Remote sensing of wetlands in the Lake Whangape catchment, Waikato, New Zealand.

Pathayala Ralalage, Sumedha Kusum Amarasena

Remote sensing of wetlands in the Lake Whangape catchment, Waikato, New Zealand.

dc.contributor.advisor	Brabyn, Lars
dc.contributor.author	Pathayala Ralalage, Sumedha Kusum Amarasena
dc.date.accessioned	2022-02-24T22:14:08Z
dc.date.available	2022-02-24T22:14:08Z
dc.date.issued	2021
dc.date.updated	2022-02-21T07:40:35Z
dc.description.abstract	Wetlands are among the world's most valuable ecosystems. They provide numerous ecological and socio-economic benefits. However, wetlands continue to disappear due to the increasing demand for wetland resources. In New Zealand, more than 90% of the original extent of wetlands has been lost since the mid-eighteenth century. Therefore, legislation has been identified for the protection of wetlands as a matter of national importance. Geographic Information System (GIS) and Remote Sensing (RS) techniques have proven helpful for mapping and monitoring wetland resources. This study aims to understand how RStechniques can classify wetlands in the Lake Whangape catchment, Waikato. The parameters that can be extracted from available data and their effectiveness in the classification process are also studied. Four types of input data are collectively employed in the study. The data types are optical RS data, Synthetic Aperture Radar (SAR) data, a Digital Elevation Model (DEM), and wetland polygons provided by the Waikato Regional Council (WRC). All the steps including, accessing satellite scenes and data processing were performed within Google Earth Engine (GEE) computing platform using JavaScript language. The classification process for this study includes feature extraction, feature selection, model training, classification, and validation. Finally, the accuracy of the classification results is checked visually and statistically. The classification was carried out in two stages. In Stage one, open water, wetland, and non-wetland areas are classified (simple classification). The combined wetlands class is separated into marsh and swamp in the second stage (detailed classification). Based on the results, the Topographic Position Index (TPI) is the most influential parameter in identifying wetlands, while the Modified Normalized Water Index (MNDWI) successfully identifies open water. The overall accuracy reached 91% at the simple classification stage. However, the detailed classification results received comparatively low classification accuracies (the overall accuracy is 76%).
dc.format.mimetype	application/pdf
dc.identifier.uri	https://hdl.handle.net/10289/14756
dc.language.iso	en
dc.publisher	The University of Waikato
dc.rights	All items in Research Commons are provided for private study and research purposes and are protected by copyright with all rights reserved unless otherwise indicated.
dc.subject	Wetlands
dc.subject	GIS
dc.subject	Remote Sensing
dc.subject	Classification
dc.subject	Lake Whangape
dc.subject	Google Earth Engine
dc.subject.lcsh	Wetlands -- New Zealand -- Whangape Lake -- Remote sensing
dc.subject.lcsh	Wetlands -- New Zealand -- Waikato -- Remote sensing
dc.subject.lcsh	Wetlands -- New Zealand -- Whangape Lake -- Classification
dc.subject.lcsh	Wetland management -- New Zealand -- Whangape Lake
dc.subject.lcsh	Wetlands -- New Zealand -- Whangape Lake -- Geographic information systems
dc.subject.lcsh	Earth (Planet) -- Surface -- Remote sensing
dc.title	Remote sensing of wetlands in the Lake Whangape catchment, Waikato, New Zealand.
dc.type	Thesis
pubs.place-of-publication	Hamilton, New Zealand	en_NZ
thesis.degree.grantor	The University of Waikato
thesis.degree.level	Masters
thesis.degree.name	Master of Social Sciences (MSocSc)