Monitoring and Forecasting Green Tide in the Yellow Sea Using Satellite Imagery
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, Landsat 8 OLI_TIRS, HJ-1A/B, HY-1C, and MODIS. The results of the accuracy assessment...
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| Published in | Remote sensing (Basel, Switzerland) Vol. 15; no. 8; p. 2196 |
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| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
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Basel
MDPI AG
01.04.2023
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| Online Access | Get full text |
| ISSN | 2072-4292 2072-4292 |
| DOI | 10.3390/rs15082196 |
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| Abstract | 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, Landsat 8 OLI_TIRS, HJ-1A/B, HY-1C, and MODIS. The results of the accuracy assessment based on three indicators: Precision, 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 uncertainty for the predicted growth curve was estimated. 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 km2. |
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| AbstractList | 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, Landsat 8 OLI_TIRS, HJ-1A/B, HY-1C, and MODIS. The results of the accuracy assessment based on three indicators: Precision, 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 uncertainty for the predicted growth curve was estimated. 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[sup.2] . 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, Landsat 8 OLI_TIRS, HJ-1A/B, HY-1C, and MODIS. The results of the accuracy assessment based on three indicators: Precision, 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 uncertainty for the predicted growth curve was estimated. 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 km2. 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, Landsat 8 OLI_TIRS, HJ-1A/B, HY-1C, and MODIS. The results of the accuracy assessment based on three indicators: Precision, 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 uncertainty for the predicted growth curve was estimated. 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 km². |
| Audience | Academic |
| Author | Xu, Shuwen Pan, Xishan Yu, Yang Yu, Tan Xu, Jinmeng Shao, Weizeng Zuo, Juncheng |
| Author_xml | – sequence: 1 givenname: Shuwen surname: Xu fullname: Xu, Shuwen – sequence: 2 givenname: Tan orcidid: 0000-0003-3746-0361 surname: Yu fullname: Yu, Tan – sequence: 3 givenname: Jinmeng surname: Xu fullname: Xu, Jinmeng – sequence: 4 givenname: Xishan surname: Pan fullname: Pan, Xishan – sequence: 5 givenname: Weizeng orcidid: 0000-0003-3693-6217 surname: Shao fullname: Shao, Weizeng – sequence: 6 givenname: Juncheng surname: Zuo fullname: Zuo, Juncheng – sequence: 7 givenname: Yang surname: Yu fullname: Yu, Yang |
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| CitedBy_id | crossref_primary_10_1109_JSTARS_2024_3492533 crossref_primary_10_3390_rs16183520 crossref_primary_10_3389_fmars_2025_1546289 crossref_primary_10_3390_rs16162934 crossref_primary_10_5194_essd_16_4189_2024 crossref_primary_10_3390_rs17020326 crossref_primary_10_3390_land13091541 crossref_primary_10_1016_j_ecz_2024_100009 |
| Cites_doi | 10.1016/j.ecolind.2022.108988 10.23953/cloud.ijaese.204 10.1016/j.ecss.2019.106335 10.1016/j.ecss.2020.106943 10.1016/j.ecss.2015.06.023 10.3390/rs71215819 10.1016/j.marpolbul.2021.112801 10.1093/nsr/nwz026 10.3390/rs13163240 10.1016/j.marpolbul.2020.111845 10.1109/TGRS.1995.8746029 10.1016/j.ecss.2014.09.014 10.1073/pnas.18.1.1 10.1016/j.ecoinf.2020.101156 10.1016/j.asr.2022.04.030 10.1080/01431161.2012.663112 10.1016/j.rse.2017.01.037 10.1029/2009JC005561 10.1016/j.rse.2009.05.012 10.1016/j.marpolbul.2021.113253 10.1016/j.enzmictec.2003.07.010 10.1016/j.marpolbul.2015.10.033 10.1029/2009JC005513 10.5194/gmd-14-6049-2021 10.1007/s11284-011-0821-8 10.1016/j.marpolbul.2010.05.015 10.1016/j.hal.2016.07.004 10.1016/j.hal.2021.102001 10.2307/2527498 10.1016/j.ecss.2013.05.021 10.1016/j.hal.2014.05.013 10.4319/lo.2013.58.6.2206 10.1016/j.compag.2015.03.019 10.1016/j.protcy.2012.10.074 10.1016/j.aml.2016.09.006 10.1016/j.ecss.2021.107504 10.1016/j.marenvres.2022.105756 10.1109/JSTARS.2016.2580000 10.3390/rs11040445 10.1109/LGRS.2017.2737079 10.1016/j.rse.2016.02.065 10.1007/s11434-011-4551-7 |
