105 results about "Vegetation Index" patented technology

This application relates to the field of agricultural Internet of Things (IoT), and particularly to a method, apparatus, device, and program product for calculating vegetation indices. The method includes: receiving a user-configured vegetation index calculation task, wherein the calculation task includes at least one vegetation index to be calculated; based on a preset dependency relationship between vegetation indices and spectral channels, and combined with the vegetation indices included in the calculation task, parsing to obtain the necessary set of spectral channels required for the calculation task; acquiring the necessary spectral data corresponding to the necessary set of spectral channels, and caching the image data of the current processing row and adjacent rows in the necessary spectral data; calculating the vegetation index in the calculation task row by row based on the cached image data; and when the calculation of a single row of vegetation indices is completed, outputting the single row of vegetation indices and releasing the cached data related to the calculation of the single row of vegetation indices. This method can significantly reduce the memory usage of vegetation index calculation and improve memory utilization efficiency.

The application discloses a kind of based on remote sensing cloud computing's regional desertification automatic monitoring method, comprising: obtaining target area time series multispectral remote sensing image, extracting vegetation index and surface albedo;Based on composite ground object identification rule, eliminate non-target ground object pixel, construct basic sampling pixel set;Based on spatial homogeneity verification mechanism, sample extraction effective fitting sample set;The sample set is segmented and is statistically processed and is regressed, extracts two-dimensional characteristic space's dry side equation and wet side equation;Calculate the spatial geometric relative distance of each reserved pixel to dry and wet double sides, combined with the out-of-bound physical constraint truncation mechanism processing obtains relative desertification difference index;Based on the index, divide desertification grade.The present application overcomes the defect that extreme value fitting is fragile and threshold generalization fails, and realizes high-precision automatic monitoring.

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 present application relates to the technical field of agricultural remote sensing and machine learning, and particularly relates to a leaf area index estimation method based on improved XGBoost. The method comprises the following steps: acquiring unmanned aerial vehicle multi-spectral images and sample leaf area index measured values, and constructing a vegetation index map sample data set after preprocessing; performing feature extraction and fusion on the vegetation index map by using a deep learning network; constructing an improved beaver optimization algorithm, generating an initial population by using a two-stage initialization strategy, updating the position of the architect subpopulation by using an elite directional felling strategy, recombining individuals and the global optimal solution by using a vertical and horizontal cross strategy; optimizing the XGBoost hyperparameters by using the improved beaver optimization algorithm, and establishing a leaf area index estimation model. The leaf area index estimation method based on improved XGBoost combines deep learning feature extraction, improved swarm intelligence optimization algorithm and integrated learning regression modeling, and is helpful to improve the prediction accuracy and stability of the leaf area index estimation model.

The application relates to the technical field of remote sensing mapping, in particular to a remote sensing mapping method for a karst rocky desertification treatment grassland, which comprises the following steps: obtaining remote sensing images and DEM data of a target karst region; quantifying the shadow shielding possibility, vegetation confidence and vegetation density of a pixel based on the radiation brightness, spectral reflectance change trend of the remote sensing images and DEM topographic features, and then adaptively constructing an adjustment factor for correcting a soil-adjusted vegetation index; and calculating the soil-adjusted vegetation index at each pixel according to the adjustment factor, so as to draw a remote sensing vegetation map of the target karst region. The adjustment factor is adaptively determined by constructing a shadow vegetation characteristic value, the problem that the existing technology cannot distinguish between dense shadow vegetation and bright sparse vegetation under the complex karst topography due to the use of a unified adjustment factor is solved, and the precision and reliability of vegetation mapping in a karst rocky desertification treatment area are improved.

