355 results about "Carbon storage" patented technology

The invention discloses an intelligent measurement and calculation method and system for forest carbon reserves, and relates to the technical field of carbon reserve measurement and calculation, and the system obtains a forest electronic distribution diagram through a GIS technology and surveying and mapping data, collects various carbon reserve related data, and then carries out the correction and denoising of the collected data through a FLAASH algorithm and other preprocessing technologies; the AI sampling optimization model extracts forest features based on a convolutional neural network CNN, and dynamically adjusts a sampling area; secondary acquisition is carried out, data preprocessing is carried out, the total carbon reserve of the forest is estimated by calculating the carbon reserve of each grid, and the forest succession stage, the carbon sink change and the ecological carbon cycle are further analyzed; and finally, in combination with the dynamic influence coefficient of the carbon reserves and a preset threshold value Q, judging the change trend of the carbon reserves, triggering early warning and giving a coping strategy.

The invention discloses a forest carbon reserve and carbon sink monitoring and evaluating method based on a multi-source remote sensing technology. The method comprises the following steps: multi-source remote sensing data acquisition: respectively acquiring optical remote sensing data and radar remote sensing data; tree species classification and identification: carrying out tree species classification on the forest in the research area to realize tree species scale space distribution information extraction; for different tree species, biomass model parameters obtained through field investigation are combined, a biomass estimation model based on the tree species is established, model input parameters are biomass model parameters, and vegetation biomass is output; calculating the carbon reserve: calculating the vegetation carbon reserve by utilizing the inverted vegetation biomass and combining the carbon content coefficients of different tree species; estimating the soil carbon reserve, and finally obtaining the total carbon reserve of the forest ecosystem: calculating the carbon reserve variation of the forest ecosystem at different time points based on the multi-source remote sensing data of the long-term sequence. According to the invention, large-range, high-precision and continuous forest carbon reserve and carbon sink monitoring and evaluation considering tree species difference is realized.

The invention discloses a monitoring system for forest ecological carbon sink calculation, and belongs to the field of ecological environment monitoring and carbon sink evaluation. Comprising a foundation optical remote sensing module, a crown biomass calculation module, a soil carbon component identification module, a vertical carbon flux detection module, a regional microclimate analysis module, a time sequence big data coupling module, a carbon source and carbon sink model fusion module and a result correction feedback module. The method comprises the following steps: collecting vegetation three-dimensional structure data through a multi-frequency laser radar, calculating unit-area canopy carbon storage acceleration in combination with seasonal climate interference parameters, and fusing turbulent carbon exchange and soil carbon change data to construct a regional carbon flux coupling model; the system collects cross-rosa chinensis carbon change data to construct a multi-dimensional feature tensor diagram, dynamically updates a weight and a fitting function, realizes carbon sink dynamic estimation and feedback optimization based on a biology-climate-time three-dimensional cooperation mechanism, and improves forest carbon sink monitoring precision and stability. The beneficial effect is that scientificity and accuracy of ecological environment monitoring and carbon sink evaluation are improved.

The invention relates to the technical field of carbon sequestration capacity monitoring, in particular to a carbon sequestration capacity monitoring and evaluating method for an island ecosystem, which comprises the following steps: S1, multi-source data fusion and habitat partitioning: dividing an island habitat type partitioning map; s2, stratified sampling and carbon reserve actual measurement: measuring the carbon content, and constructing a carbon reserve actual measurement database; s3, constructing a dynamic carbon sink model: carrying out a whole growth cycle environment simulation experiment on sea grass and algae in different habitats, and establishing the dynamic carbon sink model taking habitat types as classification units; s4, carbon sequestration capability quantification and bottleneck diagnosis: calculating carbon sequestration contribution values of various habitat types, identifying low-efficiency carbon sequestration areas through contribution value comparison, and analyzing environmental limitation factors of the low-efficiency carbon sequestration areas; and S5, integrating a sink increasing technology and generating a path: designing a habitat specific sink increasing scheme for the identified low-efficiency carbon sink area. According to the invention, high-precision monitoring, intelligent evaluation and system optimization of the island ecosystem carbon sequestration capability are realized.

