10 results about "Normalized Difference Vegetation Index" patented technology

The invention provides a multi-mode remote sensing tea garden automatic identification method and system integrating phenolic indexes and phenological characteristics. According to the method, the growth amplitude GA reflecting tea phenological characteristics is calculated through medium-resolution time sequence radar data, and the normalized vegetation index NDVI, the phenolic compound index PCI and the phenol high-value dominant index PHD are calculated from optical remote sensing images so as to represent the growth condition of the tea tree and the change rule of the phenol content. A double-branch lightweight deep learning model is constructed, a first branch processes a high-resolution remote sensing image, and a second branch processes multi-modal tea garden remote sensing features; and then multi-modal geoscience knowledge of the tea garden is fully extracted and fused through a double-branch fusion module, refined and automatic extraction of large-area tea garden distribution is realized, and key technical support is provided for tea garden resource monitoring, management and the like.

The invention belongs to the field of ocean remote sensing information, and particularly relates to a characteristic spectrum-based Sargassum horneri gold tide and Enteromorpha green tide remote sensing classification and identification method. Comprising the following steps: 1) acquiring a sentinel No.2 remote sensing image of a set sea area, and preprocessing the sentinel No.2 remote sensing image; 2) calculating a normalized difference vegetation index (NDVI) of the preprocessed remote sensing image, and extracting a floating macroalgae image according to a calculation result; 3) performing non-water interference factor elimination on the floating macroalgae image by using a triple mask method to obtain a macroalgae bloom area; and 4) based on the macroalgae bloom area, carrying out classification and identification on the Sargassum horneri gold tide and the Enteromorpha green tide. The invention provides an improved spectrum ratio gradient SRG-1 algorithm so as to enhance the distinguishing capability of two algae spectrums, and aims to establish a large algae bloom classification extraction method considering both precision and efficiency so as to provide technical support for monitoring and prevention and control of algae bloom disasters.

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 long-time-sequence green land data extraction method and device based on an optimal NDVI threshold value, and the method comprises the steps: obtaining high-precision land coverage data in a specific period and NDVI data in the same period in a boundary range of a target region, extracting base green land data, generating a plurality of candidate NDVI threshold values of the NDVI data in a self-adaptive manner, and extracting the base green land data according to the candidate NDVI threshold values. Determining target green land data of the target area under each candidate NDVI threshold value, calculating green land division precision of green land distribution in the target area according to the base green land data, and finally selecting the candidate NDVI threshold value with the highest green land division precision as the optimal NDVI threshold value. The method is used for extracting long-time-sequence green land data of a target area in other periods except a specific period. Through the method and the device, the practical problems that long-time-sequence land coverage data is insufficient in precision, and high-precision land coverage data only covers a specific period and cannot meet long-time-sequence application requirements are solved.

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.

The invention relates to an early warning device for hydraulic ring ecological environment destruction, which comprises a water quality monitoring sub-module, a soil monitoring sub-module, a vegetation monitoring sub-module, a landform monitoring sub-module and a hydrodynamic monitoring sub-module, and is characterized in that the water quality monitoring sub-module comprises a pH sensor, a chemical oxygen demand sensor, an ammonia nitrogen sensor and a heavy metal ion sensor; probes of all the sensors are provided with anti-corrosion coatings. The soil monitoring sub-module comprises a soil moisture content sensor, a soil bulk density tester and a soil heavy metal sensor; the vegetation monitoring sub-module comprises a portable spectrum sensor, and normalized difference vegetation indexes and vegetation coverage rates are obtained by monitoring vegetation; the landform monitoring sub-module comprises a GNSS positioning module and a tilt angle sensor; the hydrological power monitoring sub-module comprises an ultrasonic water level gauge and an electromagnetic flow velocity sensor and is used for monitoring water level fluctuation and water flow velocity and judging the river bank erosion risk. The method can greatly improve the early recognition capability of hydraulic environment ecological environment damage.