The application provides a peanut planting area remote sensing identification method and system, relates to the peanut identification technical field, and includes the following steps: acquiring time-series remote sensing images of a target area; using known peanut planting ground sample points to automatically generate potential peanut planting areas in a feature space corresponding to the time-series remote sensing images as training samples; selecting images of at least three key phenological periods in the time-series remote sensing images to calculate vegetation indexes; using the training samples to mine multi-temporal phenological feature threshold rules for distinguishing peanut planting areas through a decision tree model; and applying the multi-temporal phenological feature threshold rules to judge the vegetation indexes to extract the peanut planting areas. The training samples are automatically generated by using the known peanut planting ground sample points, so that the dependence on a large amount of manual labeling is eliminated, and the multi-temporal phenological feature threshold rules mined by the decision tree model are closely matched with the vegetation indexes of the key phenological periods, so that the accuracy of the extraction of the peanut planting areas is improved.

This invention discloses a method for predicting the heading date of rice, comprising the following steps: S1. Acquiring multispectral images using a drone; S2. Manually recording heading date data; S3. Screening and processing the multispectral images to obtain a complete full-frame multispectral orthophoto image; S4. Segmenting the image, selecting different breeding plots, and calculating the average spectral reflectance of all pixels as spectral reflectance data; S5. Obtaining vegetation indices through band calculations; S6. Constructing a prediction model using a decision tree regression algorithm, and selecting the model with the highest Pearson correlation coefficient as the final prediction model. The drone-based multispectral prediction model for assessing the heading date of rice constructed in this invention takes approximately 20 minutes to collect data from 400 breeding plots. The model's predictive correlation is 0.89, reaching a highly significant level, demonstrating significant application value in improving the efficiency of high-quality rice breeding.

This invention provides a handheld method and device for detecting vegetation index of crop leaves, relating to the field of plant phenotyping technology. The method includes: controlling a contact linear array image sensor to scan the leaf of the crop to be tested line by line in the shaded leaf channel to obtain multi-channel reflectance grayscale digital quantities corresponding to each band; sequentially performing radiation baseline drift suppression, fluorescence cross-channel crosstalk compensation, and crosstalk bias correction on the multi-channel reflectance grayscale digital quantities to obtain multi-channel response data; determining the vegetation index of the leaf of the crop to be tested based on the multi-channel response data, and extracting the leaf phenotypic parameters of the leaf of the crop to be tested; determining the growth status of the leaf of the crop to be tested based on the vegetation index and leaf phenotypic parameters. This method can suppress the influence of ambient light interference, fluorescence crosstalk, and multi-band crosstalk on the measurement results, improve the accuracy of the vegetation index and the reliability of the leaf phenotypic parameters, and is beneficial for high-frequency, continuous, single-person handheld operation under field conditions.

The application relates to a soil erosion risk assessment method, which comprehensively considers the influence of vegetation, land use, topography and rainfall and other factors on soil erosion by acquiring multi-source ecological data of each grid area in a target area. First, the vegetation coverage is determined according to the vegetation index, and the initial erosion risk level is determined in combination with the land use type to evaluate the basic erosion risk under different land use types and vegetation coverage conditions. Then, the risk correction parameters are determined by using the terrain slope and rainfall data to correct the initial erosion risk level, and the comprehensive erosion risk level is obtained to more accurately reflect the actual erosion risk. Then, the sensitive coefficient is introduced, the management priority index is calculated in combination with the comprehensive erosion risk level, and the erosion sensitivity difference of different land types is fully considered. Finally, the management priority of each grid area is determined according to the management priority index, and a scientific and reasonable decision basis is provided for soil erosion management.

The present application relates to the technical field of water and soil monitoring, in particular to a water and soil loss data intelligent analysis system based on unmanned aerial vehicle multi-spectral monitoring, which aims to solve the problems of inconsistent brightness between multi-spectral image frames, pixel saturation failure and inability to intervene in flight behavior in real time caused by environmental light fluctuation; the technical scheme comprises a data acquisition module, which ensures that the skyward light sensor and the groundward multi-spectral camera are triggered along the same direction through a hardware synchronization circuit, and outputs original multi-spectral data frames; an analysis and processing module, which divides the data into a steady-state queue and an abnormal queue by gradient energy comparison, and interpolates and repairs the saturated pixels in the abnormal queue along the texture direction; and an early warning output module, which splices the repaired frames and the steady-state frames, calculates the vegetation index to identify the water and soil loss area, converts the area coordinates into flight control instructions and issues the unmanned aerial vehicle; the present application can eliminate light interference, repair failed pixels, realize perception and flight control closed loop, and improve the accuracy and real-time performance of water and soil loss monitoring.

