Multi-source remote sensing big data collaborative resource management and environment monitoring method and application

Abstract

The invention discloses a multi-source remote sensing big data collaborative resource management and environment monitoring method, which comprises the following steps of: obtaining water environment remote sensing image data of a water environment monitoring area through earth observation of different sensors; carrying out smooth filtering on the abnormal pixels, and correcting the water environment remote sensing image data from which the abnormal pixels are removed; processing the corrected water environment remote sensing image data to obtain remote sensing data; establishing various water quality parameter inversion models; inputting remote sensing data into the inversion model, and calculating through various water quality index inversion models to obtain a corresponding water quality index concentration diagram and a black and odorous water body distribution diagram; and further analyzing and processing through a chart to obtain a water quality grade and water environment monitoring report. The multi-source remote sensing big data collaborative resource management and environment monitoring method is efficient in data processing, high in monitoring result accuracy, capable of avoiding human intervention, reliable in analysis and capable of being widely applied to flood and lake environment resource monitoring and analysis processing.

Description

technical field [0001] The invention relates to the technical field of remote sensing monitoring, in particular to a resource management and environmental monitoring method and application of multi-source remote sensing big data collaboration. Background technique [0002] Traditional water environment monitoring in river basins mainly involves setting up fixed-point monitoring stations, sampling surveys, on-site observation and measurement. These monitoring methods and methods are conducive to accurately reflecting the local microscopic water body characteristics. If the water environment conditions of river basins are mapped macroscopically, Adopting such methods not only consumes manpower, material resources, and financial resources, but also is very inefficient. [0003] With the continuous improvement of remote sensing technology and the continuous improvement of related theoretical models, remote sensing monitoring technology has been applied more and more in water env...

AI Technical Summary

Problems solved by technology

[0002] Traditional water environment monitoring in river basins mainly involves setting up fixed-point monitoring stations, sampling surveys, on-site observation and measurement. These monitoring methods and methods are conducive to accurately reflecting the local microscopic water body characteristics. If the water environment conditions of river basins are mapped macroscopically, Adopting such methods not only consumes manpower, material resources, and financial resources, but also is very inefficient.

[0003] With the continuous improvement of remote sensing technology and the continuous improvement of related theoretical models, remote sensing monitoring technology has been applied more and more in water environment monitoring. Since the remote sensing data of water environment monitoring are point cloud data, and these data need to go through a series of Complicated processing is required to obtain the data needed by people. However, a set of point cloud data is usually processed without considering the abnormal pixels in the original image data, so that the accuracy of the monitoring results is not high, and it cannot be used as a water environment monitoring tool. Reliable guarantee for later analysis, therefore, we propose a resource management and environmental monitoring method and application of multi-source remote sensing big data collaboration

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