238 results about "Vegetation type" patented technology

The invention discloses a CCER forestry carbon sink project development system based on artificial intelligence technology monitoring, and relates to the technical field of carbon sink monitoring and optimization. According to the CCER forestry carbon sink project development system based on artificial intelligence technology monitoring, space-time estimation is carried out on CCER forestry carbon sink project data through the deep learning model, and the dynamic change of the carbon sink amount is accurately predicted. Spatial features are extracted through a convolutional neural network, time sequence data are analyzed in combination with a long-short-term memory network, and a carbon sink prediction model is constructed. And through statistical analysis and a spatial interpolation algorithm, carbon sink quantity change is monitored in real time, an abnormal trend is identified, and early warning is provided. Based on a mixed integer linear programming algorithm, forest land resource allocation and vegetation types are optimized, a carbon sink optimization scheme is generated, and carbon sink benefit maximization is promoted. And through an intelligent carbon sink management scheme, sustainable ecological development and green and economic popularization are assisted.

The invention discloses a forest prevention and control method based on disease and pest monitoring, and belongs to the technical field of forestry prevention and control, and the method comprises the steps: dividing a target forest into regions according to vegetation types, collecting historical multi-source data, filling the missing data, analyzing a time sequence and causal relationship to construct a time sequence causal diagram, and simulating a disease and pest spatio-temporal dynamic state in combination with current data. And determining a prevention and control risk level and a key induction factor of each region, executing a prevention and control strategy according to the level, and updating a causal diagram through reinforcement learning according to forest health response data. According to the method, a closed loop is formed from data processing to model construction, accurate prevention and control are achieved, efficiency and scientificity are improved, forest ecological changes can be dynamically optimized and adapted, and the prevention and control effect is guaranteed.

The invention discloses an intelligent forest fire monitoring method and device, electronic equipment and a medium, and the monitoring method employs a multi-mode deep learning model based on a double-attention mechanism to enhance the capturing capability of early flame thermal radiation characteristics and smoke form characteristics, and greatly improves the recognition sensitivity. In combination with triple verification and a confidence coefficient decision-making mechanism, errors caused by environmental interference are effectively avoided, and the recognition reliability is comprehensively improved; meanwhile, based on intelligent gridding three-dimensional monitoring of terrain complexity and vegetation types, the blind area coverage rate is greatly reduced, normalized inspection and post-disaster quick response are performed through the unmanned aerial vehicle platform, the monitoring coverage range is enlarged, and the response efficiency is improved.

The invention discloses a construction method for governing and repairing desertification degraded grassland in an arid area, and relates to the technical field of sand governing. The method comprises the following steps: evaluating a native vegetation type, vegetation coverage, a soil degradation and desertification degree, a soil type and a water resource condition; performing classified measures according to natural conditions, and determining different types of dune (flowing, fixed or semi-fixed) treatment measures; construction preparation; grid sand barriers are laid on the windward sides of the moving dunes and the semi-fixed dunes, and sand stabilization operation is conducted in a mechanical and manual combined mode; digging pits to plant indigenous shrubs subjected to special treatment, and using rooting water retention bags to improve the survival rate; the granular organic fertilizer and the seeds are mixed and stirred according to a certain proportion and then sown into soil at a time, the operation frequency is reduced, and precise fertilization is achieved. Through effective sand prevention and control measures, the sand wind damage is reduced, the living environment of farmers and herdsmen is protected, and the living quality is improved.

The invention relates to the technical field of wetland ecosystem soil carbon sequestration capability, in particular to a seasonal waterlogged wetland soil carbon estimation and dynamic monitoring method, which comprises the following steps of: selecting a sampling zone and a quadrat according to an equal-elevation water level gradient and a vegetation type; an optimal remote sensing estimation carbon model is determined through remote sensing image interpretation and screening of characteristic factors such as image waveband reflectivity, vegetation indexes, principal component characteristics and single-waveband textures, and soil organic carbon spatial distribution characteristics are analyzed through remote sensing inversion and GIS interpolation; constructing a Wetland-DNDC wetland carbon dynamic simulation model based on a DNDC model to predict wetland long-term carbon dynamic change and greenhouse gas emission; the invention provides a method for simulating carbon reserves and carbon dynamic monitoring based on field investigation and coupling a 3S technology with a Wetland-DNDC model. Compared with a traditional field observation method, the method has obvious technical and cost advantages, and can realize the technical advantages of spatial analysis and long-time sequence monitoring. The research method is suitable for a wetland ecosystem with seasonal hydrological characteristic changes.

