18 results about "Enhanced vegetation index" patented technology

The invention belongs to the field of remote sensing monitoring, and particularly relates to a land ecological condition monitoring system and method based on remote sensing data, and the method comprises the steps: obtaining a remote sensing image sequence through a preprocessing module, and obtaining initial cultivated land segmentation sub-regions and edge features; a feature extraction module extracts spatial features and fluctuation values such as normalized vegetation indexes and enhanced vegetation indexes based on the feature extraction module, and constructs a space-time coordinate system; the region determination module determines a real cultivated land area and a cultivated land area fluctuation value; the inversion module inverts the vegetation coverage, the moisture content and the soil fertility, and obtains an ecological degradation fluctuation value by combining with phenological period anomaly recognition; the early warning module performs real-time early warning based on the cultivated land area fluctuation value and the ecological degradation fluctuation value, and visually marks the position, the type and the severity of a change region through a GIS platform; according to the invention, precise monitoring and dynamic early warning of the ecological condition of the land are realized.

The application provides a tea garden identification method and device based on satellite images, which comprises the following steps: determining a evergreen vegetation area of a to-be-identified area based on Sentinel-2 image data and Landsat image data of the to-be-identified area; and identifying a tea garden area from the evergreen vegetation area through a decision tree model; wherein the decision tree model is constructed based on the following features and the classification threshold values corresponding to the features: a tea leaf phenology feature index, a terrain feature, and a spectral index determined by a separability index, wherein the SI is used to reflect the spectral reflectance separability of the tea garden and other evergreen vegetation; wherein the tea leaf phenology feature index is determined by an enhanced vegetation index in month N and a land surface water index in month M; the SI between the enhanced vegetation index in month N and the land surface water index in month M is the largest compared with the SI between the EVI in any other month and the LSWI in any other month, and N and M are integers greater than 0 and less than 13.

The invention relates to the field of agricultural yield prediction, and particularly discloses a crop yield prediction method and system based on artificial intelligence, and the method comprises the steps: S1, recognizing crop types through the spectral features of a remote sensing image and field sampling data, and dividing corresponding key growth stages; s2, collecting multi-source feature data for each key growth stage, wherein the multi-source feature data comprises remote sensing image data, an extracted normalized difference vegetation index NDVI, an enhanced vegetation index EVI and a topographic factor; acquiring hourly temperature, humidity, precipitation and illumination intensity of each sub-region through a distributed sensor according to micro-meteorological data; obtaining plant height, leaf area index and fruit development parameters through unmanned aerial vehicle three-dimensional imaging according to crop phenotype data; by adopting the technical scheme of the invention, the terrain, microclimate and crop dynamic growth characteristics can be accurately fused, and the problem of non-uniform sample distribution is solved, so that the accuracy and regional adaptability of yield prediction are improved.

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 kind of estimation method and system of vegetation canopy combustible moisture content based on meteorology and remote sensing data, comprising the following steps: first, combustible moisture content and various meteorological data and remote sensing data and other several kinds of subsidiary data are selected as combustible moisture content estimation data.Meteorological data includes air temperature, relative humidity, rainfall and wind speed.Remote sensing data includes two vegetation indexes: enhanced vegetation index and normalized vegetation index.Subsidiary data includes: root zone soil moisture, vapor pressure difference, drought index, fire weather factor.Then the long time sequence characteristics of meteorological data are extracted.The size of time window is determined first, and the experimental results show that the correlation coefficient of most sites is relatively high under the time window of 90-210 days.90 days, 150 days and 210 days of time window are selected respectively to extract the time characteristics of four kinds of meteorological data.Secondly, the samples of experimental area are divided into five vegetation classifications, which are closed shrub, sparse shrub, multi-tree tropical grassland, tropical savanna and grassland.Finally, the data sets of the five different vegetation types are sequentially adjusted to obtain the respective estimation model.

The application 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.

This application provides a method and system for identifying the operational boundary of an agricultural drone. When the index difference value between vegetated and non-vegetated areas in farmland is lower than the hard scenario threshold of the agricultural drone, vegetation endmember spectra and soil endmember spectra are extracted from the multispectral image. Based on the vegetation and soil endmember spectra, spectral mixing decomposition is performed on each pixel in the multispectral image to obtain the vegetation endmember abundance of each pixel. A vegetation abundance map is constructed based on all vegetation endmember abundance values. The original vegetation index map is enhanced using the vegetation abundance map to obtain an enhanced vegetation index map. The enhanced vegetation index map is input into a pre-trained semantic segmentation network model, which outputs a binarized operational area mask. The contour of the operational area mask is used as the boundary coordinates of the operational area. Based on the above scheme, spectral identification of crop boundaries in low-contrast farmland scenes can be achieved.

