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

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.