A Method for Mangrove Extraction Integrating Multi-Source Remote Sensing Data with Topographic Mechanism Correction

(1) Background: The accurate remote sensing extraction of mangroves is often impeded by spectral confusion, particularly the misclassification of stagnant water bodies as mangroves in flat coastal regions. (2) Methods: To overcome this challenge, we propose a novel “spectral-spatial-terrain” stepwise correction framework. This approach integrates multi-source data: Sentinel-2 imagery for spectral pre-screening, Gaofen-2 (GF-2) imagery for geometric refinement, and a newly developed Potential Waterlogging Index (PWI), derived from a digital elevation model (DEM), for topographic correction. The framework was applied to evaluate mangrove damage following Typhoon Yagi (2024) in the East Harbour National Nature Reserve. (3) Results: The method achieved high extraction accuracy, with a Kappa coefficient of 0.97. The remote sensing-based damage assessment revealed that 48.2% of the mangrove area was affected, with a significantly higher damage rate of 63.0% observed within the PWI-identified potential waterlogging zones. (4) Conclusions: The high classification accuracy confirms the effectiveness of the proposed framework. More importantly, the spatially consistent damage pattern provides strong ecological evidence supporting the mechanistic rationale behind the terrain-based correction. This study presents a reliable and transferable remote sensing methodology for high-precision, dynamic monitoring and assessment of mangrove ecosystem after disaster.