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32 results about "Enhanced vegetation index" patented technology
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The enhanced vegetation index (EVI) is an 'optimized' vegetation index designed to enhance the vegetation signal with improved sensitivity in high biomass regions and improved vegetation monitoring through a de-coupling of the canopy background signal and a reduction in atmosphere influences. EVI is computed following this equation: EVI=G×(NIR-RED)/(NIR+C1×RED-C2×Blue+L) where NIR/red/blue are atmospherically-corrected or partially atmosphere corrected (Rayleigh and ozone absorption) surface reflectances, L is the canopy background adjustment that addresses non-linear, differential NIR and red radiant transfer through a canopy, and C1, C2 are the coefficients of the aerosol resistance term, which uses the blue band to correct for aerosol influences in the red band.
Grassland productivity estimation method based on remote sensing and GIS (geographic information system)
The invention relates to a grassland productivity estimation method based on remote sensing and a GIS (geographic information system). The method solves the defects that the traditional method is time-consuming and labor-consuming when estimating grassland productivity, and difficultly realizes large-range and long-term continuous acquisition. The method comprises the steps of: Step I, preprocessing grassland raw data, Step II, selecting an NDVI (normalized difference vegetation index), an RVI (ratio vegetation index), an MSAVI (modified soil adjusted vegetation index) and an EVI (enhanced vegetation index) to construct various grassland productivity estimation models based on the vegetation indices, Step III, selecting the optimal grassland productivity estimation model, Step IV, making grid operation by using the optimal grassland productivity estimation model and the vegetation indices to obtain a grassland productivity spatial distribution chart, estimating total grassland productivity according to total grassland area, and Step V, establishing a grassland productivity prediction model by taking the vegetation indices as independent variables and the grassland productivity in a future certain period as a dependent variable. The method is applied to the field of ecology and the remote sensing.
Owner:NORTHEAST INST OF GEOGRAPHY & AGRIECOLOGY C A S
Drought remote sensing monitoring method suitable for high altitude areas
ActiveCN108760643AReduce the impact of uneven distributionMaterial analysis by optical meansKernel principal component analysisVegetation
The invention discloses a drought remote sensing monitoring method suitable for high altitude areas. The method comprises: S10, collecting data of a target area, S20, preprocessing the data obtained in step S10 to obtain an enhanced vegetation index, a surface temperature index, a land cover type and downscaling rainfall data, S30, calculating a vegetation state index, a temperature condition index, a rainfall state index, a reclassified land cover type and elevation by the data in step S20, and S40, constructing a drought remote sensing monitoring model based on spatial principal component analysis. The method comprehensively considers various factors affecting drought, wherein the various factors include a vegetation factor, a surface temperature factor, a rainfall factor, a land cover type factor and a topographic factor, a drought monitoring model is constructed by a spatial principal component analysis method, can effectively eliminate variables with large correlation in the selected variables and can extract few unrelated comprehensive indicators.
Owner:SOUTHWEST PETROLEUM UNIV
Remote monitoring method for soil moisture of wheat field
The invention discloses a remote monitoring method for soil moisture of a wheat field. A corrected thermal inertia model is W=B1*ATI+B2*EVI+B, wherein W is relative soil water content; ATI is apparent thermal inertia; B1 and B2 are coefficient items; EVI is an enhanced vegetation index; and B is a constant item. In the method, the closing actions of wheat plants on the soil background are fully considered, and an enhanced vegetation index which is sensitive to vegetation reflection in a low-vegetation covering area is introduced and is taken as a vegetation influencing factor according to a thermal inertia process during inversion of soil moisture by using EOS / MODIS (Embedded Operation System / Moderate Resolution Imaging Spectroradiometer) remote sensing data. The average inversion accuracy of a corrected thermal inertia model can be over 80 percent before ridge sealing of wheat. According to comparative analysis of the synchronous inversion result of the conventional thermal inertia model, the corrected thermal inertia model has higher inversion accuracy, and a suitable time domain is expanded by about one growth period.
