Strip mine dump ecological system carbon sink evaluation method and device and electronic equipment

Abstract

The invention provides a strip mine waste dump ecological system carbon sink evaluation method and device and electronic equipment, and relates to the technical field of environmental science and ecological restoration, and the method comprises the steps: determining remote sensing characteristic parameters based on multispectral remote sensing image data of a strip mine waste dump; constructing a vegetation net primary productivity model based on the remote sensing characteristic parameters and ground actual measurement data, and performing inversion to generate an estimated value of vegetation net primary productivity NPP; constructing a soil carbon storage model based on the soil sample index data and the microbial respiration intensity, wherein the soil carbon storage model is used for predicting soil carbon storage capability index data; and according to the NPP estimated value of each target soil sample area generated by the NPP model in each time period and the corresponding soil carbon storage capacity index data, determining a soil dump carbon sink space-time distribution result, thereby solving the technical problem that the carbon sink capacity is difficult to accurately and dynamically assess in the prior art, and improving the carbon sink capacity evaluation efficiency. The spatial-temporal dynamic accurate evaluation of the carbon sequestration capability of the ecological system of the strip mine dumping site is realized.

Description

Technical Field

[0001] This invention relates to the field of environmental science and ecological restoration, and more specifically, to a method, apparatus, and electronic equipment for assessing carbon sequestration in open-pit mine spoil heap ecosystems. Background Technology

[0002] Open-pit mine spoil heaps are among the areas most severely affected by ecological damage during mineral resource development. Their large areas of bare land not only significantly reduce soil quality and inhibit plant growth, but also lead to vegetation degradation, soil erosion, and nutrient loss, further weakening the carbon sequestration capacity of the regional ecosystem. Carbon sequestration capacity, as a crucial indicator of ecosystem health and function, is of great significance in improving the ecological environment quality of mining areas and achieving carbon neutrality goals.

[0003] However, current research methods for ecological restoration measures are mostly focused on the analysis of single ecological factors, such as vegetation cover and soil organic matter. A systematic and integrated carbon sink evaluation and optimization technology system has not yet been formed, making it difficult to fully reflect the carbon sink potential and dynamic change characteristics of the mining area ecosystem. Summary of the Invention

[0004] The purpose of this invention is to provide a method, apparatus, and electronic device for assessing the carbon sequestration capacity of open-pit mine spoil heaps, in order to solve the technical problem that existing technologies are unable to accurately and dynamically assess the carbon sequestration capacity of open-pit mine spoil heaps.

[0005] In a first aspect, the present invention provides a method for assessing the carbon sink of an open-pit mine spoil heap ecosystem. The method includes: determining remote sensing characteristic parameters based on multispectral remote sensing image data collected from the open-pit mine spoil heap; constructing a vegetation net primary productivity (NPP) model based on the aforementioned remote sensing characteristic parameters and pre-acquired ground-based measured data; using the aforementioned NPP model to invert and generate an estimated value of the vegetation's NPP; constructing a soil carbon storage model based on soil sample index data and microbial respiration intensity measured for target soil samples; using the aforementioned soil carbon storage model to predict and generate soil carbon storage capacity index data; and determining the spatiotemporal distribution of the spoil heap carbon sink based on the estimated NPP values ​​of each target soil sample area generated by the aforementioned NPP model within each time period, and the corresponding soil carbon storage capacity index data predicted by the aforementioned soil carbon storage model.

[0006] In an optional implementation, the aforementioned multispectral remote sensing image data includes remote sensing image data and hyperspectral data; the step of determining remote sensing characteristic parameters based on the multispectral remote sensing image data collected for open-pit mine spoil heaps includes: preprocessing the aforementioned remote sensing image data, and then extracting reflectance and key vegetation indices for each band in conjunction with the aforementioned hyperspectral data; the aforementioned key vegetation indices include: Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Red Edge Chlorophyll Index (RECI), and Faint Vegetation Cover (FVC); and performing inversion based on the aforementioned reflectance for each band and the aforementioned key vegetation indices to generate vegetation chlorophyll content and soil surface characteristic parameters; the aforementioned remote sensing characteristic parameters include: reflectance for each band, key vegetation indices, vegetation chlorophyll content, and soil surface characteristic parameters.

[0007] In an optional implementation, the aforementioned soil surface characteristic parameters include: canopy vegetation reflectance index NIRv, nuclear normalized difference vegetation index kNDVI, soil adjusted vegetation index SAVI, soil surface temperature LST, and soil moisture content; the formula for calculating the aforementioned canopy vegetation reflectance index NIRv is:

[0008] Where NIR is the reflectance in the near-infrared band, Red is the reflectance in the red band, and C is a correction constant; The formula for calculating the nuclear normalized differential vegetation index (kNDVI) is as follows:

[0009] in, ; The formula for calculating the soil-adjusted vegetation index (SAVI) is as follows:

[0010] Where L is the adjustment factor, and its value is between 0 and 1.

[0011] In an optional implementation, the above-mentioned ground-measured data includes plant photosynthetic parameters obtained for the area where each target soil sample is located; the above-mentioned plant photosynthetic parameters include: net photosynthetic rate Pn, transpiration rate Tr, stomatal conductance Gs, water use efficiency WUE, and leaf assimilation rate Aleaf; the above-mentioned method further includes: generating observed NPP values ​​of vegetation plots based on the above-mentioned leaf assimilation rate Aleaf; and using the above-mentioned observed NPP values ​​to train and / or validate the above-mentioned vegetation net primary productivity model.

[0012] In an optional implementation, the soil sample index data includes: carbon content, bulk density, layer thickness, and gravel volume fraction; the soil carbon storage capacity index data predicted by the soil carbon storage model includes: soil carbon storage and the spatiotemporal distribution results of soil carbon storage at different times and in different regions; wherein, the calculation formula for the soil carbon storage is:

[0013] In the formula, This represents the soil carbon storage in soil layer k. ρk represents carbon content, dk represents layer thickness, and fragsk represents volume fraction of crushed stone.

[0014] In an optional implementation, the aforementioned soil sample index data also includes organic carbon content, total nitrogen, available phosphorus, enzyme activity, pH, and moisture content measured for soil samples from different regions; the aforementioned method further includes: based on the aforementioned soil sample index data and the aforementioned soil carbon storage capacity index data, using statistical software to perform structural equation modeling and determine the structural equation model; the aforementioned structural equation model is used to determine the contribution rate of each of the aforementioned soil sample index data to carbon storage capacity.

[0015] In an optional implementation, the method further includes: continuously monitoring the experimental area inoculated with different microorganisms and the uninoculated control area, generating monitoring results, the monitoring results including changes in vegetation carbon uptake rate, soil carbon storage, and microbial activity in the corresponding area; using the above-mentioned vegetation net primary productivity model and the above-mentioned soil carbon storage model to predict the above-mentioned experimental area and the above-mentioned control area, and determine the spatiotemporal distribution results of carbon sink in the corresponding area; based on the above-mentioned monitoring results and the spatiotemporal distribution results of carbon sink in the above-mentioned area, determining an ecosystem restoration and carbon sink enhancement plan.

[0016] Secondly, the present invention provides an ecosystem carbon sink assessment device for open-pit mine spoil heaps. The device includes: a first determining module for determining remote sensing characteristic parameters based on multispectral remote sensing image data collected from the open-pit mine spoil heap; a first model building module for constructing a vegetation net primary productivity (NPP) model based on the aforementioned remote sensing characteristic parameters and pre-acquired ground-based measured data; the aforementioned NPP model is used to invert and generate an estimated value of the vegetation's NPP; a second model building module for constructing a soil carbon storage model based on soil sample index data and microbial respiration intensity measured for target soil samples; the aforementioned soil carbon storage model is used to predict and generate soil carbon storage capacity index data; and a second determining module for determining the spatiotemporal distribution results of the spoil heap carbon sink based on the estimated NPP values ​​of each target soil sample area generated by the aforementioned NPP model within each time period, and the corresponding soil carbon storage capacity index data predicted by the aforementioned soil carbon storage model.