The invention provides a carbon neutralization evaluation method for an ecological park, and the method comprises the steps: obtaining the basic data of various carbon assets in the ecological park, and the basic data comprises the natural ecological system carbon sink capability and human activity carbon emission intensity data; according to the carbon asset basic data, a time sequence analysis method is adopted to carry out modeling on the change trend of the carbon sink capacity and the carbon emission intensity, and a change trend model is obtained; modeling the spatial distribution of the carbon reserves and the carbon flux by utilizing a geographic information system technology and combining a change trend model to obtain a spatial distribution model; key variables are extracted from the spatial distribution model, and the effects of different factors on the carbon assets are quantified by adopting a multiple regression analysis method; acquiring carbon asset monitoring data, evaluating data quality, and if an abnormal value exists, performing correction by adopting a data cleaning algorithm; and according to the corrected carbon asset data, adopting a random forest model to predict the carbon sink capacity and the carbon emission intensity, and generating a dynamic evaluation result.

The invention relates to the technical field of ecological environment monitoring and geographic information, in particular to a coastal wetland soil sample point quality evaluation method and system based on multi-source environmental data. According to the method, remote sensing, climate, terrain, ocean and historical land coverage data are fused, and indexes are extracted and standardized; an environment similarity score (weighted Markov / neighbor weighted Euclidean), a historical consistency score (multi-temporal coverage + transfer penalty) and a source reliability score (metadata integrity + anomaly detection + NDVI-SOC residual) are constructed respectively; comprehensive scores are obtained through self-adaptive weight fusion, A / B / C grades are divided, and a report and a high-quality sample point set are output. The system comprises a data access module, a scoring engine module and a visualization module, and the precision and robustness of soil mapping and carbon storage evaluation can be remarkably improved.

The invention discloses a coastal zone carbon sink data acquisition management system and method, and particularly relates to the technical field of carbon sink evaluation, and the method comprises the following steps: dividing carbon flux parameter partitions based on vegetation types, sediment characteristics and historical carbon storage levels, and establishing initial reference values; plant biomass, soil organic carbon content and non-carbon dioxide greenhouse gas flux are regularly collected according to fixed sampling points, and a carbon sink observation data sequence is formed; when data exception triggers an evaluation process, generating a carbon sink credibility index and an ecological interference response index; inputting the two indexes into a prediction model to output an adjustment vector, generating an adjustment strategy in combination with a weight, and dynamically updating an initial carbon flux reference value of each partition; according to the method, regionalized management is realized by constructing carbon flux parameter partitions, a carbon sink observation data sequence is established, an evaluation index is dynamically generated, intelligent updating of a carbon flux reference value is completed in combination with a prediction model, and the precision, responsiveness and adaptive adjustment capability of carbon sink monitoring are improved.

The invention relates to the technical field of forestry investigation, and discloses a forestry investigation system for carbon sink forest management, which comprises a data acquisition module, a data processing module, a data integration module, a model construction and analysis module, a decision and early warning module, an output module and an implementation monitoring module. Through multi-dimensional index investigation, information can be obtained from multiple levels of an ecological system, the carbon cycle process and ecological functions of the carbon sink forest are comprehensively known, more comprehensive data support is provided for scientific management, in addition, more variables influencing carbon sink are introduced, so that the model better conforms to the actual situation, the accuracy and generalization ability of the model are enhanced, and the method is suitable for popularization and application. According to the method, carbon reserve changes of the carbon sink forest under different conditions can be better predicted, finally, effect prediction of multiple management schemes is provided for managers through scene simulation and model analysis, scientific and reasonable management decision making is assisted, and meanwhile, an early warning mechanism can find potential risks in time and guarantee stable development of the carbon sink forest.