Owner:SHANDONG AGRI SUSTAINABLE DEV INST
Forest dynamic change mode automatic extraction method
InactiveCN105718936AAvoid analysisAvoid the hassle of refactoringCharacter and pattern recognitionData setVegetation cover
The invention relates to a forest dynamic change mode automatic extraction method. Time series data sets of a vegetation index enhanced day-by-day for many years and a snow accumulation index of a research area are established. Each pixel extracts indexes such as vegetation cover strength, dispersion, sustainability and the like and extracts a brightness index year by year based on the enhanced vegetation index time series data. Each pixel detects secular variation trends of the above indexes in sequence and establishes a forest dynamic change mode recognition flow chart based on the variation trends of the indexes such as the vegetation cover strength, the dispersion, the sustainability, the brightness and the like, and finally, the purpose of forest dynamic change automatic monitoring is achieved. According to the invention, a plurality of indexes are designed from aspects such as the cover time, the average state, the variation amplitude and the like, based on the secular variation trends of the indexes, the forest dynamic change mode is recognized effectively, training data of a known sampling area and man-machine interaction are not needed, the robustness is good, the classification precision is high, and the automation extent and the anti-interference capability are high.
Owner:FUZHOU UNIV
Identification method of corns for seed on the basis of high resolution remote sensing data texture analysis
InactiveCN106373150AStop illegal seed production such as private multiplicationStop illegal seed productionImage enhancementImage analysisField cropSensing data
The invention provides an identification method of corns for seed on the basis of high resolution remote sensing data texture analysis. The method comprises the following steps of: obtaining the moderate-resolution remote sensing image of a corn in a whole growth period and the high-resolution remote sensing image of the corn in a tasseling stage in a target detection zone; preprocessing the remote sensing images; according to the enhanced vegetation index of the preprocessed moderate-resolution remote sensing image in the growth period, identifying all corn fields in the target detection region; and extracting the texture information of all corn fields in the preprocessed high-resolution remote sensing image in the tasseling stage, and identifying the corn fields of the corns for seed in all corn fields. The non-artificial distinguishing of the corns for seed and corns which are planted in lands for growing field crops is realized, the identification method has an accurate, quick and reliable identification result, the corn fields of the corns for seed can be effectively monitored so as to effectively forbid illegal seed production behaviors including secret breeding, excessive manufacture and the like which aim at corns, and the order of a corn planting industry is guaranteed.
Owner:CHINA AGRI UNIV
Agricultural drought remote sensing monitoring method
ActiveCN105929406AImprove fitting accuracyTrue Reflection BreedingRadiation pyrometryScattering properties measurementsVegetationThermal infrared remote sensing
The invention discloses an agricultural drought remote sensing monitoring method, which comprises the following steps: 1) calculating enhanced vegetation index and land surface temperature according to surface reflectance and thermal infrared remote sensing images; 2) constructing an ETVDI model through the surface temperature and the enhanced vegetation index calculation result, and obtaining maximum value and minimum value of the surface temperature under the same enhanced vegetation index value and the number of pixel points at the points of same surface temperature values corresponding to different enhanced vegetation index values; and 3) carrying out polynomial fitting on the maximum value and minimum value corresponding to the same enhanced vegetation index value to obtain wet and dry boundary equations in the ETVDI model, and obtaining fitting parameters. The method overcomes the defect that a conventional TVDI model has vegetation index saturation easily in a dense vegetation area and the defect of uncertainty of wet and dry boundary equation fitting, thereby improving fitting precision; precision is higher in the expression aspect of temporal and spatial development of the drought; and the method can reflect the process of drought growing, development and elimination more truly, and provides reference for early warning and monitoring of the agricultural drought.
Owner:PEARL RIVER HYDRAULIC RES INST OF PEARL RIVER WATER RESOURCES COMMISSION
Rice mapping method based on self-adaptive feature selection
InactiveCN105893977AImprove classification resultsAvoid the impact of interferenceCharacter and pattern recognitionData setSpatial distribution
The invention relates to a rice mapping method based on self-adaptive feature selection. The rice mapping method comprises the steps of establishing a time sequence data set of enhanced vegetation indexes and water indexes in a research area; establishing time sequence data of cloud distribution in the research area; based on the cloud distribution of remote sensing images in a rice crucial phonological period, dividing the research area into a cloud area and a cloud-free area; based on a time sequence analysis method, acquiring rice classification results in the cloud-free area; extracting pixel-based remote sensing image features; selecting images with the least cloud interference, and segmenting in sequence to obtain remote sensing image objects respectively for the cloud area and the cloud-free area; integrating the pixel-based remote sensing image features, and extracting object-oriented remote sensing image features; taking the rice classification result in the cloud-free area as training data, and obtaining rice classification result in the cloud area; and integrating the rice classification result in the cloud-free area and the rice classification result in the cloud area, and obtaining a rice spatial distribution map in the research area. The invention has the characteristics of high automation degree, ease of use, good robustness, high classification accuracy and the like.
