126 results about "Vegetation types" patented technology

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 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 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 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 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 provides a desert steppe area mining area gangue discharge field ecological vegetation screening method and system, and the method comprises the steps: determining the classification factors of the site types of topographic regions based on site attribute information, and dividing a plurality of topographic regions into a plurality of different site types; identifying vegetation state information on the terrain area of the target site type to obtain a vegetation mode; calculating an evaluation score of the vegetation mode based on the vegetation type related to the vegetation mode; optimizing the vegetation mode to obtain a target vegetation type on the basis of correlation features among various types of vegetation in the vegetation mode; the method comprises the following steps: determining classification factors of site types according to real area information, dividing a waste rock discharge field into multiple types, determining vegetation modes based on vegetation states of the site types, calculating evaluation scores of the vegetation modes, and further optimizing the vegetation modes to obtain optimal vegetation types of the site types. Thus, vegetation types adapted to various site types are generated to obtain a stable and effective ecosystem.

The invention provides a mountain freeze-thaw desertification early warning method and related equipment. The method comprises the following steps: acquiring thermal neutron intensity and epithermal neutron intensity of a target area; based on a target intensity ratio and a pre-obtained empirical fitting parameter set, target parameters are determined, and the target intensity ratio is the ratio of the thermal neutron intensity to the epithermal neutron intensity of the target area; determining a target moisture content according to the target parameter and the thermal neutron intensity or epithermal neutron intensity of the target area; and when the target moisture content is lower than a preset critical soil moisture threshold value, carrying out mountain freeze-thaw desertification early warning. Soil moisture is inverted through a cosmic ray neutron detection technology, environmental interference factors such as a non-soil water hydrogen source are corrected, local soil moisture of different vegetation types and soil types is accurately captured, freeze-thaw desertification is judged in combination with a critical soil moisture threshold, and early warning is given.

The invention discloses a mountain vegetation classification method based on a remote sensing image and a vegetation index, and relates to the technical field of mountain vegetation classification, and the method comprises the steps: obtaining remote sensing image data of a research region in a set time span, and dividing the remote sensing image data into a plurality of sub-regions; and analyzing and extracting vegetation index time sequence characteristics and seasonal growth characteristics of each sub-region in a set time span, preliminarily determining the vegetation type of each sub-region as an initial label, and using the obtained initial label, vegetation index time sequence characteristics and seasonal growth characteristics of each sub-region to train a classification model. According to the method, the change of the vegetation index of each sub-region in the time sequence is calculated, and the change of the seasonal vegetation index is combined, so that the static vegetation type is considered, the growth cycle of the vegetation and the change characteristics of the vegetation in different seasons are dynamically captured, and the real change condition of the mountain vegetation can be reflected more accurately.

The invention discloses a forest fire danger early warning system and method based on meteorological and vegetation data analysis, and relates to the technical field of fire risk prediction and control. According to the forest fire danger early warning system and method based on meteorological and vegetation data analysis, fine grid division is carried out on a target forest area through a high-resolution DEM and a multispectral or hyperspectral image, and vegetation type identification is carried out on each monitoring subunit; calculating a wet-dry cycle index WDI, a monsoon effect index MFI and a dry thunderstorm risk factor DRF by combining hourly wind speed, wind direction, relative humidity, precipitation and vapor pressure difference meteorological data; through extracting canopy height, canopy density, under-forest shrub and grass layer thickness and high VOC tree species coverage rate, fuel continuity coefficient FCI and volatile matter flammability index VEI are calculated, and accurate quantification of continuity and flammability of vertical ladder fuel and horizontal canopy is realized. And a comprehensive fire danger index FFI is constructed and subjected to hierarchical management after judgment, so that the dynamism, the hierarchical property and the pertinence of fire danger early warning are realized, and the forest fire danger prevention and control precision and the patrol resource utilization efficiency are improved.

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

The present application relates to the technical field of ecological restoration scene modeling, in particular to an ecological restoration scene modeling method and system platform applied to land space planning, comprising the following steps: based on successful ecological restoration technical scheme cases and land units to be restored.The present application realizes modeling in data source selection granularity and expression dimension by normalizing the multi-dimensional attributes between successful ecological restoration technical scheme cases and land units to be restored, constructing source-target land feature fingerprint sets, and combining annual mean temperature, precipitation, soil type, pH value, altitude, native vegetation type and labor cost and other specific attributes.Further, by setting importance coefficients according to the influence degree of each dimension in historical restoration success cases, the present application guides feature difference weight calculation, and obtains technical migration adaptation score by combining normalized difference value calculation, thereby establishing a quantifiable source land adaptation capability evaluation index, and breaking the original limitations of relying on experience judgment.