The invention relates to the technical field of carbon sink calculation, in particular to a forestry carbon sink calculation model, a forestry carbon credit calculation method and a storage medium. The model comprises an overground biological carbon sink amount estimation module used for calculating overground biological carbon sink data of a target area, an underground soil carbon sink amount estimation module used for calculating soil carbon sink data of the target area, and a litter carbon reserve estimation module used for calculating litter carbon sink data of the target area. The combined loss calculation module is used for calculating carbon loss data; and the final carbon sink total quantity fusion module is used for calculating the carbon sink quantity according to the aboveground biological carbon sink data, the soil carbon sink data, the litter carbon sink data and the carbon loss data. A forestry carbon sink calculation model based on multiple dimensions is established through multiple types of carbon sink estimation modules, and the problem that the requirement for rapid authentication of the global carbon market cannot be met due to high manual dependency is solved; the problems of low calculation precision, high cost and the like caused by insufficient model dynamics, single model calculation dimension and the like are solved.

A method for calculating carbon storage in a mixed forest ecosystem is provided. The method includes: acquiring basic geographic data, meteorological data, eco-physiological parameter, thinning management history data, and validation data; proposing an improved biome-biogeochemical cycles (Biome-BGC) model suitable for simulating carbon storage of a mixed forest ecosystem under management by improving a phenology module, adding a thinning operation management module, and optimizing the eco-physiological parameter, based on an existing Biome-BGC model; simulating, by taking a pine-oak mixed forest as a research object, the carbon storage based on the improved Biome-BGC model; validating the improved model; analyzing sensitivity of the eco-physiological parameter by an extended Fourier amplitude sensitivity test (EFAST) method; and selecting a highly sensitive parameter, and analyzing an effect of the highly sensitive parameter on the carbon storage by a path analysis method. The improved model exhibits good performance in calculating the carbon storage of the mixed forest.

The invention provides a single tree segmentation-biological parameter estimation method based on a vehicle-mounted LiDAR point cloud, and belongs to the technical field of urban landscaping intelligent monitoring. The point cloud individual tree segmentation algorithm based on tree geometric feature constraint is designed for solving the problem that individual tree segmentation is difficult due to crown overlapping in an urban scene, and the method takes a tree geometric structure as a constraint, combines point cloud reflection intensity information, a clustering algorithm, a main direction index and other means, and obtains the individual tree segmentation algorithm based on the tree geometric feature constraint. And accurate extraction of trunks and crowns in the scene point cloud is realized. Aiming at the problems of high feature redundancy, poor model interpretability and the like in an existing estimation method, a random forest model is taken as a basis, an adaptive feature selection algorithm is introduced to improve variable screening efficiency, hyper-parameters are dynamically adjusted by utilizing pigeon inspired optimization, and generalization and stability of the model are enhanced; an estimation model used for estimating biological parameters such as leaf area index, biomass and carbon reserve is designed.

The invention discloses a forest aboveground carbon reserve estimation system and method based on machine learning, and relates to the field of forest carbon reserves, and the method comprises the steps: collecting radar data, optical remote sensing data and forest aboveground carbon reserve actual measurement data, and carrying out the data preprocessing of the radar data and the optical remote sensing data; acquiring forest vertical structure information by using the radar data after data preprocessing; utilizing the optical remote sensing data after data preprocessing to obtain vegetation spectral characteristics; constructing a carbon reserve estimation model by adopting XGBoost, and adjusting hyper-parameters of the carbon reserve estimation model through a Bayesian optimization framework; and carrying out forest aboveground carbon reserve estimation to generate a forest aboveground carbon reserve distribution map with continuous space. According to the method, the hyper-parameters of the carbon reserve estimation model are adjusted through the Bayesian optimization framework, the complex mapping relation between the hyper-parameters and the performance of the carbon reserve estimation model is learned, and oriented efficient exploration of the hyper-parameter space is achieved.