[0017] Thirdly, the present invention provides an electronic device, including a memory and a processor, wherein the memory stores a computer program executable on the processor, and the processor executes the computer program to implement the steps of the method described in any of the first aspects above.

[0018] Fourthly, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when invoked and executed by a processor, cause the processor to perform the method described in any of the first aspects above.

[0019] In summary, this invention provides a method, apparatus, and electronic device for assessing the carbon sequestration of open-pit mine spoil heap ecosystems. The method includes: first, determining remote sensing characteristic parameters based on multispectral remote sensing image data collected from open-pit mine spoil heaps; then, constructing a vegetation net primary productivity (NPP) model based on the remote sensing characteristic parameters and pre-acquired ground-based measured data to estimate the net primary productivity (NPP) of vegetation; next, constructing a soil carbon storage model based on soil sample index data and microbial respiration intensity measured for target soil samples to predict soil carbon storage capacity index data; and finally, determining the spatiotemporal distribution of carbon sequestration in the spoil heap based on the NPP estimates generated by the vegetation NPP model for each target soil sample area within each time period, and the corresponding soil carbon storage capacity index data predicted by the soil carbon storage model. This method, by combining multi-source remote sensing and measured data to construct a vegetation NPP model and a soil carbon storage model, solves the technical problem of existing technologies' difficulty in accurately and dynamically assessing the carbon sequestration capacity of open-pit mine spoil heaps, achieving a comprehensive, quantitative, and dynamic evaluation of the spatiotemporal distribution of carbon sequestration in spoil heaps. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments of the present invention will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 A flowchart illustrating a method for assessing the carbon sink of an open-pit mine spoil heap ecosystem, provided as an embodiment of the present invention; Figure 2 This is a test area layout map of an open-pit mine spoil heap ecosystem carbon sink assessment method provided in an embodiment of the present invention; Figure 3A schematic diagram illustrating the carbon sequestration capacity of vegetation in a demonstration area, provided as an embodiment of the present invention; Figure 4 A spatial distribution map of vegetation carbon sequestration capacity in different experimental areas of a demonstration zone provided in this embodiment of the invention; Figure 5 A statistical chart of vegetation carbon sequestration capacity in various experimental areas of a demonstration zone provided as an embodiment of the present invention; Figure 6 A graph showing the change of soil organic carbon in different months is provided as an embodiment of the present invention; Figure 7 This invention provides a statistical chart of soil carbon flux in various experimental areas of a demonstration zone. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0023] Current research methods for ecological restoration measures mostly focus on the analysis of single ecological factors, such as vegetation cover and soil organic matter, and have not yet formed a systematic and integrated carbon sequestration assessment and optimization technology system, making it difficult to comprehensively reflect the carbon sequestration potential and dynamic change characteristics of mining area ecosystems. Based on this, this invention provides a method, device, and electronic equipment for assessing carbon sequestration in open-pit mine spoil heap ecosystems to solve the aforementioned problems of existing technologies.

[0024] To facilitate understanding of this embodiment, a detailed description of the carbon sink assessment method for open-pit mine spoil heap ecosystem disclosed in this embodiment of the invention will be provided first. Figure 1 This is a flowchart illustrating a method for assessing the carbon sequestration of an open-pit mine spoil heap ecosystem, provided by an embodiment of the present invention. The method mainly includes the following steps S120 to S180: S120: Determine remote sensing characteristic parameters based on multispectral remote sensing image data collected from open-pit mine spoil heaps.

[0025] Multispectral remote sensing image data can be acquired using multispectral remote sensing equipment mounted on drones, including high-resolution remote sensing image data and hyperspectral data. Typically, based on vegetation growth cycle patterns, climate characteristics, and key time nodes for carbon sequestration dynamic monitoring, data collection at open-pit mine spoil heaps is conducted during three periods each year: spring, summer, and autumn, for example: April-May, June-August, and September-October.

[0026] Preferably, data is collected three times a year, in April, August, and October, with each collection covering at least 100 hectares. April is the period of vegetation greening and sprouting, when temperatures rise and plants begin to sprout and turn green, entering the initial stage of the growing season. Collecting remote sensing data during this period can provide information on vegetation recovery status, survival rate after the previous winter, soil exposure, and early cover conditions, providing baseline data for subsequent growth trends. This is crucial for evaluating whether ecological restoration measures (such as microbial inoculation) promote early spring plant recovery. August is the peak period for vegetation growth and carbon absorption. Data collected at this time reflects the highest levels of maximum vegetation cover (FVC), leaf area index (LAI), net primary productivity (NPP), and the actual performance of plant carbon sequestration capacity. It is a true reflection of the growth-promoting effect of microbial inoculation on plants and can capture maximum carbon sequestration capacity. October marks the end of the growth period. Data collected during this phase can be used to assess the total annual carbon sequestration, monitor underground biomass accumulation and the initial state of soil carbon input, determine whether the ecosystem has achieved "effective carbon sequestration," and serve as a time point for annual carbon sink accounting to assess the total annual carbon sequestration.

[0027] Therefore, selecting a combination of April, August, and October for the collection of multispectral remote sensing image data can achieve full-cycle monitoring of the carbon sequestration process in the open-pit mine spoil heap ecosystem. This approach not only conforms to ecological rhythms but also meets the technical requirements for remote sensing modeling and ecological restoration effect evaluation, demonstrating scientific validity, practicality, and operability.

[0028] In one embodiment, after acquiring multispectral remote sensing image data, remote sensing feature parameters can be generated through the following steps: First, the remote sensing image data is preprocessed; then, reflectance and key vegetation indices for each band are extracted by combining hyperspectral data; next, based on the reflectance and key vegetation indices for each band, inversion is performed to generate vegetation chlorophyll content and soil surface feature parameters, thereby determining the remote sensing feature parameters. The determined remote sensing feature parameters may include: reflectance for each band, key vegetation indices, vegetation chlorophyll content, and soil surface feature parameters.

[0029] Preprocessing of remote sensing image data can include atmospheric correction, noise removal, and reflectance conversion. The preprocessed data can then be combined with hyperspectral data for inversion at time intervals (e.g., quarterly) to generate vegetation chlorophyll content and soil surface characteristic parameters, providing high-precision input data for subsequent model analysis. It should be noted that the accuracy of chlorophyll content generated from a single hyperspectral data inversion should be controlled within ±5%, and standardized atmospheric correction methods should be used to process the remote sensing image data.

[0030] In this embodiment, "hyperspectral data" is used in a broad sense and can refer to multispectral or hyperspectral remote sensing image data in general. In one embodiment, a multispectral remote sensing device (which may include a remote sensing sensor and a multispectral sensor) mounted on a UAV can be configured to include the following multiple bands: blue band (450–510 nm), green band (520–600 nm), red band (630–690 nm), red edge band (700–750 nm), and near-infrared band (780–900 nm). Correspondingly, the reflectance of each band (ρB, G, R, RE, NIR) usually refers to the surface reflectance received by the remote sensing sensor in different bands, which is the basic data for calculating vegetation indices, estimating vegetation productivity, and inverting surface parameters.

[0031] Furthermore, the process of inversion using hyperspectral data can include first extracting reflectance and key vegetation indices for each band, and then performing inversion based on these indices. The reflectance for each band can be obtained through existing remote sensing image preprocessing methods, which will not be elaborated here. Using the extracted reflectance, various key vegetation indices are calculated according to predetermined formulas, which can more sensitively reflect vegetation structure, chlorophyll content, water status, and carbon sequestration potential. Key vegetation indices can include: Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Red-edged Chlorophyll Index (RECI), and Faint Vegetation Cap (FVC), etc.

