57 results about "Vegetation canopy" patented technology

The invention discloses an ecological restoration area carbon sink increment real-time prediction method and system based on artificial intelligence, and the method comprises the following steps: multi-source data collection: obtaining a monthly vegetation coverage image through a satellite remote sensing platform, collecting the sensor data of soil temperature and humidity, air CO2 concentration and the like through a laid ground sensor network, and carrying out the real-time prediction of the carbon sink increment of an ecological restoration area; adopting an unmanned aerial vehicle laser radar to obtain vegetation canopy point cloud data according to a preset period, and collecting biomass actual measurement data of a restoration area in a historical preset age limit; preprocessing data, performing radiometric calibration, atmospheric correction and cutting splicing on a monthly vegetation coverage image acquired by a satellite remote sensing platform, and extracting a vegetation coverage and vegetation index time sequence; denoising, ground point separation and single tree canopy segmentation are carried out on vegetation canopy point cloud data acquired by the unmanned aerial vehicle laser radar, and the height and crown breadth of single plant vegetation are calculated. According to the invention, carbon sink increment prediction can be realized more accurately.

The invention discloses a forestry restoration vegetation state identification method and system based on image identification. The method comprises the following steps: obtaining a standard image sequence; segmenting the visible light image through an improved SegFormer network to obtain vegetation areas of different scales, and calculating vegetation canopy temperature distribution characteristics based on the thermal infrared image; extracting vegetation index features based on the multispectral image to obtain multi-scale feature data; according to the multi-scale feature data, utilizing an improved GAT model to process a vegetation spatial relationship graph, learning a spatial dependency relationship between vegetation areas through a multi-head attention mechanism, and generating a vegetation feature vector containing spatial information; and classifying the vegetation states by using an improved multi-scale fusion classifier in combination with the vegetation feature vectors, outputting a vegetation recovery index, and classifying the forestry restoration vegetation states according to the vegetation recovery index. The whole forestry restoration monitoring process is automatic, the monitoring efficiency is improved, and the labor cost is greatly reduced.

The invention discloses a resource evaluation method and system based on multi-landform feature correction, which remarkably improves the accuracy of wind speed and solar radiation data by combining physical constraint correction and machine learning methods, and particularly improves the accuracy of the wind speed and solar radiation data under the conditions of complex terrains and multiple earth surface types. The problem of systematic errors existing in ERA5 reanalysis data in the prior art is solved. Specifically, through elimination of invalid values of radiation data at night and correction of wind speed data based on surface roughness, deviation caused by factors such as terrain shielding, vegetation canopy reflectivity and urban building group wind resistance effect is effectively eliminated. The ERA5 data is trained and optimized by using a machine learning model such as a random forest, so that the wind speed and radiation data at different geographic positions, seasons and time periods can be more accurately corrected, and the corrected data is more in line with an actual observation result.

The invention relates to a combustible moisture content inversion method based on a mechanism model and an artificial neural network and application. The combustible moisture content inversion method comprises the following steps: S1, constructing a canopy reflectivity simulation data set; s11, simulating the canopy reflectivity of the grassland vegetation by using a PROSAIL model; s12, simulating a non-uniform vegetation canopy reflectivity by using a 4-Scale geometrical optical model; s13, excluding unreasonable data by using the function relationship among the EWT, the DMC, the Cab and the LAI value; s14, eliminating unreasonable data based on Gaussian white noise; s15, acquiring simulated canopy reflectivity data corresponding to the MODIS wavelength range; s2, constructing an FMC inversion model; s21, training a mechanism model; S22, optimizing the mechanism model to obtain an FMC inversion model; and S3, realizing FMC inversion. The method has the characteristics of high precision, self-learning, rapidness, high efficiency and the like.

The application discloses a kind of based on spectrum-process-terrain coordination's farmland low-lying quick extraction method of waterlogging, comprising the following steps: step one, satellite remote sensing image acquisition and pretreatment;Step two, waterlogging area grading mark;Step three, process quantity extraction;Step four, red edge area index calculation;Step five, terrain factor processing and feature combination;Step six, model training and optimal feature subset screening;The application is by constructing bare soil period soil moisture variation process quantity and growth period crop growth change process quantity, accurately depict the evolution track of waterlogging with growth stage, break through the limitation that single phase cannot reflect process difference;By constructing red edge area index, enhance the spectrum difference of waterlogging, distinguish bare soil period easily confused ground class, avoid growth period vegetation canopy shielding interference;Through terrain factor, reflect the influence of topography on water depth, holding water time length, improve the identification accuracy of waterlogging spatial heterogeneity;By using random forest algorithm, improve the accuracy of waterlogging grading.

