Recognition of aquatic vegetation above water using shortwave infrared baseline and phenological features

Abstract

Accurate monitoring of emergent aquatic vegetation (EAV) and floating-leave aquatic vegetation (FAV), is vital because vegetation provides a critical habitat for aquatic animals and plays a critical role in protecting biodiversity. However, owing to the interference of water spectrum signals, it is difficult to extract the emerging aquatic vegetation information from remote sensing images, especially to distinguish between EAV and FAV. This study first proposed an enhanced aquatic vegetation index based on the difference in two short-wave infrared (SWIR) bands to extract the aquatic vegetation above the water surface, and then, the EAV and FAV were further distinguished coupled with the phenological characteristics, subsequently, a case study of Taihu Lake for accurately extracting seasonal and annual distributions. The results demonstrate that the proposed aquatic vegetation index is highly sensitive to EAV and FAV, and can further distinguish EAV from them coupled with phenological difference. Additionally, the dramatic changes in EAV and FAV in time and space indicated that human purse seine culture has had a great influence on the succession of aquatic vegetation, which may lead to the deterioration of the water ecology environment. Using the SWIR bands coupled with phenological difference is a promising method for recognizing aquatic vegetation above the water surface owing that it can weaken and eliminate the impact of algal bloom at the same time.