Home Articles Establishing a global algorithm for water quality mapping from multi-dates images

Establishing a global algorithm for water quality mapping from multi-dates images

PDF

Establishing a global algorithm for water quality mapping from multi-dates images

H. S. Lim

H. S. Lim, M. Z. MatJafri, K. Abdullah and M. N. A. Bakar
School of Physics
Universiti Sains Malaysia,
11800 Penang

Introduction
Water quality assessment of ocean and inland waters using satellite data has been carried
out since the first remote sensing satellite Landsat-MSS has been operational (Thiemann
and Kaufmann, 2000). Many researchers used satellite images in their investigations
[Allee, et al., (1999), Forster, et al., (1993) and Ritchie, et al., (1990)]. However, in this
study we used airborne remote sensing. A digital camera was used as a sensor to capture
the images at altitude of 8000 feet. The main objective of the present study is to update
our proposed algorithm for mapping total suspended solids in marine environments using
digital camera images from previous study (MatJafri, et al., 2002). We also attempted to
develop a simple correction technique for the airborne images acquired from different
dates, locations, and different flying altitudes. Data from seven image scenes were
combined in the present analysis. This study also proposed a cheaper and economical
alternative to overcome the problem of obtaining cloud-free scenes in the Equatorial
region.

Study Area
In this study, a Kodak DC290 digital camera was used as a sensor and a Cessna 172Q
aircraft was used as a platform to capture images of the study areas. The study areas were
the Prai, Muda, and Merbok river estuaries, located within latitudes 5o 22′ N to 5o 24’N
and longitudes 100o 21’E to 100o 23’E; 5o 34′ N to 5o 36’N and longitudes 100o 19’E to
100o 21’E; and 5o 39′ N to 5o 41’N and longitudes 100o 20’E to 100o 24’E, respectively.
The images were captured from the altitude of 3,000 ft on 28 October 2001 and 8,000ft
on 9 March 2002 for Prai River estuary, 8,000ft on 20 January 2002 and 9 March 2002
for Muda River estuary, and also 8,000 ft on 5 May 2002, 25 October 2002 and 22 March
2003 for Merbok River estuary. The study areas are shown in Figure 1. Water samples
were collected from a small boat within the areas covered by the scenes simultaneously
with the airborne image acquisition and later analyzed in the laboratory.



(Source: Microsoft Corp., 2001.)
Figure 1. Study area

PDF

Establishing a global algorithm for water quality mapping from multi-dates images

Optical model of water
A physical model relating radiance from the water column and the concentrations of the
water quality constituents provides the most effective way of analyzing remotely sensed
data for water quality studies. Reflectance is particularly dependent on inherent optical
properties: the absorption coefficient and the backscattering coefficient. The irradiance
reflectance just below the water surface, R(l), is given by Kirk (1984) as

R(l) = 0.33b(l)/a(l)   (1)

where
l = the spectral wavelength
b = the backscattering coefficient
a = the absorption coefficient

The inherent optical properties are determined by the contents of the water. The
contributions of the individual components to the overall properties are strictly additive
(Gallegos and Correl, 1990). For a case involving two water quality components, i.e.
chlorophyll, C, and suspended sediment, P, the simultaneous equations for the two
channels given by Gallie and Murtha (1992) can be expressed as

where
bbw(i) = backscattering coefficient of water
bbc* = specific backscattering coefficients of chlorophyll
bbp = specific backscattering coefficients of sediment
aw(i) = absorption coefficient of water
ac* = specific absorption coefficients of chlorophyll
ap* = specific absorption coefficients of sediment
C = chlorophyll
P = suspended sediment

Regression Algorithm
TSS concentration can be obtained by solving the two simultaneous equations to get the
series of terms R1 and R2 that is given as

where aj, j = 0, 1, 2, … are the coefficient for equation (3) that can be solved empirically
using multiple regression analysis. This equation can also be extended to the three-band
method given as

where the coefficient ej, j = 0, 1, 2, … can also be solve empirically.

PDF

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.

