Generation of Large-scale Map of Surface Sedimentary Facies in Intertidal Zone by Using UAV Data and Object-based Image Analysis (OBIA)

被引:7
作者
Kim, Kye-Lim [1 ,2 ]
Ryu, Joo-Hyung [2 ,3 ]
机构
[1] Korea Inst Ocean Sci & Technol, Korea Ocean Satellite Ctr, Busan, South Korea
[2] Univ Sci & Technol, Dept Ocean Sci, Daejeon, South Korea
[3] Korea Inst Ocean Sci & Technol, Ocean Res Operat & Support Dept, Busan, South Korea
关键词
Tidal flat; Large scale surface sedimentary facies; UAV; Object-based image analysis (OBIA); GRAIN-SIZE; CLASSIFICATION; AREA;
D O I
10.7780/kjrs.2020.36.2.2.5
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
The purpose of this study is to propose the possibility of precise surface sedimentary facies classification and a more accurate classification method by generating the large-scale map of surface sedimentary facies based on UAV data and object-based image analysis (OBIA) for Hwang-do tidal flat in Cheonsu bay. The very high resolution UAV data extracted factors that affect the classification of surface sedimentary facies, such as RGB ortho imagery, Digital elevation model (DEM), and tidal channel density, and analyzed the principal components of surface sedimentary facies through statistical analysis methods. Based on principal components, input data to be used for classification of surface sedimentary facies were divided into three cases such as (1) visible band spectrum, (2) topographical elevation and tidal channel density, (3) visible band spectrum and topographical elevation, tidal channel density. The object-based image analysis classification method was applied to map the classification of surface sedimentary facies according to conditions of input data. The surface sedimentary facies could be classified into a total of six sedimentary facies following the folk classification criteria. In addition, the use of visible band spectrum, topographical elevation, and tidal channel density enabled the most effective classification of surface sedimentary facies with a total accuracy of 63.04% and the Kappa coefficient of 0.54.
引用
收藏
页码:277 / 292
页数:16
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