IDENTIFICATION OF SEABED COVER USING AIRBORNE LIDAR BATHYMETRY

The presence can well know characteristics of a coastal area of bathymetric information and the type of seabed coverage. Using the two pieces of information can make the utilization of the information more precise and maximal, such as for ship navigation, coastal development, and coastal conserva...

Full description

Saved in:
Bibliographic Details
Main Author: Rangga Saputra, Lufti
Format: Theses
Language:Indonesia
Online Access:https://digilib.itb.ac.id/gdl/view/67589
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Institut Teknologi Bandung
Language: Indonesia
Description
Summary:The presence can well know characteristics of a coastal area of bathymetric information and the type of seabed coverage. Using the two pieces of information can make the utilization of the information more precise and maximal, such as for ship navigation, coastal development, and coastal conservation. Currently, there is little information on seabed cover type compared to bathymetric information. Remote sensing methods can be an alternative acceleration. One method is using Airborne LiDAR bathymetry (ALB). The LiDAR laser pulse will interact with the environment in its paths, such as the atmosphere, air surface, air column, and bottom of the water, until it returns to the receiver. The interaction can be seen by extracting the full waveform from LiDAR. The different types of coverage of the bottom of the water reflecting the laser pulse will affect the value and form of the energy of the returned pulse. Therefore, this research approach will add a variable from the waveform, namely width, and area. All these variables will be analyzed in the formation of seabed classification using the Random Forest method. Classification is divided into three classes: sand, rock, and coral. The research location is on the coast of Bagus Beach, South Lampung Regency. The classification results were tested using field survey data of seabed bottom type. The accuracy test shows that adding the waveform variable can increase the classification accuracy from 74 to 83, resulting kappa value of 74.5%. In addition, the analysis results find important variables in making predictions, namely width, depth, and area. With these promising results, LiDAR bathymetry can be a solution in supporting the provision of complete seabed information.