76 results about "Red edge" patented technology

The invention discloses a soil water and salt dynamic monitoring and quality evaluation method based on multispectral remote sensing, and relates to the field of geological monitoring, and the method comprises the steps: carrying out the scattering deviation correction and roughness correction of an observation spectrum through a satellite remote sensing image, an unmanned aerial vehicle multispectral image, a ground sample and meteorological driving data; the soil intrinsic reflectivity, the vegetation coverage component and the salt crusting component are obtained; a water-salt inversion model is established by combining thermal infrared and red edge wave band information, and multi-temporal soil volumetric moisture content and surface salinity index are inverted; by introducing meteorological conditions and vegetation dynamic characteristics, a water-salt coupling partial differential equation and a graph space-time constraint model are constructed, and continuous space-time distribution of soil water content and salinity is obtained; extracting salinity, moisture, vegetation response and soil health indexes, establishing a soil quality comprehensive evaluation model, and outputting high-risk plaques and a treatment priority sequence. The method realizes dynamic monitoring and quality grading evaluation of soil water and salt, and is suitable for saline-alkali soil treatment and ecological restoration.

The invention relates to the technical field of image recognition, and discloses a multispectral image and deep learning-based forest disease and insect pest automatic recognition method, which comprises the following steps of 1, carrying a multispectral camera containing a red edge wave band through an unmanned aerial vehicle to obtain a forest region image; 2, calculating a red edge normalized vegetation index of the image; 3, performing time sequence modeling on the red edge normalized vegetation index data of more than five consecutive periods, and inputting a time sequence convolutional network to generate an early lesion probability graph; 4, detecting a pest and disease damage target by adopting a multi-scale adaptive feature pyramid network; 5, outputting a disease and pest distribution thermodynamic diagram; and 6, driving the unmanned aerial vehicle cluster to execute precise pesticide spraying. According to the method, through the high sensitivity of the red-edge wave band to chlorophyll degradation and in combination with sequential convolutional network dynamic modeling, an initial lesion area can be recognized 7-10 days before disease development, the early disease recognition capability is remarkably improved, the disease discovery period is shortened, and large-scale disease diffusion is effectively avoided.

The invention relates to the field of vegetation index monitoring, and discloses a method for monitoring solanum roselle based on a multi-spectral index change rate of an unmanned aerial vehicle, which comprises the following steps: acquiring multi-spectral image data of a target area under a continuous time sequence; extracting NDVI, GNDVI and red edge vegetation index change layers from the reconstructed time sequence multispectral image by adopting a self-adaptive spectrum calibration algorithm to obtain a vegetation index change rate initial map; the method aims at the spectrum confusion problem of the eggplant at different growth stages and peripheral plants in change rate expression. Carrying out deep fusion on the initial vegetation index change rate map and the growth fluctuation contour model, constructing a change rate anomaly response factor map layer, and outputting suspected distribution candidate regions of the solanum aureocauda var. Aureocauda var. Aureocauda; and combining the change rate evolution trend of the historical extension path with a neighborhood growth consistency index, and dynamically generating a high-confidence identification map of the solanum aureum. The method has the advantage of improving the change rate discrimination precision.

The present invention provides a method, device and storage medium for crop fertilizer decision-making based on multispectral images. The method comprises: receiving an image to be processed sent by a multispectral drone via a local area network; the image to be processed is a multi-band image of a field collected by the multispectral drone; determining NDVI according to the near-infrared band reflectivity and red band reflectivity of the image to be processed, and determining RECI according to the near-infrared band reflectivity and red-edge band reflectivity of the image to be processed; performing growth classification on the crops in the image to be processed based on RECI and NDVI to obtain multiple regions with independent boundaries; determining the fertilizer requirement of the centroid point of each region, and obtaining a fertilization prescription map based on the fertilizer requirement of the centroid points of all regions. The method, device and storage medium for crop fertilizer decision-making based on multispectral images provided by the present invention realize the automatic acquisition of multispectral images, and realize the rapid determination of fertilizer deficiency information of the entire field through pixel-level fertilizer deficiency analysis, thereby improving the efficiency of crop fertilizer decision-making.