The application provides a vegetation carbon storage prediction method and device and a storage medium, wherein the method comprises the following steps: extracting a feature set of a to-be-measured region according to a remote sensing image, an elevation model and historical vegetation index information of the to-be-measured region; inputting the feature set into a random forest vegetation classification model for prediction to obtain at least one vegetation type of the to-be-measured region and regional information of each vegetation type in the remote sensing image; performing segmentation processing on the remote sensing image of the to-be-measured region based on a pre-trained vegetation segmentation model to obtain at least one vegetation proportion information of the to-be-measured region and corresponding regional information of each vegetation proportion information in the remote sensing image; and determining the carbon storage of the to-be-measured region according to the vegetation type of the to-be-measured region, the regional information of each vegetation type in the remote sensing image, the vegetation proportion information and the corresponding regional information of each vegetation proportion information in the remote sensing image. The application effectively improves the accuracy of forest carbon storage estimation.

This invention belongs to the field of ecological environment monitoring, specifically a dynamic monitoring method for carbon flux in wetland ecosystems that integrates eddy covariance (ECD) and satellite remote sensing. The method includes the following steps: S1: Monitoring station establishment: Based on the ECD method, flux monitoring stations are established in typical areas of lake wetlands. The selected typical areas should represent the main ecological characteristics and vegetation types of the wetlands to ensure the representativeness and reliability of the monitoring data. This invention combines ECD observation with satellite remote sensing technology to achieve real-time visual monitoring of carbon flux. It provides both data accumulation and visualization, improving the accuracy and reliability of wetland carbon source / sink assessment results. Simultaneously, the visualized spatial distribution of carbon sources / sinks facilitates the detection of changes in carbon sources / sinks in wetland areas, enabling timely detection and response to changes in wetland ecosystems, and scientifically increasing wetland carbon sinks through artificial regulation.

This invention relates to a method, system, and equipment for evaluating the quality of carbon cycle data, specifically in the field of carbon cycle quality assessment. The method includes: predicting carbon emissions from coal mining subsidence areas to obtain predicted carbon emissions; predicting the scale changes in soil, vegetation, and water bodies based on subsidence characteristic information; collecting soil physicochemical property parameters, vegetation type parameters, and water composition parameters, and calculating the predicted carbon sequestration amount by combining these parameters with the scale changes in soil, vegetation, and water bodies; and calculating the carbon cycle quality assessment result based on the predicted carbon sequestration amount and predicted carbon emissions. This invention addresses the technical problem that traditional assessment methods fail to fully consider the unique geographical characteristics and changing patterns of coal mining subsidence areas, resulting in inaccurate predictions of carbon emissions and carbon sequestration capacity, and consequently, an inability to effectively quantify and assess carbon cycle quality. It significantly improves the scientific rigor, accuracy, and reliability of carbon emission and carbon sequestration capacity predictions, achieving precise quantitative assessment of carbon cycle quality.

The present application relates to a kind of grassland vegetation coverage measuring device and measuring method, device includes connecting column, lifting mechanism, host computer and camera, host computer is connected with lifting mechanism and camera communication;Connecting column includes vertical connection column and cross bar, column one end is fixed on ground, host computer is installed on column;Lifting mechanism includes lifting block and telescopic machine, the output shaft of telescopic machine connects lifting block, the top of lifting block is equipped with guide shaft, the one end of cross bar is equipped with with guide shaft cooperation installation guide hole, camera is connected with lifting block;Camera changes observation height according to vegetation type.Compared with prior art, the height of camera is controlled by lifting mechanism in the present application, when using, camera hangs under cross bar to collect information, when height needs to be adjusted, telescopic machine can be started by host computer or remote signal control, drive camera to rise or fall, so as to change the effect of camera height control collection range, and the measuring effect is better.

