Cyanobacteria biomass spatial-temporal change monitoring and visualization method based on remote sensing image
A technology of remote sensing images and temporal and spatial changes, applied in photogrammetry/video metrology, surveying and navigation, measuring devices, etc., can solve the problems of cyanobacteria bloom cause analysis, lack of watershed and water network analysis, inability to analyze and intuitively express And other issues
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Embodiment 1
[0065] A method for monitoring and visualizing temporal and spatial changes in cyanobacterial biomass based on remote sensing images, the method comprising the following steps:
[0066] 1) Obtain the remote sensing image of the cyanobacteria area, and perform image preprocessing on it to construct a normalized cyanobacteria index;
[0067] 2) Using the feature optimization model based on VPRS_GID to optimize the features of remote sensing images, and obtain the optimized multi-feature space;
[0068] 3) Establish a double-weighted SVM classification model based on wavelet kernels according to the multi-feature space, obtain the optimal classification decision surface of SVM, and use the classification model to extract and identify the spatial distribution information of cyanobacteria (Microcystis) and detect changes, and combine the field Survey data for comprehensive verification and precision analysis;
[0069] 4) Superimpose and display the processed remote sensing image, ...
Embodiment 2
[0177] The method for monitoring and visualizing temporal and spatial changes of cyanobacterial biomass based on remote sensing images in Example 1 is used below for actual operation.
[0178] 1. Cyanobacteria (Microcystis) spatial distribution extraction and change detection results
[0179] The Landsat7ETM+ images acquired on August 1 and September 2, 2000 were selected. Using the wavelet kernel double weighted SVM classification model, the identification and detection results of the spatial distribution pattern of Microcystis in the Dianshan Lake area were obtained.
[0180] Set up 10 sampling areas along the Dianshan Lake to conduct field sampling of cyanobacterial biomass. It is found that the extraction results of remote sensing images are consistent with the actual survey data. And the image of August 1, 2000, where Microcystis is more obvious, was selected for error matrix statistics.
[0181] Table 1 Error matrix of information extraction of cyanobacteria (Microcys...
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