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

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).

Our study showcases the LSMM’s superior performance over the established High-resolution Global Mangrove Forest (HGMF) map. The LSMM demonstrates enhanced classification accuracy, improved spatial delineation, and more precise area calculations, along with a robust framework of spatial analysis. Notably, compared to the HGMF, the LSMM achieves a 22.0 % increase in overall accuracy and a 0.27 improvement in the F1 score. In terms of mangrove coverage within China, the LSMM estimates a reduction of 4,345 ha (15.4 %), decreasing from 32,598 ha in the HGMF to 28,253 ha. This reduction is further underscored by a significant 61.7 % discrepancy in spatial distribution areas when compared to the HGMF, indicative of both commission and omission errors associated with the 10-m HGMF. Additionally, the LSMM identifies a fivefold increase in the number of mangrove patches, totaling 40,035, compared to the HGMF’s 7,784. These findings underscore the substantial improvements offered by sub-meter resolution products over those with a 10-m resolution. The LSMM and its automated mapping methodology establish new benchmarks for comprehensive, long-term mangrove mapping at sub-meter scales, as well as for the detailed mapping of extensive land cover types. Our study is expected to catalyze a shift toward high-resolution mangrove mapping on a large scale.