The invention relates to the technical field of remote sensing recognition, in particular to a garlic recognition system and method based on remote sensing deep learning. According to the method, visible light and red edge wave band information in a multi-temporal remote sensing image for the phenological period of garlic are combined, the alignment precision of image data in the time sequence is improved through spectrum normalization and spatial calibration, and a multi-source classification sample set is constructed by superposing vegetation spectrum indexes and spatial sample positioning labels; a deep learning structure of semantic segmentation and an attention mechanism is introduced to enhance fine expression of spatial coherence and feature response of region recognition, the discrimination definition of a garlic region in an image is improved through channel spatial feature fusion, and a garlic pattern spot spatial boundary is subjected to continuous recombination and comparison verification with a reference sample. And the pattern spot precision and the sample consistency of the final identification area are ensured, so that the classification accuracy and the identification stability of the garlic planting area are remarkably improved under the complex background.

The invention relates to the technical field of image analysis, in particular to a vegetation fine classification and recognition method and system based on an unmanned aerial vehicle, and the method comprises the following steps: obtaining a multispectral image through the unmanned aerial vehicle, extracting red edge reflectivity, NDVI and gray-level co-occurrence contrast, generating a feature vector in a standardized manner, calculating neighborhood offset to obtain a dynamic weight, and combining the dynamic weight into a weighted vector; high discrete features are screened as effective channels, multi-scale clustering is carried out, center and region growth extension recognition is optimized, and a vegetation classification atlas is generated. According to the method, a neighborhood pixel feature offset dynamic weight mechanism is introduced, multi-spectral feature dimension contribution degree is adjusted in a self-matching mode, effective channels are screened based on full-image dispersion, redundant interference is eliminated, image pyramid multi-scale clustering and consistency constraint are fused, the complex vegetation boundary recognition capability is improved, dynamic weight and multi-scale optimization are coordinated, and the method is high in robustness and high in robustness. Sample dependence is reduced, and accurate distinguishing of spectrum similar vegetation is achieved.

The invention relates to the technical field of salicornia europaea spectral feature extraction, and discloses a salicornia europaea lithium stress time sequence spectral feature database construction method which comprises the following steps: culturing salicornia europaea in a greenhouse by utilizing water culture vessels containing different lithium concentrations to obtain a plurality of salicornia europaea water culture vessels containing different lithium concentrations; measuring hyperspectral images of the salicornia europaea in the sampling intervals to obtain hyperspectral images of the salicornia europaea and the reference white board in each sampling interval; extracting a hyperspectral reflectivity image and hyperspectral data of the salicornia europaea in each sampling interval so as to obtain absorption characteristic parameters and red edge positions of each continuum removal spectrum under different set wavelengths, and calculating the hyperspectral reflectivity of the salicornia europaea according to the hyperspectral reflectivity image of the salicornia europaea in each sampling interval. Respectively constructing a time sequence spectrum library and a standard spectrum library of salicornia europaea at different sampling intervals under the stress of different lithium concentrations; according to the method, the spectral feature precision of salicornia europaea is improved, and meanwhile, the spectral database of salicornia europaea is enriched.

The invention belongs to the technical field of remote sensing, and discloses a wolfberry planting distribution high-precision remote sensing monitoring method based on multi-temporal fusion and spectral feature optimization. According to the method, a multi-temporal and multi-spectral satellite image is used as a data source, and preprocessing and multi-temporal image fusion are firstly carried out; the method comprises the following core steps: establishing a time sequence characteristic curve according to a unique phenological period (such as bare soil characteristics in a dormancy period and high vegetation coverage in a rapid growth period) of wolfberry; in a spectral domain, screening out a characteristic spectrum dimension combination with the highest discrimination degree between the wolfberry and other crops through a characteristic wave band optimization algorithm (such as vegetation index difference degree and red edge characteristics); and in combination with an object-oriented classification or deep learning classification model, constructing a space-time coupling classifier, and performing high-precision extraction and distribution mapping on the Chinese wolfberry planting region. The method can effectively solve the problem of confusion classification of Chinese wolfberry and similar ground features (such as other shrubs and orchards), and realizes rapid and accurate monitoring of Chinese wolfberry planting area and spatial distribution.

The invention relates to the field of remote sensing image processing, in particular to a farmland soil type classification-oriented remote sensing image data acquisition method, which comprises the following steps of: regularly acquiring remote sensing image data of a target area according to a preset period; obtaining a distance measure between any two pixel points in each remote sensing image, and segmenting each remote sensing image to obtain a ground feature region; analyzing the trend and the amplitude of each pixel point on the spectral curve, and determining the red edge expression degree of each pixel point; determining the vegetation growth performance degree of each pixel point; determining the farmland soil segmentation quality of each remote sensing image; and screening a to-be-classified image from all remote sensing images of the target area, screening a farmland soil area to construct a bare soil mask image, and performing farmland soil classification on a ground feature area in the to-be-classified image in combination with a neural network. According to the method, the farmland soil area in the remote sensing image is accurately divided, and the accuracy of farmland soil type classification is improved.