The invention relates to the technical field of ecological vegetation information configuration, and discloses a landslide surge attenuation method and system based on biological vegetation configuration, and the method comprises the steps: constructing a landslide surge physical model, and determining a vegetation configuration range according to basic features; calculating a flood position and a normal water level according to the vegetation configuration range to determine an elevation area of vegetation configuration; determining the vegetation type of each elevation area of vegetation configuration; preprocessing data obtained by the landslide surge physical model to form a data set; calculating an average value and a standard deviation of the data set to obtain a standard data set; constructing a vegetation configuration model, inputting the standard data set into the vegetation configuration model, and simulating the landslide surge to obtain an influence mechanism of vegetation on the surge; and analyzing the influence mechanism of the vegetation on the surge to obtain a plurality of vegetation configuration schemes. The system comprises an acquisition module, a processing module and a vegetation configuration module. High-water-level protection is provided, rain wash can be effectively prevented, and the shallow slope is stable.

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 provides a park green land vegetation optimization system based on carbon sink capability improvement and a method thereof, relates to the field of urban ecological environment, and aims to improve the carbon sink benefit of a green land through scientific optimization, the system comprises a data acquisition module, a carbon sink evaluation module, a vegetation optimization module and an intelligent monitoring module. The data acquisition module acquires data such as soil quality, climate conditions and vegetation types by using a multi-source sensor; the carbon sequestration evaluation module quantifies the carbon sequestration capacity of the green land through a carbon fixed quantity formula and a carbon storage quantity model; the vegetation optimization module adopts an intelligent optimization algorithm to generate a capability of giving consideration to carbon sink based on the optimization objective function; and the intelligent monitoring module evaluates a green land optimization effect through real-time monitoring data and dynamically adjusts vegetation configuration. The carbon sink optimization method provided by the invention comprises four steps of data acquisition, carbon sink evaluation, vegetation optimization design and dynamic monitoring, and efficient configuration is realized for different green land types by utilizing an accurate model and an optimization algorithm.

The invention discloses a vegetation gross primary productivity monitoring method based on downscaling and multi-source remote sensing data, and belongs to the field of remote sensing image processing. In order to accurately monitor the GPP change of an engineering scale, the vegetation total primary productivity monitoring method comprises the following steps: acquiring first resolution multisource remote sensing data, downscaling by using CNN, inputting data containing DEM and LUCC in a layer, and dynamically adjusting background weight in a loss function through an LUCC binarization mask (a vegetation region is 1, and a non-vegetation region is 0); a CASA method is used for simulating GPP, a multi-year target area GPP data set is generated, and the maximum light energy utilization rate is determined by the vegetation type; analyzing GPP spatial and temporal change characteristics of the data set by using a Theil-Sen slope estimation method and the like, and extracting spatial distribution and a time change mode; extracting principal components of similar factors by using a principal component analysis method, calculating local correlation coefficients of the principal components and the GPP by using a partial correlation method, identifying dominant influence factors by using a contribution degree decomposition model, and extracting an environmental factor driven map. The method is applied to the field of ecological remote sensing monitoring.

The invention discloses an urban block green land planning method and device based on thermal environment big data, and the method comprises the steps: obtaining the thermal environment data, vegetation coverage data and a vegetation database of a target block; constructing a thermal environment map of the target block based on the thermal environment data, and obtaining a thermal environment feature vector of each sub-block according to the thermal environment map; acquiring a microclimate influence value of each vegetation type; performing correlation analysis on the thermal environment feature vector and the microclimate influence value of each sub-block to obtain the microclimate adjustment contribution degree of each vegetation type to each sub-block; and according to the microclimate adjustment contribution degree, determining a target vegetation type to be planted in each sub-block and a planting proportion of each target vegetation type, and obtaining a vegetation configuration scheme of the target block. The microclimate adjusting capability of the urban green land can be improved.

