77 results about "Vegetation canopy" patented technology

The invention provides a garden plant environment ecological assessment method and system. According to the method, self-adaptive layered scanning of vegetation canopies is executed through a flight device, multi-spectral reflection data and three-dimensional morphological parameters are synchronously obtained, and a reflection database for space-time calibration is constructed. High-reflection interference is suppressed by adopting a polarization compensation technology, layered reflectivity weighted calculation is performed based on penetration level identification, and a three-dimensional photosynthetic efficiency map is generated by combining vertical section attenuation gradient and photosynthetic parameter mapping. Secondary scanning is started for the abnormal area, the root system absorption delay error is corrected by fusing soil moisture parameters, and finally an accurate ecological evaluation result is output. According to the technical scheme provided by the invention, three-dimensional dynamic monitoring of the photosynthetic efficiency of the plant community is realized through an air-ground cooperative scanning and dynamic compensation algorithm, and the precision and reliability of ecological assessment in a complex canopy environment are effectively improved.

The invention relates to the technical field of geological disaster detection, in particular to a geological disaster hidden danger point detection method and system. Comprising the following steps: S1, synchronously acquiring vegetation canopy penetration data, multispectral soil humidity spatial distribution data and lithology distribution data by adopting an unmanned aerial vehicle carried image data acquisition device and a multi-band laser radar, and generating an orthoimage and a three-dimensional point cloud data set; s2, fusing the orthoimage and the three-dimensional point cloud data through an aerial triangulation encryption algorithm to establish a three-dimensional model, loading the multispectral soil humidity spatial distribution data into the three-dimensional model to generate a dynamic digital elevation model, and generating a contour line topological structure through a TIN triangulation network algorithm; s3, extracting a slope factor, topographic relief, vegetation coverage, fracture distance and a lithologic factor based on the three-dimensional model to construct a geological disaster hidden danger system; according to the system, the geological disaster occurrence time can be estimated, and the rescue and protection treatment period can be effectively planned based on the geological disaster occurrence time.

The invention discloses a vegetation coverage and management measure remote sensing inversion method combining multi-angle remote sensing data and layered vertical vegetation coverage, and the method comprises the following steps: (1), obtaining a multi-angle remote sensing data set, and carrying out the preprocessing of the multi-angle remote sensing data set; (2) selecting an optimal wave band through Borson correlation analysis and XGBoost feature screening; (3) estimating a C factor and a structured vegetation index S-Cs; (4) simulation of leaf reflectivity and transmissivity is carried out, and then vegetation canopy reflectivity is simulated; (5) converting the high spectral reflectivity of the vegetation canopy into equivalent remote sensing reflectivity of a satellite wave band, calculating vegetation indexes, and selecting the vegetation index with the highest correlation to participate in inversion and modeling of a leaf area index LAI; (6) determining an optimal angle combination and evaluating an inversion result of LAI; (7) establishing a novel quantitative coupling relation model between the multi-angle LAI and the SCs and between the SCs and the C factor, and then inverting the C factor; the invention provides a technical means with high practicability for monitoring the dynamic change and expansion process of soil erosion.

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 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 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 discloses a vegetation canopy nitrogen content prediction method based on a remote sensing image spectrum, and belongs to the field of agricultural remote sensing, and the method comprises the steps: introducing a Sobol sequence, a tippling foraging strategy and a multi-scale cooperative variation mechanism into an original black wing optimization algorithm, then obtaining an improved black wing optimization algorithm, and enabling the improved black wing optimization algorithm to be combined with a machine learning model, thereby achieving the prediction of the nitrogen content of a vegetation canopy. Especially a random forest model is combined to obtain a vegetation canopy nitrogen content prediction model. The model can efficiently and effectively predict the nitrogen content of the vegetation canopy through the spectral index and the texture index of the vegetation, so that the application of the nitrogen fertilizer can be accurately regulated and controlled according to the growth period demand of the plant.

