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Prediction of Total Suspended Sediments in Penang Waters Using Remote Sensing

Tan, Sek Aun (2004) Prediction of Total Suspended Sediments in Penang Waters Using Remote Sensing. Masters thesis, Universiti Putra Malaysia.

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Total suspended sediments (TSS) are one of the main causes of pollution in the country’s coastal areas. Land-based loaded and seabed resuspension are two main sources of TSS in coastal and estuary areas. In this study, remote sensing techniques were used to predict TSS concentrations. Landsat-5 TM satellite imagery was used simultaneously with groundtruth data collected on 27th May 2000 in the Penang Straits. Various image processing steps such as geometric correction, radiometric correction and atmospheric correction were carried out in this study. Initially, digital number (DN) of imagery was corrected and converted into reflectance values for algorithm development. Subsequently combinations of various radiometric correction methods were used in this study to reduce the errors from various sources prior to statistical analysis. Data generated from corrected satellite imagery and TSS concentrations measured from field sampling were compared and tested using statistical analysis. Only the best-fit algorithm developed in this study was selected to predict the TSS concentrations from satellite imagery. Out of the six algorithms derived, Algorithm 6 showed the best correlation with the ground-truth data (R2 value of 0.9755 and RMSE value of 4.0107). The developed algorithm was then applied to predict the TSS concentrations on historical Landsat imagery acquired on 1st February 1993. The historical satellite image was normalized and converted to reflectance for the biophysical study. Besides the derived algorithm, models suggested by other researchers were tested in this study. However, the Algorithm 6 showed the best results in predicting TSS concentration for the Penang waters. The predicted TSS concentrations distribution maps were generated and compared with the GIS platform.

Item Type:Thesis (Masters)
Chairman Supervisor:Associate Professor Shattri Bin Mansor, PhD
Call Number:FK 2004 77
Faculty or Institute:Faculty of Engineering
ID Code:231
Deposited On:24 May 2008 03:40
Last Modified:27 May 2013 14:46

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