Power of SAR Imagery and Machine Learning in Monitoring Ulva prolifera:A Case Study of Sentinel-1 and Random Forest  

作　　者：ZHENG Longxiao WU Mengquan XUE Mingyue WU Hao LIANG Feng LI Xiangpeng HOU Shimin LIU Jiayan 

机构地区：[1]College of Resources and Environmental Engineering,Ludong University,Yantai 264039,China [2]North China Sea Marine Forecasting and Hazard Mitigation Center,Ministry of Natural Resources,Qingdao 266061,China [3]School of Life Sciences,Lanzhou University,Lanzhou 730000,China [4]Marine Development and Fisheries Bureau of Yantai,Yantai 264039,China [5]Modern Marine Industry Development Promotion Center of Yantai,Yantai 264003,China

出　　处：《Chinese Geographical Science》2024年第6期1134-1143,共10页中国地理科学（英文版）

基　　金：Under the auspices of National Natural Science Foundation of China(No.42071385);National Science and Technology Major Project of High Resolution Earth Observation System(No.79-Y50-G18-9001-22/23)。

摘　　要：Automatically detecting Ulva prolifera(U.prolifera)in rainy and cloudy weather using remote sensing imagery has been a long-standing problem.Here,we address this challenge by combining high-resolution Synthetic Aperture Radar(SAR)imagery with the machine learning,and detect the U.prolifera of the South Yellow Sea of China(SYS)in 2021.The findings indicate that the Random Forest model can accurately and robustly detect U.prolifera,even in the presence of complex ocean backgrounds and speckle noise.Visual inspection confirmed that the method successfully identified the majority of pixels containing U.prolifera without misidentifying noise pixels or seawater pixels as U.prolifera.Additionally,the method demonstrated consistent performance across different im-ages,with an average Area Under Curve(AUC)of 0.930(+0.028).The analysis yielded an overall accuracy of over 96%,with an average Kappa coefficient of 0.941(+0.038).Compared to the traditional thresholding method,Random Forest model has a lower estimation error of 14.81%.Practical application indicates that this method can be used in the detection of unprecedented U.prolifera in 2021 to derive continuous spatiotemporal changes.This study provides a potential new method to detect U.prolifera and enhances our under-standing of macroalgal outbreaks in the marine environment.

关 键 词：Ulva prolifera Random Forest Sentinel-1 Synthetic Aperture Radar(SAR)image machine learning remote sensing Google Earth Engine South Yellow Sea of China 

分 类 号：X87[环境科学与工程—环境工程] X834[电子电信—信号与信息处理] TN957.52[电子电信—信息与通信工程] TP181[自动化与计算机技术—控制理论与控制工程]