The invention relates to a subtropical forest aboveground carbon reserve estimation method fused with a multi-modal remote sensing technology. The method comprises the following steps: acquiring and preprocessing multi-modal remote sensing data of a research area; feature extraction is carried out based on the preprocessed multi-modal remote sensing data; adopting a multi-scale feature fusion method to perform complementary fusion on the extracted feature data; based on the fused feature data, performing model training by adopting a hybrid machine learning method to obtain a carbon reserve estimation model; and estimating the carbon reserve by using the carbon reserve estimation model. According to the invention, subtropical forest aboveground carbon reserves can be accurately estimated, and spatial distribution characteristics and main influence factors of the subtropical forest aboveground carbon reserves can be systematically analyzed.

The invention discloses a highway area vegetation carbon sink plaque calculation method. The method comprises the following steps: step 1, acquiring multi-source remote sensing data; 2, data preprocessing; step 3, dividing vegetation types; step 4, vegetation feature extraction; 5, constructing a biomass estimation model; step 6, laboratory determination; step 7, spectral analysis; 8, calculating carbon reserves; step 9, carbon sink plaque identification; and step 10, carbon sink plaque feature analysis. The invention provides the carbon sink measuring and calculating method specially aiming at the highway area vegetation, the carbon sink quantity of the highway area vegetation is quickly obtained by comprehensively applying multi-source data and various technical means, and the carbon sink plaque characteristics of the highway area vegetation are deeply analyzed, so that a scientific and comprehensive basis is provided for ecological protection and carbon sink management.

A method for monitoring subterranean hydrocarbon storage sites includes simulating a seismic experiment using a baseline model. The method also includes perturbing the baseline model to produce one or more perturbed baseline models. The method also includes selecting a plurality of candidate acquisition geometries to probe the one or more perturbed baseline models. The method also includes extracting a set of simulated data from the simulated seismic experiment that corresponds to the plurality of candidate acquisition geometries. The method also includes executing a model probing exercise for the extracted set of simulated data for each candidate acquisition geometry and each of the one or more perturbed baseline models.

The invention discloses a coastal culture pond expansion recognition and mangrove forest ecological occupation and supplement balance evaluation method, and belongs to the technical field of environment remote sensing monitoring and ecological evaluation. Aiming at the problems that a mangrove forest ecological system is damaged by expansion of a coastal culture pond and a dynamic monitoring and quantitative evaluation mechanism is lacked, the evaluation method is provided and comprises the following steps: preprocessing a multi-temporal satellite remote sensing image; using a convolutional neural network computer model to supervise, classify and distinguish culture ponds, mangrove forests and other land types; extracting boundaries of the two-time-phase culture pond and identifying a newly-added area; in combination with the mangrove forest distribution map, obtaining an occupied area through spatial overlay analysis; establishing a biomass regression model based on the vegetation index value of the infringed area to estimate the biomass per unit area, and hierarchically calculating the carbon reserve loss amount; and calculating a compensation area according to the carbon loss amount and the unit carbon reserve of the mature mangrove forest in the target area, and generating an occupation and compensation balance scheme in combination with unutilized mud flat spatial distribution. The method is used for dynamic monitoring, accurate quantification and scientific compensation of mangrove forest ecological loss.

The invention discloses a forest land ecosystem carbon reserve accounting method and system, and the method comprises the steps: obtaining and screening feature variables, such as optical remote sensing data, vegetation indexes, texture factors, satellite-borne laser radar feature parameters, so as to determine features having the most influence on a target variable, and removing redundant and irrelevant variables; the method comprises the following steps of: screening characteristic variables, reserving variables which contribute to the model prediction effect, improving the precision and stability of the model, constructing a canopy height inversion model by adopting a random forest method based on the screened characteristic variables, realizing accurate estimation of the canopy height, and finally calculating the biomass of vegetation of the forest ecological system by using a different-speed growth equation. And finally, summarizing the biomass of the vegetation of the forest ecological system to obtain the total biomass of the vegetation, and multiplying the total biomass of the vegetation by the carbon-containing coefficient to obtain the vegetation carbon reserve of the forest ecological system, so that wide-area and efficient forest parameter monitoring is realized, the monitoring cost is reduced, the environmental adaptability is improved, and the problems of data continuity and standardization are solved.

