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Detection of bottom substrate type using single-beam echo sounder backscatter: a case study in the east coastal of Banyuasin by Fauziyah, ; Purwiyanto, A I S; Agustriani, F; Putri, W A E; Liyani, M; Aryawati, R; Ningsih, E N; Suteja, Y is a Agricultural and Biological Sciences article available to read on EtoBox.
What is Detection of bottom substrate type using single-beam echo sounder backscatter: a case study in the east coastal of Banyuasin about?
## Abstract This research was aimed to identify substrates type in the east part of the Banyuasin coastal waters using quantitative backscatter data from single-beam echo-sounding. The SIMRAD EK-15 was used to classify the seafloor substrate types. The ground truth was required for calibrating the acoustic result. Wet sieving methods and Shepard’s triangular diagram were used to analyze the ground truth samples. The acoustic data were filtered to extract the volume backscattering strength of bottom surface (SV) using Echoview 4.0. The data of bottom surface backscattering strength (SS) and SV were classified by using Hierarchical Cluster Method. Data of substrate type from the ground truth will be used as a guideline to classify the SS data to identify the substrate type based on the SS characteristic which associated with various types of the bottom substrate. The results showed the single beam capability in distinguishing the types of bottom substrate, namely clayed sand with the SS value ranges from -47.29 to -46.32 dB), silt + sand + clay with the SS value ranges from -51.00 to -48.54 dB), clayed silt with the SS value ranges from -53.47 to -52.24 dB), and silty clay with the S
Who reads Detection of bottom substrate type using single-beam echo sounder backscatter: a case study in the east coastal of Banyuasin?
It is typically read by researchers, students, and practitioners in Agricultural and Biological Sciences.
- Author
- Fauziyah, ; Purwiyanto, A I S; Agustriani, F; Putri, W A E; Liyani, M; Aryawati, R; Ningsih, E N; Suteja, Y
- Publisher
- IOP Publishing (ISSN 1755-1307)
- Published
- 2019
- Language
- EN
- Field
- Agricultural and Biological Sciences (Physical Sciences)