The invention discloses a farmland tree remote sensing image optimization method, relates to the technical field of remote sensing image processing, and solves the problems that in an existing optical image processing technology, a data processing flow is too complex in an optical image processing process, and wrong division and missing division are likely to occur, so that farmland tree images in optical images are not clear, and the image quality is poor. Therefore, the application of optical images in agricultural operation activities is restricted. A farmland tree remote sensing image optimization method comprises a data acquisition stage, a vegetation and non-vegetation distinguishing stage, a vegetation type distinguishing stage, a farmland tree and non-farmland tree distinguishing stage and a farmland tree remote sensing image optimization stage. The method is suitable for the fields of farmland tree remote sensing monitoring, ecology, remote sensing technology, geographic information systems and the like.

The invention discloses a vegetation species identification method for enhancing AI computing power and time sequence tracing, and the method comprises the steps: collecting and preprocessing multi-source remote sensing data, and constructing a vegetation classification data set with time-space alignment and uniform resolution; generating a vegetation mask based on the vegetation classification data set, obtaining a standardized sample slice, and constructing a training data set in combination with spectral features; performing time-phase-sharing processing on the training data set based on vegetation phenological characteristics, and outputting a preliminary vegetation classification result; performing object-level optimization on the preliminary vegetation classification result to obtain an optimized vegetation classification result; and correcting an optimized vegetation classification result in combination with topographic data, generating a vegetation species classification map of a target year, and realizing vegetation dynamic change inversion in a specified time period through transfer learning. Therefore, traditional resolution limitation can be broken through, the classification error problem caused by independent use of multi-source data is solved, the distinction degree of complex vegetation types is remarkably improved, and historical vegetation dynamic backtracking analysis is supported.

The invention discloses a resident-free island vegetation classification method. The method comprises the steps of S1, data acquisition and preprocessing; s2, constructing a random forest model; s3, feature importance evaluation; s4, evaluating feature correlation; s5, feature optimization; s6, precision evaluation; according to the method, the problem that high-precision classification and time sequence dynamic monitoring of non-resident island coastal blue carbon resources in a complex environment are difficult to realize is solved, a remote sensing fine monitoring method is provided, and only satellite remote sensing and ground monitoring data are used; and fine classification and time sequence change detection of resident-free island vegetation types are realized.

A cascade vegetation surface moisture content detection method based on a visual image is characterized by comprising the following steps: step 1, constructing a cascade vegetation surface moisture content detection system based on the visual image; 2, the image acquisition module acquires a vegetation image of a target area; 3, the surface state recognition module quickly recognizes attachments on the surface of the vegetation in the vegetation image, and classifies the vegetation surface state into a saturated water-containing state and a surface dry state; step 4, the vegetation type identification module carries out vegetation type identification on the vegetation image I in the surface dry state, and identifies the biological type of each vegetation in the vegetation image I; and 5, according to the biological category of each vegetation, the moisture content prediction module calls the exclusive moisture content regression prediction model of the corresponding category from the model library to predict the moisture content of the vegetation of the biological category, and outputs a moisture content prediction result of each vegetation.

The application relates to the technical field of water environment remote sensing monitoring, and provides a water vegetation type monitoring method and device based on optical and SAR satellite images. The method comprises the following steps: acquiring optical remote sensing image data and radar image data of the same water area after spatiotemporal registration; acquiring optical image band data, near-infrared reflectivity and red light band reflectivity of each pixel point from the optical remote sensing image data; selecting sub-channel features corresponding to the pixel points from cross-polarization channel feature data; obtaining normalized vegetation indexes of each pixel point based on the near-infrared reflectivity and the red light band reflectivity; obtaining classification results corresponding to each pixel point based on the normalized vegetation indexes, the optical image band data and the sub-channel features, so as to determine all water vegetation types in the water area. The method realizes large-range, high-frequency and high-precision monitoring of water vegetation types in large shallow lakes.

The invention discloses a data-driven actual evapotranspiration estimation method fusing vegetation biophysical characteristics, which comprises the following steps: firstly, collecting actually measured meteorological and remote sensing data of a flux tower station, and carrying out quality control and energy balance correction to obtain an actual evapotranspiration reference value; then, a PMLV2 model is constructed station by station based on the processed data, and vegetation biophysical characteristic variables such as stomatal conductance are obtained through simulation; summarizing the features with meteorological and remote sensing data to form an input factor set, and summarizing training sets and test sets of all stations according to vegetation types; and finally, taking the factor set as input, constructing an LSTM actual evapotranspiration estimation model by using the training set, and evaluating the precision of the model through the test set. According to the method, vegetation biophysical characteristic variables simulated by the PMLV2 model are fused, so that the adaptability and generalization ability of the data driving model when environmental conditions change are enhanced, and the problems of overfitting and performance reduction when samples are scarce or representativeness is insufficient in a traditional data driving method are effectively relieved.