The invention discloses a crop nitrogen nutrition monitoring and diagnosis method and device based on a multispectral remote sensing image, a medium and a product, and relates to the technical field of modern agricultural intelligence, and the method comprises the steps: determining a multispectral reflectivity image of a to-be-detected region based on multispectral remote sensing images of the to-be-detected region and a target radiation correction plate; determining a soil adjustment vegetation index grid map based on the multispectral reflectivity image so as to determine a vegetation canopy area; determining a near infrared-red edge index grid map based on the reflectivity grid map of the red edge wave band and the reflectivity grid map of the near infrared wave band; determining canopy coverage according to the pixel number of the vegetation canopy area and the pixel number of the multispectral remote sensing image; inputting the canopy coverage into a nitrogen nutrition diagnosis model to obtain an optimal near infrared-red edge index confidence interval; and determining a crop nitrogen nutrition deficiency distribution map based on the near infrared-red edge index grid map and the optimal near infrared-red edge index confidence interval. According to the invention, large-scale crop nitrogen nutrition monitoring and diagnosis are realized.

The invention relates to a leaf area index time sequence processing method, which comprises the following steps of: inputting a time sequence remote sensing image and suburb forest and economic forest classification data of the same region; calculating a normalized differential vegetation index of the research area and carrying out time sequence sorting; calculating vegetation coverage data of the research area and carrying out time sequence sorting; synthesizing to obtain monthly FVC time sequence data of the suburb forest region and the economic forest region; calculating a suburb forest leaf area index LAI in the research area and performing time sequence sorting; calculating the economic forest leaf area index LAI of the research area and performing time sequence sorting; performing seasonal decomposition on the suburb forest time sequence data after adaptive filtering; performing seasonal decomposition on the economic forest time series data after adaptive filtering; merging the suburb forest time sequence data and the economic forest time sequence data; and outputting to obtain final optimized data. The invention further relates to a leaf area index time sequence processing system. According to the invention, a purification LAI time sequence product which clearly represents long-term trend, mutation and gradual change signals of respective vegetation canopy structures can be output.

The present application provides a high-vegetation-covered area target mining area range prediction method, device and electronic equipment, comprising: acquiring hyperspectral data and laser radar point cloud data of a high-vegetation-covered research area, and registering; performing point cloud data layering on the point cloud data; manufacturing each layer of point cloud data into a height interval mask; performing spatial layering on the hyperspectral data based on the height interval mask, and extracting a hyperspectral image of a vegetation canopy bottom; extracting stressed vegetation according to abnormal spectral information, and manufacturing a sample; dividing all samples into a training set and a verification set according to a proportion; constructing a high-vegetation-covered area target mining area range prediction model based on deep learning DeeplabV3+; performing training and verification of the prediction model through the training set and the verification set respectively; acquiring a hyperspectral image of a target high-vegetation-covered area, and predicting the target mining area range in the target high-vegetation-covered area through the trained prediction model. The present application can efficiently and accurately realize high-vegetation-covered target mining area range prediction.

The application provides a city surface area classification method and related equipment. The city surface area classification method comprises: obtaining a first target parameter of a to-be-determined city surface area, the first target parameter comprising a characteristic building height, an average building height, a building height standard deviation, a building coverage rate, an impervious surface ratio, a characteristic vegetation canopy height, an average vegetation canopy height, and a vegetation coverage rate of the to-be-determined city surface area; inputting an electronic map corresponding to the to-be-determined city surface area and the first target parameter into a city surface area classification model to obtain a city surface area type corresponding to the to-be-determined city surface area output by the city surface area classification model, the city surface area type being used for analysis or simulation of heat waves, air quality, and extreme weather of the to-be-determined city surface area. In the application, the fineness and determination accuracy of the city surface area type are improved.

