[0053] The heterogeneity attenuation coefficient is obtained by calculating the heterogeneity core parameters using the heterogeneity attenuation calculation formula. Calculate and output the heterogeneity attenuation coefficient ;in, These are the nugget value, sill value, and snow cover range of the heterogeneity core parameters, respectively. The nugget coefficient reflects the proportion of random heterogeneity caused by mining disturbances in the total variation. The larger the value, the stronger the snow heterogeneity. The standard snow cover range is preset for the undisturbed control area. In the heterogeneity attenuation calculation formula, the insulation performance of snow depends on continuous and intact snow cover. The stronger the snow heterogeneity caused by mining disturbance, the larger the nugget coefficient, and the smaller the spatially autocorrelated snow cover range, the more the continuous insulation layer of the snow is disrupted, and the greater the attenuation of insulation performance. The ratio of the snow cover range of the target collection area to the undisturbed control is given. The smaller the value, the smaller the spatial autocorrelation range of the snow cover and the higher the degree of fragmentation. The product of the two terms is subtracted by 1 to achieve positive vectorization with stronger heterogeneity and larger attenuation coefficient. The value range is fixed at 0~1.

[0054] S5: Obtain in-situ monitoring data of frozen soil hydrothermal activity, soil profile physicochemical parameters, and boundary data of disturbance types. Then, use the conjugate gradient method to solve the transient heat conduction inverse problem to analyze and obtain the insulation performance attenuation coefficient, specifically: Based on the disturbance type boundary data, the area at a predetermined surface depth in the target acquisition area is marked as the organic matter layer. A forward problem model of transient heat conduction in the organic matter layer is constructed based on in-situ hydrothermal monitoring data of frozen soil, soil profile physicochemical parameters, and disturbance type boundary data. This includes soil dry bulk density extracted based on soil profile physicochemical parameters. Specific heat capacity of soil mass ; Soil temperature; The effective thermal conductivity of the organic matter layer is denoted as , and is the parameter to be inverted. The upper boundary of the model equation is set as the surface temperature at the preset surface starting line, for example, the surface temperature at the 0cm position. The lower boundary of the equation is the soil temperature at the corresponding surface depth. The initial conditions are the distance from the surface starting line to the surface depth at the start of the simulation, and the soil depth of the organic matter layer. The soil temperature profile is shown above. The left side of the model represents the heat conservation term, which indicates the rate of change of heat per unit volume of soil; the right side represents the heat conduction flux divergence term, which indicates the spatial transport process of soil heat. The spatiotemporal variation of soil temperature is determined by the heat conduction process. Under the premise that the soil bulk density and specific heat capacity are fixed, the dynamic change of soil temperature is determined by the thermal conductivity.

[0055] Using soil temperature profiles as observed values ​​and the effective thermal conductivity of the organic matter layer as the parameter to be inverted, a conjugate gradient method is used to construct an inverse problem solution model. The objective function is: The effective thermal conductivity of the organic matter layer is obtained by inversion using this model. The iteration termination condition is set to the rate of change of the objective function being less than a preset upper limit value for the rate of change of the objective function, for example... If the number of iterations is less than the preset upper limit, calculate and output the effective thermal conductivity of the organic matter layer for each pixel; where, The objective function is defined as the model that minimizes the objective. The soil temperature output by the transient heat conduction problem model of the organic matter layer is... Soil temperature extracted from in-situ hydrothermal monitoring data of frozen soil; The preset total number of simulation time steps matches the length of the time series acquired from multiple data sources. In the objective function above, the inverse problem is solved to make the model simulation results approximate the measured data. The sum of squared residuals is the optimal index for measuring the goodness of fit between the simulated and measured values. By minimizing this index, a unique optimal thermal conductivity solution can be obtained.

[0056] The effective thermal conductivity of the organic layer is input into the preset formula for quantifying the thermal insulation performance degradation. The thermal insulation performance attenuation coefficient was calculated. ;in, The standard reference thermal conductivity is set to the pre-defined standard organic layer.

[0057] S6: The permafrost assessment parameters are obtained by comprehensively and quantitatively evaluating aerodynamic roughness, water and heat flux contribution rate, heterogeneity attenuation coefficient, and insulation performance attenuation coefficient. Specifically: The four core parameters of permafrost were normalized and their values ​​were taken, that is, the aerodynamic roughness, water and heat flux contribution rate, heterogeneity attenuation coefficient, and insulation performance attenuation coefficient were normalized to the interval [0, 1]. A comprehensive weighting factor was assigned to each core parameter of permafrost. The TOPSIS method was used to calculate and output the permafrost assessment parameters for the four core parameters and the comprehensive weighting factor. The specific parameters are as follows: Construct a weighted standardized decision matrix: ; Optimal solution output: ; Output of worst solution: ; Calculate the Euclidean distance between each pixel and the optimal and worst solutions: ; Input the optimal and worst solutions into the permafrost assessment quantification formula. Calculated frozen soil assessment parameters The value range is [0, 1]. At this time, it indicates that the permafrost is completely undisturbed and in a naturally stable state; This indicates that the permafrost has been subjected to extremely strong disturbance and is in a state of severe degradation; the larger the value, the stronger the permafrost disturbance, the greater the depth of melting of the active layer, and the more significant the impact on vegetation carbon sinks.

