A Pixel-object-knowledge-based Approach for Fine-grained Coastal Wetland Mapping: A Case Study of Shandong Province

Wetlands play a crucial role as key ecosystems in water quality purification, carbon storage, and biodiversity conservation. To achieve effective protection and scientific management of wetlands, precise and accurate wetland mapping is of utmost importance. Existing high-resolution wetland classification methods still face significant challenges in boundary identification and type differentiation, particularly in complex terrains and areas with high spectral similarity between wetland types. To address these issues, this study proposed a Pixel-Object-Knowledge-based Wetland Mapping (POK-WM) method. The POK-WM method overcomes the limitation of traditional approaches that rely solely on spectral information by integrating pixel-level spectral features, object-level spatial features, and regional knowledge rules, enabling accurate mapping of seven wetland types in the coastal areas of Shandong Province. Based on validation with test samples, the wetland map achieved an overall accuracy of 90.4% and a Kappa coefficient of 0.89. Compared with datasets such as EA, GLC_FCS, and GLWD, the POK-WM method demonstrates superior performance in wetland type identification and boundary clarity in complex regions, indicating that the integration of pixel–object– knowledge offers significant advantages in improving classification accuracy and spatial consistency. This provides reliable data support for the systematic mapping of complex coastal wetlands as well as regional wetland conservation and ecological management.