The invention relates to the technical field of agricultural environmental protection, and discloses a farmland emission reduction and carbon sequestration collaborative optimization system, which is characterized in that a data processing module is used for collecting farmland multi-source data of main grain products and preprocessing the collected data to obtain preprocessed multi-source data; the model building module is used for screening key factors which have obvious influence on farmland carbon reserves on the basis of the preprocessed multi-source data, and building a farmland emission reduction and carbon sequestration model; the collaborative optimization module is used for searching a Pareto optimal solution set in a parameter space by adopting a non-dominated sorting genetic algorithm, and introducing a fuzzy logic controller to process target conflicts to obtain a collaborative optimization scheme of farmland emission reduction and carbon sequestration; the real-time monitoring module is used for monitoring farmland environment change and emission reduction and carbon sequestration effects in real time and regularly adjusting and optimizing a collaborative optimization scheme; the synergism and effectiveness of the technology are improved, collaborative improvement of farmland yield increase and emission reduction carbon sequestration is achieved, and the problem that yield increase and emission reduction carbon sequestration are not collaborative in the prior art is solved.

The disclosed method includes: determining a subsurface space of interest; arranging a source sensor array relative to a receiver sensor array of a Surface Distributed Acoustic Sensing (S-DAS); transmitting, from the source sensor array, a first set of seismic signals indicating baseline measurements; receiving, using the receiver sensor array, the baseline measurements and thereby generate baseline data; injecting fluid into the subsurface space of interest; transmitting, using the source sensor array, a second set of seismic signals indicating fluid plume measurements; receiving, using the receiver sensor array, the fluid plume measurements and thereby generate fluid plume data; determining, based on the baseline data and the fluid plume data, time-shift data and thereby generate fluid plume extent data; formatting the fluid plume extent data into data values that characterize an extent or rate of spread of the fluid plume; and visualizing the extent or rate of spread of the fluid plume.

The invention relates to the technical field of environmental science and engineering, in particular to a method for accounting water and soil conservation small watershed carbon sink. The method for accounting the water and soil conservation small watershed carbon sink comprises the steps that a small watershed area is selected; obtaining parameter data of the small watershed area based on the selected small watershed area; calculating a reference line carbon reserve CBSL, t and a project scene carbon sink CPROJ, t after the small watershed region is implemented for t years; and based on the reference line carbon reserve CBSL, t and the project scene carbon sink CPROJ, t of the small watershed region after t years after the small watershed region is implemented, the carbon sink CDRt of the small watershed region after t years after the small watershed region is implemented is obtained by combining net carbon sink accounting, the small watershed region and related parameter data are selected, a calculation formula is adopted to calculate the reference line carbon reserve CBSL of the small watershed region after t years after the small watershed region is implemented, and the carbon sink CDRt of the small watershed region after t years after the small watershed region is implemented is obtained. According to the method, the accurate carbon sink amount CDRt is finally obtained through calculation of the project scene carbon sink amount CPROJ, t and the project scene carbon sink amount CPROJ, t, the vacancy in the prior art is made up, and the practicability is high.

The invention discloses a vegetation carbon density and carbon reserve estimation method and device based on machine learning, and relates to the field of vegetation carbon density and carbon reserve accounting, the vegetation carbon density and carbon reserve estimation method based on machine learning mainly comprises the following steps: obtaining land cover and vegetation type data of a research area; setting a standard sample plot in the research area and investigating to obtain overground, earth surface and underground vegetation carbon density and carbon reserve data of the sample plot; performing preprocessing and information extraction on the land cover data of the research area to obtain remote sensing ecological factors of the sample plot; according to the sample plot remote sensing ecological factors and sample plot carbon density measured data, vegetation carbon density model construction and optimization are carried out, and an optimal estimation model is obtained; and according to the vegetation type and the vegetation area, utilizing the optimal estimation model to obtain the vegetation total carbon reserve in the research area. According to the vegetation carbon density and carbon reserve estimation method and device based on machine learning provided by the invention, rapid, dynamic and accurate estimation of multi-element vegetation carbon density and carbon reserve of regional scale forest grass wet and rough vegetation can be realized.

