National scale sub-meter mangrove mapping using an augmented border training sample method

Abstract

This study presents the development of China’s first national-scale sub-meter mangrove map, addressing the need for high-resolution mapping to accurately delineate mangrove boundaries and identify fragmented patches. To overcome the current limitation of 10-m resolution, we developed a novel Semi-automatic Sub-meter Mapping Method (SSMM). The SSMM enhances the spectral separability of mangroves from other land covers by selecting nine critical features from both Sentinel-2 and Google Earth imagery. We also developed an innovative automated sample collection method to ensure ample and precise training samples, increasing sample density in areas susceptible to misclassification and reducing it in uniform regions. This method surpasses traditional uniform sampling in representing the national-scale study area. The classification is performed using a random forest classifier and is manually refined, culminating in the production of the pioneering Large-scale Sub-meter Mangrove Map (LSMM).