[0032] The Normalized Difference Vegetation Index (NDVI) is a widely used vegetation monitoring index in remote sensing. It quantifies vegetation cover and growth status, providing crucial data support for research in ecology, agriculture, and climate. It is typically calculated by normalizing the differences in reflectance characteristics of vegetation in the near-infrared (NIR) and red (Red) bands. The Enhanced Vegetation Index (EVI) assesses surface vegetation cover, growth status, and biomass. Compared to the traditional NDVI, EVI offers significant advantages in reducing atmospheric interference, soil background effects, and canopy structure effects, making it particularly suitable for monitoring high-density vegetation areas. The Red Edge Chlorophyll Index (RECI) is a vegetation index designed based on the characteristics of the red edge region of the vegetation spectrum. It is specifically used to estimate the chlorophyll content in the plant canopy. Fractional Vegetation Cover (FVC) is a quantitative indicator that describes the proportion of the vertical projection area of ​​vegetation on the ground to the total area of ​​a statistical region. It can represent the percentage of the vertical projection area of ​​the canopy and branches of vegetation on the ground to the total area of ​​the statistical region.

[0033] In this embodiment, the Normalized Difference Vegetation Index (NDVI) can be used as the base vegetation index (the starting point for calculating vegetation remote sensing indices). The canopy reflectance index (NIRv), which is essentially a near-infrared reflectance index, can be calculated using the NDVI index, effectively reducing the influence of soil background reflectance. The Enhanced Vegetation Index (EVI) can be used to quantitatively analyze vegetation cover and spatial distribution characteristics.

[0034] In this embodiment, the Red Edge Chlorophyll Index (RECI) can be calculated using existing general formulas based on surface reflectance data in the red-edge and near-infrared bands acquired by a multispectral sensor on a UAV, after standardized atmospheric correction and radiometric calibration. RECI characterizes vegetation chlorophyll content and serves as an important input variable for subsequent vegetation net primary productivity models. Vegetation cover (FVC) can be calculated using vegetation indices from remote sensing imagery, such as regression models based on vegetation indices. Commonly used indices include the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI). The calculation formula is: FVC = (NDVI - NDVI) / ... S ) / (NDVI V - NDVI S ), of which NDVI S and NDVI V These represent the NDVI values ​​for bare soil and pure vegetation, respectively.

[0035] In one embodiment, soil surface characteristic parameters may include: canopy vegetation reflectance index (NIRv), kernel normalized differential vegetation index (kNDVI), soil-adjusted vegetation index (SAVI), and soil surface temperature (LST). The canopy vegetation reflectance index (NIRv) (essentially a near-infrared reflectance index) can effectively reduce the influence of soil background reflectance; the kernel normalized differential vegetation index (kNDVI) can be calculated based on red and near-infrared bands acquired by a multispectral sensor mounted on a UAV, used to reduce the saturation phenomenon of the normalized vegetation index (NDVI) in areas with high vegetation cover; the soil-adjusted vegetation index (SAVI) can reduce the influence of soil background on vegetation information extraction, thereby further improving the accuracy and reliability of vegetation indices.

[0036] The formula for calculating the canopy vegetation reflectance index (NIRv) is:

[0037] Where NIR is the reflectance in the near-infrared band, Red is the reflectance in the red band, and C is a correction constant.

[0038] On the other hand, the canopy vegetation reflectance index NIRv can be calculated using the normalized vegetation index NDVI, i.e.: NIRv=(NDVI-C)·NIR, where the correction constant C can generally be set to 0.08.

[0039] The formula for calculating the nuclear normalized differential vegetation index (kNDVI) is:

[0040] in, The value range of kNDVI is [-1, 1], with the main value range being between 0 and 1.

[0041] The formula for calculating the Soil Adjusted Vegetation Index (SAVI) is:

[0042] Where L is an adjustment factor, with a value between 0 and 1, used to reduce the impact of soil surface reflection.

[0043] Soil surface temperature (LST) is typically obtained using remote sensing technology, particularly through inversion calculations using multispectral remote sensing equipment (including thermal infrared sensors) mounted on unmanned aerial vehicles (UAVs). Commonly used LST retrieval algorithms include single-window algorithms, split-window algorithms, and temperature and emission separation methods. To improve the accuracy of LST retrieval, ground meteorological observation data (such as air temperature and humidity) can be further incorporated to constrain the retrieval process, and atmospheric profile parameters can be provided using numerical weather prediction models (such as ERA5).

[0044] In addition, soil surface characteristic parameters may also include soil moisture content, which can be obtained by directly measuring soil samples using existing methods or instruments (such as laboratory drying, time-domain / frequency-domain reflectometer, neutron probe, etc.).

[0045] S140: Based on remote sensing feature parameters and pre-acquired ground measurement data, construct a vegetation net primary productivity model; the vegetation net primary productivity model is used to invert and generate an estimate of the vegetation net primary productivity (NPP).

[0046] In this embodiment, the remote sensing feature parameters may include the following data obtained based on remote sensing image data: Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Red Edge Chlorophyll Index (RECI), Vegetation Cover (FVC), Canopy Reflectance Index (NIRv), Core Normalized Difference Vegetation Index (kNDVI), Soil Adjusted Vegetation Index (SAVI), reflectance of each band (ρB, G, R, RE, NIR), and Soil Surface Temperature (LST).

[0047] In one embodiment, the ground-based measured data includes plant photosynthetic parameters obtained for the area where each target soil sample is located; the plant photosynthetic parameters include: net photosynthetic rate Pn, transpiration rate Tr, stomatal conductance Gs, water use efficiency WUE, and leaf assimilation rate Aleaf.

[0048] The target soil samples can be selected based on the different crops planted. The number of sampling points to be tested in the corresponding experimental area and the number of plants randomly selected at each sampling point can be determined according to the planting area of ​​different crops. The above-mentioned plant photosynthetic parameters can be measured using existing portable photosynthesis systems / equipment at preset time intervals.

[0049] For example, a portable photosynthesis system was used to measure the net photosynthetic rate (Pn), transpiration rate (Tr), stomatal conductance (Gs), water use efficiency (WUE), and leaf assimilation rate (Aleaf) of plants once a month. For each measurement, 10 sampling points from different experimental areas were selected, and 10 plants were randomly selected from each sampling point for data collection. The sampling frequency of plants in the experimental area was once a month to ensure the representativeness of the measurement data.

[0050] The above method may also include: generating observed NPP values ​​for vegetation plots based on leaf assimilation rate Aleaf; and using the observed NPP values ​​to train and / or validate the vegetation net primary productivity model.

[0051] Leaf assimilation rate (Aleaf) can refer to the net photosynthetic rate of a leaf, and its unit is: , which represents the amount of CO2 absorbed per unit leaf area per unit time.

[0052] In ecology and remote sensing monitoring, net primary productivity (NPP) is an important indicator for measuring the carbon uptake capacity of an ecosystem and is often used to assess the productivity and carbon cycle dynamics of terrestrial ecosystems.

[0053] When constructing a vegetation net primary productivity (NPP) model, a set of "real" NPP data is needed as the basis for model training and validation (i.e., "ground truth"). These real NPP values ​​can be obtained by integrating the photosynthetic rate (Aleaf) at the leaf scale (accumulation of time and spatial dimensions) to obtain the plot-level observed NPP values ​​(NPPobs).

[0054] Then, using observed NPP values ​​as the target variable and remote sensing and ground-measured parameters as input features, a machine learning algorithm was used to establish a vegetation net primary productivity (NPP) model. The model accuracy was evaluated through cross-validation (model validation requirement: coefficient of determination R). 2 ≥ 0.75, RMSE ≤ 5%, and at least 100 sets of data for each validation (preferred, the machine learning algorithm may be Random Forest Regression (RFR) algorithm).

[0055] S160: Based on soil sample index data and microbial respiration intensity measured for target soil samples, a soil carbon storage model is constructed; the soil carbon storage model is used to predict and generate soil carbon storage capacity index data.

[0056] In one embodiment, soil sample index data may include: carbon content, bulk density, etc. ρk ( The soil sample parameters, including layer thickness and gravel volume fraction, can be obtained by measuring the collected soil samples using existing carbon flux instruments.

[0057] In establishing a soil carbon storage model, microbial respiration intensity can be determined using standard laboratory culture methods. For example, an appropriate amount of air-dried and sieved target soil sample is taken and cultured in a sealed environment under constant temperature (e.g., 25℃) and suitable humidity conditions. The amount of CO2 released from the culture bottle is measured periodically. This indicates the intensity of microbial respiration.

