Assessing the Extent of Water Overflows and Vegetation Dieback along Ok Tedi Mine Affected River Systems (Ok Tedi and Fly Rivers, Papua New Guinea)

Author: Willie Kurie

Kurie, Willie, 2017 Assessing the Extent of Water Overflows and Vegetation Dieback along Ok Tedi Mine Affected River Systems (Ok Tedi and Fly Rivers, Papua New Guinea) , Flinders University, School of the Environment

This electronic version is made publicly available by Flinders University in accordance with its open access policy for student theses. Copyright in this thesis remains with the author. This thesis may incorporate third party material which has been used by the author pursuant to Fair Dealing exceptions. If you are the owner of any included third party copyright material and/or you believe that any material has been made available without permission of the copyright owner please contact with the details.


Mining has been one of humankind’s most impactful activities, affecting human well-being and transforming the natural environment and landscapes. Its high waste-to-product ratio necessitates assessments of mining wastes and monitoring of the environmental health for damage mitigation and management. Environmental monitoring and assessment requires up-to-date data, and approaches or techniques applicable for detecting changes across a range of time spans, especially between two or more dates. Satellite imagery, GIS, and remote sensing techniques facilitate the detection of environmental changes. This is particularly important for large catchments that are remote, and/or hindered by climate and geography. This study uses Landsat Imagery from three sensors: Multi Spectral Scanner (MSS), Thematic Mapper (TM) and Operational Land Imager (OLI), to provide datasets to assess changes in the vegetation, hydrology, and sedimentation along the Ok Tedi and Fly River floodplains of Western Province, Papua New Guinea. Images of the same location taken in 1984, 1988, 1996, 2004, and 2015 were processed for analysis with Image Classification (Unsupervised Classification), Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), and Image Differencing. Feature types were grouped and quantified using the classification technique. Vegetation health and structure were assessed using NDVI, and NDWI/MNDWI were used to study the water bodies and sediments/built-ups. Image differencing was used to detect the changes that occurred during the span of years from1984-1988, 1988-1996, 1996-2004, and 2004-2015. Analysis of the results of Unsupervised Classification, NDVI, NDWI/MNDWI, and Image Differencing indicates that there were changes in the features on each image set; changes of one feature type to another, changes in brightness values, and increase or decrease in quantity of each feature type. The results of the NDVI and NDWI/MNDWI show drastic changes in the brightness values in the floodplains of Ok Tedi and Fly Rivers where mine wastes have been deposited, when compared with a control. These features along the Ok Tedi and Fly River floodplains have reacted to the mining wastes. This study also confirms the past studies that showed reduced vegetation health and dieback, and sedimentation. The latest changes, which are the stabilization and declining of sedimentation and vegetation dieback, show reaction to the mitigation processes already put in place by Ok Tedi Mining Limited and the government of Papua New Guinea. These findings can be improved with a non-optical based satellite imagery such as SPOT -5/6 that reduces the impact of the cloud and shadow cover for accuracy and precision. Furthermore, other image classification techniques, such as Object-based classification, and ground-truthing, could better classify and quantify each feature class or types accurately.

Keywords: Change Detection, Environment, Landsat Imagery, MNDWI, NDVI, NDWI, Mining Wastes, Remote Sensing
Subject: Environmental Science thesis

Thesis type: Masters
Completed: 2017
School: School of the Environment
Supervisor: Stephen Fidles and Prof. Andrew Millington