Owner:FUZHOU UNIV
Remote sensing rating method for growth vigor of crops on large scale
ActiveCN103971199AAvoid phenological changesAvoid Planting Structure ChangesResourcesImage resolutionVegetation Index
The invention discloses a remote sensing rating method for growth vigor of crops on a large scale. The remote sensing rating method includes A, acquiring temporal MODIS (moderate-resolution imaging spectroradiometer) data of evaluation years, and preprocessing the temporal MODIS data to obtain temporal EVI (enhanced vegetation index) data; B, revising the temporal EVI data; C, acquiring temporal EVI data of comparison years, analyzing the similarity among the temporal EVI data of the comparison years and the revised temporal EVI data and finding out reference temporal EVI data for rating the growth vigor; D, grading and evaluating the temporal EVI data of specified numbers of periods of the evaluation years according to the reference temporal EVI data. The MODIS data contain quality evaluation information. The remote sensing rating method has the advantages that the remote sensing rating method is adaptive to the requirement on monitoring the growth vigor of the crops in complicated planting systems on a large scale, and influence of phenological change of the crops and change of crop planting structures can be effectively prevented as compared with the traditional simple historical same-period comparison method.
Owner:WUHAN HEXUN AGRI INFORMATION TECH
Method for obtaining net carbon budget of regional scale forest ecological system
InactiveCN102592049AAchieving scalingOvercome the inability to reflect the carbon budget of large-scale forest ecosystemsSpecial data processing applicationsVegetation IndexModerate-resolution imaging spectroradiometer
The invention discloses a method for obtaining net carbon budget of a regional scale forest ecological system, belonging to the technical field of the combination of related vorticity technology and remote sensing image processing. The invention provides the method for obtaining the net carbon budget of the regional scale forest ecological system, which aims at solving the problems that the related vorticity technology only represents the carbon budget condition of an ecological system at the periphery of an observing tower, but can not reflect the carbon budget condition of the forest ecological system in a large scale. The method comprises the steps of selecting different forest flux observing stations; obtaining and preprocessing the data of MODIS (Moderate Resolution Imaging Spectroradiometer); obtaining and processing data NEE of the flux observing stations; according to the types of the forest ecological systems, utilizing SPSS (Statistical Product and Service Solution) software to respectively record net carbon exchanging NEE data of representative stations and corresponding parameter relevance of EVI (Enhanced Vegetation Index), LSWI (Land Surface Water Index), LST (Land Surface Temperature) and LST'; generating a quantitative inverse model of the net carbon exchanging NEE of all types of forests; and testing and verifying the simulated results of the model so as to check the effectiveness. The method is used for obtaining the carbon budget condition of the forest ecological system in the large scale.
Owner:NORTHEAST INST OF GEOGRAPHY & AGRIECOLOGY C A S
Regional carbon flux estimation method based on remote sensing data
InactiveCN108121854ADesign optimisation/simulationSpecial data processing applicationsCarbon fluxSurface water
The invention discloses a regional carbon flux estimation method based on remote sensing data. A mesoscale meteorological model (WRF) is used to output results, to provide spatial information within atarget region range and required meteorological field data for a vegetation photosynthetic respiration model (VPRM). High-precision vegetation type data (SYNMAP with a resolution of 1 km) is extracted, to classify land use types in a target area; an enhanced vegetation index (EVI) and a land surface water index (LSWI) are obtained by inversion of medium-high-resolution remote sensing data (MODIS09A1); parameters such as the EVI and the LSWI are placed in a target area grid by a spatial difference method; for different regional characteristics, more than one year of historical observation datais used, and these parameters are optimized by using a light response equation (Michaelis-Menten); and finally, based on the data, the carbon flux calculation is performed on the target area using the VPRM model.
