一种基于深度学习的红树林生长状况监控方法及系统
By employing a deep learning-based mangrove growth monitoring method, which utilizes dual-temporal remote sensing imagery and deep semantic feature extraction, the problem of traditional monitoring methods being unable to assess growth decline is solved, enabling high-precision monitoring of mangrove growth status and automatic generation of hierarchical management recommendations.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SOUTH CHINA SEA PLANNING & ENVIRONMENT RES INST SOA
- Filing Date
- 2026-03-20
- Publication Date
- 2026-07-17
AI Technical Summary
Traditional mangrove monitoring methods are insufficient for rapid and synchronous assessment of growth status on a large spatial scale, and existing methods cannot accurately quantify quality degradation phenomena such as growth decline and vitality reduction, leading to a disconnect between ecological protection and management.
Using a deep learning-based approach, candidate growth areas for mangroves are extracted and deep semantic features are extracted by acquiring dual-temporal remote sensing images. Feature matching and rationality assessment are then performed by combining these with a pre-defined set of evolutionary pattern features to generate a mangrove growth status index and hierarchical management recommendations.
It achieves high-precision monitoring of mangrove growth, can identify range dynamics and internal changes, automatically generates hierarchical management suggestions, and enhances the model's adaptability and feature representation reliability in complex environments.
Smart Images
Figure CN122024070B_ABST