The invention discloses a rice nutrient accurate monitoring and fertilization decision-making system based on multi-source data fusion, particularly relates to the field of precision agriculture, and is used for solving the problems of nutrient monitoring deviation and inaccurate fertilization caused by spectrum saturation in a rice vigorous growing period. The method comprises the following steps of: acquiring red edges and near-infrared reflection from multiple angles, synchronously acquiring texture and structure images, fusing to generate an anti-saturation characteristic cube, outputting a peak period characteristic packet by phonological transition point space-time segmentation, eliminating saturation response to construct a separable nitrogen trace spectrum, and calculating the nitrogen trace spectrum. The method is realized by calculating an energy concentration ratio and a water layer confluence indicating quantity in a situation constraint graph, outputting a calibration coefficient to adjust a prescriptional graph, and finally combining a historical track and a microtopography check drop point to output an execution prescriptional graph, so that the peak period diagnosis precision is improved, excessive or insufficient fertilization is avoided, and efficient nutrient management is realized.

The invention discloses an intelligent monitoring, prevention and control system for tobacco brown spot, and relates to the technical field of agricultural disease monitoring and intelligent prevention and control. A lower canopy monitoring array, elevation thermal infrared and red edge near infrared imaging and spore sampling (fluorescent quantitative PCR / loop-mediated isothermal amplification) are used as inputs; fog drop deposition D, leaf surface water film W and spore concentration S are predicted through multi-modal alignment and a physical field generator; carrying out long sequence risk assessment by inputting a correlation state space model and superposing a liquid state self-correlation kernel, and carrying out long sequence risk assessment by using concentric wheel patterns and narrow yellow halo (rlt; 0.15) + near-infrared / short-wave infrared black mould layer triple discrimination and checking, and a prescription is formed by combining double-pulse triggering that leaf surface wetting is larger than or equal to 8 h or a spore index reaches a threshold. The prescription comprises night microclimate intervention, variable spraying and leaf thinning control; resistance rotation compliance is realized through prescription uplink pre-check and electronic control unit unlocking; based on row belt / Thiessen polygon scheduling, 36 h coverage is larger than or equal to 95%, the dosage and residues are reduced, and the hit rate and timeliness are improved.

The invention relates to the technical field of water quality data analysis, in particular to a multispectral image-based water phosphate concentration prediction method, which is characterized by comprising the steps of data acquisition, image preprocessing, absorption characteristic determination, key wave band selection and characteristic extraction, model establishment and training and real-time prediction and optimization. The continuity and the precision in the prediction area are improved; according to the invention, a high-precision, real-time and online phosphate concentration prediction system is constructed by utilizing a method of combining laboratory simulation and field data and directly utilizing the specific absorption characteristic of a complex generated by reacting phosphate with metal ions such as iron and aluminum in a red edge or near-infrared band; through multiple links of image preprocessing, key wave band selection, spectral feature extraction, nonlinear regression model establishment, spatio-temporal data optimization and the like, the defects that a traditional remote sensing method depends on indirect indexes and is greatly interfered by the environment and the like are successfully overcome, and the accuracy and reliability of water quality monitoring are remarkably improved.

The invention relates to a machine learning-based intelligent identification method for investigating and monitoring forest and grass resources by an unmanned aerial vehicle, and the method comprises the steps: determining a target region, and carrying out the multidirectional target image and spectrum data collection of the forest and grass resources in the target region according to a preset cruise mode; inputting the spectral data into a vegetation recognition model to obtain a vegetation recognition result; the vegetation identification model is an optimal machine learning model evaluated by training a plurality of machine learning models by using a training set; the training set comprises original spectral data; extracting near-infrared wave band data and red edge wave band data of the spectral data, and acquiring the vegetation generation condition and the chlorophyll content by using the near-infrared wave band data and the red edge wave band data; and processing the multidirectional target image, constructing a forest and grass resource three-dimensional model by using the processed multidirectional target image, and importing the vegetation identification result and the vegetation generation condition as labels into the forest and grass resource three-dimensional model to obtain a forest and grass resource identification model.