The embodiment of the invention relates to the technical field of artificial intelligence, and provides a coastal salt marsh vegetation carbon sink estimation method and system based on multi-temporal phenological characteristics. The method comprises the following steps: acquiring a remote sensing image of a target coastal salt marsh area in a key phenological period; extracting an initial vegetation contour of a vegetation coverage range in the target area from the remote sensing image by adopting an unsupervised classification method; constructing a phenological decision tree model by taking an NDVI threshold method as a core, and integrating a machine learning enhanced node division mechanism and a phenological period slope analysis method to enhance the phenological decision tree model; inputting the initial vegetation contour into a phenology decision tree model, and performing multi-level classification on vegetation types according to the initial vegetation contour through the phenology decision tree model to distinguish vegetation coverage ranges of various types of vegetation in the target coastal salt marsh area; and vegetation carbon density parameters are obtained, and the estimation of the total carbon sink amount of the target coastal salt marsh area is completed through spatial superposition calculation in combination with a vegetation coverage range, so that accurate classification of vegetation and efficient estimation of carbon sink are realized.

The invention discloses an urban traffic three-dimensional model vegetation element automatic generation method and system based on streetscape recognition, a terminal and a storage medium. The method comprises the steps of constructing a vegetation element knowledge graph, defining a vegetation type and typical attributes, directionally collecting a three-dimensional model and a multi-view image, unifying metadata and storing the metadata. Pixel-level semantic segmentation is carried out on the streetscape image, vegetation categories are identified based on transfer learning and an attention mechanism, and ROI and feature vectors are output; screening the candidate set based on ontology mapping, selecting a best matched vegetation model for the local feature vector of the recognized ROI and a pre-stored feature vector in a material library, and recording mapping; based on reference object correction, knowledge graph fusion and automatic discrimination or optimization strategies, reliable scale correction, number or position deduction and batch three-dimensional model import placement meeting ecological constraints are carried out on the vegetation ROI. According to the invention, efficient, intelligent and standardized technical support is provided for traffic simulation and urban visualization.

The invention relates to the technical field of geological engineering, in particular to a comprehensive evaluation method and system for a mine geological environment, and the method comprises the following steps: collecting mine surface geological data based on a target mine region, collecting vegetation types and vegetation coverage area data, evaluating the occurrence probability of landslide and land subsidence of a plurality of regions of a mine, and obtaining a comprehensive evaluation result; and obtaining a surface risk assessment result. According to the method, the accuracy and the reliability of risk assessment are improved by integrating the surface geological data and the vegetation coverage data and combining meteorological information and topographic analysis, the judgment for geological stability is enhanced by combining fixed-point assessment and stratigraphic analysis, so that early warning for a hidden danger area is more timely and specific, and the risk assessment accuracy and the risk assessment reliability are improved. By monitoring surface deformation in real time and combining long-term data analysis, a more dynamic and continuous geological environment change trend is provided, mining strategies and disaster prevention and reduction measures can be adjusted in time, and the environmental influence of mining activities is reduced.

The invention relates to the technical field of ecological restoration scene modeling, in particular to an ecological restoration scene modeling method and system platform applied to territorial space planning, and the method comprises the following steps: based on a successful ecological restoration technical scheme case and a to-be-restored land parcel unit. According to the method, normalization processing is carried out on multi-dimensional attributes between successful ecological restoration technical scheme cases and to-be-restored land parcel units, a source-target land parcel feature fingerprint set is constructed, and specific attributes such as annual average temperature, precipitation, soil types, pH values, altitudes, native vegetation types and labor cost are combined; modeling is realized on data source selection granularity and expression dimension. Furthermore, an importance coefficient is set according to the influence degree of each dimension in a historical repair success case, feature difference weight calculation is guided, and a technical migration adaptation degree score is obtained by combining normalized difference calculation, so that a quantifiable source-place adaptation capability evaluation index is established, and the limitation that judgment depends on experience in the prior art is broken through.

