240 results about "Carbon stock" patented technology

The present application relates to a carbon stock monitoring technique, and specifically relates to an evaluation method and system for the carbon sink effect of a coastal wetland. The evaluation method comprises: acquiring a remote sensing image set, within a preset time period, of a coastal wetland to be subjected to evaluation; respectively identifying evolution data of different types of coastal wetlands in the remote sensing image set, wherein the evolution data comprises conversion data between different types of wetlands, and net change data, other than the conversion data, of each type of coastal wetland; and on the basis of the conversion data and the net change data, evaluating the carbon sink effect of the coastal wetland to be subjected to evaluation, so as to obtain a carbon sink effect evaluation result. In addition to performing qualitative analysis on a mature wetland area in the prior art, the present application further takes into comprehensive consideration factors which represent a carbon sink effect, such as evolution data and evolution types before and after the evolution of a coastal wetland to be subjected to evaluation, so as to comprehensively perform carbon sink effect evaluation on said coastal wetland, thereby more accurately obtaining the carbon sink effect of said coastal wetland.

The invention relates to the technical field of forest carbon sequestration, in particular to a forest carbon sequestration remote sensing monitoring method and system, and aims to solve the problems that in the prior art, a carbon sequestration prediction result can be obtained by inputting a target area image into a preset carbon sequestration model, a more comprehensive carbon reserve prediction model cannot be constructed in combination with a vegetation index, and the carbon sequestration prediction result cannot be obtained by using the preset model. The problem that the prediction result is inaccurate due to the fact that the features of specific tasks cannot be effectively handled is solved. According to the invention, ground survey data and vegetation indexes are combined through the prediction model construction module to construct a comprehensive carbon reserve prediction model, and the carbon reserve prediction model is obtained through machine training learning and can effectively cope with the characteristics of a specific task, so that the prediction accuracy and flexibility are improved; and a high-resolution carbon reserve distribution map is generated through an efficient machine learning algorithm, scientific decision and management are supported, and an important tool is provided for carbon reserve research.

The present invention relates to the technical field of dynamic carbon sink accounting, and specifically relates to a dynamic carbon sink accounting system based on multi-modal AI remote sensing monitoring and blockchain-based evidence storage. The system collects optical remote sensing, radar, photosynthetically active radiation and meteorological data, performs unified spatiotemporal calibration on the data, and then fuses the calibrated data by means of a cross-modal attention mechanism, so as to generate multi-modal feature vectors, and inputs same into a TCN model for carbon stock and trend prediction. A prediction result and metadata are uploaded to a blockchain by means of smart contracts, so as to generate a carbon sink NFT including a geographic fence and a confidence level, thereby realizing trusted evidence storage. A residual mapping function is established in view of on-chain historical data, so as to dynamically optimize the model, and improve the accounting accuracy. The system improves the fusion capability and prediction accuracy, and enhances the credibility and transparency of carbon asset management and transactions.

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 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.

Provided is a method for calculating a carbon stock of a mixed forest ecosystem, which method belongs to the technical field of carbon sink estimation of forest ecosystems. The method comprises: acquiring basic geographic data, meteorological data, physiological and ecological parameters, historical operation data and verification data; on the basis of an existing Biome-BGC model, improving a phenological module, adding a thinning operation management module, and optimizing the physiological and ecological parameters, so as to propose an improved Biome-BGC model suitable for simulating a carbon stock of a managed mixed forest ecosystem; taking a mixed pine and oak forest as a research object, and using the improved model to simulate a carbon stock; verifying the improved model; using an extended Fourier amplitude sensitivity test method to analyze the sensitivity of the physiological and ecological parameters; and on the basis of selecting high-sensitivity parameters, using a path analysis method to analyze the influence of the high-sensitivity parameters on the carbon stock. The proposed improved model exhibits a better performance in the aspect of a carbon stock of a mixed forest.

The invention discloses a multi-model coupled regional terrestrial ecosystem carbon reserve estimation method, which is suitable for the field of carbon cycle and carbon reserve estimation. The method comprises the following steps: firstly, collecting remote sensing data, and then estimating overground, underground and soil annual carbon density mean values of a regional terrestrial ecosystem by adopting an ARIMA model; the method comprises the following steps: clustering regions with different carbon densities by adopting a CatBoost model, dividing the carbon densities of a regional ecological system into low, medium and high levels, and avoiding the influence of spatial heterogeneity among different density levels on subsequent model training; an RNN model is constructed to evaluate the carbon density conditions of different levels of unit grids, and interannual dynamic carbon density parameters of different types of ecosystems are obtained based on multi-source data; and finally, calculating the carbon reserves of the unit grids, analyzing the annual average change trend and the spatial distribution condition of the carbon reserves, and realizing accurate evaluation of the regional terrestrial ecosystem carbon sink. The method is simple in step and good in using effect, and the change condition of the carbon sink capacity in the corresponding time can be obtained according to the sampling time interval of the input data.