This disclosure relates to the field of remote sensing and mapping technology, and provides an adaptive distributed scatterer interferometry method, apparatus, and medium for mountainous areas. The method includes: acquiring radar image data and digital elevation model (DEM) data of the target monitoring mountainous area; calculating the radar vegetation index of each pixel in the dual-polarization synthetic aperture radar (DAP) image sequence and classifying it into multiple vegetation coverage levels to obtain a vegetation coverage grading atlas; identifying statistically homogeneous pixels in each image scene under the spatial constraints of vegetation coverage levels according to the grading atlas to determine a candidate set of distributed scatterers; further, using the grading atlas, performing adaptive coherence threshold filtering on each candidate point to obtain multiple distributed scatterers; and determining the long-term surface deformation results of the mountainous area based on the multiple distributed scatterers, the DAP image sequence, and the DEM data. This embodiment effectively improves the accuracy and reliability of distributed scatterer extraction and is suitable for deformation monitoring in complex mountainous areas.

This disclosure relates to a method, system, electronic device, and computer storage medium for identifying green areas around a tower base. The method includes acquiring UAV images and initial images after shadow detection; identifying a disturbance zone within the initial image based on preset tower base design data and the UAV images, where the disturbance zone represents the area within the temporary construction land area; calculating a vegetation index based on the image of the disturbance zone, and stacking the vegetation index and multiple bands from the image of the disturbance zone to obtain a multi-channel image; classifying the channel image using preset sample data to generate a classification probability map, where the classification probability map represents the probability of vegetation and non-vegetation; and calculating the green coverage rate based on the classification probability map. This application can quickly and accurately identify the green area around the tower base and calculate the green coverage rate.

This invention relates to the field of food security monitoring technology, specifically a method for monitoring and warning of food security yield based on big data analysis. The method includes: collecting multi-source monitoring data such as remote sensing vegetation index time-series data, meteorological observations, soil moisture, crop growth surveys, and historical yield statistics. An improved time-series decomposition algorithm combining crop phenological patterns and interannual cycle characteristics is employed to decompose multiple components of the remote sensing time-series data. Spatiotemporal fusion and feature derivation are performed on farmland environment and growth data to construct a multi-dimensional feature vector set. A yield prediction integration model is used to calculate the predicted grain yield and confidence interval, and combined with yield security thresholds, graded risk warning information is output. Multiple data types are integrated to generate a structured warning report, which is then pushed to a decision support platform, achieving accurate grain yield calculation and standardized risk warning.

This invention discloses a method and apparatus for fine extraction of aquatic vegetation and landscape connectivity assessment in rivers and lakes based on high-resolution remote sensing, belonging to the field of environmental remote sensing monitoring and water ecology assessment technology. It includes: acquiring and preprocessing high-resolution satellite imagery to obtain a standardized remote sensing image dataset; extracting water body masks and aquatic vegetation coverage areas through hierarchical threshold segmentation; constructing a dual-threshold discrimination rule based on the normalized vegetation index and soil-regulated vegetation index to finely divide the distribution areas of floating and submerged plants; constructing a binary raster map of vegetation distribution based on the distribution areas; combining a preset connectivity distance threshold; calculating the landscape connectivity index of vegetation patches based on graph theory; determining the ratio of equivalent connected area to effective connected area; and completing a comprehensive assessment of the ecological effectiveness of the aquatic vegetation community. This invention is mainly used for remote sensing monitoring of aquatic vegetation in urban rivers and lakes and for assessing the effectiveness of water ecology restoration, effectively mitigating spectral interference from complex water body sediment backgrounds.

