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
  • [31] Deep learning-based mapping of total suspended solids in rivers across South Korea using high resolution satellite imagery
    Moon, JunGi
    Suh, SungMin
    Jung, SangJin
    Baek, Sang-Soo
    Pyo, Jongcheol
    GISCIENCE & REMOTE SENSING, 2024, 61 (01)
  • [32] A Study on Developing a Model for Predicting the Compression Index of the South Coast Clay of Korea Using Statistical Analysis and Machine Learning Techniques
    Lee, Sungyeol
    Kang, Jaemo
    Kim, Jinyoung
    Baek, Wonjin
    Yoon, Hyeonjun
    APPLIED SCIENCES-BASEL, 2024, 14 (03):
  • [33] Author Correction: Using hyperspectral imagery to investigate large-scale seagrass cover and genus distribution in a temperate coast
    Kenneth Clarke
    Andrew Hennessy
    Andrew McGrath
    Robert Daly
    Sam Gaylard
    Alison Turner
    James Cameron
    Megan Lewis
    Milena B. Fernandes
    Scientific Reports, 11
  • [34] A comparison of machine learning methods for target recognition using ISAR imagery
    Uttecht, Karen D.
    Chen, Cindy X.
    Dickinson, Jason C.
    Goyette, Thomas M.
    Giles, Robert H.
    Nixon, William E.
    AUTOMATIC TARGET RECOGNITION XXI, 2011, 8049
  • [35] Identifying artificially drained pasture soils using machine learning and Earth observation imagery
    O'Hara, Rob
    Green, Stuart
    McCarthy, Tim
    Cahalane, Conor
    Fenton, Owen
    Tuohy, Pat
    JOURNAL OF APPLIED REMOTE SENSING, 2020, 14 (03)
  • [36] PHURIE: hurricane intensity estimation from infrared satellite imagery using machine learning
    Amina Asif
    Muhammad Dawood
    Bismillah Jan
    Javaid Khurshid
    Mark DeMaria
    Fayyaz ul Amir Afsar Minhas
    Neural Computing and Applications, 2020, 32 : 4821 - 4834
  • [37] Recognizing protected and anthropogenic patterns in landscapes using interpretable machine learning and satellite imagery
    Stomberg, Timo T.
    Leonhardt, Johannes
    Weber, Immanuel
    Roscher, Ribana
    FRONTIERS IN ARTIFICIAL INTELLIGENCE, 2023, 6
  • [38] River vegetation discrimination method using satellite imagery and topographic data by machine learning
    Miyawaki S.
    Ikawa K.
    Suzuki K.
    Suzuoki Y.
    Ikeuchi K.
    Ecology and Civil Engineering, 2020, 23 (02) : 261 - 278
  • [39] Mapping Coastal Wetlands Using Satellite Imagery and Machine Learning in a Highly Urbanized Landscape
    Munizaga, Juan
    Garcia, Mariano
    Ureta, Fernando
    Novoa, Vanessa
    Rojas, Octavio
    Rojas, Carolina
    SUSTAINABILITY, 2022, 14 (09)
  • [40] PHURIE: hurricane intensity estimation from infrared satellite imagery using machine learning
    Asif, Amina
    Dawood, Muhammad
    Jan, Bismillah
    Khurshid, Javaid
    DeMaria, Mark
    Minhas, Fayyaz ul Amir Afsar
    NEURAL COMPUTING & APPLICATIONS, 2020, 32 (09): : 4821 - 4834