The invention relates to a novel drought index construction method based on multi-factor coupling, and belongs to the technical field of agricultural drought monitoring. The method comprises the following steps: calculating vegetation coverage FVC, land surface temperature LST, soil water content SMC and enhanced vegetation index EVI by using multi-source remote sensing image data, calculating chaos characteristic parameters of each factor, determining sensitivity weight of each factor to drought according to the chaos characteristic parameters, adopting an improved random forest algorithm as a multi-factor coupling model, inputting a weighted characteristic matrix for training, and determining drought sensitivity of each factor. And optimizing key parameters of the random forest by adopting a particle swarm optimization algorithm, determining nonlinear mapping from a weighted feature to a chaos coupling multi-factor drought index CCMDI, and judging a drought level according to an index value. According to the method, the comprehensive process of drought formation and development is represented more truly, and high-precision inversion of the drought grade is realized.

The application discloses a salt marsh vegetation classification method, system, device, medium and product, relates to the field of salt marsh vegetation classification, and comprises the following steps: generating a vegetation index time sequence according to satellite remote sensing data; dividing a coastal zone latitude into several classification latitude zones as classification intervals, calculating the mean value and variance index of each vegetation index in each interval per month, and the mean value index and variance index of the current year; based on the normalized difference vegetation index time sequence and the enhanced vegetation index time sequence, calculating the growth period and decay period indexes of the vegetation by using a double logistic function; calculating the J-M distance of each candidate classification index on different vegetation classification samples; based on the J-M distance of the indexes on different vegetation classification samples and the preliminary classification contribution evaluation, screening out the most separable classification index, and constructing a final random forest classification model to classify the salt marsh vegetation in the coastal salt marsh wetland. The application can efficiently complete the coastal plant classification task at low cost.

The present application relates to a kind of irrigation farmland crop low temperature cold injury monitoring and diagnosis method, device, equipment and medium, comprising: collecting irrigation farmland multi-source data;Key indicators are calculated based on farmland multi-source data, wherein, key indicators include: land surface temperature LST, enhanced vegetation index EVI, sunlight-induced chlorophyll fluorescence SIF and solar declination delta;The key indicators calculated are calculated by the air temperature estimation model of pre-constructed irrigation farmland to obtain farmland daily average air temperature, and based on the low temperature cold injury determination standard of farmland crop, the monitoring and diagnosis of farmland crop low temperature cold injury are carried out.The present application can be based on the actual situation of irrigation farmland crop growth, utilize multi-source remote sensing data, consider the influence of surface temperature, crop growth, canopy physiology, solar radiation and other multi-factor, accurately estimate the air temperature of irrigation farmland, have strong operability, practicality is strong, it is easy to promote.

The application belongs to the field of remote sensing monitoring, and particularly relates to a land ecological condition monitoring system and method based on remote sensing data, which comprises: a preprocessing module that acquires a remote sensing image sequence and obtains an initial cultivated land segmentation sub-region and edge features; a feature extraction module that extracts spatial features such as normalized vegetation index and enhanced vegetation index and wave values based on the same, and constructs a space-time coordinate system; a region determination module that determines a true cultivated land area and a cultivated land area wave value; an inversion module that inverts vegetation coverage, water content and soil fertility, and obtains an ecological degradation wave value in combination with abnormal identification of a phenological period; and an early warning module that performs real-time early warning based on the cultivated land area wave value and the ecological degradation wave value, and visually marks the position, type and severity of a changed region through a GIS platform; and the application realizes accurate monitoring and dynamic early warning of land ecological conditions.

A rapid monitoring and identification method for winter wheat drought levels includes: 1) obtaining multispectral images and ground-measured LAI through UAV-based multi-payload low-altitude remote sensing technology, and calculating vegetation indices such as Normalized Difference Vegetation Index (NDVI), Difference Vegetation Index (DVI), Ratio Vegetation Index (RVI), Enhanced Vegetation Index (EVI), Optimized soil adjusted vegetation index (OSAVI) and Transformed Chlorophyll Absorption Reflectance Index; 2) establishing regression equations between the calculated vegetation indices and measured LAI for different growth stages, and selecting the optimal model equation for each growth stage; 3) using the optimal model equation to invert the LAI of winter wheat at various growth stages, and calibrating the LAI thresholds for different drought stress levels; 4) acquiring the multispectral images of target plot through real-time monitoring, calculating the required vegetation indices, inverting to obtain the LAI value, and comparing it with the threshold to determine the current drought stress level.

The application provides a farmland forest network rate monitoring method and device, electronic equipment and storage medium, and relates to the technical field of remote sensing. The method comprises the following steps: determining a vegetation area in a target remote sensing image based on the spectral information of the target remote sensing image of a region to be monitored; obtaining a characteristic parameter value corresponding to the vegetation area; determining a forest belt area in the vegetation area based on the characteristic parameter value corresponding to the vegetation area; obtaining the farmland forest network rate of the region to be monitored based on the forest belt area and a farmland area in the target remote sensing image; the characteristic parameters comprise a normalized vegetation index and an enhanced vegetation index, and a length-width ratio and / or information entropy; and the farmland area is determined according to the geographic information of the farmland in the region to be monitored. The farmland forest network rate monitoring method and device, electronic equipment and storage medium provided by the application can more accurately and efficiently monitor the farmland forest network rate of a large area, and can reduce the cost input of farmland forest network rate monitoring.

