81 results about "Soil carbon" patented technology

The invention relates to the field of agricultural data analysis, in particular to a japonica rice paddy field soil carbon nitrogen ratio dynamic intelligent assessment method, which comprises the following steps: acquiring and preprocessing japonica rice paddy field environment time sequence data to obtain a standardized environment data set; performing nonlinear congestion analysis on the soil moisture content state to obtain a corrected moisture dimension distance component; obtaining a corrected temperature dimension distance component by evaluating the metabolism permissible degree of the soil moisture on the temperature effect; performing double weighted fusion on the moisture and temperature correction distance components to obtain a soil environment state clustering result; through coupling calculation of a soil environment state clustering result and a first-order dynamic decomposition model, a dynamic intelligent evaluation result of the carbon-nitrogen ratio of the japonica rice paddy field soil is obtained, so that the problem that existing clustering cannot accurately distinguish aerobic and anaerobic environment states in a japonica rice paddy field scene and causes deviation of the evaluation result of the carbon-nitrogen ratio of the soil is solved.

Disclosed is a method and device for predicting soil organic carbon based on spectrum-guided ensemble learning. The method includes: obtaining a soil sample and a real organic carbon content and an original soil spectrum thereof, pre-processing the original soil spectrum to obtain a soil spectrum sample, constructing, based on the soil spectrum sample, a sample set, grouping the sample set into a first training set and a validation set, and using the real organic carbon content as a label; training, based on the first training set and the corresponding labels, a partial least squares regression model, a Cubist model and a random forest model to obtain carbon content predicted value sets of the three models; constructing, based on the carbon content predicted value sets of the three models and soil spectrum principal component data, a second training set, and training a second random forest model with the second training set and corresponding labels to obtain a spectrum-guided ensemble model. The method combines the advantages of different predictive models and can accurately predict the soil carbon content.

The invention relates to the technical field of forest wetland carbon sink monitoring and evaluation, and particularly discloses a forest wetland carbon sink intelligent monitoring and evaluation system and method. According to the method, a vegetation carbon sequestration potential evaluation result is obtained by analyzing vegetation structure feature data of forest, water and soil composite ecology of the forest wetland, a water-soil-carbon cooperative evaluation result is obtained by analyzing specific hydrological-soil carbon cycle key elements of the forest wetland, and environmental disturbance response factors are obtained by combining various environmental disturbance factors. Through multi-source data fusion and intelligent modeling, a carbon sink quantitative evaluation value is output, and abnormal feedback is triggered; the problems that in the prior art, adaptability to a forest wetland compound ecological system is insufficient, evaluation refinement is insufficient, and dynamic response is lacked are solved. According to the method, accurate and dynamic monitoring and evaluation of forest wetland carbon sink are realized, carbon sink short plates can be accurately positioned, optimization support is provided, and technical support is provided for protection, restoration and optimization management of forest wetland carbon sink resources.

The invention discloses a carbon sink intelligent evaluation platform based on solid waste resource utilization, and the platform constructs a digital elevation model and a canopy height model through aerial remote sensing and ground environment data collection, and evaluates the vegetation net primary productivity in combination with a light energy utilization rate model. Training a soil organic carbon inversion model by adopting machine learning, and analyzing multi-stage hyperspectral image data; and establishing a soil carbon dynamic model to simulate a carbon solid storage and turnover process. Remote sensing data, LiDAR structure parameters and ground actually measured biomass are fused through random forest regression, and the biomass estimation precision is optimized. And finally integrating the plant and soil carbon sequestration amounts, calculating the total carbon sequestration amount of the ecological system and evaluating the temporal and spatial variation characteristics of the total carbon sequestration amount. According to the platform, intelligent and precise carbon sink evaluation is realized, and a scientific basis is provided for ecological restoration and carbon sink management.

A method for calculating total organic carbon (TOC) for a sample location includes obtaining an intact core soil sample from a sample location, the core soil sample having a depth and mass; transferring a predetermined fixed mass M from the intact core soil sample into an analysis sample container to obtain an analysis sample; and providing a subsample of the analysis sample for soil organic carbon (SOC) concentration testing.