The invention relates to the technical field of ocean engineering, in particular to a marine ranch blue carbon monitoring and intelligent regulation and control system based on offshore wind power complementary energy supply, which comprises a wind power complementary energy capture and standardization module, an environmental parameter acquisition module, a blue carbon reserve calculation module, an ecological regulation and control parameter generation module and an execution feedback optimization module. Wherein the wind power complementary energy capture and standardization module is used for collecting excess electric energy of an offshore wind plant in real time; the environmental parameter acquisition module is used for acquiring a multi-source environmental parameter set; the blue carbon reserve calculation module is used for calculating and outputting a blue carbon reserve index hour by hour; and the ecological regulation and control parameter generation module is used for generating a regulation and control parameter set. According to the invention, by constructing a wind power complementary energy and blue carbon regulation and control closed-loop linkage mechanism, integrated coordination of multi-source environment monitoring, carbon reserve dynamic evaluation and intelligent ecological regulation and control is realized, and the blue carbon formation efficiency and the wind power resource utilization rate of the marine ranch are effectively improved.

The invention discloses a method and device for intelligently monitoring and estimating forest carbon reserve based on tree diameter at breast height, and belongs to the technical field of tree growth and carbon sink monitoring and estimation.The device comprises a rotating disc wound with a surrounding belt, the surrounding belt is used for surrounding a tree, and along with growth of the tree, the surrounding belt is pulled out to drive the rotating disc to rotate; the deflection angle of the turntable is detected through the angle sensor, so that the diameter at breast height of the tree can be obtained through conversion; and the rear end calculates the biomass of the single tree based on the tree diameter at breast height data so as to calculate the forest carbon reserve, and predicts the carbon sink trend in the short time in the future by using an autoregression model. Through an automatic and intelligent mode, real-time monitoring and accurate estimation of the tree growth condition and the forest carbon reserve are realized, and the monitoring efficiency and the data precision are improved.

The invention relates to the technical field of plant ecological carbon sink measurement and calculation, in particular to a tea garden carbon reserve dynamic monitoring and prediction method based on meteorological data, a typical sample plot in a tea garden is selected, meteorological data and basic carbon reserve of the typical sample plot are acquired, and the meteorological data comprise real-time data and forecast data; establishing dynamic monitoring, and performing heat damage identification and freeze damage identification on real-time data; establishing a carbon reserve dynamic correction model; a carbon reserve prediction model is established, and when heat damage and / or freezing damage occur in prediction data, the carbon reserve dynamic correction model is used for prediction to obtain a prediction result; according to the method, the damage of high-temperature stress to the tea trees has a gradual superposition characteristic, high-temperature inhibits photosynthesis, so that calculation is performed in a summation form, cold waves increase respiratory consumption, and the damage mainly depends on the intensity peak value and the cooling rate of a single event; by providing more accurate dynamic measurement and calculation as the supplement of static measurement and calculation, the change condition of the carbon reserves of the tea garden can be reflected in time.

The disclosed methods include: determining a central reference point for fluid migration associated with the stored subsurface fluid; generating a radial coverage detection space extending from the central reference point for the fluid migration associated with the stored subsurface fluid; initializing a subsurface computing model for the stored subsurface fluid; overlaying the radial coverage detection space or detection system over the subsurface model for the stored subsurface fluid; execute one or more simulations by adapting or varying the one or more parameters of the subsurface computing model based on fluid plume data or fluid pressure data associated with the stored subsurface fluid; generating, based on the fluid plume data or fluid pressure data, spatial or temporal fluid distribution data associated with the stored subsurface fluid; and generating, based on the spatial or temporal fluid distribution data, PP data and PS data associated with the stored subsurface fluid.