The invention provides a crop fertilization decision-making method and system based on an unmanned aerial vehicle multispectral image, which are applied to the technical field of crop fertilization, and the method comprises the steps: carrying out growth vigor partitioning based on red light band reflectivity, red edge band reflectivity and near-infrared band reflectivity, and obtaining a growth vigor partitioning result of the multispectral image; centroid extraction is carried out on each independent area in the growth vigor zoning result, and sampling points of each independent area are obtained; determining global positioning coordinates of sampling points; determining a normalized difference red edge index of the sampling point based on the red light band reflectivity, the red edge band reflectivity and the near-infrared band reflectivity; based on the global positioning coordinates of the sampling points and the normalized difference red edge indexes of the sampling points, generating a crop growth visualization image; and based on the crop growth visualization image and a preset target yield, determining a recommended topdressing amount of the target crop. The method can dynamically match and recommend the fertilization amount in combination with the target yield demand.

The invention provides a method for monitoring the population density of rice planthoppers by an unmanned aerial vehicle for the field of agricultural pests. In order to solve the problems that rice planthoppers are seriously damaged and efficient monitoring methods are lacked, the method for monitoring the population density of the rice planthoppers by collecting rice multispectral images through an unmanned aerial vehicle is provided and is characterized in that the unmanned aerial vehicle is used for carrying a multispectral camera to collect rice canopy multispectral images, and the population density of the rice planthoppers is investigated; extracting green, red, red edge and near-infrared reflectivity and texture indexes of the rice canopy; utilizing a continuous projection algorithm to screen reflectivity and texture indexes sensitive to the rice planthopper population density, and utilizing the screened indexes to establish a rice planthopper population density monitoring BP neural network and a random forest model; performing unmanned aerial vehicle multispectral image acquisition on rice in a to-be-detected area, extracting spectral reflectivity and texture indexes, and inputting the spectral reflectivity and the texture indexes into the model to monitor the rice planthopper population density. According to the method, the unmanned aerial vehicle is used for collecting the multispectral image simply and quickly, the monitoring precision is high, and a new method is provided for monitoring large-area rice planthoppers.

The invention discloses a forest tree DBH (diameter at breast height) inversion method based on a multispectral remote sensing image, which relates to the technical field of remote sensing information inversion, and comprises the following steps of: performing radiometric calibration and geometric correction processing on original multispectral observation data, generating a standardized multispectral data set, and performing single tree identification and canopy segmentation processing to obtain a tree DBH (diameter at breast height); generating single-plant tree crown image data; combining the illumination direction features with the red-edge wave band and the near-infrared wave band, performing multi-scale feature analysis and fusion processing, and outputting canopy feature data; performing nonlinear processing and illumination correction on the canopy feature data to generate breast diameter prediction data; and carrying out space mapping and visual mapping processing on the diameter at breast height prediction data to generate a diameter at breast height distribution diagram result, carrying out precision evaluation and quality inspection processing, and outputting a forest tree diameter at breast height inversion result. The stability, the precision and the environmental adaptability of the breast diameter inversion are improved, and high-reliability technical support is provided for forest structure monitoring and resource evaluation.

The invention discloses a remote sensing image reconstruction method and device cooperating with spatial channel attention and dynamic frequency domain multi-scale fusion, and the method comprises the steps: carrying out the decomposition, fusion and reconstruction of a frequency spectrum of a down-sampling feature in an attention enhancement module, thereby enhancing the expression of high-frequency information through the extraction of a frequency domain feature; according to the method, the Landsat-8 image slice sample set with month information is taken as the training sample set, the month information in the training sample is embedded into the Landsat-8 image data through the time embedding layer to obtain a comprehensive feature map including time and space for training, and thus the influence of dynamic change of the earth surface is considered. Through the two points, image data missing caused by factors such as cloud layer shielding can be filled, meanwhile, the red edge wave band of Sentinel-2 is reconstructed, and more accurate and comprehensive data support is provided for the fields of agriculture, ecological monitoring and the like.

The invention discloses a vegetation coverage index algorithm and system based on an unmanned aerial vehicle. The algorithm comprises the following steps: S1, data acquisition and multi-source data preprocessing; s2, dynamic environment correction and multi-source data fusion; s3, red edge enhanced vegetation index calculation and terrain correction; s4, vegetation coverage intelligent prediction and precision verification; and S5, result visualization and decision support: generating a vegetation coverage spatial distribution map and a statistical report, and supporting a resource management decision. By integrating the unmanned aerial vehicle, the multispectral imaging sensor, the dynamic environment correction model and the data fusion algorithm, efficient and high-precision technical support is provided for precision agriculture, ecological resource management and disaster monitoring.