The utility model discloses a reservoir area hydro-fluctuation belt ecological slope protection structure, and relates to the field of ecological slope protection structures, the reservoir area hydro-fluctuation belt ecological slope protection structure comprises a soil base and a concrete connecting seat, underwater gabion protection feet are attached to the bottom of the side of the soil base at equal intervals, and a gravel permeation area is laid on the outer surface of the soil base; and a vegetation type Reynolds protection pad is fixedly arranged on the outer surface of the gravel permeation area. According to the ecological slope protection structure for the reservoir area hydro-fluctuation belt, through the arrangement of the vegetation type Reynolds protection pad, when the vegetation type Reynolds protection pad covers the outer surface of the gravel permeation area, it can be effectively ensured that the gravel permeation area does not slip off when being impacted by water flow; according to the ecological slope protection structure, the plant seeds can have an excellent soil fixation function during germination and growth, so that the ecological slope protection structure is ensured not to have a large-area soil loss phenomenon during a water swelling period, and the ecological slope protection structure can be ensured to be normally used after being used for a long time.

The present invention relates to a modular grow house system designed for optimizing the cultivation of various vegetation types through advanced environmental control. The system comprises multiple grow chambers, each equipped with sensors to monitor temperature, humidity, light, and soil moisture, and actuators to regulate these conditions. A central control unit, including receiving, monitoring, processing, and regulating units, manages the environmental conditions based on plant profiles stored in digital formats like JSON, XML, or CSV. The modular design features interchangeable panels, standardized sensor and actuator mounts, and quick-connect fittings, allowing flexible configuration and expansion. Safety features, such as temperature and humidity alarms, and a user interface for manual control, enhance the system's functionality. Additionally, the control unit supports remote updates via a cloud platform, ensuring up-to-date plant profiles and control algorithms. This invention provides a scalable, automated solution for controlled environment agriculture, improving productivity and sustainability in various settings.

The application provides a kind of geological disaster post-vegetation recovery monitoring method based on high-resolution remote sensing image, comprising: constructing fusion type "spectrum-vegetation index-texture" feature set;In sunny weather conditions, more evenly select n typical vegetation samples in the target area;Construct bagging type ensemble learning model;Analysis of the obvious differences of arbor, shrub and herbaceous vegetation in vegetation height, single plant vegetation horizontal projection coverage area, root depth and root extension range;By analyzing the vegetation change rate index and vegetation type change rate index of several continuous time phase remote sensing images according to the time phase change information, the vegetation recovery of the target area is quantitatively analyzed, so as to effectively improve the accuracy of pixel classification method, greatly improve the accuracy of quantitative analysis of vegetation in the target area, and greatly reduce the cost of long-term accurate monitoring.

The invention discloses a system and a method for monitoring longitudinal discrete coefficients of river pollutants under vegetation influence. A data acquisition unit acquires parameter data of a vegetation river in real time; the data processing unit performs data preprocessing on the parameter data; the modeling correction unit introduces a vegetation interference factor on the basis of the hydraulic model, constructs a longitudinal discrete coefficient dynamic calculation model, performs adaptive parameter correction on the longitudinal discrete coefficient dynamic calculation model, and outputs a longitudinal discrete coefficient and a pollutant concentration change trend; and the intelligent early warning unit sends out early warning and generates a corresponding treatment scheme based on a preset multi-level threshold triggering mechanism. Vegetation interference factors are introduced and combined with vegetation density, height and resistance coefficients, so that the pollutant discrete monitoring precision is improved; in combination with real-time data acquisition and intelligent early warning, response measures are made for different pollution risks; model coefficients are adjusted according to river vegetation types and hydrological conditions, and the method is suitable for various vegetation-containing water bodies and has wide application prospects in the field of water environment protection and treatment.

