32 results about "Enhanced vegetation index" patented technology

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.