The application discloses a canopy coverage considering aggregation index inversion method and correction method, relates to the technical field of remote sensing image processing and aggregation index inversion, collects remote sensing images of a vegetation canopy, extracts hot spot reflectivity and dark spot reflectivity respectively, calculates a normalized hot spot and dark spot index NDHD, calculates an initial value of an aggregation index CI according to a linear relationship between the NDHD and the CI, and corrects the initial value of the CI by using a canopy coverage Ccan, wherein an error between the initial value of the CI and a real value of the CI has a linear relationship with the Ccan. The application first establishes a CI correction model based on the Ccan, and solves the deviation problem of a traditional algorithm in a sample field with high or low Ccan. By improving a mainstream CI inversion algorithm based on the NDHD, the application significantly improves the precision of CI inversion, and provides more reliable technical support for forest ecological monitoring, carbon cycle research and remote sensing application.

This invention discloses a non-prior detection method and device for targets obscured by vegetation canopy based on UAV multispectral point clouds, belonging to the field of three-dimensional target detection technology in remote sensing. To address the problem of insufficient target samples due to vegetation canopy obstruction in target detection methods based on multispectral point cloud data, this invention first clusters non-ground points in the spectral point cloud, statistically analyzes the circumradius of each cluster, and extracts multi-scale spatial features of the multispectral point cloud based on the clustering results, constructing a spatial-spectral feature profile. Then, it constructs a radius-adaptive spherical shell model, obtains the local background point set within the spherical shell corresponding to any point, and further analyzes the p... i The spatial-spectral features are jointly expressed, and the residuals between all expressed results and the original features are calculated, thereby realizing the detection of occluded targets.

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 application relates to the technical field of vegetation canopy water content inversion, and provides a vegetation canopy water content inversion method, device, equipment, product and medium. The method comprises the following steps: inputting a candidate vegetation data set into a vegetation radiation transmission model, taking the minimum loss function of the vegetation radiation transmission model as a target, and obtaining a basic data set in the candidate vegetation data set; obtaining a high-dimensional feature data set based on the basic data set and a vegetation index data set; inputting the high-dimensional feature data set into a proxy model, obtaining optimal feature data set in the high-dimensional feature data set based on a score output by the proxy model, and calculating the vegetation canopy water content based on the optimal feature data set; and the loss function is a multi-dimensional physical constraint constructed based on a multi-source remote sensing data set. The application can effectively solve the problems of parameter compensation of a physical inversion mechanism, insufficient precision in a complex environment and low efficiency of feature selection in the prior art.

The invention discloses a vegetation management method and device based on a dynamic threshold value, terminal equipment and a storage medium, and belongs to the technical field of ecological hydrology and vegetation management. The method comprises the steps that the soil layer water content, the to-be-managed vegetation area, the underground water level amplitude, the underground water aquifer area, the current precipitation amount, the current soil heat flux, the daily average temperature, the wind speed, the daily saturation water vapor pressure, the actual water vapor pressure, the vegetation surface net radiation and the vegetation canopy area are obtained, and a water source utilization proportion dynamic threshold value is calculated; then obtaining the actual utilization ratio of each to-be-managed vegetation to each potential water source; and finally, respectively comparing the dynamic threshold value of the water source utilization ratio with each actual utilization ratio, performing score assignment and grading according to a comparison result, and managing the vegetation to be managed. According to the invention, the accuracy and rationality of the vegetation management method can be improved.

This invention provides a method, system, device, and medium for identifying vegetation lodging risk around power facilities, relating to the field of power facility risk identification. The method includes: extracting vegetation canopy data of the transmission corridor of the target power facility from satellite multispectral images; calculating the initial tilt angle and lodging direction; obtaining a list of potential lodging areas; acquiring visible light images of the transmission corridor using ground image acquisition equipment; extracting vegetation pixels and obtaining satellite normalized vegetation index values ​​for corresponding locations at multiple time phases to construct a seasonal fluctuation feature sequence; extracting texture features from corresponding locations in the ground visible light image; constructing a fused feature vector and inputting it into a convolutional neural network for vegetation type classification; and calculating the risk identification result for each vegetation pixel by combining the initial tilt angle and lodging direction. This invention improves the accuracy of vegetation lodging risk detection around power facilities.

