Author Type

Graduate Student

Date of Award

2019

Document Type

Thesis

Publication Status

Version of Record

Submission Date

October 2026

Thesis/Dissertation Advisor [Chair]

Nayak, Aditya

Abstract

The thesis objective is to develop protocols that provide analysis and interpretation for data from a pulsed laser serial scanning lidar system for underwater imaging. The specific focus is remotely observing marine organisms in the centimeter size range in the poorly understood mesopelagic realm of the ocean. The Unobtrusive Multi-Static Lidar Imager (UMSLI) system captures an expansive volume scan field with differentiating imaging resolution per planar slice, allowing precise assignment of location for organisms in the field of view. The multi-static highly collimated beams are recorded by a photo-multiplier tube receiver as a time lapse waveform of the returned energy flux, each waveform comprehensibly represents an image pixel in spatially and temporally. Complied lidar waveforms produce an array of returns which signify the magnitude of backscatter from varying sized particles across the observed volume. These volume scans are uniquely evaluated and transformed for each time bin through a processing method which extracts particle characteristics and statistics based on adaptive spatial and temporal techniques. The post processing method aims to greatly extend the capabilities of the lidar imaging system to extract particles. Results of the processing method are presented as particle counts and particle size distributions of the water columns during observed vertical migration periods. Methods are compared with other optical devices for validation, and results are interpreted to better understand the organism distribution in the mesopelagic and their behavior, with respect to diel vertical migrations.

Extent

76 p.

Comments

Includes bibliography.|Thesis (M.S.)--Florida Atlantic University, 2019.|FAU Electronic Theses and Dissertations Collection|Includes bibliography.

Identifier

FA00013396

Format

pdf

Download

Share

COinS