Remote sensing of dynamics and aboveground biomass of seagrass in Tauranga Harbor (Bay of Plenty), New Zealand
type: CreativeWork
description: Seagrasses are angiosperm plants that are completely adapted to life in seawater. They are distributed widely across the climatic regions, ranging from the tropics to temperate regions, in both inter-tidal and sub-tidal zones. Multiple ecosystem services are recognized as being supported by seagrass meadows, which recently has included appreciation role in carbon sequestration. Seagrass meadows, however, have been degraded in both terms of area and habitat quality across the globe, leading to a significant loss of ecosystem services and human livelihood support. This ongoing degradation has resulted in an urgent need to develop tools for assessing the temporal changes of extant meadows and accurate estimation of seagrass biological parameters, which will contribute to a sustainable conservation strategy into the future. This thesis describes the use of a range of freely available Earth observation products, including multi-spectral imagery from Landsat and Sentinel-2, and synthetic aperture radar (SAR) products from Sentinel-1, coupled with a range of machine learning (ML) and meta-heuristic optimization algorithms, to develop novel and advanced techniques for remote sensing of seagrass. The work used field validation data from Tauranga Harbor, New Zealand, and specifically targeted mapping, change detection, and estimation of seagrass distribution and biomass. The relatively small and patchy meadows of Zostera muelleri in this harbor can be mapped using a three-category classification (dense, sparse and none) with up to 91% accuracy for dense and 75% for sparse meadows using the machine learning algorithm Rotation Forest with Sentinel-2 imagery. Despite a slightly lower accuracy (90%), the algorithm Canonical Correlation Forest also shows merit for categorical seagrass mapping. Historic Landsat multispectral satellite data used with ML models was able to map accurately the change in distribution of seagrass meadows over 29 years (1989 - 2019). For this binary mappi…
abstract: Seagrasses are angiosperm plants that are completely adapted to life in seawater. They are distributed widely across the climatic regions, ranging from the tropics to temperate regions, in both inter-tidal and sub-tidal zones. Multiple ecosystem services are recognized as being supported by seagrass meadows, which recently has included appreciation role in carbon sequestration. Seagrass meadows, however, have been degraded in both terms of area and habitat quality across the globe, leading to a significant loss of ecosystem services and human livelihood support. This ongoing degradation has resulted in an urgent need to develop tools for assessing the temporal changes of extant meadows and accurate estimation of seagrass biological parameters, which will contribute to a sustainable conservation strategy into the future. This thesis describes the use of a range of freely available Earth observation products, including multi-spectral imagery from Landsat and Sentinel-2, and synthetic aperture radar (SAR) products from Sentinel-1, coupled with a range of machine learning (ML) and meta-heuristic optimization algorithms, to develop novel and advanced techniques for remote sensing of seagrass. The work used field validation data from Tauranga Harbor, New Zealand, and specifically targeted mapping, change detection, and estimation of seagrass distribution and biomass. The relatively small and patchy meadows of Zostera muelleri in this harbor can be mapped using a three-category classification (dense, sparse and none) with up to 91% accuracy for dense and 75% for sparse meadows using the machine learning algorithm Rotation Forest with Sentinel-2 imagery. Despite a slightly lower accuracy (90%), the algorithm Canonical Correlation Forest also shows merit for categorical seagrass mapping. Historic Landsat multispectral satellite data used with ML models was able to map accurately the change in distribution of seagrass meadows over 29 years (1989 - 2019). For this binary mappi…
author: Ha, Nam-Thang
keywords: Seagrass, Mapping, Change detection, Aboveground, Sentinel, Landsat, Machine learning, Metaheuristic, Sar, Radar, Webgis, Qgis, Saga gis
license: https://researchcommons.waikato.ac.nz/bitstreams/b0e01874-ddeb-43c5-b976-39c7480f71e0/download
date published: 2021
date modified: 2026-07-24T05:57:30Z
provider: University Waikato
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: The University of Waikato