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 forest fire risk prediction method based on multi-source data, and belongs to the technical field of forest fire prevention. Aiming at the problem of low prediction precision caused by one-sided information of a single data source and insufficient multi-source data fusion in the prior art, the method is realized by the following steps: acquiring micrometeorological data including temperature and humidity, wind speed and air pressure, and image data including an infrared image and a visible light image; the data of the mountain fire-prone area comprises historical fire frequency, vegetation type and topographic information; uTC + 8 time synchronization and WGS84 coordinate system space calibration are carried out on the data, and missing values and abnormal values are processed; carrying out feature layer fusion by adopting an attention mechanism, and extracting core features such as a temperature and humidity coupling index and vegetation dryness; spatial correlation features are captured through CNN, a time sequence trend is captured through LSTM, a mountain fire occurrence probability is output by using a Sigmoid function after decision-making layer fusion, and a result is calibrated in combination with sub-region features. Through multi-source data deep fusion and spatial-temporal feature collaborative analysis, the accuracy and timeliness of forest fire risk prediction are improved, a new data source can be expanded and accessed, and the method is suitable for a complex forest fire prevention scene.

The invention provides a plant classification labeling method. The method comprises the following steps: S1, acquiring an orthoimage of a target area through an unmanned aerial vehicle, and segmenting to generate plant pattern spots; s2, superposing the orthoimage and the base map of the electronic map, and establishing a composite coordinate system; s3, displaying the superposed map on the mobile terminal, and receiving a marking instruction of a user on the pattern spots; when the user cannot identify the plant type, calling a plant identification API to classify the plant image uploaded by the user, and associating the result to the corresponding plant pattern spot; s4, storing the labeled plant data to a database, and supporting statistics and management of the vegetation types, number and distribution of the target area through a geofence function; s5, a visual interface is provided, a user is allowed to check detailed information of the plants by clicking map marks, the detailed information comprises names, photos and statistical results, through the plant classification marking method, the types, the number and the distribution of the vegetation in the statistical area can be systematically analyzed, data are accurate, and presentation is visual.

The invention provides a mining area vegetation reconstruction method based on vegetation classification and division. The method relates to the technical field of ecological environment restoration, and comprises the following steps: S1, acquiring mining area environment data by adopting a mode of combining remote sensing images, unmanned aerial vehicle aerial photography and ground sampling, performing spatial interpolation, normalization and filtering preprocessing on the environment data, and outputting a mining area ecological factor database; s2, on the basis of the ecological factor data, constructing a multi-index evaluation model by combining an analytic hierarchy process with clustering analysis to perform vegetation type division on the mining area, and generating partition templates with different ecological functions; and S3, constructing a plant phenological growth prediction model based on years of phenological observation data and weather forecast information. According to the mining area vegetation reconstruction method based on vegetation classification and division, precise planting and spatial layout are completed, the vegetation survival rate, biomass production and community diversity are remarkably improved, and the strict requirements of mining area ecological reconstruction for diversity and adaptability are met.

The invention relates to the technical field of forest resource analysis, in particular to a forest resource data analysis method and system based on forestry ecological data. The method comprises the following steps: performing altitude measurement on a target detection area to obtain an altitude measurement data set; performing landform classification on the target detection region according to the altitude measurement data set to obtain landform type data; performing ecological partition on the target detection region according to the landform type data to obtain ecological partition data; performing forestry ecological data acquisition on the target detection region according to the ecological partition data to obtain a forestry ecological data set; performing vegetation diversity determination on the target detection region based on the forestry ecological data set to obtain vegetation type distribution data; and performing land type-vegetation planting integrating degree determination on the target detection region according to the vegetation type distribution data to obtain a land-vegetation planting integrating degree set. According to the method, the efficiency and accuracy of forest resource data analysis are remarkably improved.

The invention discloses a knowledge graph ontology constrained vegetation remote sensing classification method. The method comprises the steps of data acquisition and data preprocessing, deep learning model selection and fusion of a knowledge graph ontology and a deep learning model. According to the method, the knowledge graph ontology and the FCN8s-ResNet 50 model are fused in the two stages of model training and classification post-processing. The result shows that 1) the classification accuracy of the deep learning model can be remarkably improved by fusing the mountain vegetation knowledge graph ontology in the two stages of deep learning model training and classification post-processing: the accuracy of the deep learning model constrained by the knowledge graph ontology is improved by 10%, and the overall classification accuracy of vegetation types reaches 89.2%; and 2) the deep learning model constrained by the mountain vegetation mapping knowledge domain ontology not only obtains excellent results in fine classification of vegetation types, but also improves the universality and interpretability of the model, so that powerful support is provided for wide-range remote sensing vegetation classification and application of the deep learning model.