This invention discloses a method for inverting equivalent water thickness in plant leaves across species. This method selects equivalent water thickness with more explicit physical meaning as a representative indicator of plant leaf water content. Based on traditional vegetation indices, it combines continuous removal of preprocessing parameters to construct absorption feature parameters, thereby reducing noise in hyperspectral data while deeply mining its inherent spectral characteristics. It innovatively introduces one-heat coding technology to explicitly process species information and employs a Bayesian optimized machine learning model, effectively improving the accuracy, stability, and computational efficiency of cross-species water inversion. This invention can accurately invert the water status of crops across species, providing a new method for rapidly acquiring crop water status based on hyperspectral reflectance characteristics. It can be widely applied in intercropping and relay cropping patterns and large-scale survey and analysis scenarios, greatly improving the accuracy and speed of water stress diagnosis.

The present application belongs to the technical field of plant disease monitoring, and more particularly relates to a wheat stripe rust remote sensing monitoring method based on optimized hyperspectral vegetation index. The method comprises: test design and sample collection, multi-scale spectral data acquisition, vegetation index screening and optimization, PLSR monitoring model construction, model verification and disease spatial inversion, and result output. The sensitive vegetation index is screened through a test field and optimized, and then the model adaptability is verified in a real field, so that the problems of significant decrease in precision when the existing wheat stripe rust hyperspectral monitoring technology is transplanted from a controllable test field to a real field, inability to cope with complex interference such as soil background heterogeneity, environmental fluctuation and agronomic condition difference, etc. are solved.

The application discloses a kind of transmission and transformation engineering area vegetation recovery evaluation method and system, applied to electric power engineering vegetation evaluation technical field, method includes obtaining the point cloud data and remote sensing image of target transmission and transformation engineering area;Point cloud data is processed to obtain canopy height model, each pixel in remote sensing image is identified using canopy height model, to obtain vegetation pixel set and soil pixel set;According to the reflectivity value of each pixel in vegetation pixel set and soil pixel set, the vegetation coverage of target transmission and transformation engineering area is calculated;The vegetation structure parameter and spectral vegetation index of each vegetation pixel are calculated, based on each vegetation structure parameter and each spectral vegetation index, to obtain vegetation biomass map;Using point cloud data and remote sensing image to calculate to obtain heterogeneity index map, based on vegetation coverage, vegetation biomass map and heterogeneity index map, to obtain vegetation recovery evaluation result, the accuracy of vegetation recovery condition evaluation is effectively improved by the method.

This invention discloses a spatiotemporal reconstruction method and system for coastal vegetation indices based on geographic logic gating and multi-agent systems, belonging to the fields of remote sensing ecological monitoring and deep learning. The method acquires and preprocesses multi-temporal high- and low-resolution remote sensing images to construct NDVI spatiotemporal feature sequences. Spatiotemporal features are obtained through deep encoding, and a multi-agent system incorporating information on vegetation, cloud cover, tides, and topography is established. Coastal geographic logic constraints are introduced to construct multi-scale spatiotemporal differences and generate geographic logic gating, which weights and modulates the spatiotemporal features to enhance reliable information and suppress interference noise. Multi-agent collaborative reconstruction is achieved under multi-objective constraints, outputting high-resolution vegetation indices and supporting edge deployment and online learning. This invention effectively overcomes problems such as cloud and fog obstruction, tidal inundation, and data gaps, significantly improving reconstruction accuracy, geographic rationality, and spatiotemporal continuity. It is more adaptable to the complex ecological environment of coastal zones, providing a stable and reliable technical means for long-term dynamic monitoring of vegetation.

This invention provides a rapid landslide area segmentation method based on high-resolution satellite remote sensing data and deep learning algorithms, belonging to the field of ground scene technology. The invention downsamples the original satellite remote sensing image to obtain a first image, extracts reflectance data from the red and near-infrared channels, calculates the reflectance difference and sum of reflectance values, and normalizes them to obtain a vegetation index to construct a vegetation layer. The first image and the vegetation layer are superimposed and stitched together, and feature enhancement is performed to obtain a vegetation feature map. This map is input into a convolutional neural network to output a landslide probability map. A two-dimensional coordinate set is constructed and cropped to obtain high-resolution test patches. The spatial gradient magnitude of the local vegetation index is calculated and weighted by a preset enhancement coefficient to generate edge-enhanced patches. The edge-enhanced patches are input into a semantic segmentation network to output a local landslide map, which is then backfilled into the initial image to obtain a landslide area segmentation map. This invention constructs a cascaded segmentation system to achieve efficient and accurate segmentation of landslide areas.