[0058] In one embodiment, based on the microbial respiration intensity measured above, combined with soil sample index data, a multivariate regression model (i.e., a soil carbon storage model) is established using structural equation modeling or machine learning algorithms to achieve quantitative assessment and spatiotemporal prediction of soil carbon storage capacity.

[0059] The soil carbon storage capacity index data generated by the soil carbon storage model prediction includes: soil carbon storage and the spatiotemporal distribution results of soil carbon storage at different times and in different regions. The formula for calculating soil carbon storage is as follows:

[0060] In the formula, This represents the soil carbon storage in soil layer k. ρk represents the carbon content (g / kg) and the bulk density. dk represents the layer thickness (cm), and fragsk represents the volume fraction of crushed stone.

[0061] For example: using the above stratification formula (Formula 4) for calculation, in the 0–10 cm soil layer, C k =4.73 g / kg, ρk=1.35 When dk=10 cm, the calculated SOC is 0–10 It is 0.0607 In one embodiment, the soil sample index data may further include organic carbon content, total nitrogen, available phosphorus, enzyme activity, pH, and moisture content measured from soil samples from different regions. The aforementioned soil sample index data can be obtained by measuring the collected soil samples using existing carbon flux instruments, and then determined through laboratory analysis of the soil sample data.

[0062] Furthermore, the above method may also include: using statistical software to perform structural equation modeling based on soil sample index data and soil carbon storage capacity index data, and determining the structural equation model; the structural equation model can be used to determine the contribution rate of each soil sample index data to carbon storage capacity.

[0063] In this embodiment, structural equation modeling can be used to analyze the influence pathways of soil factors' physicochemical properties (such as pH, water content, organic carbon, total nitrogen, available phosphorus, enzyme activity, etc.) on soil carbon storage capacity. Furthermore, by standardizing path coefficients, the contribution rate of each soil factor to carbon storage capacity can be quantitatively assessed, thereby identifying key influencing factors and optimizing model input variables.

[0064] Structural equation modeling (SEM) is a multivariate statistical analysis method used to explore direct, indirect, and total effects between variables and to construct causal path diagrams between variables. In this invention, SEM can be used to analyze the complex correlations between various soil factors; reveal how these factors directly or indirectly affect soil carbon storage capacity; and quantitatively assess the standardized path coefficients of each factor on carbon storage capacity, thereby deriving their contribution rate.

[0065] After obtaining the soil sample index data (including soil factors such as organic carbon content, total nitrogen, available phosphorus, enzyme activity, pH and water content), a theoretical path diagram can be constructed based on existing literature or previous studies. The relationships between various soil factors can be hypothesized. For example, total nitrogen (TN) may indirectly affect organic carbon (SOC) content by influencing enzyme activity. Furthermore, the relationship between dependent variables (such as soil carbon storage capacity) and independent variables (such as various soil factors) can be determined.

[0066] Then, statistical software (such as AMOS, LISREL, Mplus, and the `lavaan` package in R language) can be used to perform structural equation modeling. Furthermore, the model's rationality can be judged by model fit indices (such as CFI, TLI, RMSEA, GFI, etc.), and the model path can be corrected by removing insignificant paths and retaining significant paths.

[0067] Finally, the standardized path coefficients output by SEM reflect the relative strength of the influence between variables. Based on the square of the path coefficient, the proportion or contribution rate of each variable to the dependent variable can be calculated. For example, if the path coefficient of pH is 0.4, then its explained variance contribution is 0.4. 2 = 16%. The contribution rates of multiple factors can be summed to determine which factors are the key drivers of carbon storage capacity.

[0068] S180: Based on the estimated NPP values ​​of each target soil sample area generated by the vegetation net primary productivity model in each time period, and the soil carbon storage capacity index data predicted by the corresponding soil carbon storage model, determine the spatiotemporal distribution results of carbon sinks in the spoil heap.

[0069] In this embodiment, the net primary productivity model constructed in step S140 can be applied to the pixel-level inversion of the entire remote sensing image to generate a continuous NPP spatial distribution map. Furthermore, the soil carbon storage model constructed in step S160 can be used to predict soil carbon storage capacity index data for different soil layers. Finally, the two models can be fused to achieve spatial representation.

[0070] In one embodiment, a high-resolution spatiotemporal distribution map of carbon sinks in spoil heaps is generated quarterly using a constructed vegetation net primary productivity model and a soil carbon storage model (spatial resolution not less than 30m, and each monitoring area covering not less than 50 hectares). By combining UAV remote sensing images with ground measurement data, each monitoring area covers not less than 50 hectares, and the carbon storage capacity and changing trends of different regions are dynamically quantified.

[0071] In summary, the above-mentioned open-pit mine spoil heap ecosystem carbon sequestration assessment method provided by the embodiments of the present invention, by combining multi-source remote sensing and measured data, constructs a vegetation net primary productivity model and a soil carbon storage model, solves the technical problem that existing technologies are unable to accurately and dynamically assess the carbon sequestration capacity of open-pit mine spoil heaps, and realizes a comprehensive, quantitative, and dynamic evaluation of the spatiotemporal distribution of carbon sequestration in spoil heaps.

[0072] Furthermore, the purpose of step S160 is to establish an assessment model that reflects carbon storage potential and its dynamic changes by quantifying the relationship between soil organic carbon mineralization processes and microbial activity. Specific implementation methods for this step may include key processes such as sample processing, experimental design, parameter determination, model construction, and validation.

[0073] In one embodiment, sample processing may include the collection and pretreatment of target soil samples. As a specific example, sampling time and frequency may be set to once per quarter, covering different growing seasons (e.g., April in spring, August in summer, and October in autumn) to capture seasonal variations. Sampling depth stratification may include 0–10 cm (surface layer, where biological activity is most active), 10–20 cm, and 20–40 cm (deeper layer, where carbon stability is higher). The sampling point layout strategy may be: setting up no fewer than 20 random or S-shaped sampling points in the area where each target soil sample is located, with a sampling amount of no less than 500 g per hectare to ensure representativeness. Sample pretreatment may include: removing impurities such as roots and stones; air-drying and sieving through a 1 mm sieve; aliquoting and storing, with one portion used for physicochemical property analysis and the other portion frozen at –80°C for subsequent DNA extraction and microbial functional analysis, etc.

[0074] The experimental design process described above may include determining the respiration intensity of microorganisms through laboratory culture methods to obtain the rate of organic carbon decomposition. Specifically, continuous monitoring can be performed using indoor isothermal incubation with alkaline absorption or infrared gas analysis (IRGA). Parameter determination may include fitting organic carbon decomposition kinetic parameters to determine the organic carbon decomposition rate constant; a smaller value indicates more stable organic carbon and a stronger carbon storage capacity.

[0075] The model building and validation process may include building a dynamic assessment model of soil carbon storage capacity. This model can combine the above-mentioned measured microbial respiration intensity data, decomposition kinetic parameters, soil factors and microbial community characteristics to achieve quantitative and spatial prediction of carbon storage capacity.

[0076] As a specific example, specific indicators of microbial community characteristics may include: functional gene abundance, AMF / DSE relative abundance, Shannon diversity index, KEGG / MetaCyc functional prediction results, etc.

[0077] Furthermore, to restore the ecology of mining areas, existing technologies are increasingly focusing on the synergistic restoration of plants and microorganisms. Functional microorganisms such as arbuscular mycorrhizal fungi (AMF) and dark septate endophytic fungi (DSE), through symbiotic relationships with plants and their unique hyphal networks, can not only expand the absorption range of plant roots and improve the efficiency of water and nutrient use, but also enhance plant stress resistance and survival ability. Improving plant stress resistance can enhance vegetation carbon sequestration capacity. Studies have shown that the combined inoculation of DSE and AMF can produce a synergistic effect in reclaimed soils, significantly improving vegetation carbon sequestration capacity and soil carbon storage, providing technical support for the reconstruction of the ecological function of reclaimed soils.