The invention discloses a wheat field grain water content prediction method and system based on a PlanetScope image. The method comprises the following steps: acquiring 8-waveband multispectral surface reflectance data of PlanetScope, screening an image of which the cloud cover is lower than a preset percentage from the 8-waveband multispectral surface reflectance data, and calculating an enhanced vegetation index (EVI) pixel by pixel to obtain an EVI time sequence; obtaining the heading period of the winter wheat based on the preprocessed EVI time sequence; based on the heading period of the winter wheat, predicting the physiological mature period of the winter wheat through an effective accumulated temperature accumulation model; an actually measured wheat grain moisture content sample is obtained, and a field grain drying model combining a moisture diffusion mechanism, saturation vapor pressure difference adjustment and Gaussian function constraint is constructed based on the wheat grain moisture content sample; and by taking the predicted physiological mature period as a priori and combining daily meteorological data and a field grain drying model, simulating the dynamic change of the moisture content of the wheat grains day by day, and generating a spatial distribution diagram of the moisture content of the wheat grains. The method can predict the water content of the wheat grains in the field in a high-time-efficiency and high-precision manner.

The invention relates to the technical field of image processing, and particularly discloses an image processing method and device based on artificial intelligence, and the method comprises the steps of super-resolution reconstruction, reconstruction effect judgment and the like. According to the method, super-resolution reconstruction is performed through a bilinear interpolation method, an elliptical area model based on a peak-to-noise ratio and a global similarity evaluation coefficient is constructed to dynamically evaluate a reconstruction effect, iterative optimization is performed through adaptive adjustment of scaling factor parameters, the balance between image resolution improvement and information fidelity is ensured, and after the reconstruction reaches the standard, the reconstruction efficiency is improved. A multi-dimensional feature fusion technology is adopted, a fit index is calculated in combination with form, texture and color feature indexes to realize accurate classification of crops, and the growth health state of the crops is evaluated through an elliptic cylinder volume model of a normalized vegetation index, an enhanced vegetation index and a photochemical reflection index. The accuracy and scientificity of remote sensing image recognition analysis are effectively improved, and the timeliness and reliability of agricultural remote sensing monitoring are remarkably improved.

The invention relates to an urban ecological environment evaluation model construction method. The method comprises the following steps: inputting a remote sensing image, night light annual synthesis data and auxiliary space data; calculating a normalized differential vegetation index and an enhanced vegetation index; calculating the surface temperature; calculating urban heat island intensity as a pressure index; producing land cover data; calculating primary indexes of each criterion layer in the model, and performing gridding and normalization processing; designing primary indexes of the model; carrying out improved normal cloud combination empowerment; comprehensive evaluation based on the closeness of cloud similarity is carried out; and outputting a final comprehensive evaluation product. Performing space-time dynamic analysis and driving mechanism analysis; future scene simulation based on the improved LSTM model is carried out; and outputting a final comprehensive prediction product. The invention also relates to an urban ecological environment evaluation model construction system. According to the method, a new generation of urban ecological environment quantitative evaluation model with cognitive robustness, spatial insight and dynamic predictability can be constructed.

The present application provides a kind of pasture carbon sequestration amount remote sensing estimation method, device, electronic equipment and storage medium, it is related to remote sensing technical field, the method comprises: obtaining the time series of remote sensing data in target period in target area;Based on remote sensing data, obtain the regional information of pasture region in target area;Based on remote sensing data and regional information, obtain the time series of enhanced vegetation index value in pasture region in target period and the cutting frequency of pasture region in target period;Based on enhanced vegetation index value and cutting frequency, obtain the estimated value of carbon sequestration amount of pasture region in target period.The present application provides a kind of pasture carbon sequestration amount remote sensing estimation method, device, electronic equipment and storage medium, can more accurately, more efficient realization of the identification of pasture region, pasture region cutting time node and the estimation of times, can improve the accuracy and efficiency of pasture region carbon sequestration estimation.

The invention discloses a winter wheat physiological mature period prediction method and system based on a spectrum time sequence. The method comprises the following steps: acquiring multispectral remote sensing data during the growth period of winter wheat in a target area, and calculating an enhanced vegetation index (EVI); calculating the AGDD of the accumulated effective accumulated temperature growth degree days of the winter wheat, and constructing an AGDD-EVI time sequence based on the AGDD of the accumulated effective accumulated temperature growth degree days and the EVI time sequence; based on the AGDD-EVI time sequence, using a double Logistic function and K-means clustering to construct a typical growth curve shape model; performing translation matching on the AGDD-EVI time sequence and the typical growth curve shape model to obtain a matching model, selecting the matching model with the minimum root-mean-square error, and updating the double-Logistic function to obtain a final matching model; based on the final matching model, using a DSLIM (dual-single Logistic curve intersection method) to determine an AGDD value in the physiological maturation period, and converting the AGDD value into a day order after sowing to obtain the physiological maturation period time of the winter wheat. According to the method, efficient and stable intra-season prediction of the physiological mature period of winter wheat is realized.