[0058] Where a is the preset parameter number corresponding to each core parameter of permafrost, a=1, 2, 3, 4; Let a be the core parameter of the permafrost in the j-th pixel; This is the weighted standardized value of the a-th permafrost core parameter of the j-th pixel; is the comprehensive weighting factor for the a-th core parameter of permafrost, and its value is based on experimental calibration. ; These are the optimal solution and the worst solution, respectively. Let be the Euclidean distance between the j-th pixel and the optimal and worst solutions. Euclidean distance is a classic indicator of the relative position between two points in multidimensional space. The closer the pixel is to a strongly disturbed solution and the farther it is from an undisturbed solution, the stronger the permafrost disturbance in that pixel. The optimal solution corresponds to strongly disturbed permafrost, and the worst solution corresponds to undisturbed permafrost. The ratio of the distance from the pixel to the undisturbed worst solution to the total distance quantifies the relative position of the pixel within the continuous interval between undisturbed and strongly disturbed areas. The larger the ratio, the closer the pixel is to the strongly disturbed end, and the higher the degree of permafrost disturbance.

[0059] A3: Extract vegetation carbon sink parameters from multi-source data of the mining area.

[0060] A4: Perform a comprehensive analysis of permafrost assessment parameters and vegetation carbon sink parameters to output a carbon sink report for the target collection area.

[0061] Please see Figure 3 A dynamic change assessment system for vegetation carbon sequestration in high-altitude mining areas includes a multi-source data acquisition module, a permafrost quantitative assessment module, a vegetation carbon sequestration extraction module, and a comprehensive assessment module for carbon sequestration in mining areas.

[0062] The multi-source data acquisition module acquires multi-source data of the target acquisition area in the high-altitude mining area by installing multi-source data acquisition groups.

[0063] The frozen soil quantitative assessment module is used to perform dynamic quantitative analysis of frozen soil using multi-source data from mining areas, and to assess frozen soil parameters.

[0064] The vegetation carbon sequestration extraction module extracts vegetation carbon sequestration parameters from multi-source data of the mining area.

[0065] The comprehensive assessment module for carbon sequestration in mining areas performs a comprehensive analysis of permafrost assessment parameters and vegetation carbon sequestration parameters, and outputs a carbon sequestration report for the target data collection area, specifically as follows: The vegetation carbon sequestration parameters are matched with each pixel in the target acquisition area to output the vegetation carbon sequestration assessment value for each pixel; the permafrost assessment parameters and the corresponding vegetation carbon sequestration assessment values ​​for each pixel are input into the permafrost disturbance correction formula. The perturbation-corrected carbon sink assessment value of the j-th pixel is calculated. ;in, The preset permafrost disturbance correction coefficient is based on experimental calibration. For example, a value of 0.85 means that for every 1 unit increase in permafrost disturbance, the vegetation carbon sequestration assessment value overestimates the actual carbon sequestration capacity by 85%. The perturbation-corrected carbon sink assessment value is combined with the corresponding permafrost assessment parameters to obtain the comprehensive carbon sink index for each pixel, i.e., through the comprehensive quantification formula. Calculate and output the carbon sink comprehensive index of pixel j. Its value range is [0, 1]. When it is 0, it means that the carbon sequestration capacity of the pixel has reached the natural baseline level and the permafrost has no additional disturbance; when it is 1, the carbon sequestration capacity of the pixel has been completely lost and the permafrost is in a state of extreme disturbance. The larger the value, the higher the carbon sequestration optimization potential of the pixel, and the higher its priority in ecological restoration projects; a carbon sequestration report of the target collection area is output based on the comprehensive carbon sequestration index; among which... These are the preset standard reference vegetation carbon sequestration assessment values ​​and standard reference permafrost assessment parameters, respectively. In the comprehensive quantitative formula... This represents the deviation from the true carbon sequestration level, used to reflect the extent of loss in current carbon sequestration capacity. To quantify the degree of permafrost disturbance of the target pixel; the value range of both parts is [0, 1], and the value range obtained after multiplication is still [0, 1].

[0066] To facilitate calculations, all index data involved in the calculations in this embodiment of the invention have undergone data preprocessing to eliminate the influence of dimensions. The specific methods for eliminating the influence of dimensions are well-known to those skilled in the art and are not limited here.