Owner:STATE GRID JIANGSU ELECTRIC POWER CO ELECTRIC POWER RES INST +3
Remote sensing night light data desaturation method
InactiveCN110852159AHigh precisionReduce the impactScene recognitionVegetation IndexAtmospheric sciences
The embodiment of the invention discloses a remote sensing night light data desaturation method and device, a storage medium and computer equipment. The method comprises the following steps of obtaining an enhanced vegetation index and daily average surface temperature data in a space area where a city is located; calculating the mean value of the enhanced vegetation index and the daily average surface temperature data of the saturated pixel area; and performing desaturation correction on the remote sensing night light data of the saturated pixel area by utilizing the sum of the two ratios ofthe enhanced vegetation index and the average value thereof, and the average value of the daily average surface temperature data and the daily average surface temperature data. According to the remotesensing night light data desaturation method, desaturation of remote sensing night light data of metropolis can be realized, and the precision of the remote sensing night light data applied to quantitative analysis of urban economy, population and the like is improved.
Owner:GUANGZHOU INST OF GEOGRAPHY GUANGDONG ACAD OF SCI
Corn yield remote sensing prediction method and system
InactiveCN110428107AAccelerate Yield EstimationAvoid the easy problem of falling into a local optimal solutionImage enhancementImage analysisVegetation IndexRipening
The invention discloses a corn yield remote sensing prediction method and a system. The method comprises the following steps: acquiring a remote sensing image of corn in a to-be-detected area in a setgrowth period, wherein the set growth period comprises a milk ripening period and a ripening period; determining attribute parameters of each remote sensing image, wherein the attribute parameters comprise a difference vegetation index, a ratio vegetation index, an enhanced vegetation index and a greenness vegetation index; and inputting the attribute parameters into a GA-BP neural network modelto predict the yield of the corn in the to-be-detected area, wherein the GA-BP neural network model is a model obtained by training according to historical remote sensing images of the corn in the to-be-detected area in a set growth period and corresponding historical yield data. The corn yield remote sensing prediction method and the system provided by the invention have the characteristic of high prediction precision.
Owner:JILIN UNIV
Crop classification and identification method and device and electronic equipment
InactiveCN111523525AEnhanced Vegetation IndexImprove accuracyCharacter and pattern recognitionSoil scienceImaging processing
The invention provides a crop classification and identification method and device and electronic equipment, and relates to the technical field of image processing, and the method comprises the steps:obtaining a to-be-classified agricultural remote sensing image, and the geographic position and monthly average temperature data of each pixel in the to-be-classified agricultural remote sensing image; processing the to-be-classified agricultural remote sensing image to obtain target data of each pixel in the to-be-classified agricultural remote sensing image; and processing the geographic position of each pixel, the monthly average temperature data and the target data by using a target classification model to obtain distribution data of target crops in the to-be-classified agricultural remotesensing image. According to the method, the self-learning capability of a neural network model is utilized; the target classification model learns the characteristics of various crops; the method hasthe capability of classifying and identifying the crops in the agricultural remote sensing image to be classified according to the surface reflectance of the pixel, the normalized vegetation index, the enhanced vegetation index, the geographic position and the monthly average temperature data, and the accuracy of the distribution data of the target crops obtained through classification and identification is high.
Owner:BEIJING AEROSPACE HONGTU INFORMATION TECH
Alpine meadow ecosystem respiratory carbon emission estimation method based on remote sensing
The invention discloses an alpine meadow ecosystem respiratory carbon emission estimation method based on remote sensing. The alpine meadow ecosystem respiratory carbon emission estimation method comprises the following steps: 1) acquiring and processing flux data of observation stations of a field vortex motion related system; 2) downloading the time sequence MODIS remote sensing data, and calculating to obtain parameters such as an enhanced vegetation index, a leaf area index, a surface water index and a day and night average temperature which are matched with the scale of the flux station; 3) performing seasonal dynamic analysis on respiratory carbon emission of the ecosystem and environmental variables of the respiratory carbon emission; 4) determining a key environmental control factor, 5) establishing an ecological system respiratory carbon emission regression model, 6) estimating the ecological system respiratory carbon emission by utilizing the obtained model, and 7) performing precision evaluation and verification. The method can realize to obtain large-scale alpine meadow ecological system respiratory carbon emission data by utilizing a remote sensing environmental parameter product.
