耦合SAM与地学知识的红树林-盐沼生态交错带无人机精细制图与碳储量估算OA

To address the challenges faced by conventional classification methods in balancing"salt and pepper noise"suppression and complex boundary extraction for fine-scale mapping of mangrove-salt marsh ecotones,this study proposes a mapping framework by coupling the Segment Anything Model(SAM)with geoscientif-ic knowledge.The Yifengxi Wetland Park in Shantou,Guangdong was selected as the study area.A multi-scale hierarchical constraint strategy was utilized.First,the object-oriented random forest classification was a-dopted to the resampled unmanned aerial vehicle Digital Orthophoto Map(DOM,2 m spatial resolution)to obtain a coastal vegetation mask,serving as a macro-scale geospatial background constraint.Subsequently,this mask was applied as a regional constraint for the SAM model to perform zero-shot segmentation on the resampled unmanned aerial vehicle DOM(20 cm spatial resolution),and fine-scale object patches were ex-tracted.Finally,spectral and textural features were coupled to achieve precise identification of mangroves and salt marshes.The results demonstrated that the proposed method achieved the Overall Accuracy(OA)of 98.67％and Kappa coefficient of 0.98,outperforming the conventional object-oriented random forest classifi-cation,and it showed a superior capability in delineating complex boundaries.Combination of the fine-scale mapping results and the InVEST model estimated the carbon stocks of mangroves ecosystem and salt mar-shes ecosystem in the study area as 14 953.53 and 923.58 Mg C,respectively.This study proves that the fine-scale mapping of unmanned aerial vehicle by coupling SAM and geoscientific knowledge provides reliable baseline data for the refined management of blue carbon ecosystems,offering significant application value.