The invention provides a method for segmenting an earth surface evapotranspiration component based on a double-source wave-text ratio. The method comprises the following steps: step 1, collecting meteorological data; step 2, function expressions of an ecological system wave-text ratio, a vegetation canopy wave-text ratio and a soil layer wave-text ratio are respectively constructed based on the collected meteorological data; step 3, substituting the Bowen ratio expression in the step 2 into an energy balance equation and a water vapor balance equation, and deducing plant transpiration and soil evaporation segmentation factors; and step 4, calculating the plant evaporation amount and the soil evaporation amount according to the segmentation factor. According to the method, the ecosystem evapotranspiration can be accurately divided into plant transpiration and soil evaporation only through conventional meteorological data driving. According to the method, a theoretical basis can be provided for ecosystem evapotranspiration component simulation, a technical support is provided for improving the calculation efficiency of an existing global mode in the ecosystem evapotranspiration component simulation process, and a scientific basis is provided for ecological hydrology research.

The invention relates to the technical field of photogrammetry, in particular to a communication line engineering investigation design method and system adopting an unmanned aerial vehicle for surveying and mapping. The method comprises the following steps of: 1, acquiring multi-source data driven by communication constraints; 2, reconstructing a three-dimensional geographic scene, separating the ground surface and the vegetation elevation through laser point cloud, and generating a digital elevation model and a vegetation canopy height model; performing semantic classification on the obstacles based on the multispectral image; 3, dynamically optimizing the position of the tower, generating tower candidate points along the path, iteratively checking the sag safety distance by combining a digital elevation model and a lead catenary equation, and adjusting the position and the type of the tower according to the path corner constraint; 4, verifying the electromagnetic compatibility of the obstacle; and step 5, integrated output of engineering parameters. According to the method, the vegetation area needing to be cleared can be accurately recognized, excessive felling is avoided, meanwhile, the electromagnetic compatibility of the obstacle is verified, and the negative influence on the environment is avoided.

The present invention discloses a vegetation environment coupling prediction method and system based on multi-source remote sensing data fusion, which relates to the field of vegetation environment monitoring. Specifically disclosed are vegetation canopy structure data, vegetation environment temperature detection data, and vegetation component characteristic data. Vegetation canopy structure parameters are calculated through a data fusion algorithm; vegetation canopy structure parameters are introduced to construct a vegetation transpiration intensity outlier detection model; local drought or waterlogged areas are identified based on the vegetation transpiration intensity outlier detection model; trunk insect pest infestation vibration signals and trunk wormhole scanning data are detected based on local drought or waterlogged areas; and pest spread paths are simulated based on trunk insect pest infestation vibration signals and trunk wormhole scanning data. The present invention realizes the analysis of the specific ecological structure of vegetation, as well as the in-depth research and application of the immediate response mechanism for disasters.

The present application relates to a kind of rice growth process three-dimensional scene construction method based on crop growth model and rendering engine, steps are as follows: through field experiment, plant density, distribution, leaf angle distribution and leaf spectrum, canopy spectrum and substrate reflectivity are obtained, and single rice 3D model is constructed;Coupling crop growth model, the canopy structure parameter and leaf BRDF are used to describe the reflection characteristics of vegetation canopy, and the canopy reflection factor is simulated;According to water biological optics model and wave water surface reflection model, water scattering phase function and water surface BRDF are calculated respectively;Three-dimensional water volume model is generated by AVRM model;By using Blender software, LuxCoreRender rendering engine is coupled into incident radiation, vegetation canopy reflection factor, water optical property and other related input parameters, and real rice scene is constructed.The method establishes high-precision three-dimensional rice scene model, improves the precision and efficiency of rice remote sensing radiation modeling, and further provides important technical support for agricultural remote sensing and wetland monitoring, with popularization and application prospect.

The invention discloses a canopy coverage-considered aggregation index inversion method and correction method, and relates to the technical field of remote sensing image processing and aggregation index inversion. Remote sensing images of vegetation canopies are collected, and hotspot reflectivity and dark spot reflectivity are extracted respectively; a normalized hot spot and dark spot index NDHD is calculated, and a CI initial value is calculated according to the linear relation between the NDHD and the aggregation degree index CI; the CI initial value is corrected through the canopy coverage Ccan, and the error between the CI initial value and the CI true value is in linear relation with the Ccan. According to the method, the Ccan-based CI correction model is established for the first time, and the problem of deviation of a traditional algorithm in a sample plot with higher or lower Ccan is solved. By improving a mainstream CI inversion algorithm based on NDHD, the precision of CI inversion is remarkably improved, and more reliable technical support is provided for forest ecological monitoring, carbon cycle research and remote sensing application.