The invention provides a method and a system for estimating organic carbon reserve change of soil in a vegetable field in a short time, and belongs to the technical field of soil measurement. The method comprises the following steps: acquiring net ecosystem carbon income and expenditure (NECB); and estimating the organic carbon reserve of the vegetable field soil by using net ecosystem carbon income and expenditure (NECB). According to the method, the organic carbon reserve of the soil in the vegetable field can be quickly estimated, the influence of different farming measures on the organic carbon reserve of the soil in the vegetable field in a short period can be known, and farmers and agricultural managers can optimize management measures such as fertilization, irrigation and planting modes; carbon emission and fixed conditions of a farmland ecosystem can be determined, so that the speed and the degree of climate change can be better predicted, and reasonable vegetable planting management measures can be taken in time.

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 provides a desert area carbon reserve prediction method and system based on a PLUS-I nVEST model, and relates to the technical field of carbon reserve estimation and prediction. The desert region carbon reserve prediction method based on the PLUS-I nVEST model comprises an acquisition module used for acquiring land utilization data and carbon density data with equal year intervals in multiple periods, and driving factor and limiting factor data corresponding to the land utilization data; according to the method, the land utilization types are systematically and completely classified, the carbon density corresponding to each land utilization type is optimized, the estimation precision of the carbon reserves is improved, potential changes of driving factors and model parameter setting are fully considered, the simulation precision of future land utilization changes is higher, and the simulation efficiency is improved. The method can provide a theoretical basis for ecological construction and sustainable development of a research area, and is of great significance to optimization of a land utilization pattern of the research area and reasonable ecological decision making.

The invention discloses a method and a system for detecting metabolism kinetics of organic carbon and microbial carbon in deep soil, and relates to the technical field of soil science and environmental science. Comprising the following steps: acquiring the content and structural characteristics of organic carbon in the soil of the rice field, and determining the composition, function and dynamic response mechanism of microbial communities to environmental change; setting a control group and a treatment group, and obtaining soil organic carbon reserve change rates, CO2 emissions and influence factors in the control group and the treatment group at different time points; and constructing a data analysis model according to the soil organic carbon reserve change rate, CO2 emission and influence factors in the control group and the treatment group at different time points, and generating a comprehensive report of deep soil organic carbon and microbial carbon metabolism kinetics. According to the invention, a scientific basis is provided for optimizing agricultural management and coping with climate change, the positive influence of biomass charcoal addition on soil health is disclosed, and the development of sustainable agriculture is promoted.

The present invention relates to the technical field of forest carbon sinks, and more specifically, to a forest carbon sink remote sensing monitoring method and system, which are used to solve the problem that the prior art can obtain a carbon sink prediction result by inputting a target area image into a preset carbon sink model, but cannot combine with a vegetation index to build a more comprehensive carbon storage prediction model, and the use of the preset model cannot effectively cope with the characteristics of a specific task, resulting in inaccurate prediction results. The present invention combines ground survey data and vegetation indices through a prediction model construction module to build a comprehensive carbon storage prediction model, and obtains a carbon storage prediction model through machine training and learning, which can effectively cope with the characteristics of a specific task, thereby improving the accuracy and flexibility of the prediction, and also generates a high-resolution carbon storage distribution map through an efficient machine learning algorithm, supports scientific decision-making and management, and provides an important tool for carbon storage research.

The invention relates to the technical field of mine ecological restoration and carbon reserve estimation, in particular to a coal mining subsidence area ecological system carbon reserve estimation method and system.The method comprises the steps that S1, a coal mining subsidence area is divided according to land utilization types, and the range and area of each land utilization type are determined; s2, dividing the coal mining subsidence area according to a space state, and forming a land utilization grid table with a unique code with each land utilization type; s3, obtaining an initial carbon density parameter corresponding to the land utilization type, calculating the surface subsidence, and carrying out differential correction on the carbon density parameter of each subarea of the coal mining subsidence area; and S4, calculating the total carbon reserves of different collapse stages, analyzing the carbon reserves, the carbon sequestration amount and the spatial distribution of each land utilization type, and comprehensively analyzing the time change characteristics of the carbon reserves. According to the method, the carbon density change is analyzed in combination with land utilization and the collapse state, and the time-space evolution characteristics of the carbon reserves in the coal mining subsidence area are evaluated.