The invention discloses a homeland space ecological protection and restoration effect evaluation method based on big data analysis, and relates to the technical field of ecological environment monitoring, multi-source big data and a remote sensing time sequence are utilized to divide a target restoration area into space units, and ecological carbon sink classification, vegetation index change, a biomass model and a soil carbon model are combined to evaluate the ecological protection and restoration effect of the homeland space. According to the method, quantification of carbon reserves of each space unit is achieved, the contrast unit which is not repaired but has similar conditions is matched, the carbon sink change prediction model is introduced to calculate the additional carbon sink increment of the repair unit relative to the contrast unit, contribution of natural succession and manual repair can be clearly distinguished, and quantification and traceability of the repair effect are achieved. According to the method, the extra carbon sink increment of each space unit is summarized to the region, and meanwhile, the space distribution pattern and the time change trend are analyzed, so that quantitative evaluation from a single point to the region and from the local to the whole is realized, and scientific reference is provided for ecological restoration planning and management.

The invention discloses a device and method for increasing soil carbon reserve by using organic fertilizer, the device comprises a storage part, the device is characterized in that the storage part comprises an upper support plate, and the upper support plate is fixedly connected with a first-layer barrel assembly, a second-layer barrel assembly and a third-layer barrel assembly; the first-layer barrel assembly comprises a first-layer barrel, the first-layer barrel is fixedly connected with the upper supporting plate and fixedly communicated with a first material pipe, the first material pipe penetrates through the upper supporting plate and is fixedly connected with first-layer circular plates on the upper side, and the two first-layer circular plates are fixedly connected with connecting blocks correspondingly; and the center of the first-layer circular plate on the upper side is fixedly connected with a first motor. The invention relates to the technical field of fertilization equipment, in particular to equipment and a method for increasing soil carbon reserve by using organic fertilizer. The technical problem to be solved by the invention is to provide the equipment and the method for increasing the soil carbon reserve by using the organic fertilizer, so that the application of the organic fertilizer is facilitated.

The present application relates to the field of crop cultivation and soil improvement technology, and discloses a wheat-corn-sunflower-legume green manure crop rotation carbon fixation and water saving method suitable for arid irrigation areas, which comprises wheat planting, corn planting, sunflower planting and legume green manure planting. The present application replaces traditional flooding irrigation with high-frequency low-amount drip irrigation mode, combines with precise water supply according to the water requirement characteristics of each crop growth period, and avoids invalid irrigation. Meanwhile, through straw returning to field, soil water evaporation is reduced, and soil conservation capacity is improved. Moreover, the present application constructs a double carbon fixation path of straw returning to field and green manure turning, straw is crushed and returned to field, organic carbon is input into soil every year, legume green manure is turned and decomposed, and soil organic matter content is improved. The present application also reduces nitrogen fertilizer application through symbiotic nitrogen fixation of legume green manure and rhizobium, protects soil microbial activity, optimizes soil carbon cycle, relieves soil degradation, and improves long-term carbon fixation potential of soil.

The utility model discloses a kind of for simulating root litter in situ sustained input device and its application, through the design of the soil column with stainless steel handle, 1 μm aperture nylon net, the PVC pipe with hole in bottom and multi-generation root litter in situ sustained input simulation system, the dynamic simulation of root litter sustained input process under soil profile scale is realized.The device solves the limitation of traditional decomposition experiment, can dynamically add root litter without destroying soil structure, while effectively blocking exogenous interference, ensuring the accuracy and consistency of experiment.The utility model provides key technical platform for studying the influence of root litter in situ sustained input on soil carbon stability, and is suitable for ecology, carbon cycle research and ecological system management and other fields.

The present application provides a kind of wind erosion sand transport rate vegetation correction factor dynamic optimization method, introduce the inherent cementation index of soil inorganic / organic carbon ratio, by quantifying carbon ratio influence coefficient, the inherent influence of soil carbon component structure on soil aggregate stability, sand critical condition is integrated into vegetation correction system, so that vegetation correction process is no longer separated from soil base attribute, substantially improve the physical mechanism rationality of wind erosion model. Meanwhile, through remote sensing per-pixel modeling and multi-factor spatial overlay operation, realize the dynamic adjustment of each pixel vegetation correction factor with the difference of soil carbon structure, vegetation coverage, vegetation type, solve the distortion problem caused by the parameter uniformization and staticization of traditional model.

