Combination of Google Earth imagery and Sentinel-2 data for mangrove species mapping

Abstract

Knowledge gained about mangrove species mapping is essential to understand mangrove species’ development and to better estimate their ecological service value. Spectral bands and spatial resolution of remote sensing data are two important factors for accurate discrimination of mangrove species. Mangrove species classification in Shenzhen Bay, China, was performed using Sentinel-2 (S2) multispectral instrument (MSI) data and Google Earth (GE) high-resolution imagery as data sources, and their suitability in mapping mangrove forest at a species level was examined. In the classification feature groups, the spectral bands were from the S2 MSI data and the textural features were based on GE imagery. The support vector machine classifier was used in mangrove species classification processing with eight groups of features, which were based on different S2 spectral bands and different GE spatial resolution textural features. The highest overall accuracy of our mapping results was 78.57% and the kappa coefficient was 0.74, which indicated great potential for using the combination of S2 MSI and GE imagery for distinguishing and mapping mangrove species.