Table 1. Correlation coefficients of equation (2)

Coefficients ao a1 a2 a3 a4 a5 a6 a7 a8 a9
Values 43.794 -2.021 -9.964 10.710 0.121 -7.278×10–2 0.279 -1.640×10-2 -0.141 -0.151



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:

PDF

Establishing a global algorithm for water quality mapping from multi-dates images

Verification analysis
For the verification analysis, sea truth data were divided into two groups, half of the
numbers of water samples were radom selected for algorithm calibration and the another
half of the numbers of water samples were radom selected for verification analysis. The
calibrated algorithm was produced high accuracy with R value of 0.9685 and RSM value
of 13.19 mg/l in the verification analysis. Figure 6 shows the relationship of the measured
TSS versus estimated TSS concentration for algorithm calibration analysis. Figure 7
shows the relationship of the measured TSS versus estimated TSS concentration for
verification analysis.



Figure 6. Measured TSS versus estimated TSS concentration for algorithm calibration
analysis



Figure 7. Measured TSS versus estimated TSS concentration for verification analysis
Conclusion

This study gives a cheaper way to overcome the problem of difficulty of obtaining cloudfree
scenes at the Equatorial region. Traditional water quality monitoring method based
on water sample collection is time consuming and requires a high operating cost. It is
good for determined the water pollution for real time. The proposed algorithm is
considered superior to other tested algorithms based on the values of the correlation
coefficient, R=0.97 and root-mean-square error, RMS=15mg/l. This indicates that the
TSS maps can be generated using digital camera imagery with the proposed algorithm.

Acknowledgement
This project was carried out using the Malaysian Government IRPA grant no. 08-02-05-
6011 and USM short term grant FPP2001/130. We would like to thank the technical staff
and research officers who participated in this project. Thanks are extended to USM for
support and encouragement.

References

  • Allee, R.J., and Johnson, J.E., (1999). Use of satellite imagery to estimate surface
    chlorophyll-a and Secchi disc depth of Bull Shoals, Arkansas, USA.
  • International Journal of Remote Sensing, 20, 1057-1072.
  • Forster, B.C., Xingwei, I.S., and Baide, X., 1993, Remote sensing of water quality
    parameters using landsat TM. International Journal of Remote Sensing, 14,
    2759-2771.
  • Gallegos, C.L., and Correl, D.L., (1990). Modeling spectral diffuse attenuation,
    absorption and scattering coefficients in a turbid estuary. Limnology and
    Oceanography, 35, 1486-1502.
  • Gallie, E.A., and Murtha, P.A., (1992). Specific absorption and backscattering spectra for
    suspended minerals and chlorophyll-a in Chilko Lake, British Columbia.
  • Remote Sensing of Environment, 39, 103-118.
  • Kirk, J. T. O. (1984). Dependence of relationship between inherent and apparent optical
    properties of water on solar altitude. Limnology and Oceanography, 29,
    350356.
  • Lopez Garcia, M.J., 1990, A multi-temporal study of chlorophyll-a concentration in the
    Albufera lagoon of Valencia, Spain, Using Thematic Mapper data.
  • International Journal of Remote Sensing, 11, 301-311.
  • Microsoft Corp., Map of Kedah, Malaysia. (2001). [online].
  • M. Z. MatJafri, K. Abdullah, H. S. Lim, M.N. AbuBakar, Z.B. Din, and S. Marshall,
    (2002). Algorithm For Total Suspended Solids Mapping Using Digital
    Camera Images. Proceeding in SPIE’s Third International Asia-Pacific
    Environment Remote Sensing Symposium – Remote Sensing Of The
    Atmosphere, Ocean, Environment, and Space: Ocean Remote Sensing Dan
    Applications, 23 – 27 October 2002, HangZhou, China.
  • Ritchie, C.J., Cooper, C.M., and Schiebe, F.R., 1990, The relationship of MSS and TM
    digital data with suspended sediment, chlorophyll and temperature in Moon
    Lake, Mississippi. Remote Sensing of environment, 33, 137-148.
  • Thiemann, S. and Kaufmann, H. (2000). Determination of chlorophyll content and
    trophic state of lakes using field spectrometer dan IRS-1C satellite data in the
    Mecklenburg Lake District, Germany. Remote Sensing of Environment, 73,
    227 235.