An enhanced monitoring method for spatio-temporal dynamics of salt marsh vegetation using google earth engine

Abstract

Coastal salt marsh wetlands constitute one of the three blue carbon ecosystems, playing a pivotal ecological role in carbon sequestration and greenhouse gas mitigation. The spatio-temporal monitoring of coastal salt marsh facilitates the assessment of their resilience to climate change and aids decision-makers in formulating effective wetland policies. The Google Earth Engine (GEE), a remote sensing cloud platform, offers a new perspective for such endeavors. This study, leveraging GEE, integrates climatic features and the Soil Adjusted Vegetation Index (SAVI) for the precise extraction of salt marsh vegetation, enabling remote sensing monitoring of spatial and temporal dynamics of over an extended time series. Using random forest classification, three types of typical coastal salt marsh vegetation in Yancheng City were accurately extracted. The refined extraction method demonstrated superior efficiency and convenience in subdividing and monitoring coastal salt marsh vegetation compared with traditional methods, with high accuracy. In addition, the study observed distinct change characteristics in the Normalized Difference Vegetation Iindex (NDVI) values of three typical salt marsh vegetation types in Yancheng, China, from 2000 to 2020. Specifically, Phragmites australis and Spartina alterniflora showed an expansion trend, while the area of Suaeda salsa contracted. Precipitation exhibited a more pronounced impact on salt marsh vegetation growth compared to temperature. Consequently, this study holds significant implications for the precise extraction of salt marsh vegetation and long-time monitoring, playing a crucial role in wetland ecological protection and sustainable development.