Marine organism monitoring method and system based on remote sensing technology
Through the multi-dimensional integration of remote sensing images and environmental data, the problem of insufficient linkage of environmental factors in marine biological monitoring in the existing technology is solved, more accurate biometric identification and dynamic monitoring are achieved, and the analysis ability of ecological response is improved.
Patent Information
- Application Number
- CN202510513772.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-23
- Publication Date
- 2025-08-05
AI Technical Summary
The existing remote sensing marine biological monitoring technology is difficult to fully consider the multi-dimensional linkage of complex environmental factors, resulting in a deviation in biodistribution recognition accuracy, lack of integration of dynamic environmental factors such as water temperature and salinity, and cannot effectively reflect the coupling relationship between biological distribution and environmental conditions. It is difficult to capture the spatial and temporal evolution dynamics of biological populations and reduce the analytical ability of ecological response changes.
Through formatting and denoising processing based on remote sensing images and environmental data, combining spectral reflection values and multi-dimensional environmental factors such as water temperature, salinity, and current velocity, key bands and hot salt structures are extracted, target cells are identified and geographical coordinates are superimposed, continuous position changes are analyzed, dynamic spatiotemporal evolution perspectives are constructed, and timing synchronization characteristics between multiple environmental factors are analyzed.
It has achieved more accurate data foundation construction, improved the target biometric efficiency and spatial distribution interpretation depth, clearly restored the species migration path, enhanced ecological early warning and dynamic monitoring capabilities, revealed key turning points in ecological response, and broke through the traditional static distribution model.