The invention discloses a soil organic carbon mineralization rate determination method, and relates to the technical field of soil carbon circulation and greenhouse gas monitoring. The method comprises the following steps: carrying out hydrochloric acid back titration on an absorption liquid by adopting an automatic potentiometric titration method, converting to obtain the mass of a carbon element contained in carbon dioxide, calculating the mineralization rate by combining the dry soil weight and the culture interval, and finally subtracting an inactivation control value to obtain the organic carbon mineralization rate driven by pure microorganisms. A thin-wall stainless steel undisturbed soil drill matched with the inner diameter of a culture bottle is used for collecting soil columns in a layered manner, PVC liner tube sealing and constant-temperature transportation are matched, and a cold chamber for simulating the in-situ temperature is combined, so that the in-situ structure and the microbial habitat of the loose high-water-content peat soil are reserved to the maximum extent, and damage of traditional pretreatment to soil pores and microbial communities is avoided.

A carbon amount acquisition device (100) comprises: an information acquisition unit (11) that acquires information indicating a first soil carbon amount contained in the soil in a specific area at a first time point; a plant carbon amount acquisition unit (12) that acquires information indicating the plant carbon amount contained in the plant in the specific area at the first time point; a discharged carbon amount acquisition unit (13) that acquires information indicating the discharged carbon amount discharged from the soil in the specific area during a period from the first time point to a second time point which is after the first time point; and a soil carbon amount acquisition unit (14) that acquires, on the basis of the result of subtracting the discharged carbon amount from the total of the first soil carbon amount and the plant carbon amount, information indicating a second soil carbon amount contained in the soil in the specific area at the second time point.

The deep soil bulk density measuring device comprises a well digging device and a soil taking device, the soil taking device is installed on a drill rod of the well digging device, the soil taking device is composed of a drill bit, a cutting ring and a pressure sensor, the drill bit is in a barrel shape, an opening of the drill bit faces downwards, the upper end of the drill bit is connected with the drill rod, and the lower end of the drill bit is connected with the cutting ring. A pressure sensor is installed in the center of the bottom of the barrel, and a cutting ring is installed below the pressure sensor in a pressing mode. According to the utility model, the characteristic that a well digging machine can collect deep soil and the characteristic that a cutting ring method is simple and operable are combined, and the deep soil is collected through the cutting ring, so that the purpose of accurately measuring the bulk density of the deep soil is achieved, and the device has certain positive significance on research in related fields such as soil physicochemical property analysis, civil engineering and soil carbon sink.

The invention relates to the technical field of carbon sink prediction management, and discloses an LCA-based tea garden full-flow carbon metering system, which comprises the following steps: analyzing a tea garden into ladder surfaces, ladder walls and inter-row landform units, and constructing a runoff transportation network; calculating chemical activity parameters representing residual polyphenol release potential and mineral interface reaction sensitivity parameters representing interaction of organic carbon and iron; the primary soil carbon mineralization is checked based on the positive excitation effect in the vertical dimension, and the lateral organic mineral leaching flux which is driven by a ligand dissolution promoting mechanism and has a nonlinear amplification attribute is quantified in the lateral dimension; and generating a management optimization scheme for avoiding the lateral leaching risk. According to the method, the problem of carbon footprint accounting distortion caused by neglecting a tea garden microtopography hydrological process and a soil chemical mechanism in the prior art is solved, and accurate prediction and low-carbon management of carbon emission in a complex mountain tea garden scene are realized.

The invention relates to the technical field of soil carbon sink management, in particular to a soil carbon sink visual management system and method. The method comprises the following steps: determining a target soil area, collecting historical carbon sink data and vegetation distribution data of the target soil area, and receiving a preset monitoring window uploaded by a user side; dividing the target soil area according to the vegetation distribution data, determining a plurality of soil sub-areas, and obtaining a plurality of carbon sink conversion rates of the plurality of soil sub-areas in combination with the historical carbon sink data and the window duration of a preset monitoring window; based on the plurality of carbon sink conversion rates, predicting expected carbon sink values of the plurality of soil sub-regions after the preset monitoring window is ended, and generating a carbon sink visual distribution diagram of the target soil region; and performing carbon sink management on the target soil area according to the carbon sink visual distribution diagram. By implementing the method, the precision, visualization and prospect of soil carbon sink management can be realized.