[0078] Based on this, this invention proposes a method for determining ecosystem restoration and carbon sequestration enhancement schemes. By combining remote sensing monitoring technology, it can quickly and accurately assess the regional carbon sequestration capacity, providing an important basis for the scientific design and optimization of ecological restoration schemes.

[0079] In another embodiment, the above method may further include: (1) continuously monitoring the experimental area inoculated with different microorganisms and the uninoculated control area, generating monitoring results, including changes in vegetation carbon uptake rate, soil carbon storage, and microbial activity in the corresponding area; (2) using a vegetation net primary productivity model and a soil carbon storage model to predict the experimental area and the control area, and determining the spatiotemporal distribution of carbon sinks in the corresponding area; and (3) based on the monitoring results and the spatiotemporal distribution of carbon sinks in the area, determining an ecosystem restoration and carbon sink enhancement plan.

[0080] The different microorganisms can be arbuscular mycorrhizal fungi (AMF) and dark septate endophytic fungi (DSE). Experimental areas inoculated with different microorganisms can include AMF inoculation experimental areas, DSE inoculation experimental areas, and AMF and DSE combined inoculation experimental areas. Preferably, the inoculation rates of AMF and DSE are 750-800 kg / ha and 800-1000 kg / ha, respectively, with the total combined inoculation amount controlled at 1000-1200 kg / ha.

[0081] The detection results in step (1) above were all obtained by experimental determination or field measurement of soil samples from the target area (the experimental area inoculated with different microorganisms and the uninoculated control area). Among them, the vegetation carbon absorption rate can be the net photosynthetic rate Pn of the plant directly measured through the photosynthetic system.

[0082] As a specific example, three treatment methods can be set up in the spoil heap experimental area: inoculation with arbuscular mycorrhizal fungi (AMF, 750 kg of fungicide per hectare), inoculation with dark septate endophytic fungi (DSE, 800 kg of fungicide per hectare), and combined inoculation (AMF+DSE, 1000 kg of fungicide per hectare); at the same time, a control area (uninoculated) is set up to monitor vegetation carbon uptake rate and soil carbon storage, once per quarter, with an experimental period of two years.

[0083] In another embodiment, the method may further include: calculating the carbon neutrality contribution potential of different remediation schemes based on annual monitoring data; and using a formula in the carbon benefit assessment. By converting the increased carbon sequestration volume into economic value and then combining it with data from the carbon trading market to calculate carbon sequestration revenue, an assessment of ecological and economic benefits can be achieved. Furthermore, by comprehensively considering ecological and economic benefits, an optimized restoration and management plan can be proposed.

[0084] As a concrete example, the economic benefits of carbon sequestration can be calculated using a market price of 100-120 RMB per ton of CO2, while the comprehensive ecological benefits need to be assessed through carbon neutrality contribution potential. This invention integrates UAV multispectral remote sensing technology, plant photosynthetic parameter monitoring, soil and microbial function analysis, and a multi-model dynamic evaluation system to achieve precise quantification and optimization of the carbon sequestration capacity of open-pit mine spoil heaps. Utilizing the synergistic effect of arbuscular mycorrhizal fungi (AMF) and dark septate endophytic fungi (DSE), the efficiency of vegetation absorption of soil nutrients and water can be significantly improved, while simultaneously improving soil structure and carbon storage capacity. Experimental results show that inoculation with AMF and DSE not only enhances the carbon sequestration capacity of vegetation but also significantly increases soil organic carbon content and microbial activity, especially under combined inoculation conditions, where the improvement in carbon storage capacity is most pronounced. This invention is applicable to the ecological restoration and carbon sequestration assessment of open-pit mine spoil heaps, not only improving the ecological function of land resources but also taking into account carbon neutrality goals and sustainable development needs, providing important technical support for ecological environmental protection in mining areas.

[0085] Next, we will describe in detail the open-pit mine spoil heap ecosystem carbon sequestration assessment method provided by the present invention with reference to a specific embodiment. This embodiment may include the following steps: (Step 1) Remote Sensing Data Acquisition: Using a drone equipped with multispectral remote sensing equipment, image data of the spoil heap will be collected in April, August, and October. Each flight will cover an area of ​​no less than 100 hectares, with a flight altitude of 150 meters, acquiring remote sensing image data with a resolution of no less than 30 cm. Vegetation indices such as NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index) will be extracted from the remote sensing data to quantitatively analyze vegetation cover and spatial distribution characteristics.

[0086] (Step 2) Hyperspectral data inversion: Atmospheric correction and noise removal are performed on the acquired hyperspectral data, which are then converted into surface reflectance data. Based on the hyperspectral inversion model, the chlorophyll content of vegetation, soil surface temperature (LST), and soil moisture content are inverted quarterly to provide key data for the input of the carbon sink model.

[0087] (Step 3) Measurement of plant photosynthetic parameters: Using a portable photosynthesis system, the net photosynthetic rate, transpiration rate, stomatal conductance, and water use efficiency of the plants were measured monthly during the growing season (May-October). The measurement area was divided into 10 sampling points, and 10 plants were selected from each sampling point for data collection.

[0088] (Step 4) Soil Sample Collection and Analysis: Soil samples are collected from different areas of the spoil heap every quarter, with 4-6 sampling points per hectare. Sampling depths are 0-10cm, 10-20cm, and 20-40cm. Each sample should contain no less than 500g. Laboratory analysis includes key indicators such as soil organic carbon, total nitrogen, available phosphorus, soil enzyme activity, and moisture content.

[0089] (Step 5) Microbial inoculation and monitoring: Set up experimental areas inoculated with arbuscular mycorrhizal fungi, dark-colored septate endophytic fungi, and a combination of both (see [link to experimental area]). Figure 2 As shown in the figure, the application rates are: 750-800 kg / ha of arbuscular mycorrhizal fungi inoculant and 800-1000 kg / ha of dark-colored septate endophytic fungi inoculant, with a total combined inoculation amount controlled at 1000-1200 kg / ha. Soil samples were collected at 30, 60, and 90 days after inoculation to analyze the microbial community structure and functional activity.

[0090] (Step 6) Determination of vegetation biomass and carbon storage: During the peak vegetation growth period (August) and harvest period (October), the aboveground and belowground biomass of the vegetation were measured, respectively. Three sampling points were randomly selected per hectare, and plant samples were collected to calculate their biomass and carbon storage. The aboveground parts were measured by drying and weighing, and the underground roots were washed through a sieve, dried, and weighed.

[0091] (Step 7) Carbon sink model construction: Based on remote sensing image data, ground photosynthetic parameters, and soil carbon storage data, a vegetation net primary productivity (NPP) estimation model and a soil carbon storage model are constructed. The NPP model adopts the random forest regression method, and the model performance is optimized through cross-validation to ensure that the model accuracy is within ±5%.

[0092] (Step 8) Spatiotemporal distribution analysis of carbon sinks: Using the NPP model and soil carbon storage model, a spatiotemporal distribution map of carbon sinks in spoil heaps is generated quarterly. The dynamics of vegetation cover, the trend of carbon storage changes, and the effects of different inoculation treatments on carbon sink capacity are analyzed in conjunction with remote sensing imagery. The spatial resolution of the generated spatiotemporal distribution map is no less than 30m, and the coverage area of ​​each monitoring area is no less than 50 hectares.

[0093] (Step 9) Carbon sink comparison and evaluation: Compare and analyze the changes in vegetation carbon absorption rate, soil carbon storage and microbial activity in the experimental areas inoculated with AMF, DSE and combined inoculation and the uninoculated control areas, and screen out the best remediation and carbon sink enhancement scheme.

[0094] (Step 10) Ecological and Economic Benefit Assessment: Based on annual carbon storage data, calculate the carbon neutrality contribution under different restoration models. Calculate the economic benefits of ecological restoration based on carbon trading market prices (100-120 RMB / ton CO2) and propose an optimized management strategy that balances ecological and economic benefits.