The invention provides a land deep saline water layer-oriented carbon sequestration determination method and apparatus, and an electronic device. Comprising the steps of obtaining carbon sequestration data, and constructing a carbon storage mathematical model based on the carbon sequestration data; a first determination step: determining the component mass fraction of the components in each phase in the carbon storage mathematical model of the current time step according to the carbon sequestration data and a flash evaporation equation; a solving processing step: based on the component mass fraction, solving the carbon storage mathematical model by adopting a Newton-krylov algorithm based on a field splitting precondition to obtain a solving result; a second determination step: determining the step length of the next time step according to the solving result; the first determining step, the solving processing step and the second determining step are repeatedly executed at least once until the preset simulation duration is reached, and a carbon sequestration simulation result is obtained; and evaluating the carbon sequestration capability of the land deep saline water layer by using a carbon sequestration simulation result. The problem that the carbon sequestration capacity of the land deep saline water layer is not accurately evaluated in the prior art is solved.

The invention provides a forest carbon reserve prediction method, device and equipment based on forestry carbon sink monitoring, and relates to the technical field of data analysis. According to the method, firstly, a forest optical image and climate-related data are acquired, and the forest optical image at least comprises a red light band and a near-infrared band; secondly, extracting an optical global semantic vector from the forest optical image, and performing semantic constraint on the optical global semantic vector based on growth condition semantic information in the forest optical image to form an optical growth semantic vector; then, a climate global semantic vector is extracted from climate related data; thirdly, performing semantic fusion on the optical growth semantic vector and the climate global semantic vector to obtain a forest global semantic vector; and finally, performing semantic reduction based on the forest global semantic vector to form a carbon reserve prediction result. Based on the above content, the problem that the reliability of forest carbon reserve prediction is relatively low in the prior art can be improved.

The invention discloses a composite carbon sink estimation method based on multi-mode mangrove forest canopy data, and belongs to the technical field of carbon sink calculation in ecological environment monitoring, and the method comprises the steps: obtaining laser radar point cloud data, a hyperspectral remote sensing image and ground actual measurement data of a mangrove forest region; preprocessing the laser radar point cloud data, the hyperspectral remote sensing image and the ground actual measurement data to obtain standardized data; structure parameters and biochemical parameters are inversed based on the standardized data; fusing the structure parameters and the biochemical parameters to construct a comprehensive inversion model; the mangrove forest carbon reserve is calculated through the comprehensive inversion model; and verifying the precision of the comprehensive inversion model through ground measured data, and outputting a final carbon sink estimation result.

The invention discloses a vegetation type-considered wetland carbon reserve inversion and contribution analysis method, which comprises the following steps of: (1) obtaining a spatial mode of a wetland dominant vegetation type according to multi-source remote sensing data and ground observation plant community sample data; (2) constructing a multi-model collaborative learning framework on the spatial mode of the wetland advantageous vegetation type obtained in the step (1); (3) analyzing the carbon reserve difference of different vegetation types on the basis of the spatial pattern of the carbon reserve of each advantageous vegetation type of the wetland predicted in the step (2) so as to evaluate the influence value of the vegetation types on the soil carbon reserve; and (4) preferentially selecting characteristic factors from the key environment factors identified in the step (3) to obtain direct and indirect influence of each characteristic on carbon reserve change. The method has the following beneficial effects that a multi-model collaborative learning framework integrated with the advantages of various machine learning is combined with a data-driven regression model, and scientific support is provided for fine management and ecological protection strategies of the wetland.

The invention discloses a power system carbon emission time sequence simulation method and device considering energy storage carbon storage characteristics. The method comprises the following steps: acquiring a time-interval calculation result and energy storage data of time sequence production simulation of a power system; based on the energy storage data and a pre-constructed energy storage unit carbon emission storage model, calculating a total carbon emission amount and an electrical carbon factor of each energy storage unit; and according to the total carbon emission amount and the electric carbon factor of each energy storage unit, correcting the time-interval time sequence carbon emission data of the power system to obtain the corrected time-interval time sequence carbon emission data and the corrected electric carbon factor of the power system.