This paper presents the utilities of remote sensing technique for water quality assessment in Kelantan Delta, Malaysia. Remote sensing is one of the effective methods for water quality monitoring through image analysis of study area. Spectral reflectance signatures of Kelantan Delta were measured from 20 stations using ASD Handheld
spectroradiometer from regions with different turbidity level. Water samples collected from these stations were taken to the laboratory for measure turbidity in Nephelometric Turbidity Unit (NTU). The objective of this study is to examine the potential of ALOS on Japanese Earth Observing Satellite (JEOS) for assessing water quality in Kelantan Delta. There is a large correlation between NTU and the in-situ reflectance at 500 - 620 nm (maximum spectra band between 300 and 1100 nm) is shown by multiple linier regression model, resulting from increasing of turbidity levels, was developed and applied to ALOS band 2 and band 3 (0.42-069 nm). A simple atmospheric correction, based on darkest pixel technique was performed in this study. The ALOS data provides accurate estimates of the mean water quality (R2 = 0.95 and RMSE = 2.26 NTU). The result acquired is reliable to estimate of water quality values for the Kelantan Delta and its implication for future operation.
Remote sensing sensors are now able to deliver greatly increased amount of information with the used of high resolution
sensor. But high or very high resolution sensors lead to noise in generally homogeneous classes as the data contains
increased information with more internal variability. Conventional classification methods commonly cannot handle the
complex landscape environment in the image. The result of each method has often "a salt and pepper appearances"
which is a main characteristic of misclassification. It seems clear that information from neighboring pixels should
increase the discrimination capabilities of the pixel based measured, and thus, improve the classification accuracy and
the interpretation efficiency. This information is referred to as the spatial contextual information. In this paper, we shall
present a contextual classification method based on a frequency-based approach for the purpose of land cover mapping.
Additionally, classification maps are produced which have significantly less speckle error. In order to evaluate the
performances of the classifier, 9 different window sizes ranging from 3x3 to 19x19 with an increment of 2 is tested.
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