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 application discloses a kind of fusion multi-source multi-temporal remote sensing's semi-arid shallow covering area stratum lithology classification method, collects multi-period multi-spectral remote sensing image and polarization SAR data and pre-processes, calculates optical and radar vegetation index, normalizes and constructs feature dataset;Combined with field investigation label sample, construct pixel-level sample set, after inputting multi-scale time series attention network training, output GeoTIFF format classification result graph. Overall realization is based on the stratum lithology classification of semi-arid shallow covering area of multi-source multi-temporal remote sensing data, breaks through the application limit of existing remote sensing lithology classification method in semi-arid shallow covering area, gets rid of the dependence of many existing deep learning classification algorithms on regional level image sample, overcomes the training data acquisition bottleneck caused by stratum lithology unit boundary blur in semi-arid shallow covering area, improves the reliability of lithology classification under complex environmental conditions in semi-arid shallow covering area.

This invention relates to the field of forestry remote sensing monitoring technology and discloses a leaf SPAD prediction method based on UAV multispectral imaging. The method involves acquiring images of the target forest area through low-altitude multispectral aerial photography using a UAV, and extracting spectral data based on the locations of measured ground sampling points. Enhanced feature engineering is applied to the data to construct a multi-source feature set including original bands, basic vegetation indices, red-edge vegetation indices, and texture features. An early fusion strategy is used to construct a high-dimensional fusion feature vector, which is then input into an ensemble learning regression model based on ExtraTrees for training and prediction. Finally, a spatial distribution map of leaf SPAD values ​​in the forest area is generated. This invention effectively overcomes the shortcomings of conventional multispectral imaging, such as limited band size and saturation. By fusing the red-edge index and the ensemble learning algorithm, it significantly improves the accuracy and robustness of SPAD inversion, providing an effective technical means for precise management and rapid nutrient diagnosis of plantations.

The soybean aboveground biomass dynamic across growth period estimation method, system and program product relate to the crop yield technical field, fill the existing technology in the problem of combining research soybean AGB based on UAV remote sensing and machine learning relative blank. Through the unmanned aerial vehicle to soybean multiple growth stage data collection, and based on soybean growth stage data, respectively generate multispectral orthophoto and digital surface model; based on multispectral orthophoto and digital surface model, respectively extract the crown structure features, image texture features and multiple vegetation indexes related to aboveground biomass of crops; after improving the stacking model, using multiple data of soybean, crown structure features, image texture features and multiple vegetation indexes related to aboveground biomass of crops to train the improved stacking model, obtain the trained stacking model; based on the trained stacking model, complete the soybean aboveground biomass growth period estimation.

The application relates to the field of agricultural information technology and provides a crop freezing injury monitoring method and device. The method comprises the following steps: acquiring multispectral images, three-dimensional point cloud data and canopy temperature data of a target area; extracting a vegetation index from the multispectral images; generating a terrain model from the three-dimensional point cloud data; and fusing the vegetation index, the terrain model and the canopy temperature data to obtain freezing injury monitoring results of different terrain parts in the target area. The crop freezing injury monitoring method provided by the application can realize rapid, accurate and fine monitoring of crop freezing injury by acquiring multi-source data such as multispectral images, three-dimensional point cloud data and canopy temperature data.

The present invention relates to the field of vegetation index monitoring, and discloses a method for monitoring Solanum rostratum based on the change rate of drone multi-spectral indices, including obtaining multi-spectral image data of a target area in continuous time series; using an adaptive spectral calibration algorithm for the reconstructed time-series multi-spectral images to extract NDVI, GNDVI and red-edge vegetation index change layers, and obtaining an initial map of vegetation index change rates; aiming at the spectral confusion problem of Solanum rostratum at different growth stages and surrounding plants in terms of change rate performance; deeply fusing the initial map of vegetation index change rates with a growth fluctuation profile model to construct a change rate abnormal response factor layer and output candidate areas for suspected distribution of Solanum rostratum; combining the change rate evolution trend of the historical expansion path and the neighborhood growth consistency index, and dynamically generating a high-confidence identification map of Solanum rostratum. The present invention has the advantage of improving the discrimination accuracy of change rates.