Owner:SOUTHWEST UNIVERSITY
Winter wheat yield per unit prediction method and system
ActiveCN111062526AImprove applicabilityEstimates are accurateForecastingCharacter and pattern recognitionAgricultural sciencePredictive methods
The invention relates to a winter wheat yield per unit area prediction method and system. The method includes calculating the cumulative average enhanced vegetation index of the to-be-measured area, the cumulative average enhanced vegetation index of the to-be-measured area and the technical yield of the to-be-measured area, establishing a municipal yield estimation model and a provincial yield estimation model by utilizing a multiple linear regression method of a least square method, and monitoring and predicting the unit yield of the winter wheat by utilizing the two models. The yield estimation model established by the invention has very high applicability to estimation of unit yield of municipal and provincial crops, the estimation result is accurate, dynamic yield prediction can be realized, and the growth condition and change of winter wheat can be known in time.
Owner:BEIJING NORMAL UNIVERSITY
Forest interannual phenology monitoring method based on multi-source remote sensing
ActiveCN113850139AImprove time resolutionSynthesis is accurate and reliableCharacter and pattern recognitionSatellite dataVegetation
The invention discloses a forest interannual phenology monitoring method based on multi-source remote sensing, which comprises the following steps: firstly, collecting all available satellite remote sensing images of which the cloud cover is less than 80%, and then correcting an integration method of different satellite remote sensing images to improve the space and spectrum matching degree of different sensors; then generating a daily vegetation index curve by using an improved continuous change detection and classification model; and finally, based on the daily synthetic image, using a logistic regression model to test the enhanced vegetation index, the normalized vegetation index and the surface water body index to extract the optimal forest interannual SOS. According to the invention, the integration method of different satellite data is improved, and the observation frequency is increased; an MCCDC model is proposed, radiation differences are taken into consideration, a model algorithm is optimized, the calculation time is shortened while the precision is ensured, and finally a daily clear cloudless remote sensing image is generated; 3 vegetation indexes are introduced to estimate forest interannual SOS, and the difference of different indexes in evaluating forest SOS is evaluated.
Owner:NANJING FORESTRY UNIV
Automatic corn mapping method based on active growth stage NMDI (normalized multi-band drought index) increase and decrease ratio indexes
InactiveCN106772429AEliminate distractionsClear methodColor/spectral properties measurementsElectromagnetic wave reradiationVegetationMulti band
The invention relates to an automatic corn mapping method based on active growth stage NMDI (normalized multi-band drought index) increase and decrease ratio indexes. The mapping method includes the steps: building a vegetation index time sequence dataset and NMDI time sequence dataset of a research area pixel by pixel; calculating EVI (enhanced vegetation index) maximum values of crops in each growth period pixel by pixel, and acquiring growth peak value time of the crops; determining a front interval of the active growth stage of the crops and a back interval of the active growth stage of the crops according to the growth peak value time of the crops; respectively building NMDI increment indexes of the front interval of the active growth stage of the crops and NMDI increment indexes of the back interval of the active growth stage of the crops; respectively building NMDI decrement indexes of the front interval of the active growth stage of the crops and NMDI decrement indexes of the back interval of the active growth stage of the crops; building NMDI increase and decrease ratio indexes based on the NMDI increment indexes and the NMDI decrement indexes; performing mapping on corns in the research area according to the NMDI increase and decrease ratio indexes. The mapping method has the advantages of high automation degree and classification accuracy, simplicity, easiness in use, good robustness and the like.