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| References | Shi (ref_12) 2009; 114 Winsor (ref_37) 1932; 18 Hu (ref_14) 2009; 113 Sun (ref_34) 2021; 98 Zhang (ref_41) 2008; 5 Hu (ref_10) 2017; 192 Xu (ref_13) 2016; 9 ref_31 Hartigan (ref_40) 1979; 28 Qiao (ref_3) 2011; 56 Xing (ref_43) 2017; 14 Romera (ref_21) 2004; 34 Huang (ref_32) 2015; 7 Bhandari (ref_30) 2012; 6 Jiang (ref_16) 2020; 245 Liu (ref_39) 2015; 163 Xing (ref_15) 2016; 178 Qi (ref_35) 2021; 103 Huo (ref_36) 2013; 58 Wang (ref_20) 2022; 181 Rouse (ref_11) 1974; 351 Ye (ref_2) 2011; 26 Yu (ref_26) 2022; 70 Liu (ref_1) 2013; 129 ref_25 Cui (ref_24) 2012; 33 Zhuang (ref_42) 2021; 162 Li (ref_44) 2021; 172 Hu (ref_9) 2010; 115 Li (ref_4) 2014; 39 Pena (ref_28) 2015; 114 Liu (ref_17) 2010; 60 Zheng (ref_46) 2022; 140 Li (ref_33) 2019; 227 Li (ref_23) 2021; 260 Banks (ref_22) 2017; 64 Zhou (ref_19) 2021; 14 Cao (ref_7) 2020; 60 Qi (ref_45) 2016; 58 Anbazhagan (ref_27) 2016; 5 Zhou (ref_5) 2015; 163 Cui (ref_8) 2015; 101 Zhang (ref_6) 2019; 6 Yuan (ref_18) 2022; 174 Myneni (ref_29) 1995; 33 Nelder (ref_38) 1961; 17 |
| References_xml | – volume: 140 start-page: 108988 year: 2022 ident: ref_46 article-title: What causes the great green tide disaster in the South Yellow Sea of China in 2021? publication-title: Ecol. Indic. doi: 10.1016/j.ecolind.2022.108988 – volume: 5 start-page: 333 year: 2016 ident: ref_27 article-title: Statistical correlation between land surface temperature (LST) and vegetation index (NDVI) using multi-temporal landsat TM data publication-title: Int. J. Adv. Earth Sci. Eng. doi: 10.23953/cloud.ijaese.204 – volume: 227 start-page: 106335 year: 2019 ident: ref_33 article-title: Characteristics and influence of green tide drift and dissipation in Shandong Rongcheng coastal water based on remote sensing publication-title: Estuar. Coast. Shelf Sci. doi: 10.1016/j.ecss.2019.106335 – volume: 245 start-page: 106943 year: 2020 ident: ref_16 article-title: A novel index to detect green-tide using UAV-based RGB imagery publication-title: Estuar. Coast. Shelf Sci. doi: 10.1016/j.ecss.2020.106943 – volume: 163 start-page: 3 year: 2015 ident: ref_5 article-title: Introduction to the Special Issue on green tides in the Yellow Sea publication-title: Estuar. Coast. Shelf Sci. doi: 10.1016/j.ecss.2015.06.023 – volume: 7 start-page: 16024 year: 2015 ident: ref_32 article-title: Automatic labelling and selection of training samples for high-resolution remote sensing image classification over urban areas publication-title: Remote Sens. doi: 10.3390/rs71215819 – volume: 172 start-page: 112801 year: 2021 ident: ref_44 article-title: Research on the dissipation of green tide and its influencing factors in the Yellow Sea based on Google Earth Engine publication-title: Mar. Pollut. Bull. doi: 10.1016/j.marpolbul.2021.112801 – volume: 6 start-page: 825 year: 2019 ident: ref_6 article-title: Ulva prolifera green-tide outbreaks and their environmental impact in the Yellow Sea, China publication-title: Natl. Sci. Rev. doi: 10.1093/nsr/nwz026 – ident: ref_31 doi: 10.3390/rs13163240 – volume: 162 start-page: 111845 year: 2021 ident: ref_42 article-title: Sargassum blooms in the East China Sea and Yellow Sea: Formation and management publication-title: Mar. Pollut. Bull. doi: 10.1016/j.marpolbul.2020.111845 – volume: 33 start-page: 481 year: 1995 ident: ref_29 article-title: The interpretation of spectral vegetation indexes publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.1995.8746029 – volume: 163 start-page: 17 year: 2015 ident: ref_39 article-title: Cruise observation of Ulva prolifera bloom in the southern Yellow Sea, China publication-title: Estuar. Coast. Shelf Sci. doi: 10.1016/j.ecss.2014.09.014 – volume: 351 start-page: 309 year: 1974 ident: ref_11 article-title: Monitoring vegetation systems in the Great Plains with ERTS publication-title: NASA Spec. Publ. – volume: 18 start-page: 1 year: 1932 ident: ref_37 article-title: The