A Random Forest-Based Algorithm to Distinguish Ulva prolifera and Sargassum From Multispectral Satellite Images

Abstract

In 2017, large-scale macroalgae blooms with different dominant species of Ulva prolifera and Sargassum occurred concurrently in the Yellow and East China Seas, which poses a challenge to the cognition and control of macroalgae disaster. Therefore, it is necessary to develop an algorithm to distinguish U. prolifera and Sargassum from satellite images. In this study, the spectral difference between U. prolifera and Sargassum and the capability of several multispectral satellite missions to distinguish them is first analyzed. The results show that the reflectance peak in visible wavelength is always in ~550 nm for U. prolifera whether it is floating in clear open water or turbid nearshore water. However, the reflectance of Sargassum floating in clear and turbid water shows totally different characteristics, because most of Sargassum body is submerged in the water and the observed Sargassum reflectance is seriously affected by water reflectance. Compared with Landsat 8 Operational Land Imager (OLI), HuanJing-1, Charge-Coupled Devices (HJ-1 CCD), Aqua Moderate-resolution Imaging Spectroradiometer (MODIS), and Sentinel 2 Multi-Spectral Instrument (MSI), GaoFen-1, Wide Field of View (GF-1 WFV) can preferably capture the spectral difference between U. prolifera and Sargassum. Based on the spectral difference analysis, we propose a random forest-based algorithm to distinguish U. prolifera and Sargassum from GF-1 WFV images with an overall accuracy of 97.6% except when U. prolifera and Sargassum mix together. The algorithm is more robust than the existing ones as it allowed more Sargassum samples from different ocean regions to be used in the training; in addition, it avoids negative effects caused by the selection of a threshold. The proposed algorithm is proved effective in distinguishing U. prolifera and Sargassum in the Yellow and East China Seas in May and June 2017 and in detecting Sargassum in the Atlantic Ocean. Thus, this method can be used in researches including floating macroalgae traceability and competition and succession between different macroalgae species in different regions of the ocean with similar environments. IEEE