Comparison of Satellite Imagery for Identifying Seagrass Distribution Using a Machine Learning Algorithm on the Eastern Coast of South Korea

被引:4
|
作者
Widya, Liadira Kusuma [1 ,2 ]
Kim, Chang-Hwan [3 ]
Do, Jong-Dae [3 ]
Park, Sung-Jae [1 ]
Kim, Bong-Chan [1 ]
Lee, Chang-Wook [1 ,4 ]
机构
[1] Kangwon Natl Univ, Div Sci Educ, Chunchon 24341, South Korea
[2] Sunan Bonang Univ, Dept Civil Engn, Tuban 62313, Indonesia
[3] Korea Inst Ocean Sci & Technol, East Sea Res Inst, Uljin 36315, South Korea
[4] Kangwon Natl Univ, Dept Smart Reg Innovat, Chuncheon Si 24341, South Korea
关键词
seagrass; remote sensing; support vector machines (SVM); classification models; ATMOSPHERIC CORRECTION; WATER DEPTH; SUN GLINT; BOTTOM; LANDSAT;
D O I
10.3390/jmse11040701
中图分类号
U6 [水路运输]; P75 [海洋工程];
学科分类号
0814 ; 081505 ; 0824 ; 082401 ;
摘要
Seagrass is an essential component of coastal ecosystems because of its capability to absorb blue carbon, and its involvement in sustaining marine biodiversity. In this study, support vector machine (SVM) technologies with corrected satellite imagery data, were applied to identify the distribution of seagrasses. Observations of seagrasses from satellite imagery were obtained using GeoEye-1, Sentinel-2 MSI level 1C, and Landsat-8 OLI satellite imagery. The satellite imagery from Google Earth has been obtained at a very high resolution, and was to be used within both the training and testing of a classification method. The optical satellite imagery must be processed for image classification, throughout which radiometric correction, sunglint, and water column adjustments were applied. We restricted the scope of the study area to a maximum depth of 10 m due to the fact that light does not penetrate beyond this level. When classifying the distribution of seagrasses present in the research region, the recently developed SVM technique achieved overall accuracy values of up to 92% (GeoEye-1), 88% (Sentinel-2 MSI level 1C), and 83% (Landsat-8 OLI), respectively. The results of the overall accuracy values are also used to evaluate classification models.
引用
收藏
页数:13
相关论文
共 50 条
  • [1] Machine Learning Application for Coastal Area Change Detection in Gangwon Province, South Korea Using High-Resolution Satellite Imagery
    Park, Sung Jae
    Achmad, Arief Rizqiyanto
    Syifa, Mutiara
    Lee, Chang-Wook
    JOURNAL OF COASTAL RESEARCH, 2019, : 228 - 235
  • [2] Detecting Arsenic Contamination Using Satellite Imagery and Machine Learning
    Agrawal, Ayush
    Petersen, Mark R.
    TOXICS, 2021, 9 (12)
  • [3] Vineyard Segmentation from Satellite Imagery Using Machine Learning
    Santos, Luis
    Santos, Filipe N.
    Filipe, Vitor
    Shinde, Pranjali
    PROGRESS IN ARTIFICIAL INTELLIGENCE, EPIA 2019, PT I, 2019, 11804 : 109 - 120
  • [4] Estimation of Evapotranspiration in South Eastern Afghanistan Using the GCOM-C Algorithm on the Basis of Landsat Satellite Imagery
    Wali, Emal
    Tasumi, Masahiro
    Klemm, Otto
    HYDROLOGY, 2024, 11 (07)
  • [5] Quantification ofMargalefidinium polykrikoidesBlooms along the South Coast of Korea Using Airborne Hyperspectral Imagery
    Shin, Jisun
    Kim, Soo Mee
    Kim, Keunyong
    Ryu, Joo-Hyung
    REMOTE SENSING, 2020, 12 (15)
  • [6] Mapping seagrass and colonized hard bottom in Springs Coast, Florida using WorldView-2 satellite imagery
    Baumstark, Rene
    Duffey, Renee
    Pu, Ruiliang
    ESTUARINE COASTAL AND SHELF SCIENCE, 2016, 181 : 83 - 92
  • [7] Comparison of Land Cover Classification of Ir Sutami Dam Using Machine Learning and Multisource Satellite Imagery
    Walidaroyani, Ainia
    Ramdani, Fatwa
    Kurniawan, Tri Astoto
    PROCEEDINGS OF 2021 INTERNATIONAL CONFERENCE ON SUSTAINABLE INFORMATION ENGINEERING AND TECHNOLOGY, SIET 2021, 2021, : 184 - 190
  • [8] Comparison of Land Use Land Cover Classifiers Using Different Satellite Imagery and Machine Learning Techniques
    Basheer, Sana
    Wang, Xiuquan
    Farooque, Aitazaz A.
    Nawaz, Rana Ali
    Liu, Kai
    Adekanmbi, Toyin
    Liu, Suqi
    REMOTE SENSING, 2022, 14 (19)
  • [9] Quantifying urban flood extent using satellite imagery and machine learning
    Composto, Rebecca W.
    Tulbure, Mirela G.
    Tiwari, Varun
    Gaines, Mollie D.
    Caineta, Julio
    NATURAL HAZARDS, 2025, 121 (01) : 175 - 199
  • [10] Automatic Target Detection from Satellite Imagery Using Machine Learning
    Tahir, Arsalan
    Munawar, Hafiz Suliman
    Akram, Junaid
    Adil, Muhammad
    Ali, Shehryar
    Kouzani, Abbas Z.
    Mahmud, M. A. Pervez
    SENSORS, 2022, 22 (03)