The invention relates to the technical field of soil environment monitoring, in particular to a forest soil carbon sink dynamic monitoring and data acquisition device which comprises a shell and a probe used for measuring soil carbon content data in real time, a roller mechanism is arranged at the bottom of the shell, and a processor, a power supply module, a communication module and a storage module are arranged in the shell; a shielding mechanism is arranged at the top of the shell and comprises a plurality of detachable arc-shaped plates, the arc-shaped plates are annularly arranged with the shell as the center, solar power generation modules are further arranged on the concave faces of the arc-shaped plates, a rotating mechanism used for driving the arc-shaped plates to rotate is arranged on the shell, and the solar power generation modules are electrically connected with the power supply module. A drilling mechanism used for soil drilling is arranged at the bottom of the shell and comprises a lifting mechanism used for adjusting the drilling depth and a cutting mechanism used for cutting off tree roots in soil. The method is used for meeting the data transmission requirement under the forest complex environment, and continuous analysis of the carbon sink data is guaranteed.

An in-situ, or interfacial, soil sensor is provided, which includes one or more electrodes. Th soil sensor is configured to detect levels of total soil carbon in a sample of soil, the sensor including: a working electrode coated in a composite sensing coating, where the composite sensing coating includes: an active sensing component comprising an organic carbon sensing element functionalized to detect organic carbon moieties and an inorganic carbon sensing element functionalized to detect inorganic carbon moieties, and further includes an encapsulant component.

The invention relates to the technical field of agricultural films, and discloses agricultural recarburization fertilizing mulching film paper, which is prepared from the following raw materials in parts by mass: 200 to 300 parts of modified straw fiber pulp, 20 to 30 parts of polylactic acid, 10 to 15 parts of rice husk charcoal, 4 to 8 parts of polyhydroxyalkanoate, 3 to 5 parts of accelerant, 2 to 4 parts of humectant and 0.5 to 1.5 parts of anti-aging agent. The raw materials are agricultural waste resource utilization products, polylactic acid and polyhydroxyalkanoate are degradable resin, the cost is controllable, environment friendliness is achieved, the soil carbon content can be increased, the soil fertility can be improved, the covering material with ecological benefits and economic benefits is provided for agricultural production, and development of low-carbon agriculture and sustainable agriculture is effectively promoted.

The present application relates to the technical field of ecological environment management, and provides a regional scale future soil carbon spatio-temporal variation prediction method based on a geographic weighted regression model, comprising the following steps: S1: collecting data; S2: constructing a soil carbon future spatio-temporal variation prediction model and performing future spatio-temporal variation prediction of soil carbon density in different periods, comprising: S21: calculating the annual variation rate of soil carbon density; S22: selecting a prediction model auxiliary variable; S23: constructing a soil carbon future spatio-temporal variation prediction model; S24: future spatio-temporal variation prediction of soil carbon density in different periods. The present application uses the geographic weighted regression model to capture the empirical relationship between the annual variation rate of soil organic carbon density and the influencing factors and the spatial difference thereof, integrates the influence of the initial soil carbon density on the annual variation rate of soil carbon density, and effectively realizes the prediction of the spatio-temporal distribution trend of soil carbon density under different future temperature change scenarios in a region.

The invention discloses a rice product carbon emission measuring and calculating method based on data cleaning, and belongs to the technical field of agricultural product carbon emission. The method comprises the following steps: step S1, collecting full-flow data such as a farmland cultivation mode, agricultural material investment, agricultural machinery energy consumption, soil and crop carbon reserve changes, processing and packaging and the like; s2, on the basis of a regional rice database, abnormal values are removed through an isolated forest and random forest algorithm, missing values are supplemented, credible data are obtained, and the database is updated; s3, selecting a rice field equivalent emission factor and a soil carbon change factor according to a cultivation mode, inputting cleaning data through a grey prediction model to predict a plant carbon-containing parameter, calculating a plant carbon content change factor, and calling a preset factor library to determine emission factors such as agricultural materials and energy; s4, based on the credible data and each factor, completing measurement and calculation through a whole-process carbon emission formula; according to the invention, through data cleaning and parameter prediction, the data quality and the measurement and calculation precision are improved, and the parameter acquisition cost is reduced.