The embodiment of the invention discloses a carbon sink monitoring system based on an existing carbon sink monitoring methodology, and the system comprises a data module which is used for obtaining the whole forest data in a to-be-monitored region; wherein the whole forest data is obtained based on remote sensing technology monitoring; the biomass calculation module is used for calculating by combining the whole forest data with the verified biomass remote sensing model to obtain biomass data; the data processing module is used for performing estimation processing on the biomass data in combination with a constructed regression model for carbon reserve estimation so as to obtain carbon reserves; subtracting the carbon reserves at the starting time and the ending time to obtain corresponding carbon sinks; the method has the advantages that multiple technologies based on satellite remote sensing are integrated, a calculation method is provided for key parameter variables in existing methodology, the data acquisition difficulty is reduced, and more complete and comprehensive monitoring is provided for forest land carbon sink monitoring.

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.

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 discloses a regulation and control method for recarburization and nitrogen fixation of newly-reclaimed desertification saline-alkali soil, which is characterized in that the soil microenvironment is improved by combined application of bio-organic fertilizer / charcoal and attapulgite fertilizer, and nitrogen fixation of soil in a growth period and improvement of a soil structure are realized by mixed sowing of gramineous green manure crops and leguminous green manure crops. And after the growth period ends, returning the green manure to the field to realize soil recarburization. According to the recarburization and nitrogen fixation regulation and control method for the newly-reclaimed desertification saline-alkali wasteland, the soil nutrient and carbon reserve can be remarkably improved, and compared with other green manure crops or combinations, the total nitrogen and organic carbon of the soil are remarkably improved after the method is implemented. Therefore, the method has important significance on recarburization, nitrogen fixation and fertilization regulation and control of newly-reclaimed desertification saline-alkali soil, and can provide a development idea for efficient development and utilization of saline-alkali sand and guarantee of agricultural production safety.

The invention provides a blue carbon market adaptive accounting method based on multi-source data and Bayesian updating, and belongs to the technical field of natural resource planning and ecological management. The method comprises the following steps: firstly, constructing a standardized multi-source fusion data set through a systematic process, and then preprocessing collected data to generate a standardized blue carbon multi-source fusion data set; then, through a Bayesian-based carbon reserve estimation model, outputting a monitoring point carbon reserve prior value, fusing a monitoring point carbon reserve mean value with a spatial prior value, and updating monitoring point carbon reserve distribution; and finally, constructing a carbon credit risk index system, calculating risk indexes of the plot layer and the region layer, updating a plot layer carbon reserve mean value by using the updated monitoring point carbon reserve distribution, and generating a blue carbon project reference transaction price according to the carbon reserve mean value and the risk indexes. According to the invention, a full-chain blue carbon intelligent accounting system is constructed, and interdisciplinary fusion of an ecological model, deep learning and a carbon market mechanism is realized.

The invention discloses a multi-scenario land utilization carbon reserve simulation method based on combination of deep learning and a PLUS-InVEST model. The method comprises the following steps: collecting and preprocessing historical land utilization, natural environment and social economic data; analyzing land utilization change characteristics; optimizing an LEAS module of the PLUS model by using a deep neural network, excavating a nonlinear relationship between land utilization expansion and driving factors, and generating an expansion probability graph; configuring a neighborhood weight and a transfer matrix of the CARS module; predicting future land utilization requirements under different scenes by adopting a long-short-term memory neural network; inputting the demand into a CARS module, and simulating and generating a future land utilization space pattern under multiple scenes in combination with differential constraint conditions; based on the InVEST model carbon reserve module and the carbon density data, calculating, comparing and analyzing the carbon reserve of each scene; and outputting a result, carrying out precision verification, and proposing a land utilization planning and carbon regulation strategy. According to the method, the land utilization simulation precision and the carbon reserve evaluation scientificity are improved, and decision support is provided for regional low-carbon land utilization.

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 mangrove forest wetland soil carbon reserve assessment method, device and equipment and a storage medium, and the method comprises the steps: obtaining remote sensing image data, and carrying out the ground feature coverage classification of a to-be-assessed region; the hyperspectral reflectivity data of soil at different depths are actually measured by using a ground feature spectrometer; performing layered evaluation on the actually measured hyperspectral data by combining a soil density evaluation model and an organic carbon content evaluation model to obtain the soil density and the organic carbon content of each sampling point at different depths; determining soil carbon reserves at different depths of each sampling point based on the soil density and the organic carbon content at different depths of each sampling point; and carrying out spatial interpolation processing on the soil carbon reserves at different depths of each sampling point to obtain a carbon reserve distribution diagram of the to-be-evaluated region. According to the method, the sample points are determined by using the remote sensing image data, and the soil carbon reserves at different depths are determined by combining the hyperspectral data actually measured on site of the sample points with the evaluation model, so that comprehensive and accurate evaluation of the carbon reserves of the whole ecological system is realized.

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.