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

Zheng, Longxiao; Wu, Mengquan; Xue, Mingyue; Wu, Hao; Liang, Feng; Li, Xiangpeng; Hou, Shimin; Liu, Jiayan

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 images, 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 understanding of macroalgal outbreaks in the marine environment.

CHINA; SYNTHETIC aperture radar; SPECKLE interference; RANDOM forest algorithms; REMOTE sensing; MACHINE learning

Chinese Geographical Science, 2024, Vol 34, Issue 6, p1134

1002-0063

Academic Journal

10.1007/s11769-024-1465-2