[0067] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for assessing the dynamic changes of vegetation carbon sequestration in high-altitude mining areas, characterized in that, include: A1: Multi-source data of the mining area is obtained by collecting multi-source data from the target area of ​​the high-altitude mining area; A2: Permafrost assessment parameters for dynamic quantitative analysis of permafrost in mining areas based on multi-source data; A3: Extract vegetation carbon sink parameters from multi-source data of the mining area; A4: Perform a comprehensive analysis of permafrost assessment parameters and vegetation carbon sink parameters to output a carbon sink report for the target collection area.

2. The method for assessing the dynamic changes of vegetation carbon sequestration in high-altitude mining areas according to claim 1, characterized in that, The specific analysis content of the dynamic quantitative analysis of permafrost in the multi-source data of the mining area is as follows: S1: Identify multi-source data from the mining area to obtain surface cover parameters, meteorological monitoring data, in-situ monitoring data of permafrost hydrothermal data, soil profile physicochemical parameters, snow cover remote sensing parameters, and disturbance type boundary data. S2: Acquire basic spatial data, meteorological monitoring data, and disturbance type boundary data of the mining area, and analyze them using a multi-scale geometric optical inversion algorithm to obtain aerodynamic roughness; S3: Acquire meteorological monitoring data, permafrost hydrothermal in-situ monitoring data, soil profile physicochemical parameters and aerodynamic roughness, and analyze the hydrothermal flux contribution rate through a phase change interface tracking hydrothermal coupled finite volume algorithm. S4: Acquire basic spatial data of the mining area, remote sensing parameters of snow cover, and boundary data of disturbance types, and use the GWR semi-variogram coupling algorithm to analyze and obtain the heterogeneity attenuation coefficient; S5: Obtain in-situ monitoring data of hydrothermal activity in frozen soil, physical and chemical parameters of soil profile, and boundary data of disturbance type. Then, use the conjugate gradient method to solve the transient heat conduction inverse problem algorithm to analyze and obtain the thermal insulation performance attenuation coefficient. S6: The permafrost evaluation parameters are obtained by comprehensively and quantitatively evaluating aerodynamic roughness, water and heat flux contribution rate, heterogeneity attenuation coefficient and insulation performance attenuation coefficient. Specifically, the four core permafrost parameters are normalized and their values ​​are taken, that is, the aerodynamic roughness, water and heat flux contribution rate, heterogeneity attenuation coefficient and insulation performance attenuation coefficient are normalized to the interval [0, 1]. Each core parameter of permafrost is assigned a comprehensive weighting factor, and the four core parameters and comprehensive weighting factors are calculated and output as permafrost assessment parameters using the TOPSIS method.

3. The method for assessing the dynamic changes of vegetation carbon sequestration in high-altitude mining areas according to claim 2, characterized in that, The specific analysis of S2 is as follows: The target mining area is divided into multiple regular cell grids according to the preset standard cell area. Based on the spatial basic data of the mining area, and with the average cell elevation as the benchmark, the features that exceed the preset feature definition are marked as an independent rough element. The total number of rough elements in each cell and the average height of each rough element are statistically obtained. The horizontal width of each rough element perpendicular to the annual prevailing wind direction is obtained based on meteorological monitoring data. Based on the annual prevailing wind direction from meteorological monitoring data, the windward projected area of ​​each rough element is calculated, and then the windward area ratio of the pixel is obtained through the windward area ratio calculation formula. The pixel slope of each pixel and the regional average slope of the target acquisition area in the database are obtained, and the terrain correction factor is obtained through the terrain correction calculation formula. The aerodynamic roughness is calculated and output by inputting the pixel windward area ratio, terrain correction factor, roughness element drag coefficient, and mining disturbance type coefficient into the set geometric optics inversion formula.

4. The method for assessing the dynamic changes of vegetation carbon sequestration in high-altitude mining areas according to claim 3, characterized in that, The specific analysis of S3 is as follows: Construct a hydrothermal coupling control equation for frozen soil that takes into account phase change. That is, based on the in-situ monitoring data of hydrothermal permafrost, construct a fully coupled hydrothermal control equation for the active layer of frozen soil at the target soil depth in the target collection area. This equation includes a moisture control equation and a heat control equation. The above equations are spatially discretized, and time discretized using a fully implicit scheme. The 0℃ isotherm is used as the freeze-thaw front. The phase change iteration interval is set with a preset temperature interval. Then, phase change interface tracking is enabled to solve for the freeze-thaw phase change latent heat flux of the active layer, soil heat conduction flux, and soil moisture convection heat flux. The latent heat flux of freeze-thaw phase change, soil heat conduction flux, and soil moisture convection heat flux are input into the set water and heat flux contribution calculation formula to calculate and output the water and heat flux contribution rate.