The application relates to a soil organic matter prediction method based on multi-feature fusion, and belongs to the technical field of soil organic matter prediction. The method comprises the following steps: pre-processing acquired soil data, obtaining spectral features, performing time domain reconstruction on frequency domain signals, and obtaining time domain data; obtaining an optimal delay time and an optimal embedding dimension based on the time domain data, and performing phase space reconstruction to obtain a phase space trajectory; extracting chaotic features based on the phase space trajectory, and taking the extracted chaotic features, the optimal delay time and the optimal embedding dimension as final chaotic features; obtaining a vegetation index based on the spectral features and taking the vegetation index as an index feature; and inputting the spectral features, the final chaotic features and the index feature into a constructed double-flow low-rank interaction network model to obtain a soil organic matter prediction result. The application aims to solve the technical problem that the spectral features extracted by the prior art cannot comprehensively represent the complex nonlinear characteristics of soil, thereby leading to low prediction accuracy.

This invention proposes an intelligent landslide identification method based on changes in remote sensing semantic embedding. The method includes collecting annual remote sensing semantic embedding data, optical remote sensing imagery, digital elevation models, and land cover data for the study area; constructing physically feasible landslide identification areas constrained by both slope and land cover; calculating and standardizing the intensity of land cover semantic changes in adjacent years; generating annual vegetation index results based on growing season optical remote sensing imagery to produce landslide anomaly intensity results; extracting strong landslide candidate areas using adaptive thresholding; automatically generating potential and non-landslide samples, and optimizing the samples based on time-series normalized vegetation index and spectral angle measurements; and finally, using supervised classification methods such as random forest to achieve landslide identification and obtain the final landslide classification map. This invention realizes a complete intelligent landslide identification process, from anomaly identification of changes in remote sensing semantic embedding, automatic extraction of strong landslide candidate areas, automatic sample optimization, to supervised classification mapping.

This invention discloses a method and system for inverting the nutrient content of eucalyptus canopy leaves based on multi-feature fusion, belonging to the field of UAV remote sensing and forestry information technology. The method includes: acquiring and preprocessing multispectral image data from a UAV; extracting vegetation index features and texture features of the eucalyptus canopy; filtering the fused features based on mutual information to construct a multi-feature fusion dataset; constructing an inversion model for the nitrogen, phosphorus, and potassium content of eucalyptus leaves using a random forest regression algorithm; and verifying and optimizing the accuracy of the inversion model. This invention effectively compensates for the information gaps of single features by fusing spectral and spatial structure information, significantly improving the inversion accuracy of eucalyptus canopy leaf nutrient content, and providing reliable technical support for precise nutrient management of eucalyptus plantations.

This invention discloses a method for jointly regulating water resource utilization of desert riverbank vegetation based on ecological gate groups. The method includes: obtaining vegetation change patterns using a fitting method based on the kNDVI vegetation index; dividing the vegetation in the desert watershed into three vegetation distribution zones based on the vegetation change patterns and vegetation coverage; acquiring all ecological gates in the desert watershed and dividing them into multiple ecological gate groups; determining the weights of the three vegetation distribution zones for ecological protection objectives using the analytic hierarchy process (AHP), and calculating the comprehensive score of the regulation zone based on the proportion of ecological functional zones in the regulation zone; solving the joint model of the ecological gate groups using a single-objective genetic algorithm to obtain the ecological water demand and ecological water supply of each control zone when the total water shortage of all ecological gate groups is minimized; dividing the ecological gate groups into five levels based on the comprehensive score, and then activating the ecological gate groups to irrigate the irrigation area according to the ecological gate group level, ecological water demand, and the importance of the irrigation area.

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.