Mapping and Spatial Variation of Seagrasses in Xincun, Hainan Province, China, Based on Satellite Images

Abstract

Seagrass is an important structural and functional component of the global marine ecosystem and is of high value for its ecological services. This paper took Xincun Bay (including Xincun Harbor and Li’an Harbor) of Hainan Province as the study area, combined ground truth data, and adopted two methods to map seagrass in 2020 using Chinese GF2 satellite images: maximum-likelihood and object-oriented classification. Sentinel-2 images from 2016 to 2020 were used to extract information on seagrass distribution changes. The following conclusions were obtained. (1) Based on GF2 imagery, both the classical maximum likelihood classification (MLC) method and the object-based image analysis (OBIA) method can effectively extract seagrass information, and OBIA can also portray the overall condition of seagrass patches. (2) The total seagrass area in the study area in 2020 was about 395 hectares, most of which was distributed in Xincun Harbor. The southern coast of Xincun Harbor is an important area where seagrass is concentrated over about 228 hectares in a strip-like continuous distribution along the coastline. (3) The distribution of seagrasses in the study area showed a significant decaying trend from 2016 to 2020. The total area of seagrass decreased by 79.224 ha during the five years from 2016 to 2020, with a decay rate of 16.458%. This study is the first on the comprehensive monitoring of seagrass in Xincun Bay using satellite remote sensing images, and comprises the first use of GF2 data in seagrass research, aiming to provide a reference for remote sensing monitoring of seagrass in the South China Sea.

Keywords

• seagrass monitoring and mapping
• remote sensing
• GF2 and Sentinel-2 satellite data
• MLC and OBIA