Owner:FUZHOU UNIV
Image acquisition device and enhanced vegetation index monitoring system
InactiveCN107219226AConvenient and flexible collectionAvoid defectsMaterial analysis by optical meansVegetation IndexMonitoring system
The invention relates to an image acquisition device and an enhanced vegetation index monitoring system. The image acquisition device comprises an image acquisition unit, an image sensor, a controller, a power supply module and a bracket, wherein the image acquisition unit is used for simultaneously and separately acquiring images of a plurality of wavebands; the image sensor is used for converting images acquired by the image acquisition unit into image data and transmitting the image data to the controller; the controller is suitable for storing and transmitting the image data collected and converted by the image sensor; the power supply module is used for supplying power to the image acquisition device for normal work for a long time; the bracket is used for carrying out fixing and position adjustment on the image acquisition device. The enhanced vegetation index monitoring system comprises the image acquisition device, an image receiving device and a data analyzing device, wherein the image receiving device is used for receiving the image data transmitted from the controller; the data analyzing device is used for carrying out analysis and calculation according to the image data received by the image receiving device so as to obtain an enhanced vegetation index value.
Owner:INST OF REMOTE SENSING & DIGITAL EARTH CHINESE ACADEMY OF SCI
Multi-time sequence index change trend-based automatic grain for green recognition method
InactiveCN108388832AIncrease flexibilityStrong expandabilityCharacter and pattern recognitionExtensibilityData set
The invention relates to a multi-time sequence index change trend-based automatic grain for green recognition method. The method comprises the following steps of: establishing a multi-year daily enhanced vegetation index time sequence data set of a research area, and extracting 5 time sequence indexes such as abundance, time sequence dispersion, growing period length, high-quantile sustainabilityand low-quantile sustainability pixel by pixel and year by year on the basis of enhanced vegetation index time sequence data; detecting multi-year change trends of the five time sequence indexes in sequence; and establishing an automatic grain for green recognition flow chart on the basis of the change trends of the five time sequence indexes, so as to realize automatic grain for green recognition. According to the method, vegetation growth states are divided according to quantile values, a plurality of time sequence indexes are designed from the aspects of coverage time, average state and change amplitude to describe the growth features of single-season crops, multi-season crops and forest vegetation, so that changes from crops to forests are indicated by utilizing the change trends of multi-time sequence indexes and then grain for green areas are automatically recognized; and the method has good flexibility and extensibility.
Owner:FUZHOU UNIV
Method of estimating water use efficiency of temperate forest
InactiveCN105938517AEstimated availabilityImprove usabilitySpecial data processing applicationsInformaticsSensing dataVegetation
The present invention discloses a method of estimating the water use efficiency of a temperate forest. The method comprises the following steps of (1) respectively obtaining satellite remote sensing data of enhanced vegetation index EVI from a plurality of flux sites; (2) respectively obtaining satellite remote sensing data of daytime surface temperature Ts from the plurality of flux sites; (3) obtaining WUE observed values according to daily flux data of the flux sites; (4) putting a data set of the EVI, the Ts and the WUE observed values into a model calculation formula of the water use efficiency of the temperate forest WUE=a0+a1*EVI+a2*EVI*Ts; and (5) estimating undetermined coefficients a0, a1 and a2 in the model calculation formula through a model calibration method, and obtaining an estimated value of the water use efficiency of the temperate forest. Statistical modeling is carried out by adoption of easily obtained remote sensing observation data, the water use efficiency of the temperate forest can be effectively estimated through the model calibration method and significantly related to actual observed values, and therefore support is provided for applications such as forest management.
Owner:BEIJING NORMAL UNIVERSITY
A method and system for real-time estimation of the grain crop yield
ActiveCN109359862AEstimated production is accurate and effectiveStrong real-timeResourcesComplex mathematical operationsProduction modelVegetation Index
The invention discloses a method and a system for real-time estimation of the grain crop yield. The method comprises the following steps of: acquiring historical data; calculating the anomaly of the enhanced vegetation index according to the enhanced vegetation index in different years and different periods; taking the anomaly of the i-th period enhanced vegetation index as the independent variable and the estimated yield difference as the dependent variable, the linear regression equation was constructed, and the estimated yield model in i-th period was obtained. The estimated difference is the difference between the statistical output and the trend output or the difference between the statistical output and the estimated value of the i-1 period; the anomaly of the enhanced vegetation index in the i-th period is substituted into the estimated production model of the i-th period to obtain the first estimating fluctuation value of the i period; calculating the estimated value of the model in the i-th period according to the estimated fluctuation value of the i-th period and the estimated production value of the i-th period; calculating the error of the estimated production model inthe i-th period; using the estimation model of the i-th period to correct the error for the estimated value of the model in the i-th period, and the estimated value of the i-th period is obtained. Theinvention can realize the accurate and effective and real-time estimation of the production of food crops.