This invention provides an acid- and alkali-producing bacterial combination, a compound bacterial agent, and their applications, belonging to the field of microbial technology. This invention provides a bacterial combination including Bacillus subtilis with accession number CGMCC No. 29201. Bacillus sp S1 and Arthrobacterium with accession number CGMCC No. 29202 ( Arthrobacter sp S2. In this invention, both Bacillus S1 and Arthrobacter S2 are acid- and alkali-producing strains with strong environmental adaptability. Through synergistic effects via multiple pathways, including ammonia production, bicarbonate secretion, and activation of soil enzymes, the bacterial combination can significantly increase soil pH, reduce exchangeable aluminum and hydrolyzable acids, and simultaneously increase soil bicarbonate content, thus rapidly and comprehensively improving soil acidity from its root cause. This bacterial combination is of great significance for achieving green, efficient, and sustainable remediation of acidified soils.

The invention belongs to the technical field of soil monitors, and particularly relates to a multi-parameter soil carbon sink in-situ monitor which comprises an upper column, sliding grooves are formed in the two sides of the outer surface of the upper column, an inner ring layer block is installed at the front end of the outer circumferential surface of the upper column in a sliding mode, and an extensible monitoring line is arranged in the multi-parameter soil carbon sink in-situ monitor. The invention aims to adjust the length of a cable according to actual requirements, a sensor can accurately extend to a target position and enlarge the monitoring coverage range regardless of a shallow plough layer or deep soil, and more importantly, a sealing structure is arranged at the joint of a monitoring line, so that moisture and fine particle impurities in the soil can be prevented from invading the line, and the reliability of the monitoring line is improved. Line short circuit or sensor pollution is avoided, the stability of the internal environment can be kept, the influence of external interference on data acquisition is reduced, the adaptability of equipment to the complex soil environment is improved, the service life of the instrument is prolonged by reducing the maintenance frequency, and long-term and continuous carbon sink monitoring is more feasible.

The utility model discloses a soil carbon sink sampling device, which relates to the technical field of soil carbon sink sampling and comprises a base, an upright post is fixed at the upper end of the base, a top plate is arranged at the top end of the upright post, a stable seat is arranged on the base in a sliding manner, and a sampling component is arranged on the stable seat in a sliding manner. A pressure assembly for providing downward pressure for the sampling assembly is arranged at the upper end of the sampling assembly, the pressure assembly comprises a second telescopic rod, the second telescopic rod is fixed on the top plate, and the output end of the second telescopic rod is connected with the sampling assembly. According to the soil sampling device disclosed by the utility model, by arranging the stabilizing seat with the lifting assembly, on the premise of ensuring that the device is convenient to carry and move, the situation that the sampling device topples over due to overlarge shaking amplitude when the sampling device drills for sampling is avoided, and the stability of the soil sampling device is greatly improved.

This disclosure provides a data-driven and scalable method to discover cause-and-effect relationships in data from natural systems that include sparse data sets. This technique can learn a causal graph from heterogenous data sources by combining embeddings from real data and embeddings from simulated data generated by process-based models. The causal graph is used for what-if analysis in out-of-distribution settings. One application is understanding the factors that affect soil carbon. A causal model created by these techniques can be used to discover cause-and-effect relationships that affect soil carbon. This model has applications such as forecasting soil carbon for a future time point to help inform farm practices. Farm practices, like tilling, may be modified in response to predictions provided by the model.