The present invention provides a method for correcting grassland vegetation height using radar observations based on wind speed data. The method includes a three-dimensional laser scanning radar positioned above the vegetation canopy, a wind speed sensor positioned above the vegetation canopy height, and a data collector connected to the three-dimensional laser scanning radar and the wind speed sensor. The method uses real-time wind speed data to determine whether the grass height retrieved by the laser radar is subject to wind disturbances, and automatically corrects the grass height value retrieved by the laser radar based on the wind speed. Compared to methods that do not correct grass height retrieved by the laser radar based on wind speed data, this method can quickly and accurately correct abnormal grass height data that is subject to wind disturbances, reduce grass height deviations caused by wind disturbances, and improve the data quality and reliability of the final grass height retrieval. This method provides strong support for further precise detection and research on the application of laser radar for grass height retrieval in the ecological field.

The application discloses an ecological restoration area carbon sink increment real-time prediction method and system based on artificial intelligence, which comprises the following steps: multi-source data acquisition, obtaining monthly vegetation coverage images through a satellite remote sensing platform, collecting soil temperature and humidity, air CO2 concentration and other sensor data by using a laid ground sensor network, obtaining vegetation canopy point cloud data by using a UAV laser radar according to a preset period, and collecting biomass measured data of the restoration area in a preset time limit; data preprocessing, performing radiation calibration, atmospheric correction and cutting and splicing on the monthly vegetation coverage images obtained by the satellite remote sensing platform, and extracting vegetation coverage and vegetation index time series; carrying out denoising, ground point separation and single tree crown segmentation on the vegetation canopy point cloud data obtained by the UAV laser radar, and calculating single vegetation height and crown width. The application can more accurately realize carbon sink increment prediction.

The invention belongs to the technical field of biological control, and discloses a trichogramma ball throwing device suitable for unmanned aerial vehicle throwing, which comprises an unmanned aerial vehicle mounting platform, a trichogramma ball unit group, a flexible connector, a winding device and a release control module. The bee ball unit group is formed by sequentially connecting a plurality of degradable hollow bee ball bodies; the flexible connecting bodies are connected with the adjacent bee ball bodies; the winding device comprises a rotating shaft, a circumferential limiting assembly for restraining and winding the bee ball unit group and an end part locking mechanism for locking the end part; the release control module comprises a frame in rigid connection with the unmanned aerial vehicle mounting platform, an unlocking unit and a separation assembly for connecting the unlocking unit and the locking mechanism; the unlocking unit controls the locking mechanism to be unlocked through the separation assembly, and after unlocking, the bee ball unit set breaks away from constraint and falls under the action of gravity. Controllable release of the winding type bee ball is achieved, it is ensured that the bee ball group is accurately attached to a vegetation canopy through the flexible connecting body, the device adapts to complex wind speed terrains, and the field distribution uniformity and colonization efficiency of trichogramma are improved.

The present application relates to a kind of non-fisheye digital camera-based vegetation canopy structure measurement method, comprising the following steps: (1) the field of view of non-fisheye digital camera and the actual length and width corresponding to image are calculated;(2) shooting sample is carried out, and image is obtained;(3) the RGB color space of image is converted to LAB color space;(4) the A wave band in LAB color space is preprocessed, and the binary image of background and vegetation is obtained;(5) the FVC, CC, porosity, solar direct radiation and the FAPAR of sky diffuse radiation, the FAPAR of average solar direct radiation in a day, the proportion of diffuse radiation in total radiation, and then the instantaneous FAPAR is calculated;(6) the cumulative pore size distribution of each pixel direction, the vegetation width of ridge row crop and the distance between ridges are calculated;(7) CI, slightly error LAI and LAI_e, and accurate LAI and LAI_e are calculated;(8) average leaf angle is calculated.The present application has low labor cost, no image distortion and wide applicability.

This invention is applicable to the field of ecological environmental protection, providing a method and system for dynamic assessment of biodiversity in ecologically sensitive areas. The method includes: deploying a dynamic mobile monitoring network around the periphery of the ecologically sensitive area to collect data on human activity infiltration and time-series data of ecological parameters (vegetation canopy fragmentation, soil microbial activity decay rate, and frequency of stress behavior of indicator species); analyzing data fluctuation patterns through a time-series analysis model to identify early warnings of ecological degradation; performing reverse correlation of multi-source data based on satellite imagery, pollutant diffusion paths, and historical violations to determine the location and driving factors of degradation events; and integrating data quarterly to construct a dynamic resilience map and generate customized protection recommendations. This achieves early warning and precise source tracing of ecological degradation, improving the targeting and effectiveness of ecological protection strategies, and is applicable to the dynamic assessment and protection decision-making of biodiversity in ecologically sensitive areas.