Owner:BEIJING NORMAL UNIVERSITY
A Drought Remote Sensing Monitoring Method Suitable for High Altitude Areas
ActiveCN108760643BReduce the impact of uneven distributionMaterial analysis by optical meansVegetationSoil science
Owner:SOUTHWEST PETROLEUM UNIV
A Remote Sensing Monitoring Method for Agricultural Drought
ActiveCN105929406BImprove fitting accuracyTrue Reflection BreedingRadiation pyrometryScattering properties measurementsVegetationThermal infrared remote sensing
The invention discloses an agricultural drought remote sensing monitoring method, which comprises the following steps: 1) calculating enhanced vegetation index and land surface temperature according to surface reflectance and thermal infrared remote sensing images; 2) constructing an ETVDI model through the surface temperature and the enhanced vegetation index calculation result, and obtaining maximum value and minimum value of the surface temperature under the same enhanced vegetation index value and the number of pixel points at the points of same surface temperature values corresponding to different enhanced vegetation index values; and 3) carrying out polynomial fitting on the maximum value and minimum value corresponding to the same enhanced vegetation index value to obtain wet and dry boundary equations in the ETVDI model, and obtaining fitting parameters. The method overcomes the defect that a conventional TVDI model has vegetation index saturation easily in a dense vegetation area and the defect of uncertainty of wet and dry boundary equation fitting, thereby improving fitting precision; precision is higher in the expression aspect of temporal and spatial development of the drought; and the method can reflect the process of drought growing, development and elimination more truly, and provides reference for early warning and monitoring of the agricultural drought.
Owner:PEARL RIVER HYDRAULIC RES INST OF PEARL RIVER WATER RESOURCES COMMISSION
Forest unstructured scene segmentation method based on multispectral image fusion
PendingCN114821064AImprove discriminationImprove robustnessGeometric image transformationCharacter and pattern recognitionScene segmentationFeature extraction
The invention discloses a forest unstructured scene segmentation method based on multispectral image fusion, and the method comprises the steps: introducing enhanced vegetation index (EVI) data, designing a parallel double-coding structure to extract the features of RGB and EVI for the conditions of complex background, multiple light rays, shadow interference and the like of a forest unstructured scene, and carrying out the segmentation of the forest unstructured scene. And multi-mode complementary features are formed in the encoding process. In addition, in the encoding stage, the receptive field of the network is increased by using expansion convolution, and the feature extraction process is optimized. And carrying out secondary fusion on the decoding features and fusion features generated by coding to obtain a multispectral fusion convolutional neural network, training the network, and inputting RGB and EVI images to realize semantic segmentation of the forest unstructured scene. The method effectively solves the problem that the unstructured segmentation method based on the RGB image is easy to have poor adaptability and wrong segmentation, and improves the accuracy and robustness of the semantic segmentation of the forest unstructured scene.
Owner:SOUTHEAST UNIV
Pollution condition monitoring method based on satellite remote sensing technology
The invention discloses a pollution condition monitoring method based on a satellite remote sensing technology. The method comprises the following steps: acquiring an aerosol optical thickness AOD and an enhanced vegetation index EVI through an MODIS satellite; preprocessing the enhanced vegetation index (EVI); acquiring meteorological parameter data related to equivalent salt deposit density (ESDD) through a meteorological station, and preprocessing the meteorological parameter data; performing principal component analysis on the meteorological parameter data, the aerosol optical thickness (AOD) and the enhanced vegetation index (EVI); classifying the main variables by using an artificial neural network to obtain a dirty space prediction map; establishing an ESDD prediction model, inputting predicted quantized data which is a main variable, outputting a predicted value of ESDD, and evaluating the precision of the ESDD prediction model; and drawing a pollution space classification map according to the predicted value of the ESDD. Based on multi-source satellite remote sensing data, the pollution condition diagram is obtained by modeling the multi-source remote sensing data and the measured data.