The application discloses a kind of estimation method and system of vegetation canopy combustible moisture content based on meteorology and remote sensing data, comprising the following steps: first, combustible moisture content and various meteorological data and remote sensing data and other several kinds of subsidiary data are selected as combustible moisture content estimation data.Meteorological data includes air temperature, relative humidity, rainfall and wind speed.Remote sensing data includes two vegetation indexes: enhanced vegetation index and normalized vegetation index.Subsidiary data includes: root zone soil moisture, vapor pressure difference, drought index, fire weather factor.Then the long time sequence characteristics of meteorological data are extracted.The size of time window is determined first, and the experimental results show that the correlation coefficient of most sites is relatively high under the time window of 90-210 days.90 days, 150 days and 210 days of time window are selected respectively to extract the time characteristics of four kinds of meteorological data.Secondly, the samples of experimental area are divided into five vegetation classifications, which are closed shrub, sparse shrub, multi-tree tropical grassland, tropical savanna and grassland.Finally, the data sets of the five different vegetation types are sequentially adjusted to obtain the respective estimation model.

The invention provides an intelligent remote sensing surveying and mapping method which is characterized by comprising the following steps: acquiring multi-source remote sensing data of a forest region to be measured; performing targeted preprocessing on the multi-source remote sensing data to obtain purified earth surface feature data; acquiring ground elevation data of a preset actual measurement point in the forest region to be measured; and obtaining seasonal factors and vegetation type data of the forest region to be measured. Through multi-source remote sensing data collection, the technical bottleneck that traditional single optical remote sensing cannot penetrate through dense crowns is directly broken through, and areas with different canopy densities are covered.

The invention discloses a vegetation classification method based on space-time multi-modal deep learning, and relates to the technical field of image processing, and the method comprises the steps: obtaining a multi-temporal optical image, a radar image and digital elevation model data of a to-be-classified region, and forming multi-modal data; extracting spectral features, microwave features, topographic features and texture features to form fusion features; calculating graph node features, and updating the graph node features to form graph features; and fusing the fusion features and the graph features to obtain pixel-level coarse classification logs, carrying out feature extraction and fusion on the fusion features to form region-level coarse classification logs, carrying out fusion to obtain a coarse classification probability, and carrying out fine classification on coarse basic features to obtain a final classification result. The method provided by the invention can adapt to a mountainous area environment with multiple clouds, multiple shadows and large topographic relief, improves the stability and classification fineness of vegetation type identification, and is suitable for wide-range vegetation monitoring and ecological assessment scenes.

This invention discloses a coupled simulation and prediction system for regional water and carbon cycle processes, relating to the field of watershed carbon cycle simulation. The system includes a data acquisition module that determines the watershed extent and identifies riverbank areas within a unified grid system. Simultaneously, vegetation type and soil organic carbon are mapped to riverbank grid cells, consistently expressing the spatial location of carbon sources in relation to hydrological watershed migration. This facilitates subsequent clarification of carbon source distribution. Based on a DEM (Digital Elevation Model), flow direction and confluence paths are constructed. Runoff in each grid is calculated under meteorological conditions and gradually converges into the river channel, establishing hydrodynamic transport paths with clear upstream and downstream relationships. This provides a physical carrier for carbon transport. Based on soil organic carbon and vegetation type in the riverbank grids, the system quantifies the differences in carbon supply capacity of different riverbank areas during regional water migration. Riverbank carbon sources are traced along the confluence paths. Spatial weights are constructed using distance attenuation and connectivity, mapping carbon sources to each grid cell. Carbon flux is then coupled with runoff to calculate and accumulate through gradual transport.

The invention provides a coupling MCCA-inVEST model-based carbon reserve prediction method, and relates to the technical field of mixed land utilization prediction and carbon cycling, the method comprises the steps of obtaining influence factor data corresponding to a to-be-evaluated mixed land, and generating MCCA training data; according to the influence factor data and the MCCA training data, constructing an MCCA model, and based on the MCCA model and a Markov chain, obtaining a land type prediction demand in a preset scene; constructing a land utilization conversion rule based on a random forest model and simulating different types of mixed land spatial distribution; taking the secondary land utilization classification system as a target classification, and performing spatial superposition on the Chinese carbon density data set and the vegetation type distribution data to obtain secondary land utilization carbon density parameters; and inputting mixed land spatial distribution and secondary land utilization carbon density parameters into an inVEST model to obtain a regional carbon reserve prediction result and a carbon sink capacity distribution result. According to the invention, the regional applicability and precision of carbon reserve prediction can be improved.