Author: Nadeeka Wickramasinghe
Wickramasinghe, Nadeeka, 2017 Surface water inundation mapping by remote sensing for Milingimbi Island, Northern Territory, Flinders University, School of the Environment
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Surface water is the most accessible source of water for humans. It is affected by both climate change and human activities. In situ data are not regularly available for detecting the changes in surface water. Satellite remote sensing and GIS techniques facilitate the investigation of surface water changes and overcome the lack of in situ data. The goal of this study was to use images of Landsat 5 Thematic Mapper (TM), Landsat 8 Operational Land Imager (OLI) and inundation maps from European Commission Joint Research Centre Global Surface Water (EC JRC-GSW) monthly water history database to extract and assess accuracy of mapping surface water in Milingimbi Island, Northern Territory, Australia during the period from 1987 to 2016. On the island, the spatial distribution of shallow surface water inundation of salty land is changing frequently. No previous study has been performed on surface water inundation on Milingimbi Island. Initially, surface water area was extracted from thirteen images by unsupervised and supervised image classifications, Normalised Difference Water Index (NDWI), and Modified Normalised Difference Water Index (MNDWI). Surface water for each respective month was also extracted from the EC JRC-GSW monthly water history maps. Then surface water area was extracted by supervised classification from another 11 images for both wet season (Dec- April) and dry season (May- Nov). During the wet season, cloud cover has affected almost all the wet images and the possibility to extract inundation areas during the wettest moments was limited. The extracted water area from unsupervised, supervised classifications and MNDWI methods was very similar, while the area based on the NDWI method and EC JRC-GSW corresponded closely. There were significant differences in area between the two groups. The accuracy assessment showed the highest accuracy for the supervised classification. One of the study objectives of using EC JRC-GSW data as a time series of monthly surface water area for investigating the surface inundation process was not successful. For wet season inundations, the correlation (although relatively low) between surface water area and rainfall was comparatively best with sixty days cumulative rainfall. Most of the dry season inundations were observed on days with zero or insignificant rainfall. Spatial distribution of inundations were mapped separately for wet and dry seasons. The inundation areas of higher frequency are different in wet and dry season. There is no sea level monitoring station in Milingimbi to investigate the correlation of sea level and dry season inundations. When referred to nearest SEAFRAME (SEA-level Fine Resolution Acoustic Measuring Equipment) station in Darwin, the sea level is the highest during the months of October and November. This study recommends use of non-optical satellite remote sensing for improving future inundation mapping in Milingimbi Island in investigating wet season characteristics and monitoring the sea level, and establishing accurate elevation data to investigate dry season inundation.
Keywords: Surface water, Remote sensing, Unsupervised / Supervised Classification, NDWI, MNDWI
Subject: Environmental Science thesis
Thesis type: Masters
Completed: 2017
School: School of the Environment
Supervisor: Prof. Okke Batelaan