The invention discloses a moisture prediction method and system based on airborne multispectral sensitive wave band combination, and relates to the technical field of agricultural remote sensing and image processing, and the method comprises the steps: on the basis of the response characteristics of soil and plant canopy reflection spectrums to moisture, combining with the physical mechanism of O-H bond vibration in water molecules, and predicting the moisture content of the soil and plant canopy reflection spectrums; determining the bandwidths of all central wavelengths by considering the influence of atmospheric water vapor on the near infrared spectrum; generating reflection spectrums of the soil and the vegetation canopies, generating a spectral response function by using the relationship between the central wavelength and the bandwidth, converting the simulated spectrums into a moisture sensitive wave band, and calculating the reflectivity and the spectral index; and collecting reflection spectrum images of the soil and the vegetation canopies at the selected positions, and inputting the reflection spectrum images into the moisture prediction model to predict moisture. By optimizing wave band selection and bandwidth allocation, the problems that when an existing airborne multispectral sensor executes a moisture monitoring task, the pertinence of wave band selection is insufficient, and the monitoring precision is not high can be effectively solved.

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 is applicable to the technical field of satellite remote sensing earth observation, and provides a vegetation canopy vegetation parameter inversion method based on an MOBCSM (Multi-output Band-Correlative Convolution Mixture Kernel) kernel, a storage medium and a processor. By introducing the MOBCSM kernel, a complex nonlinear relation in the hyperspectral data can be better processed, a stronger modeling capability is provided in multi-task collaborative optimization, in addition, the MOBCSM kernel can effectively model interaction between wavebands, and the precision and reliability of hyperspectral data analysis and vegetation parameter inversion are further improved. The storage medium for storing the method and the processor for operating the method also have the same technical effects.

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 invention discloses a non-prior detection method and device for a vegetation canopy shielding target based on unmanned aerial vehicle multi-spectral point cloud, and belongs to the technical field of three-dimensional target detection in remote sensing. In order to solve the problem that a target detection method based on multi-spectral point cloud data is difficult to detect due to lack of target samples caused by vegetation canopy shielding, the method comprises the following steps: firstly, clustering non-ground points of spectral point clouds, and counting the radius of a circumcircle of non-ground point clustering; extracting multi-scale spatial features of the multispectral point clouds according to a clustering result, and constructing spatial-spectral features of the multispectral point clouds; and then constructing a radius-adaptive spherical shell model, obtaining a local background point set in a spherical shell corresponding to any point, further performing joint expression on space-spectral features of pi, and calculating residual errors between all expression results and original features, thereby realizing detection of a shielded target.

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.

The present invention discloses a method and system for measuring urban green space carbon sinks based on multi-source data fusion, relating to the field of environmental monitoring technology. The method comprises the following steps: collecting and preprocessing multi-source data from urban green spaces; using a dual-channel generative adversarial network to complete the parameters of vegetation in building-blocked blind areas, obtaining complete green space leaf area index distribution data; using a ray tracing algorithm to simulate the path of light reflected from building glass curtain walls, obtaining a correction factor for photosynthetically active radiation received by the vegetation canopy, and identifying urban green space carbon sink distribution data through multi-scale data fusion; obtaining carbon sink errors based on the urban green space carbon sink distribution data, dynamically correcting them using a Bayesian optimization algorithm, and generating an urban green space carbon sink measurement report. The present invention utilizes a dual-channel generative adversarial network, utilizing a collaborative training mechanism between a U-Net generator and a PatchGAN discriminator, to achieve high-precision spatial continuity reconstruction of the leaf area index in building-blocked blind areas.

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.

A method for predicting changes in the aging rate of swamp vegetation canopies. The present invention relates to a method for predicting swamp vegetation canopies. The present invention solves the problem that existing swamp vegetation cannot accurately predict changes in the aging rate of vegetation canopies at a regional scale through field observations. Methods: Acquire and process remote sensing and meteorological data; extract the swamp vegetation distribution that has not changed during the study period as the study area; interpolate and resample the meteorological element dataset; calculate the difference between the November NDVI and the October NDVI corresponding to all pixels in the study area and extract the values of each meteorological element; construct a pixel-by-pixel prediction model for changes in the aging rate of swamp vegetation canopies under the influence of climate change; and combine future meteorological element datasets to predict future changes in the aging rate of swamp vegetation canopies within the study area. The present invention utilizes the characteristics of remote sensing data, which have a wide spatial range and strong continuity, to provide a new method for predicting changes in the aging rate of swamp vegetation canopies.

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.