Establishing a global algorithm for water quality mapping from multi-dates images Data Analysis and Result Seven sets of the colour images were selected for calibration analysis. Figure 2 shows the images that were used in this study. 
 Figure 2. Images of the study ares: (a) the oblique image of the Prai River estuary captured on 28 October 2001 from altitude of 3,000ft, (b) the oblique image of the Muda River estuary captured on 20 January 2002 from altitude of 8,000ft, (c) the vertical image of the Prai River estuary captured on 9 March 2002 from altitude of 8,000ft. (d) the vertical image of the Muda River estuary captured on 9 March 2002 from altitude of 8,000ft, (e) the oblique image of the Merbok River estuary captured on 5 May 2002 from altitude of 8,000ft, (f) the vertical image of the Merbok River estuary captured on 26 October 2002 from altitude of 8,000ft and (g) the vertical image of the Merbok River estuary captured on 22 March 2003 from altitude of 8,000ft. The colour images were separated into three bands, namely, red, green and blue bands for multispectral analysis. The image of Figure 2(a) was taken at an altitude of 3,000ft, while the rest were taken from altitude of 8,000ft. The image of Figure 2(a), Figure 2(b) and Figure 2(e) were taken obliquely. The view angle correction was first performed to the oblique images to correct for the angular dependence of image brightness. In this study, a contour map of the image brightness was plotted and the view angle effect was removed based on the map. Then, the multi-date data were corrected to remove the difference in atmospheric effects between scenes using radiometric normalization technique. The vertical image of Figure 5(c) was selected as the reference image and the average brightness of the chosen target; in this case, grass vegetation was noted. We assumed the reflectance of these targets did not change with time. This assumption is in accordance with the methods proposed by Lopez, (1990). The average brightness values of grass in other images were then recorded. The difference from the reference value was used to correct for each scene. All the brightness values of the other five images of Figure 5 (a), (b), (d), (e), (f) and (g) were adjusted using this normalization technique. This normalization technique forced the images to have the same atmospheric conditions and the effects due to different camera altitudes have also been removed. The corrected scenes were then regressed with the sea-truth data to obtain all the coefficients of equation (4) in the proposed multi-date, multi-area, and multi-altitude analysis. Image rectification was performed using second order polynomial tranformation equation. The DN values corresponding to the water sample locations were extracted from all the images. The relationship between TSS and DN of the data set is shown in Figure 3. The coefficients values are listed in Table 1. Figure 4 shows the proposed algorithm produced high correlation coefficient (R) and low root-mean-square (RMS). The TSS maps were generated using the proposed calibrated algorithm. The generated maps were then filtered by using 5 by 5 pixels average for removing random noise. Finally, the generated TSS maps were colour-code for visual interpretation as shown in Figure 5. This indicates the reliability of the calibrated proposed algorithm for TSS mapping using digital camera imagery.
 Figure. 3 TSS concentration versus digital number (DN).  Figure 4. Measured versus estimated TSS concentration 
Figure 5. TSS map for the study area estimated using the proposed algorithm. Colour code: |