The invention relates to the technical field of soil carbon and nitrogen content detection, and discloses a sensor-based soil carbon and nitrogen content detection method and system.The method comprises the steps that an electric signal is applied to the surface of a sensor probe, an electrical response signal is collected, and electrical characteristics are extracted from the electrical response signal; comparing the extracted electrical characteristics with a preset wear characteristic database to obtain a wear index representing the current physical wear degree of the surface of the sensor probe; an original carbon and nitrogen content signal is obtained, and a corrected carbon and nitrogen content signal is obtained according to the wear index correction signal; and according to the wear index and environmental data measured by a sensor arranged in the sensor probe, performing environmental interference compensation on the corrected carbon and nitrogen content signal to obtain final carbon and nitrogen content data. According to the invention, the health management concept of the sensor is integrated into the measurement system, the synchronous compensation of the self state and environmental interference is realized through an intelligent algorithm, and the credibility and practicability of data are remarkably improved.

This invention discloses a method for constructing a technical model for enhancing soil organic carbon using multiple organic materials. First, indoor simulation experiments are conducted to study the net carbon compensation effect of adding exogenous organic materials to the soil under specific regional climatic conditions, establishing a quantitative relationship between this effect and the amount of organic materials applied. Then, based on field trials conducted within the region, the optimal application ratio of different types of organic materials for increasing soil carbon and fertility while improving crop yield is determined. Finally, based on the target increase in soil organic carbon, a technical model is constructed to quantify the application amount of different types of organic materials. This invention is completely different from the traditional concept of soil organic fertilization. It can accurately assess the amount of different types of organic materials applied based on the target increase in soil organic carbon within a specific region and the type of exogenous organic material, achieving a quantitative increase in soil organic carbon. This method plays an important role in the transformation of low- and medium-yield farmland, the improvement of arable land quality, and the construction of high-standard farmland.

The application provides a closed-loop optimization method for soil organic matter carbon cycle regulation and pollutant synergistic removal, and belongs to the technical field of environmental remediation based on machine learning; first, time series data of soil physicochemical properties, soil pollutant concentration, environmental meteorology and human control parameters of a target area are acquired to construct a data set; second, a soil carbon cycle-pollutant migration coupling prediction model is designed to obtain predicted values of soil organic matter content and pollutant concentration at a future time step; finally, a set of decision variables is established, a double-objective function and a hard constraint condition are set, the trained migration coupling prediction model is taken as an agent model to input a non-dominated sorting genetic algorithm for global optimization, and a Pareto front solution set is extracted; finally, an optimal control parameter combination is obtained through an approximate ideal solution sorting method; through the designed multi-objective closed-loop optimization strategy and the prediction model, high-precision joint prediction of soil organic matter dynamics and pollutant migration evolution is realized.

The application discloses a kind of visual straw utilization characteristic analysis method and system, belong to soil carbon emission field, the method includes: collection soil physical and chemical property data and the characteristic data of different types of rotten straw, and the data collected are preprocessed, and integrated database is obtained by construction;The data in integrated database is processed by biological carbon utilization characteristic analysis model, simulates the dynamic process of soil carbon cycle after corresponding type of rotten straw is applied, obtains key variable data;According to the key variable data calculated, traverse the time series in integrated database, calculate carbon sequestration efficiency, microbial activity and physical protection intensity, obtain enhanced time series data, use enhanced time series data and key variable data as data source, and render to obtain visual diagram.The application improves the calculation accuracy and accuracy of soil carbon emission flux and accumulation.

The application discloses a kind of soil organic carbon and spectral coupling generation model method and system, belong to soil remote sensing monitoring technical field.The method includes five core steps of data preprocessing, dry soil spectrum fitting, SOC mapping model construction, morphological parameter generation, spectral reconstruction and bidirectional verification.Brightness-shape-humidity (BSM) theoretical framework separates spectral brightness and shape effect, fuses random forest algorithm and double-stage hybrid optimization model, and constructs the bidirectional reversible mapping system between spectral morphological parameter and SOC content.The application breaks through the limitation of traditional technology one-way inversion, can be based on soil spectrum high-precision inversion SOC content, and can also generate high-fidelity soil spectrum according to target SOC content, has physical explainability and data-driven flexibility, solves the technical problems of cross-regional spectral deviation and lack of explainability, and provides reliable tool for soil carbon cycle remote sensing monitoring.

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.