The invention provides a wetland forest and grass ecosystem restoration strength evaluation method under drought stress. The method comprises the steps of data collection, daily-scale ecosystem service function index ESS construction, ecological drought identification and restoration elasticity curve construction. Wherein the time resolution of the index is accurate to the day, the capture of short-time drought below the monthly scale is enhanced, and the accurate description of the dynamic process of ecological system restoration under the drought stress is realized; combined identification is carried out on the drought events in combination with the standardized ecological water deficit, and the accuracy of identification of the drought events causing significant differences of ecological system service functions is improved; a quantile regression model is utilized to quantify the ecological system recovery capability of different vegetation types, and data characteristics of different recovery time distributions of the ecological system under the same drought intensity are considered, so that the defect that a traditional mean value regression model cannot fully describe drought complex characteristics is overcome; and a theoretical support is provided for sustainable development of an ecological system under an extreme climate condition.

The invention relates to the technical field of information, in particular to a vegetation water conservation amount change monitoring system and method, and the system comprises a data obtaining module which is used for obtaining vegetation coverage data and hydrological element data of a monitoring region; the data processing module is used for inputting the vegetation coverage data and the hydrological element data into a preset water conservation amount estimation model and outputting a water conservation amount estimation result, and the water conservation amount estimation model is constructed by adopting a water balance equation according to historical vegetation types, areas and meteorological and hydrological data of the monitoring area; and the abnormity early warning module is used for carrying out abnormity early warning and change trend prediction based on the water source conservation amount estimation result, and completing vegetation water source conservation amount change monitoring of the monitoring area. According to the invention, accuracy and time-space continuity of vegetation water source conservation amount monitoring can be improved, and a scientific basis is provided for basin water resource management decision.

The invention discloses a soil humidity inversion method and device, a medium and equipment, and relates to the technical field of microwave remote sensing inversion. According to the method, the total backscattering coefficient, the normalized vegetation index, the leaf area index and the average leaf inclination angle of the electromagnetic waves of the target area pixel by pixel are obtained in combination with microwave remote sensing data and atmospheric apparent reflectance data of the target area; according to the normalized vegetation index, the leaf area index and the average leaf inclination angle, spatial clustering is carried out on vegetation, spatial heterogeneity of crop types is captured, and then based on the constraint that the surface roughness parameters and the vegetation parameters of the same vegetation type are the same and the soil humidity in the same surface space window is the same, the spatial heterogeneity of crop types is obtained. Through two-stage iteration inversion of the intra-class scale and the ground surface space window scale, the time dynamic state is adapted, the parameter calibration function of the in-situ soil humidity data is replaced, and the model parameters and the soil humidity can be determined without the in-situ soil humidity data. According to the invention, the accuracy and reliability of the soil humidity inversion method are improved.

The invention provides a global vegetation canopy moisture content inversion method, which belongs to the field of remote sensing geosciences, and comprises the following steps: acquiring vegetation character data in a global vegetation character database, and counting the distribution condition and correlation of each character in different vegetation types; training samples of different vegetation types are generated through forward simulation; performing convolution conversion on the simulated spectrum data by adopting a spectrum response function to obtain an equivalent spectrum; evaluating feature combinations of different spectrums and vegetation indexes, and determining an optimal feature combination and a machine learning algorithm suitable for different vegetation types; a corresponding inversion model is adaptively called according to the vegetation type and the canopy structure, and remote sensing estimation of the global vegetation canopy moisture content is achieved. According to the method, the canopy moisture content of different vegetation types can be efficiently inverted to generate a global vegetation canopy moisture content remote sensing product with space adaptability and relatively high precision, and data support is provided for applications such as ecological environment monitoring, drought evaluation and carbon-water flux simulation.