Gompertz curve as a growth curve publication-title: Proc. Natl. Acad. Sci. USA doi: 10.1073/pnas.18.1.1 – volume: 60 start-page: 101156 year: 2020 ident: ref_7 article-title: Drift path of green tide and the impact of typhoon “Chan-hom” in the Chinese Yellow Sea based on GOCI images in 2015 publication-title: Ecol. Inform. doi: 10.1016/j.ecoinf.2020.101156 – volume: 70 start-page: 70 year: 2022 ident: ref_26 article-title: Coastline detection using optical and synthetic aperture radar images publication-title: Adv. Space Res. doi: 10.1016/j.asr.2022.04.030 – volume: 33 start-page: 5513 year: 2012 ident: ref_24 article-title: Satellite monitoring of massive green macroalgae bloom (GMB): Imaging ability comparison of multi-source data and drifting velocity estimation publication-title: Int. J. Remote Sens. doi: 10.1080/01431161.2012.663112 – volume: 192 start-page: 217 year: 2017 ident: ref_10 article-title: Remote estimation of biomass of Ulva prolifera macroalgae in the Yellow Sea publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2017.01.037 – volume: 115 start-page: C05017 year: 2010 ident: ref_9 article-title: On the recurrent Ulva prolifera blooms in the Yellow Sea and East China Sea publication-title: J. Geophys. Res. Ocean. doi: 10.1029/2009JC005561 – volume: 113 start-page: 2118 year: 2009 ident: ref_14 article-title: A novel ocean color index to detect floating algae in the global oceans publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2009.05.012 – volume: 174 start-page: 113253 year: 2022 ident: ref_18 article-title: A new assessment of the algal biomass of green tide in the Yellow Sea publication-title: Mar. Pollut. Bull. doi: 10.1016/j.marpolbul.2021.113253 – volume: 34 start-page: 392 year: 2004 ident: ref_21 article-title: Growth and nutrient removal in free and immobilized planktonic green algae isolated from pig manure publication-title: Enzym. Microb. Technol. doi: 10.1016/j.enzmictec.2003.07.010 – volume: 101 start-page: 660 year: 2015 ident: ref_8 article-title: Adaptability of free-floating green tide algae in the Yellow Sea to variable temperature and light intensity publication-title: Mar. Pollut. Bull. doi: 10.1016/j.marpolbul.2015.10.033 – volume: 114 start-page: C12010 year: 2009 ident: ref_12 article-title: Green macroalgae blooms in the Yellow Sea during the spring and summer of 2008 publication-title: J. Geophys. Res. Ocean. doi: 10.1029/2009JC005513 – volume: 14 start-page: 6049 year: 2021 ident: ref_19 article-title: The Lagrangian-based floating macroalgal growth and drift model (FMGDM v1. 0): Application to the Yellow Sea green tide publication-title: Geosci. Model Dev. doi: 10.5194/gmd-14-6049-2021 – volume: 26 start-page: 477 year: 2011 ident: ref_2 article-title: ‘Green tides’ are overwhelming the coastline of our blue planet: Taking the world’s largest example publication-title: Ecol. Res. doi: 10.1007/s11284-011-0821-8 – volume: 60 start-page: 1423 year: 2010 ident: ref_17 article-title: Recurrence of the world’s largest green-tide in 2009 in Yellow Sea, China: Porphyra yezoensis aquaculture rafts confirmed as nursery for macroalgal blooms publication-title: Mar. Pollut. Bull. doi: 10.1016/j.marpolbul.2010.05.015 – volume: 58 start-page: 35 year: 2016 ident: ref_45 article-title: Long-term trend of Ulva prolifera blooms in the western Yellow Sea publication-title: Harmful Algae doi: 10.1016/j.hal.2016.07.004 – volume: 5 start-page: 169 year: 2008 ident: ref_41 article-title: Improved K-means clustering algorithm publication-title: Image Signal Process. – volume: 28 start-page: 100 year: 1979 ident: ref_40 article-title: Algorithm AS 136: A k-means clustering algorithm publication-title: J. R. Stat. Soc. Ser. C (Appl. Stat.) – volume: 98 start-page: 102302 year: 2021 ident: ref_34 article-title: Using Landsat 8 OLI data to differentiate Sargassum and Ulva prolifera blooms in the South Yellow Sea publication-title: Int. J. Appl. Earth Obs. Geoinf. – volume: 103 start-page: 102001 year: 2021 ident: ref_35 article-title: To what extent can Ulva and Sargassum be detected and separated in satellite imagery? publication-title: Harmful Algae doi: 10.1016/j.hal.2021.102001 – volume: 17 start-page: 89 year: 1961 ident: ref_38 article-title: The fitting of a generalization of the logistic curve publication-title: Biometrics doi: 10.2307/2527498 – volume: 129 start-page: 2 year: 2013 ident: ref_1 article-title: The world’s largest macroalgal bloom in the Yellow Sea, China: Formation and implications publication-title: Estuar. Coast. Shelf Sci. doi: 10.1016/j.ecss.2013.05.021 – volume: 39 start-page: 40 year: 2014 ident: ref_4 article-title: Tempo-spatial distribution and species diversity of green algae micro-propagules in the Yellow Sea during the large-scale green tide development publication-title: Harmful Algae doi: 10.1016/j.hal.2014.05.013 – volume: 58 start-page: 2206 year: 2013 ident: ref_36 article-title: Green algae blooms caused by Ulva prolifera in the southern Yellow Sea: Identification of the original bloom location and evaluation of biological processes occurring during the early northward floating period publication-title: Limnol. Oceanogr. doi: 10.4319/lo.2013.58.6.2206 – volume: 114 start-page: 43 year: 2015 ident: ref_28 article-title: An automatic object-based method for optimal thresholding in UAV images: Application for vegetation detection in herbaceous crops publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2015.03.019 – volume: 6 start-page: 612 year: 2012 ident: ref_30 article-title: Feature extraction using Normalized Difference Vegetation Index (NDVI): A case study of Jabalpur city publication-title: Procedia Technol. doi: 10.1016/j.protcy.2012.10.074 – volume: 64 start-page: 213 year: 2017 ident: ref_22 article-title: Statistical error model comparison for logistic growth of green algae (Raphidocelis subcapitata) publication-title: Appl. Math. Lett. doi: 10.1016/j.aml.2016.09.006 – volume: 260 start-page: 107504 year: 2021 ident: ref_23 article-title: Analysis of environmental factors affecting the large-scale long-term sequence of green tide outbreaks in the Yellow Sea publication-title: Estuar. Coast. Shelf Sci. doi: 10.1016/j.ecss.2021.107504 – volume: 181 start-page: 105756 year: 2022 ident: ref_20 article-title: Distribution characteristics of green tides and its impact on environment in the Yellow Sea publication-title: Mar. Environ. Res. doi: 10.1016/j.marenvres.2022.105756 – volume: 9 start-page: 5172 year: 2016 ident: ref_13 article-title: Monitoring and tracking the green tide in the Yellow Sea with satellite imagery and trajectory model publication-title: IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. doi: 10.1109/JSTARS.2016.2580000 – ident: ref_25 doi: 10.3390/rs11040445 – volume: 14 start-page: 1815 year: 2017 ident: ref_43 article-title: High-resolution satellite observations of a new hazard of golden tides caused by floating Sargassum in winter in the Yellow Sea publication-title: IEEE Geosci. Remote Sens. Lett. doi: 10.1109/LGRS.2017.2737079 – volume: 178 start-page: 113 year: 2016 ident: ref_15 article-title: Mapping macroalgal blooms in the Yellow Sea and East China Sea using HJ-1 and Landsat data: Application of a virtual baseline reflectance height technique publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2016.02.065 – volume: 56 start-page: 2236 year: 2011 ident: ref_3 article-title: Drift characteristics of green macroalgae in the Yellow Sea in 2008 and 2010 publication-title: Chin. Sci. Bull. doi: 10.1007/s11434-011-4551-7 |
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| Snippet | 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... |
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| SubjectTerms | Accuracy Algae algal blooms Artificial satellites in remote sensing China Coasts Datasets Environmental monitoring Green tides growth curve growth curves Landsat Landsat satellites Methods multi-source RS NDVI prediction Predictions Prevention Remote sensing Satellite imagery Satellites Sensors source tracing Trends uncertainty Vegetation Weather forecasting Yellow Sea Yellow Sea green tide |
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| Title | Monitoring and Forecasting Green Tide in the Yellow Sea Using Satellite Imagery |
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