The application discloses a rice nutrient precision monitoring and fertilization decision system based on multi-source data fusion, and particularly relates to the field of precision agriculture, and is used for solving the problems of nutrient monitoring deviation and inaccurate fertilization caused by spectral saturation during the rice vigorous growth period, and is achieved by the following steps: collecting red edge and near-infrared reflection from multiple angles, synchronously acquiring texture and structure images and fusing to generate an anti-saturation feature cube, outputting a vigorous growth period feature package by spatiotemporal segmentation at the phenological transition point, constructing a separable nitrogen trace spectrum by eliminating saturated response, outputting a prescription front graph by adjusting the balance coefficient under the calculation of energy concentration and water layer confluence indicators in a context constraint graph, and finally outputting an execution prescription graph by combining historical trajectories and micro-terrain to check the landing point, so that the vigorous growth period diagnosis precision is improved, over-fertilization or under-fertilization is avoided, and efficient nutrient management is realized.

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 belongs to the field of composite material preparation, and relates to a preparation method of a flexible vegetation-imitating green leaf camouflage material, which comprises the following steps: doping chrome green Cr2O3 particles in a polydimethylsiloxane (PDMS) matrix to form a bottom film with the appearance color similar to that of green vegetation, and then coating a layer of film doped with Au or Ag and other metal nanoparticles on the bottom film to obtain the flexible vegetation-imitating green leaf camouflage material. Therefore, the wave modulation method based on surface plasmon enhanced absorption is realized, the material can imitate the reflectivity of green vegetation in a near-infrared band, PDMS is taken as a matrix and doped with chrome green particles, so that the color of the film is similar to that of a green plant macroscopically, Ag nanorods doped in the upper PDMS coating can enable the chrome green coating to imitate a red edge effect, and the reflectivity of the green vegetation can be simulated in a near-infrared band. Vegetation is simulated in a near-infrared band. The two layers of films are compounded, so that the camouflage material and the vegetation green leaves have the same color and spectrum, and the preparation method and the material have very wide application prospects.

The invention relates to the technical field of intelligent prediction of forest degradation, and discloses a forest degradation early warning platform fusing vegetation spectral features and reinforcement learning, and the platform comprises the steps: obtaining a satellite / unmanned plane image through a multi-source remote sensing data collection module; the spectral feature extraction module calculates a vegetation index, a red edge slope and a water stress index; the multi-feature fusion module integrates spectrum, texture and phenological features and outputs a degradation probability; the reinforcement learning prediction module constructs a state transition model and predicts a degradation gradient; the early warning decision module generates a dynamic early warning level based on the gradient value; the feedback optimization module updates network parameters through a loss function; therefore, the problems of difficult early recognition and early warning lag are solved, the recognition rate is improved by 40%, and the early warning response is shortened to 15 days.

The invention discloses a moso bamboo forest big-year and small-year classification method suitable for different areas, and belongs to the technical field of remote sensing monitoring, and the method comprises the following steps: obtaining a plurality of remote sensing images of a selected area in two consecutive years; calculating a normalized vegetation index, a land surface moisture index, a near-infrared wave band reflectivity and a red edge wave band reflectivity of each pixel in each remote sensing image; calculating a moso bamboo time sequence index of each pixel; calculating a phenological difference index of each pixel; judging whether the position represented by each pixel is a moso bamboo forest or a non-moso bamboo forest based on the moso bamboo time sequence index of each pixel, and obtaining a binary image; and based on the binary image and the phenological difference index of each pixel, judging whether each pixel is a mao bamboo forest, a young mao bamboo forest or a non-mao bamboo forest. According to the method, the classification precision of the moso bamboo forest in all years and the regional applicability can be improved.

The present application relates to the intelligent mining direction of remote sensing data, and particularly relates to a poplar single tree crown segmentation method, device and equipment based on aerial image. The method comprises the following steps: acquiring a first multi-spectral unmanned aerial vehicle aerial image, and preprocessing the first multi-spectral unmanned aerial vehicle aerial image; inputting the first multi-spectral unmanned aerial vehicle aerial image into a crown top identification model based on a D-LinkNet network to obtain a poplar crown top seed point graph; using a watershed segmentation algorithm based on label control, taking the poplar crown top seed point graph and a first band of the first multi-spectral unmanned aerial vehicle aerial image as inputs to perform calculation, and segmenting a poplar single tree crown based on the first multi-spectral unmanned aerial vehicle aerial image; wherein the first band is an infrared band or a red edge band.