5. The method for assessing the dynamic changes of vegetation carbon sequestration in high-altitude mining areas according to claim 4, characterized in that, The specific analysis of S4 is as follows: The snow cover of each pixel is obtained based on the snow cover remote sensing parameters, and the spatial coordinates, elevation, slope and aspect parameters of each pixel are obtained based on the spatial basic data of the mining area. Then, a GWR model is constructed, and the snow distribution residual of each pixel is extracted based on the model. Spatial structure analysis of snow distribution residuals in each pixel is performed using a semivariogram to calculate the semivariogram value. The variation law of the semivariogram value with the preset lag distance is extracted based on the semivariogram. Based on the variation law, the heterogeneity core parameters, including nugget value, sill value and snow cover range, are extracted. The heterogeneous decay coefficient is obtained by calculating the heterogeneous core parameters using the heterogeneous decay calculation formula.

6. The method for assessing the dynamic changes of vegetation carbon sequestration in high-altitude mining areas according to claim 1, characterized in that, The specific analysis of S5 is as follows: Based on the disturbance type boundary data, the area at a preset surface depth in the target collection area is marked as the organic matter layer. Based on the in-situ monitoring data of frozen soil hydrothermal activity, soil profile physicochemical parameters and disturbance type boundary data, a transient heat conduction problem model of the organic matter layer is constructed. Using soil temperature profile as the observed value and effective thermal conductivity of organic matter layer as the parameter to be inverted, the conjugate gradient method is used to construct an inverse problem solution model. The effective thermal conductivity of organic matter layer is obtained by inversion output through this model. The iteration termination condition is set as the rate of change of objective function is less than the preset upper limit of objective function change. The effective thermal conductivity of organic matter layer of each pixel is solved and output. The effective thermal conductivity of the organic layer is input into the preset thermal insulation performance attenuation quantification formula to calculate the thermal insulation performance attenuation coefficient.

7. The method for assessing the dynamic changes of vegetation carbon sequestration in high-altitude mining areas according to claim 1, characterized in that, The process of acquiring multi-source data from the target area in the high-altitude mining area to obtain multi-source data for the mining area specifically involves: Multi-source data acquisition includes: obtaining basic spatial data of the mining area through remote sensing and aerial surveys of the target acquisition area; obtaining meteorological monitoring data through meteorological stations; obtaining in-situ hydrothermal monitoring data of frozen soil through permafrost monitoring boreholes; obtaining soil profile physicochemical parameters by collecting soil samples at a predetermined depth in the target acquisition area; acquiring remote sensing parameters of snow cover based on remote sensing image data; and obtaining boundary data of disturbance types based on the basic spatial data of the mining area. The multi-source data for the mining area includes spatial basic data of the mining area, meteorological monitoring data, in-situ monitoring data of permafrost hydrothermal parameters, soil profile physicochemical parameters, snow cover remote sensing parameters, and disturbance type boundary data.

8. A dynamic change assessment system for vegetation carbon sequestration in high-altitude mining areas, characterized in that, The system includes a multi-source data acquisition module, a permafrost quantitative assessment module, a vegetation carbon sink extraction module, and a mining area carbon sink comprehensive assessment module, so that the high-altitude mining area vegetation carbon sink dynamic change assessment system can perform the high-altitude mining area vegetation carbon sink dynamic change assessment method as described in any one of claims 1-7.

9. A dynamic change assessment system for vegetation carbon sequestration in high-altitude mining areas according to claim 8, characterized in that, The multi-source data acquisition module is used to acquire multi-source data of the target acquisition area in the high-altitude mining area by installing the multi-source data acquisition group; The frozen soil quantitative assessment module is used to perform frozen soil assessment parameters for dynamic quantitative analysis of frozen soil from multi-source data in the mining area. The vegetation carbon sink extraction module is used to extract vegetation carbon sink parameters from multi-source data of the mining area. The comprehensive assessment module for carbon sequestration in the mining area is used to comprehensively analyze permafrost assessment parameters and vegetation carbon sequestration parameters and output a carbon sequestration report for the target collection area.

10. A dynamic change assessment system for vegetation carbon sequestration in high-altitude mining areas according to claim 9, characterized in that, A comprehensive analysis of the permafrost assessment parameters and vegetation carbon sequestration parameters is performed to output a carbon sequestration report for the target data collection area, specifically as follows: The vegetation carbon sink parameters are matched with the pixels of the target collection area to output the vegetation carbon sink assessment value of each pixel; the permafrost assessment parameters and the corresponding vegetation carbon sink assessment values ​​of each pixel are input into the permafrost disturbance correction formula to calculate the disturbance correction carbon sink assessment value of each pixel. The perturbation-corrected carbon sink assessment value and the corresponding permafrost assessment parameters are combined to quantify the carbon sink comprehensive index of each pixel. Carbon sink reports for the target collection area are generated based on the comprehensive carbon sink index.

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