Owner:YUNNAN POWER GRID CO LTD ELECTRIC POWER RES INST
Maize automatic mapping method based on nmdi increase-decrease ratio index in peak growth period
InactiveCN106772429BEliminate distractionsClear methodColor/spectral properties measurementsElectromagnetic wave reradiationMulti bandVegetation
Owner:FUZHOU UNIV
Wetland vegetation carbon sequestration rate remote sensing estimation method based on reconstructed vegetation index time sequence image
ActiveCN112991226AFast trackSimple and fast operationImage enhancementImage analysisVegetation IndexWetland vegetation
The invention discloses a wetland vegetation carbon sequestration rate remote sensing estimation method based on a reconstructed vegetation index time sequence image. The method comprises the following steps: S1, reconstructing an enhanced vegetation index (EVI) image time sequence with high space-time continuity in a wetland vegetation distribution area; S2, establishing an optimal wetland vegetation carbon stock remote sensing inversion model based on measured data and EVI observation values of corresponding pixels in the same period; S3, reconstructing the annual dynamic change process of the wetland vegetation carbon stock in combination with the optimal wetland vegetation carbon stock remote sensing inversion model and the reconstructed EVI time sequence image; and S4, calculating the annual cumulative growth amount of the carbon stock of the wetland vegetation pixel by pixel according to a cumulative stock method to obtain the annual carbon sequestration rate of the wetland vegetation. The method is suitable for a wetland ecological system with frequent cloudy and rainy weather and large water level variation, and is simple and convenient to operate and convenient to popularize and apply.
Owner:HOHAI UNIV
Land desertification extraction method and device fusing multiple vegetation indexes
PendingCN113822107AGuaranteed accuracyEnsure reliabilityImage enhancementImage analysisSatellite dataVegetation
The embodiment of the invention provides a land desertification extraction method and device fusing multiple vegetation indexes, and the method comprises the steps: firstly obtaining the landsat satellite data of a target region, and carrying out the multiple correction, and obtaining the target satellite data; secondly, generating an image map of the target area according to the target satellite data, clipping the image map by adopting a preset first shp file to obtain a plurality of sub-maps, and then extracting a vegetation coverage index, a soil vegetation removal index and an enhanced vegetation index corresponding to each sub-map by adopting a preset second shp file; and finally, judging a desertification result of the target region based on the vegetation coverage index, the soil vegetation removal index and the enhanced vegetation index corresponding to each sub-graph, generating script data corresponding to the desertification evaluation model according to the desertification result, and storing the script data. Therefore, fusion analysis can be carried out on the vegetation coverage index, the soil vegetation removal index and the enhanced vegetation index, so that the accuracy and reliability of a desertification judgment result are ensured.
Owner:SHANDONG UNIV OF TECH
Method and device for mapping VLAN (Virtual Local Area Network) among DC (Data Center) sites
The invention provides a method and a device for mapping a VLAN (Virtual Local Area Network) among DC (Data Center) sites, which are applied to any edge equipment in an EVI (Enhanced Vegetation Index) network. The method comprises the steps of receiving the announcement messages of other sites in the EVI network, and acquiring identity labels and VLAN mapping information in the announcement messages; judging whether the identity labels are the identification tags of issuers, if so, locally storing the VLAN mapping information in the announcement messages, and completing the ID switching of the VLAN of sites according to the stored VLAN mapping information. Through the method, the complexity of the configuration of same businesses of different sites in the prior art can be simplified, the degree of dependence on user configuration can be reduced, and the usability of the VLAN mapping can be enhanced.
Owner:NEW H3C TECH CO LTD
A winter wheat yield prediction method and system
ActiveCN111062526BImprove applicabilityEstimates are accurateForecastingCharacter and pattern recognitionSoil sciencePredictive methods
The invention relates to a winter wheat yield prediction method and system, which calculates the cumulative average enhanced vegetation index of the area to be measured, the cumulative average enhanced vegetation index of the area to be measured and the technical yield of the area to be measured, and uses the multiple linear regression method of the least square method to establish the market. Yield estimation model and provincial yield estimation model, and use the two models to monitor and predict the yield of winter wheat. The yield estimation model established by the invention has high applicability to the estimation of crop per unit yield at municipal and provincial levels, the estimation result is accurate, and dynamic yield forecast can be realized, which is conducive to timely understanding of the growth status and changes of winter wheat.
Owner:BEIJING NORMAL UNIVERSITY
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