[0095] This embodiment accurately assesses the carbon sequestration capacity of open-pit mine spoil heap ecosystems by combining hyperspectral remote sensing, plant photosynthetic parameter measurement, and microbial function optimization. Inoculation with arbuscular mycorrhizal fungi and dark-colored septate endophytic fungi effectively promotes vegetation carbon absorption efficiency, improves soil organic carbon content and microbial activity, and enhances soil water use efficiency and fertility restoration. This invention is applicable to the ecological restoration of open-pit mine spoil heaps and has application value in improving carbon storage capacity, optimizing soil quality, and achieving carbon neutrality goals.

[0096] Next, we will provide a detailed description of the open-pit mine spoil heap ecosystem carbon sink assessment method provided by the present invention, using another specific embodiment as an example.

[0097] (I) Description of Experimental Conditions: The project site is located at the northern end of the Loess Plateau and the southeastern edge of the Mu Us Desert, characterized by a typical mid-latitude semi-arid continental climate. Winters and springs are influenced by the Mongolian cold air mass, resulting in scarce rainfall, dry and cold weather, and the prevailing northwest monsoon, which is the main period for sandstorms. Summers and autumns have concentrated rainfall, mild weather, and predominantly southeasterly winds. The region is arid with an average annual precipitation of approximately 420 mm (according to the standard for classifying arid and humid climate zones based on average annual precipitation, 200-450 mm is considered semi-arid). Annual precipitation is unevenly distributed seasonally, with summer and autumn accounting for approximately 70% of the annual precipitation. The region has a large per capita land area and abundant light and heat resources, making it suitable for dryland agriculture.

[0098] The soil mainly consists of aeolian sandy soil and chestnut calcareous soil; soil erosion is primarily caused by strong water erosion and moderate wind erosion. It falls within a national-level key monitoring and control area for soil erosion (the coal development monitoring area bordering Shanxi, Shaanxi, and Inner Mongolia, and the sandy and coarse sand control area between the Helong River and other regions), as well as a key monitoring and control area for soil erosion in Shaanxi Province. The soil is slightly alkaline and has low fertility. The soil on the terraces of the spoil heap is all backfill soil, compacted quite tightly by the vehicles dumping the spoil.

[0099] The vegetation consists of herbaceous and shrub vegetation, with a forest and grassland coverage rate of 20%–40%. It mainly comprises three major vegetation groups: shrub vegetation, psammophytic shrubs, and meadow, saline-soil, and marsh vegetation. The main vegetation types include *Salix psammophila*, *Artemisia scoparia*, *Ligustrum lucidum*, *Juniperus chinensis*, *Salix matsudana*, *Caragana korshinskii*, *Pinus tabuliformis*, *Salix matsudana*, *Nitraria tangutorum*, *Chrysanthemum indicum*, *Allium chinense*, *Alfalfa*, *Agrostis pilosa*, and *Haloxylon ammodendron*.

[0100] The experimental area was located at the spoil heap of the Xiwang open-pit coal mine. The experimental area was 20 × 30 m in size, with a spacing of 0.5 m between areas. The experiment included four treatments: control (CK), inoculated with AM fungi (AMF), inoculated with DSE (DSE), and a combination of AM fungi and DSE (AMF+DSE).

[0101] In mid-May 2023, the test plants, *Amorpha fruticosa* and *Alfalfa*, were planted. The *Amorpha fruticosa* planting pattern was 3 m row spacing × 2 m plant spacing. Alfalfa was sown in the demonstration area using drone seeding at a density of 8 g / m³. Simultaneously with planting, each treatment was inoculated. Specifically, after placing the *Amorpha fruticosa* plants in the planting pits, microbial inoculum was applied to the plant roots. The application rate of AM fungal inoculum was 50 g per plant per pit, and the application rate of DSE inoculum was 50 ml per plant per pit (100-fold dilution of the original inoculum solution). For the alfalfa planting area, shallow trenches were dug between the *Amorpha fruticosa* plants, the microbial inoculum was applied, and then covered. The application rates were 300 kg / ha for AM fungal inoculum and 1500 L / ha for DSE inoculum. After application, the topsoil was re-covered. After each treatment, the soil was watered to its maximum saturation water holding capacity, and then watered every two weeks. After one month, watering was stopped, and the plant was allowed to grow naturally. The planting end date for the control area and the AMF-inoculated area was May 16, 2023, while the planting end date for the DSE-inoculated area and the AMF+DSE double-inoculated area was slightly later, on May 24, 2023.

[0102] (II) UAV Multispectral Data Acquisition and Processing: The multispectral image data in this embodiment was acquired using a DJI Jingwei M300 RTK UAV (DJI, Shenzhen, China) system, achieving a horizontal and vertical positioning accuracy of ±0.1 meters. It carried a Rededge multispectral sensor (MicaSense, Seattle, WA, USA). The Rededge camera includes five bands: blue (475nm), green (560nm), red (668nm), near-red (840nm), and red-edge (717nm). Detailed parameters are shown in Table 1. Following a pre-planned flight path, the Rededge multispectral sensor underwent radiometric calibration using a MicaSense Altum calibration reflector before flight. The flight altitude was set to 50m, with 75% and 80% overlap in the heading and lateral directions, respectively. Multispectral image acquisition and data processing were performed twice, in June and October 2023.

[0103] (III) Sample Collection: Soil samples were collected from different treatment areas after planting in June and October 2023. The sampling method was as follows: Four points were randomly selected along an S-shaped route. After removing approximately 1 cm of topsoil, about 500 g of soil from the 0-20 cm depth was collected. After the soil was naturally air-dried, impurities such as dead branches and leaves were removed, and the soil was sieved through a 1 mm sieve. Soil physicochemical properties and other indicators were measured. Simultaneously, a Li-8100A soil carbon flux analyzer was used to measure soil respiration.

[0104] (iv) Statistical Analysis: Remote sensing data, including MODIS data, was used to obtain vegetation indices EVI, NDVI, NIRv, kNDVI, MSAVI, OSAVI, and SAVI, which were then used in NPP modeling regression. Fractional Vegetation Cover (FVC) was calculated based on UAV multispectral imagery to characterize the vegetation cover of the demonstration area. Multiple linear regression (MLR), random forest regression (RFR), support vector machine regression (SVR), and deep neural network (DNN) were used for modeling regression, with a training set to test set ratio of 7:3. The root mean square error (RMSE) and coefficient of determination (R²) were used as the statistical indicators. 2 Accuracy evaluation was performed. Soil factor and plant photosynthetic data were processed and statistically analyzed using Excel 2007, and LSD multiple comparison tests and ANOVA were performed using SPSS 19.0. The R language packages ggplot2, ggdist, and gghalves were used for box plotting.

[0105] (v) Spatial distribution of NPP in a UAV-based multispectral plant-microbe demonstration area: Figure 3This is a schematic diagram of the vegetation carbon sequestration capacity in the demonstration area, where (a)-(c) represent the spatial distribution of vegetation carbon sequestration capacity in the demonstration area in June 2023, October 2023, and June 2024, respectively.

[0106] See Figure 3 It can be seen that the vegetation carbon sequestration capacity in the demonstration area gradually increased from 2023 to 2024. The average vegetation carbon sequestration capacity in the demonstration area in June 2023 was 179.57. ( Figure 3 (Part a) In October, the average vegetation carbon sequestration capacity of the demonstration area increased to 232.36. ( Figure 3 (Part b) In June 2024, the average vegetation carbon sequestration capacity of the demonstration area reached 257.23. ( Figure 3 (Part c), the minimum NPP in the demonstration area is 122.58. The maximum value is 290.41. Spatial distribution results show that areas with lush vegetation have a greater carbon sequestration capacity, while sparsely vegetated roads and bare land have a relatively weaker carbon sequestration capacity.

[0107] From 2023 to 2024, with the accumulation of time, the vegetation restoration effect in areas inoculated with AMF and DSE was more significant, and the vegetation carbon sequestration capacity was improved. (See [reference needed]). Figure 4 The spatial distribution map of vegetation carbon sequestration capacity in different experimental areas of the demonstration zone is shown, and Figure 5 The diagram shows the statistical chart of vegetation carbon sequestration capacity in each experimental area of ​​the demonstration zone. Figure 4 (a)-(d) in the figure represent the four experimental areas of the demonstration area, where (a) is inoculated with AMF, (b) is inoculated with AMF and DSE in combination, (c) is the experimental area control, and (d) is inoculated with DSE.