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 provides a global land-sea ecosystem productivity estimation method based on SIF and light response, and the method comprises the steps: 1, carrying out the data fusion of satellite remote sensing image data and meteorological data, and carrying out the consistency verification; 2, on each pixel point, inputting the input remote sensing data into a mechanical light response (MLR) theoretical formula to carry out total primary productivity GPP estimation, and calculating carbon dioxide partial pressure in a chloroplast matrix, a carbon dioxide compensation point of chloroplast, an open proportion of a photosynthetic system reaction center, an energy ratio of plant heat dissipation to plant fluorescence emission, and an escape rate; step 3, acquiring the distribution of C3 plants and C4 plants by using the vegetation type distribution data; and respectively calculating the total primary productivity of the C3 plant and the C4 plant. According to the scheme, the required number of vegetation parameters is simplified in the inversion process, the parameter acquisition difficulty is greatly reduced while the inversion precision is ensured, and meanwhile, the result uncertainty caused by the parameters is greatly reduced.

The invention discloses a vegetation resource distribution high-resolution prediction method, a vegetation resource distribution high-resolution prediction system, a terminal and a medium, and relates to the technical field of vegetation resource prediction. The prediction precision and generalization ability of the model are significantly improved, the improvement ensures that the model can process growth laws of complex geographic features and different vegetation types, the output prediction result better conforms to ecological laws and geographic features, and reliable support is provided for vegetation resource management.

The invention discloses a road construction carbon emission monitoring method and system, and relates to the technical field of carbon emission monitoring. The method comprises the steps of obtaining earth surface feature information, vegetation types and distribution areas of a construction area, obtaining disturbance areas of different vegetation types, calculating disturbance coefficients, loss coefficients and carbon sink loss amounts of the disturbance areas, calculating the total carbon emission amount of a construction period, constructing an emission evaluation model, and outputting a predicted emission amount of a subsequent construction period in combination with a project scheme. Judging whether the predicted emission exceeds a preset emission threshold value or not, if not, keeping the project scheme unchanged, and if yes, suggesting to modify the project scheme; the carbon sink loss amount caused by vegetation damage can be accurately calculated, so that the total carbon emission amount is more comprehensively and accurately evaluated, and a scientific basis is provided for construction scheme optimization.

The invention relates to the technical field of environmental data processing, in particular to an environmental monitoring data processing method and system based on big data analysis. The method comprises the following steps: carrying out statistical marking on an urban environment to-be-monitored area, and obtaining block vegetation type data, block environment humidity data and block soil condition data; carrying out block vegetation growth analysis and vegetation carbon storage influence analysis on the basis of the block vegetation type data to obtain actual block vegetation carbon storage; performing soil microflora abundance analysis and soil organic carbon storage estimation on the block soil condition data to obtain actual organic carbon storage of block soil, and performing time period window division and carbon sink capacity comparison division to obtain an urban green land efficient carbon sink block and an urban green land low-efficiency carbon sink block; and performing targeted optimization adjustment on the urban green land low-efficiency carbon sink block until the urban green land low-efficiency carbon sink block is converted into an urban green land high-efficiency carbon sink block. According to the invention, the regional carbon sink capability can be effectively improved and managed.

The invention discloses a carbon sequestration capability value evaluation system and method for a river and lake water ecosystem, and the system comprises a data collection module which is used for collecting different types of vegetation samples in a target region and corresponding vegetation image data; the ground monitoring terminal comprises a data analysis processing module which is used for processing the collected vegetation samples to obtain carbon content data of different types of vegetation, obtaining vegetation biomass data of different types of vegetation according to the vegetation samples and the vegetation image data, and sending the vegetation biomass data to the ground monitoring terminal; calculating carbon sequestration capability data of different types of vegetation according to the vegetation biomass data and the carbon content data; the evaluation module evaluates the carbon sequestration capacity value of the target area based on the sum of the carbon sequestration capacity data of different types of vegetation, and the mode is beneficial to understanding the influence of the vegetation types on the carbon sequestration capacity value of the river and lake water ecosystem, facilitates the formulation of ecological protection policies, and improves the pertinence and effectiveness of the control and protection of the river and lake water ecosystem.