Abstract

An outbreak of Ulva prolifera poses a massive threat to coastal ecology in the Southern Yellow Sea, China (SYS). It is a necessity to extract its area and monitor its development accurately. At present, Ulva prolifera monitoring by remote sensing imagery is mostly based on a fixed threshold or artificial visual interpretation for threshold selection, which has large errors. In this paper, an adaptive threshold model based on Google Earth Engine (GEE) is proposed and applied to extract U. prolifera in the SYS. The model first applies the Floating Algae Index (FAI) or Normalized Difference Vegetation Index (NDVI) algorithm on the preprocessed remote sensing images and then uses the Canny Edge Filter and Otsu threshold segmentation algorithm to extract the threshold automatically. The model is applied to Landsat8/OLI and Sentinel-2/MSI images, and the confusion matrix and cross-sensor comparison are used to evaluate the accuracy and applicability of the model. The verification results show that the model extraction of U. prolifera based on the FAI algorithm has higher accuracy (R2 = 0.99, RMSE = 5.64) and better robustness. However, when the average cloud cover is more than 70% in the image (based on the statistical results of multi-year cloud cover information), the model based on the NDVI algorithm has better applicability and can extract the algae distributed at the edge of the cloud. When the model uses the FAI algorithm, it is named FAI-COM (model based on FAI, the Canny Edge Filter, and Otsu thresholding). And when the model uses the NDVI algorithm, it is named NDVI-COM (model based on NDVI, the Canny Edge Filter, and Otsu thresholding). Therefore, the final extraction results are generated by supplementing NDVI-COM results on the basis of FAI-COM extraction results in this paper. The F1-score of U. prolifera extracted results is above 0.85. The spatiotemporal distribution of U. prolifera in the South Yellow Sea from 2016 to 2020 is obtained through the model calculation. Overall, the coverage area of U. prolifera shows a decreasing trend over the five years. It is found that the delay in recovery time of Porphyra yezoensis culture facilities in the Northern Jiangsu Shoal and the manual salvage and cleaning-up of U. prolifera in May are among the reasons for the smaller interannual scale of algae in 2017 and 2018.

Highlights

  • Introduction distributed under the terms andIn recent years, green macroalgae blooms (MABs) caused by the green tide have been widely reported and have become major global marine disasters [1,2,3]

  • After calculating the Floating Algae Index (FAI) and Normalized Difference Vegetation Index (NDVI) of the image, we found that the remaining cloud could not mask the cloud well, and a small part of algae was distributed at the edge of the had a high FAI value, while the NDVI value was negative

  • The model was applied to Sentinel-2/MSI and Landsat8/OLI images to extract and analyze the distribution of U. prolifera in the South Yellow Sea, China from 2016 to

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Summary

Introduction

Green macroalgae blooms (MABs) caused by the green tide have been widely reported and have become major global marine disasters [1,2,3]. Green tide is a kind of harmful algae bloom, and Ulva prolifera is the dominant algal species involved in these blooms. Since 2007, U. prolifera has broken out in the South Yellow Sea of China for 13 conditions of the Creative Commons. The main characteristics of this marine disaster are rapid outbreak and wide distribution. Studies show that the disaster could quickly spread to most coastal cities on the Shandong Peninsula [4,5,6]. If U. prolifera is not treated in time, it will harm marine life, destroy aquaculture, block the river, and affect human life.

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