[0108] The mean NPP in the trial areas that received AMF in June 2023 was 202.1. In October, the average vegetation carbon sequestration capacity of the region was 223.32. In June 2024, the region's average NPP reached 267.92. From 2023 to June 2024, the average NPP in the trial areas vaccinated with DSE increased from 174.23. Increased to 251.54 This indicates that DSE inoculation has a significant effect on carbon sequestration by vegetation in this area.

[0109] From 2023 to 2024, the carbon sequestration capacity of vegetation in areas jointly inoculated with AMF and DSE increased, but compared with areas inoculated with AMF and DSE separately, the carbon sequestration capacity of vegetation in the joint inoculation experimental areas was the weakest. (See [reference needed]). Figure 6The graph showing the changes in soil organic carbon over the months, and Figure 7 The diagram shows the soil carbon flux statistics for each experimental area in the demonstration zone. The average NPP for June and October 2023 and June 2024 was 163.45. 230.17 and 248.48 The response rate was lower than that of areas where AMF and DSE were applied during the same period. This may be because the vegetation is still in a specific growth stage within a year, and the complexity of the regional environment makes the vegetation in this area less sensitive to or less than optimal in response to the combined AMF and DSE inoculation, thus affecting its carbon sequestration capacity.

[0110] (vi) Effects of different planting methods and different experimental treatments on soil organic carbon and soil respiration: such as Figure 6 The changes in soil organic carbon content in December, April, June, August, and October are shown. Overall, the DSE and A+D groups had higher organic carbon contents in several months, especially in December and June. The DSE group reached 6.5 g / kg in December, while the A+D group reached 6 g / kg in June, indicating that the dark septate endophytic fungi (DSE) and their synergistic effect with arbuscular mycorrhizal fungi (AMF) effectively promoted carbon accumulation in winter and early summer. In contrast, the uninoculated group (CK) had a lower overall organic carbon content, especially in October, at only about 2 g / kg, indicating a weaker effect of soil carbon accumulation under uninoculated conditions. The AM group reached an organic carbon content of nearly 6 g / kg in December, but then gradually decreased, indicating that the promoting effect of AMF on carbon accumulation was significant in winter, but weakened in summer and autumn. The differences between the treatment groups may be related to the activity of microorganisms, soil environmental conditions, and the ecological functions of different fungi. In particular, the symbiotic relationship between fungi and plants can have a significant impact on soil carbon accumulation in different seasons.

[0111] In this embodiment, soil CO2 flux data for four different treatment groups were analyzed. Figure 7 The mean flux values ​​for each treatment group are as follows: The mean flux value for the unvaccinated group (CK) was 1.25. The average flux in the AMF group was 1.75. The average flux in the DSE group was 1.90. The average flux in the AD group was 2.20. These results show that the AD group had the highest mean flux value, indicating that co-inoculation of DSE and AMF may have a synergistic effect on CO2 release, leading to an increase in carbon flux. In contrast, the uninoculated group had the lowest mean flux value, reflecting the baseline of soil CO2 release under natural conditions.

[0112] Although the mean flux values ​​varied among the groups, one-way ANOVA did not show significant differences between the treatment groups. This may indicate that different fungal treatments have a relatively small impact on soil carbon release under current conditions.

[0113] (vii) Evaluation of Carbon Sequestration Potential of Different Experimental Treatments: A linear regression model was used to predict the NPP change trend of each treatment area over the next 5 years. By fitting NPP data at different time points, the NPP change trend of each treatment area was obtained, and then the NPP value for the next 60 months was predicted. The Century model was used to simulate the dynamic changes of soil and vegetation carbon sequestration. Combining soil organic carbon, plant photosynthetic rate, and predicted NPP data, the Century model assessed the carbon sequestration accumulation of different treatment areas over the next 5 years.

[0114] CK region: Linear regression prediction shows that NPP in the CK region will exhibit a stable growth trend over the next 5 years. Due to the lack of fungal inoculation, plant growth is less promoted, but good carbon sequestration capacity is still maintained. The total carbon sink is projected to be approximately 8.75 in the next 5 years. .

[0115] AMF Zone: Inoculation of the AMF zone with arbuscular mycorrhizal fungi significantly improved plant NPP and photosynthetic rates, particularly in alfalfa, resulting in a substantial increase in photosynthetic rate. This is because arbuscular mycorrhizal fungi enhance nutrient uptake, especially phosphorus, thereby promoting plant growth and photosynthesis. The total carbon sequestration is projected to be approximately 10.32 billion kcal over the next 5 years. The value was higher than that in the control (CK) zone, indicating that arbuscular mycorrhizal fungi play a significant role in improving carbon fixation.

[0116] DSE Zone: Inoculation with dark-colored septate endophytic fungi significantly increased NPP at all time points, particularly in photosynthetic rate, where both *Amorpha fruticosa* and *Alfalfa* showed superior photosynthetic rates compared to the CK and AMF zones. Dark-colored septate endophytic fungi can promote plant growth by improving root structure and increasing water and mineral absorption. The total carbon sequestration is projected to be approximately 10.75 in the next 5 years. DSE also has the advantage of enhancing plant resistance to stress, especially showing significant growth advantages under conditions of unbalanced soil nutrients or insufficient water.

[0117] AMF+DSE Zone: In the AMF+DSE zone, which was simultaneously inoculated with arbuscular mycorrhizal fungi and dark-colored septate endophytic fungi, although NPP and photosynthetic rates were high at all time points, a certain competitive effect was observed, resulting in a slightly lower carbon sequestration potential compared to the zone inoculated alone. The total carbon sequestration is projected to be approximately 9.89 billion kJ / L over the next 5 years. This competitive effect may stem from the competition between the two fungi for nutrient resources in the plant roots, leading to mutual inhibition and thus reducing overall carbon fixation efficiency. However, the diversity of the AMF+DSE zone also contributes to the system's stability and resilience, which is crucial for the long-term health of the ecosystem.

[0118] (VIII) Comparative Conclusions: Inoculation with arbuscular mycorrhizal fungi (AMF) and dark septate endophytic fungi (DSE) significantly improved vegetation carbon fixation capacity and soil organic carbon accumulation, indicating that microbial inoculation has a significant effect on enhancing carbon sequestration potential. In particular, the DSE treatment showed a greater promoting effect on photosynthesis and carbon sequestration capacity, making it suitable for arid and nutrient-deficient environments. The DSE area performed best in enhancing carbon sequestration, especially in increasing the photosynthetic rate of Amorpha fruticosa and Alfalfa, with its carbon sequestration potential exceeding that of other treatment areas. Although the AMF+DSE area showed a high carbon fixation capacity, its carbon sequestration potential was slightly lower than that of the DSE area due to potential competition. However, the diversity of AMF+DSE also provides a guarantee for the long-term stability of the ecosystem.

[0119] Based on the same inventive concept, this invention also provides an ecosystem carbon sequestration assessment device for open-pit mine spoil heaps, the device comprising: The first determining module is used to determine remote sensing characteristic parameters based on multispectral remote sensing image data collected for open-pit mine spoil heaps; The first model construction module is used to construct a vegetation net primary productivity model based on remote sensing feature parameters and pre-acquired ground measurement data; the vegetation net primary productivity model is used to invert and generate an estimated value of vegetation net primary productivity (NPP). The second model construction module is used to construct a soil carbon storage model based on soil sample index data and microbial respiration intensity measured for the target soil sample; the soil carbon storage model is used to predict and generate soil carbon storage capacity index data. The second determination module is used to determine the spatiotemporal distribution of carbon sinks in spoil heaps based on the estimated NPP values ​​of each target soil sample area generated by the vegetation net primary productivity model in each time period, and the soil carbon storage capacity index data predicted by the corresponding soil carbon storage model.

[0120] The device provided in this embodiment of the invention has the same implementation principle and technical effect as the aforementioned method embodiment. For the sake of brevity, any parts not mentioned in the device embodiment can be referred to the corresponding content in the aforementioned method embodiment.

