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
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