| While the use of marine resources has brought huge economic benefits,it is the over-exploitation of marine resources,the frequent occurrence of marine ecological disasters and the destruction of marine ecosystems.To promote the sustainable development of the marine economy,we must find a balance between exploitation and protection.How to monitor the ecological environment dynamically,quickly and accurately in real-time is the key,and remote sensing technology is one of the most important monitoring tools for environmental protection.Constant temporal and spatial monitoring of the coastline is essential for environmental protection.Waterlines were extracted from multi-source satellite remote sensing images,e.g.,Landsat8,MODIS,HY-1C,and GF-3 SAR.A semi-automatic methodology of threshold segmentation was proposed to detect coastline based on local blocks of images,which is suitable for both optical and microwave remote sensing images and threshold scaling standards across different coastline types.The accuracy assessment of artificial,sandy,and muddy coastlines for each dataset was highly dependent on spatial resolution,and the result was GF-3 SAR.<Landsat8<HY-1C<MODIS for RMSE and STD,and Landsat8>HY-1C>MODIS>GF-3 SAR for accuracy within 1 pixel.For the angle of imaging effects of them,GF-3 SAR was inferior to other datasets on muddy coastline.The extraction effect of HY-1C was more closely correlated to the tide and its imaging effect of muddy coastline was the best among all datasets we selected.This work fused HY-1C images,which might have omitted some coastline information due to cloud interference,with GF-3 SAR images recorded over the same time period.The result showed that the fusion of optical and microwave remote sensing is effective and allows for better monitoring of the coastline.This paper proposes a semi-automatic green tide extraction method based on the NDVI to extract Yellow Sea green tides from 2008 to 2022 using remote sensing(RS)images from multiple satellites:GF-1,Landsat 5 TM,Landsat8 OLI＿TIRS,HJ-1A/B,HY-1C,and MODIS.The results of accuracy assessment based on three indicators:Precise,Recall,and F1-score,showed that our extraction method can be applied to the images of most satellites and different environments.We traced the source of the Yellow Sea green tide to Jiangsu Subei shoal and the southeastern Yellow Sea and earliest advanced the tracing time to early April.The Gompertz and Logistic growth curve models were selected to predict and monitor the extent and duration of the Yellow Sea green tide,and added uncertainty estimate for the predict.The prediction for 2022 was that its start and dissipation dates were expected to be June 1 and August 15,respectively,and the accumulative cover area was expected to be approximately 1190.90-1191.21 km~2. |