[0121] Based on the same inventive concept, embodiments of the present invention also provide an electronic device, which may include: a processor, a memory, and a bus. The memory stores machine-readable instructions that can be executed by the processor. When the electronic device is running, the processor communicates with the memory through the bus, and the processor executes the machine-readable instructions to perform the steps of the above-described open-pit mine spoil heap ecosystem carbon sink assessment method.

[0122] Specifically, the aforementioned memory and processor can be general-purpose memory and processor, without any specific limitations. When the processor runs the computer program stored in the memory, it can execute the aforementioned open-pit mine spoil heap ecosystem carbon sink assessment method.

[0123] The processor may be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the above methods can be completed by integrated logic circuits in the processor's hardware or by software instructions. The processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this invention can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in the memory, and the processor reads the information in the memory and, in conjunction with its hardware, completes the steps of the above method.

[0124] Corresponding to the above-mentioned open-pit mine spoil heap ecosystem carbon sink assessment method, this embodiment of the invention also provides a computer-readable storage medium storing machine-executable instructions. When the machine-executable instructions are called and run by a processor, the machine-executable instructions cause the processor to perform the steps of the above-mentioned open-pit mine spoil heap ecosystem carbon sink assessment method.

[0125] The open-pit mine spoil heap ecosystem carbon sequestration assessment device provided in this embodiment of the invention can be specific hardware on the device or software or firmware installed on the device. The implementation principle and technical effects of the device provided in this embodiment of the invention are the same as those in the foregoing method embodiments. For the sake of brevity, any parts not mentioned in the device embodiments can be referred to the corresponding content in the foregoing method embodiments. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can all be referred to the corresponding processes in the above method embodiments, and will not be repeated here.

[0126] In the embodiments provided by this invention, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and there may be other division methods in actual implementation. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interface; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0127] For example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0128] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0129] In addition, the functional units in the embodiments provided by the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0130] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause an electronic device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0131] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0132] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship commonly used when the product of this invention is in use. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this invention. In addition, the terms "first," "second," "third," etc., are only used to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0133] In the description of this invention, it should also be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," and "link" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0134] Finally, it should be noted that the above embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the scope of the technology disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, 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. All should be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for assessing the carbon sequestration of an open-pit mine spoil heap ecosystem, characterized in that, The method includes: Based on multispectral remote sensing image data collected from open-pit mine spoil heaps, remote sensing characteristic parameters were determined. Based on the remote sensing feature parameters and the pre-acquired ground measurement data, a vegetation net primary productivity model is constructed; the vegetation net primary productivity model is used to invert and generate an estimated value of vegetation net primary productivity (NPP). Based on soil sample index data and microbial respiration intensity measured for target soil samples, a soil carbon storage model is constructed; the soil carbon storage model is used to predict and generate soil carbon storage capacity index data. Based on the estimated NPP values ​​of each target soil sample area generated by the vegetation net primary productivity model in each time period, and the corresponding soil carbon storage capacity index data predicted by the soil carbon storage model, the spatiotemporal distribution results of carbon sinks in the spoil heap are determined.

2. The method for assessing the carbon sequestration of open-pit mine spoil heap ecosystems according to claim 1, characterized in that, The multispectral remote sensing image data includes remote sensing image data and hyperspectral data; The steps for determining remote sensing characteristic parameters based on multispectral remote sensing image data collected from open-pit mine spoil heaps include: After preprocessing the remote sensing image data, reflectance and key vegetation indices for each band are extracted by combining the hyperspectral data. The key vegetation indices include: Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Red Edge Chlorophyll Index (RECI), and Faint Vegetation Value (FVC). Based on the reflectance of each band and the key vegetation index, inversion is performed to generate vegetation chlorophyll content and soil surface characteristic parameters. The remote sensing characteristic parameters include: reflectance of each band, key vegetation indices, vegetation chlorophyll content, and soil surface characteristic parameters.

3. The method for assessing the carbon sequestration of open-pit mine spoil heap ecosystems according to claim 2, characterized in that, The soil surface characteristic parameters include: canopy vegetation reflectance index NIRv, nuclear normalized differential vegetation index kNDVI, soil adjusted vegetation index SAVI, soil surface temperature LST, and soil moisture content. The formula for calculating the canopy vegetation reflectance index (NIRv) is as follows: Where NIR is the reflectance in the near-infrared band, Red is the reflectance in the red band, and C is a correction constant; The formula for calculating the nuclear normalized differential vegetation index (kNDVI) is as follows: in, ; The formula for calculating the Soil Adjusted Vegetation Index (SAVI) is as follows: Where L is the adjustment factor, and its value is between 0 and 1.

4. The method for assessing the carbon sequestration of open-pit mine spoil heap ecosystems according to claim 1, characterized in that, The ground-based measured data includes plant photosynthetic parameters obtained for the regions where each target soil sample is located; the plant photosynthetic parameters include: net photosynthetic rate Pn, transpiration rate Tr, stomatal conductance Gs, water use efficiency WUE, and leaf assimilation rate Aleaf; the method further includes: The observed NPP values ​​of the vegetation plots were generated based on the leaf assimilation rate Aleaf. The observed NPP values ​​are used to train and / or validate the vegetation net primary productivity model.

5. The method for assessing the carbon sequestration of open-pit mine spoil heap ecosystems according to claim 1, characterized in that, The soil sample data include: carbon content, bulk density, layer thickness, and gravel volume fraction. The soil carbon storage capacity index data predicted by the soil carbon storage model includes: soil carbon storage and the spatiotemporal distribution results of soil carbon storage at different times and in different regions. The formula for calculating the soil carbon storage is as follows: In the formula, This represents the soil carbon storage in soil layer k. ρk represents carbon content, dk represents layer thickness, and fragsk represents volume fraction of crushed stone.

6. The method for assessing the carbon sequestration of an open-pit mine spoil heap ecosystem according to claim 5, characterized in that, The soil sample index data also includes organic carbon content, total nitrogen, available phosphorus, enzyme activity, pH, and moisture content measured for soil samples from different regions; the method also includes: Based on the soil sample index data and the soil carbon storage capacity index data, structural equation modeling was performed using statistical software to determine the structural equation model; the structural equation model was used to determine the contribution rate of each soil sample index data to the carbon storage capacity.

7. The method for assessing the carbon sequestration of open-pit mine spoil heap ecosystems according to claim 1, characterized in that, The method further includes: Continuous monitoring was conducted on experimental areas inoculated with different microorganisms and uninoculated control areas to generate monitoring results, which included changes in vegetation carbon absorption rate, soil carbon storage, and microbial activity in the corresponding areas. The vegetation net primary productivity model and the soil carbon storage model are used to predict the spatiotemporal distribution of carbon sinks in the experimental area and the control area, and to determine the spatiotemporal distribution of carbon sinks in the corresponding areas. Based on the detection results and the spatiotemporal distribution of carbon sinks in the region, an ecosystem restoration and carbon sink enhancement plan is determined.

8. A device for assessing the carbon sequestration of an open-pit mine spoil heap ecosystem, characterized in that, The device includes: The first determining module is used to determine remote sensing characteristic parameters based on multispectral remote sensing image data collected for open-pit mine spoil heaps; The first model construction module is used to construct a vegetation net primary productivity model based on the remote sensing feature parameters and pre-acquired ground measurement data; the vegetation net primary productivity model is used to invert and generate an estimated value of vegetation net primary productivity (NPP). The second model construction module is used to construct a soil carbon storage model based on soil sample index data and microbial respiration intensity measured for the target soil sample; the soil carbon storage model is used to predict and generate soil carbon storage capacity index data. The second determining module is used to determine the spatiotemporal distribution of carbon sinks in the spoil heap based on the estimated NPP values ​​of the target soil samples in each time period generated by the vegetation net primary productivity model and the corresponding soil carbon storage capacity index data predicted by the soil carbon storage model.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions that, when invoked and executed by a processor, cause the processor to perform the method according to any one of claims 1 to 7.

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