The utility model provides full-automatic vegetation canopy spectrum auxiliary observation equipment for field trial observation. The full-automatic vegetation canopy spectrum auxiliary observation equipment for field trial observation comprises a spectrograph and a support, the spectrograph is connected with one end of a bifurcated optical fiber, the other two ends of the bifurcated optical fiber are connected with a first corrector and a second corrector respectively, the bifurcated optical fiber is provided with an electronic switch, and the electronic switch is connected with the support. The first corrector and the second corrector are both arranged on a support, and the first corrector is located under the second corrector. The spectrograph and the electronic switch are arranged in the heat preservation box; the device further comprises a controller, and the controller is connected with the spectrograph. The full-automatic vegetation canopy spectrum auxiliary observation equipment for field test observation has the advantages of being convenient to use and capable of automatically rotating by 360 degrees so as to realize automatic observation of different test fields.

The invention discloses a method for dynamically monitoring the vegetation coverage of an intertidal zone in an ecological restoration shoreline, belongs to the technical field of coastal zone ecological monitoring, and solves the problem of data distortion caused by tidal boundary drift, mixed vegetation types and topographic relief in monitoring of the vegetation coverage of the intertidal zone. Satellite and unmanned aerial vehicle images are synchronously acquired; dynamically delimiting an intertidal zone range based on real-time tidal data, generating a boundary vector layer and calculating a total area; overlapping the images and the boundaries, inputting the images and the boundaries into a convolutional neural network model, and outputting mangrove forest, salt marsh herbaceous plants and spartina alterniflora classification results; performing morphological optimization on a classification result, and calculating an initial vertical projection area; arranging sampling grids according to gradient distribution, actually measuring a vegetation canopy projection area and generating error correction factor optimization model parameters; using an optimization model to re-classify, calculate and calibrate the projection area; and finally calculating the effective coverage degree. The method is mainly used for accurate monitoring and acceptance authentication of vegetation coverage of coastal zone ecological restoration engineering.

This invention relates to a method for modeling the reflectance characteristics of three-dimensional aquatic vegetation canopy based on real-world scenarios. The steps are as follows: Leaf reflectance and transmission spectra are calculated using a plant leaf radiative transfer model; the canopy reflectance characteristics are described using canopy structure parameters and leaf BRDF (Brake-Based Flow Function) in conjunction with the actual aquatic vegetation scene; the model parameters are calibrated using the four-flow approximation and linear approximation, and then applied to approximate the solution of the canopy reflectance; finally, the LuxCoreRender physical rendering engine is used to couple incident radiation, canopy reflectance factor, water body optical properties, and observation geometry input parameters, combined with the aquatic vegetation scene, to calculate the canopy reflectance. This method establishes a high-precision three-dimensional aquatic vegetation canopy reflectance model, which helps improve the accuracy and application value of remote sensing radiation modeling. It provides an important tool platform for wetland remote sensing monitoring and ecological protection, as well as the assessment of the blue carbon sink function of coastal wetlands, and has promising prospects for widespread application.

The application discloses a kind of slope infiltration boundary reconstruction methods based on atmosphere-plant-soil collaborative monitoring, comprising: the test area of slope to be monitored is divided into the root zone close to trunk and the crown projection area away from trunk, and in two area open space, soil layer monitoring unit is arranged in pairs;Atmosphere layer and plant layer monitoring unit are arranged in test area;With single rainfall event as basic analysis unit, the data collected by each monitoring unit is time-synchronized, quality-controlled and event-processed, and the monitoring results are integrated as atmospheric input index, plant regulation index and soil hydraulic response index;Based on plant regulation index, determine the water distribution relationship after rainfall through vegetation canopy and trunk influence, and correspond the root zone throughfall and the trunk runoff converted by area to the root zone infiltration boundary, correspond the throughfall of the crown projection area to the infiltration boundary of the area, to reconstruct the partition infiltration boundary of two areas under single rainfall event.The result obtained by the present application is closer to the real situation.