The present invention discloses a method, system, device and medium for inverting the growth condition of crops, relating to the field of remote sensing technology, including: obtaining the red edge inflection point (REIP) and the integrated red edge chlorophyll index (IRECI) according to the reflectance of the spectral image of the crop planting area, and combining the REIP and the IRECI in different proportions; training a first deep neural network (DNN) with the combined index of the first proportion to predict the leaf area index, and optimizing the learning rate and regularization parameters of the first DNN by the particle swarm method during the training process to construct a leaf area index inversion model; training a second DNN with the combined index of the second proportion to predict the plant height data to obtain a plant height inversion model; obtaining the leaf area index and the plant height data according to the leaf area index inversion model and the plant height inversion model and fitting them by the entropy weight method to obtain the index weight result for evaluating the growth condition of crops. The present invention can accurately predict the growth condition of crops.

The invention discloses a forest farm monitoring-oriented vegetation reflection spectrum measurement method and system, and the method comprises the steps: obtaining an original spectrum collection signal of a forest farm target region, carrying out the canopy penetration attenuation correction, and building a reference reflection spectrum set; performing crown projection area identification and atmospheric scattering interference identification on the reference reflection spectrum set to form a real earth surface reflection spectrum, and performing red edge feature positioning and waveband ratio analysis based on the real earth surface reflection spectrum to form a vegetation reflection spectrum index; performing multi-temporal spectrum difference detection on the vegetation reflection spectrum index, eliminating phenological interference, determining a change confidence interval, screening an abnormal spectrum based on the change confidence interval, performing forest stand health grade marking after spectrum evolution coefficient correction, and outputting a forest stand coverage change measurement result. The method can effectively eliminate the interference of canopy shadow and atmospheric scattering, distinguish normal phenological fluctuation from real health abnormality, and provide reliable spectral data support for monitoring the vegetation state of the forest farm.

The invention provides an urban tree species classification method, system and device, and belongs to the technical field of tree species classification, and the method comprises the steps: firstly, selecting candidate wavebands in a red-edge spectrum range, traversing all dual-waveband combinations, and defining a vegetation index through a preset formula; secondly, for each index, generating a vegetation mask by using a plurality of candidate thresholds, and calculating a confusion matrix evaluation index by comparing the vegetation mask with a real mask; and finally, based on the index, automatically screening out an index with optimal performance and a threshold pair. Wherein the index defined by the optimal dual-band combination is the final vegetation index, and according to the final vegetation index, classification and identification of urban tree species are carried out through a machine learning model. The method solves the problem that classification of the classic vegetation index on the urban tree species is not accurate.

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 discloses a kind of vegetation cover index algorithm and system based on unmanned plane, wherein algorithm includes the following steps: S1, data acquisition and multi-source data preprocessing;S2, dynamic environment correction and multi-source data fusion;S3, red edge enhanced vegetation index calculation and terrain correction;S4, vegetation coverage intelligent prediction and precision verification;S5, result visualization and decision support: generate vegetation coverage spatial distribution map and statistical report, support resource management decision.The application provides efficient, high-precision technical support for precision agriculture, ecological resource management and disaster monitoring by integrating unmanned plane, multispectral imaging sensor, dynamic environment correction model and data fusion algorithm.

The invention discloses a satellite inversion method for the canopy chlorophyll content based on a row-sowing rice geometrical optics-radiation transfer composite model, and the method comprises the steps: 1, simulating the spectral reflectance of rice canopies at an interval of 1 nm in different planting scenes based on agricultural priori knowledge through the row-sowing rice geometrical optics-radiation transfer composite model; 2, mapping the simulated canopy spectral reflectivity data set into a satellite image broadband canopy spectral reflectivity data set by using a spectral response function; step 3, constructing red edge chlorophyll index characteristics based on the simulated broadband reflection spectrum data set; and 4, establishing a hybrid inversion model between the red edge chlorophyll index and the paired canopy chlorophyll content CCC, and carrying out canopy chlorophyll content satellite inversion by using the hybrid inversion model to realize large-range rice chlorophyll content space-time dynamic monitoring. According to the method, the precision of the rice canopy chlorophyll content hybrid inversion model is remarkably improved in the early stage of rice growth.