6 results about "Normalized Difference Vegetation Index" patented technology

The present application provides a kind of leaf area index determination method, device, equipment, medium and product, it is related to agricultural remote sensing and near-ground optical measurement technical field, to solve the defects of lower accuracy and poor efficiency in determining the leaf area index of crop in the prior art, realize the accuracy and efficiency of improving the leaf area index of crop is determined.Imcluding: the target hemispherical image collected is carried out image correction processing, obtain the target hemispherical image after correction, target hemispherical image is obtained by vertical upward shooting for shooting device in plant row center position;Determine the normalized difference vegetation index NDVI histogram corresponding to plant in the target hemispherical image after correction;Based on NDVI histogram and preset zenith angle weight, determine the void fraction of plant;Based on void fraction, the leaf area index LAI of plant is inversed.

The invention provides a method and a device for rapidly generating crop growth vigor information, which are used for monitoring the regular growth vigor condition of crops by utilizing satellite remote sensing inversion and supporting the continuous monitoring of the field growth vigor of crops such as rice, corn, rape, citrus and tea and the monitoring of the growth state of the growth stage. The method comprises the following steps: firstly, performing time sequence optical image reconstruction and data space-time fusion on an obtained multi-temporal optical image to generate high space-time remote sensing data; then, remote sensing parameters related to crop growth are extracted from the high space-time remote sensing image, a plurality of parameters such as a normalized vegetation index and a normalized yellow difference index are calculated, a high space-time crop growth grid result is obtained, and under the spatial constraint of a crop vector plot, elements are converted into grids to serve as mask data; and performing mask extraction on a resampled high-space-time crop growth grid result to obtain spectral characteristics of a corresponding unit of crops, performing wave band set statistical calculation to obtain a growth index mean value of a certain crop, and obtaining a comprehensive evaluation result of the plot-level crop growth.

The invention discloses an ecological environment quality prediction method, and relates to the technical field of ecological environment, and the method comprises the steps: obtaining remote sensing images of a research region in different periods, and calculating a vegetation normalization index NDVI, a humidity index WET, a heat index LST, a dryness index NDBSI, a PM2.5 concentration difference index DI and a comprehensive salinity index CSI of the research region in different periods; constructing a novel remote sensing ecological index RSEInew by using a principal component analysis method, and dividing the novel remote sensing ecological index RSEInew into five ecological environment quality levels; calculating novel remote sensing ecological indexes RSEInew of the monthly scale, the annual scale and the different seasonal scales of the research area, and determining ecological environment quality levels of the monthly scale, the annual scale and the different seasonal scales of the research area; a CA-Markov model is constructed, the novel remote sensing ecological indexes RSEInew of the monthly scale, the annual scale and different seasonal scales of the research area and the ecological environment quality level are used as data sources, and the data sources are input into the CA-Markov model to predict the ecological environment quality; in conclusion, the method can accurately evaluate and predict the environment quality.

The invention relates to a cotton field intercropping system yield prediction method and device fused with multi-temporal unmanned aerial vehicle images and a storage medium. The method comprises the following steps: acquiring a multi-temporal spectral image, environment variable data and intercropping plant state data of a cotton field; the multi-temporal spectral image and the environment variable data are provided with time labels; based on the multi-temporal spectral image and the intercropping plant state data, analyzing the distribution condition of the plants in the cotton field to obtain distribution characteristic data; the distribution characteristic data comprises intercropping competition index parameters and a normalized difference vegetation index matrix; based on the distribution characteristic data and the environment variable data, analyzing the growth trend of the plants in the cotton field to obtain a time sequence evolution vector; predicting the yield of the cotton in the cotton field based on the time sequence evolution vector and the environment variable data to obtain the yield prediction data of the cotton; the yield prediction data includes a yield prediction value and a yield prediction interval. By adopting the method, the cotton field yield prediction accuracy can be improved.

The application discloses a kind of urban green space extraction methods based on prior knowledge and remote sensing data, method includes: obtaining remote sensing data and pre-processing to obtain multispectral image data, select several preset bands of multispectral image data, set the prior knowledge threshold value corresponding to each band one by one, the pixel point of band is compared with corresponding prior knowledge threshold value, according to the comparison result, pixel point is initialized as first value or second value, obtain several preliminary hierarchical data, based on the normalized difference vegetation index, after the integrated data is corrected, the modified integrated data is obtained;According to the distribution result of multiple ground object types obtained by the modified integrated data and the preset classification rule. According to the result of prior knowledge extraction, the quality is high, the robustness is stronger, can quickly divide urban green space and other ground object types, reduce workload, can be widely applied in data processing technical field.

The invention relates to a coastal zone aquatic vegetation identification method, system and device, and a storage medium, and the method comprises the steps: obtaining a multispectral remote sensing image of a target coastal zone region in the same season; classifying the multispectral remote sensing images according to tide level heights to obtain a high tide level image set, a medium tide level image set and a low tide level image set; respectively calculating a normalized vegetation index and a corrected normalized water body index corresponding to each tide level image set; obtaining a vegetation index difference characteristic value and a water body index difference characteristic value according to the normalized vegetation index and the corrected normalized water body index; and combining the vegetation index difference feature value and the water body index difference feature value to obtain an aquatic vegetation identification result of the target coastal zone area. According to the method provided by the invention, the spectrum and the water body characteristics are taken as dual criteria, and the recognition precision and the automatic judgment level of the aquatic vegetation in the complex environment of the coastal zone are remarkably improved.