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Estimating Chlorophyll-a Concentration from Remotely Sensed Data in East Coast of Peninsular Malaysia

Estimating Chlorophyll-a Concentration from Remotely Sensed Data in East Coast of Peninsular Malaysia

A. Asmat
Program of Environmental Technology
Faculty of Applied Science, University Technology MARA
Shah Alam, Selangor
[email protected]

S. B Mansor
Faculty of Engineering
Universiti Putra Malaysia,Serdang, Selangor
[email protected]

M. I. Mohamed
Faculty of Science and Environmental Studies
University Putra Malaysia, Serdang, Selangor
[email protected]

M. R. Mispan
Strategic, Environment and Natural Resources
Research Centre (SENR), MARDI
[email protected]

The evolving capabilities of satellite sensors and data processing techniques provide a promising tool towards the development of fish forecasting and management techniques. The light absorbing pigment collectively known as chlorophyll-a is commonly used by oceanography as an index of phytoplankton concentration (Mansor et.al, 2001).

The remote sensing technology has been applied widely in the developed countries in relation to fish productivity (Mansor et.al, 2001). This technique has been applied in Malaysia in upgrading the industrial of fishery. Due to these, the integration between field sampling and remote sensing technique should be improved in order to improve the fishing productivity along the coastal area of Malaysia. The use of satellite remote sensing to provide synoptic measurements of the ocean is becoming increasingly important in fishing industry.

Several works on extensively chlorophyll-a can be found in Harding (1992) Mayo and Gitelson (1995), Hirawake (1996). However, these models were not for tropical region especially for Malaysian waters. Mansor et.al (2001) has highlighted the need of operational algorithm for estimation of chlorophyll-a concentration because currently there is no operational algorithm to extract chlorophyll-a for Malaysian waters.

In view of this problem this research focuses on measuring the concentration of chlorophyll-a in EEZ of East Coast Peninsular Malaysia from SeaWiFS data. In order to achieve this objective it is essential to determine an empirical relation between the chlorophyll-a and the radiances values recorded by the sensor. This study used SeaWiFS satellite data combined with in-situ data measurement of chlorophyll-a concentration in east coast of Peninsular Malaysia. Algorithm to estimate the chlorophyll-a concentration in the South China Sea generated by computation based on empirical method using radiance ratio of SeaWiFS channel.

Data Acquisition

Study Area

The study area is located at the Economic of Exclusive Zone off the east coast of Peninsular Malaysia. The area lies in an equatorial region dominated by the northeast monsoon (October through March) and the southwest monsoon (May through September). Figure 1 shows the location of the study area.



Figure 1: Location of the study area lies between 102°E-105°E and 4.5°N- 6°N.



Estimating Chlorophyll-a Concentration from Remotely Sensed Data in East Coast of Peninsular Malaysia

Materials

  • Sea truth
    The data from the sea truth campaigned of 24th August 2000 until 29th August 2000 were applied to obtain the correlation between chlorophyll-a concentration (mg/m3) and the radiance values in chosen channel of SeaWiFS image. The amount of concentration of chlorophyll-a was calculated based on blue, blue/green and green (442nm-555nm) reflectance ratios, this was done by selecting representative radiance values corresponding to in-situ data measurements.
  • Image satellite data
    For SeaWiFS data, are uses in
    this study are Level 1A (LAC) data dated on 24th August 2000 and
    29th August 2000 with FTP format can be downloaded from the
    Internet. Both images are cloudy and only apart of sampling point
    can be furthered analysis.

Methodology

This study can segregated into two parts, firstly, chlorophyll-a sampling and analysis and secondly to map the chlorophyll-a concentration from SeaWiFS data.

Laboratory analysis

Seawater samples were taken using Van Dorn Sampler for maximum chlorophyll-a layer according the information from acoustic equipment. Chlorophyll-a concentrations were measured in the laboratory.

The chlorophyll-a concentrations were estimated by using the spectrophotometer 6300 Jensey at the Aquatic laboratory. The chlorophyll-a concentration was measured by using the technique and calculation described by Parsons et.al (1984). Five litres of seawater were filtered through a filter paper (Millipore, size 0.5 mm). As the seawater is being filtered, a few drops of suspension of magnesium carbonate were added to prevent acidity on the filter. Pigments were extracted from the filters in 90% acetone. The wavelengths involved are .

All the extinction was corrected for a small turbidity blank by subtracting the from the , and absorptions. Then, the amounts of pigments in the original seawater sample were calculated using the equation given below. For Chlorophyll-a (Ca):

Ca = 11.85E664 – 1.54E647 – 0.08E630 …… (3.1)

Where:
E stands for the absorbance at different wavelength obtained above (Corrected by the 750 nm reading).
Ca is the amount of chlorophyll in mg / ml

Chlorophyll-a is Chlorophyll-a concentration, and its obtained from the following equation:

Chlorophyll-a(mg/m3) = Cxn/V x 10 …… (3.2)

Where:
C are substituted for Chlorophyll-a from equation (3.1)
n is the volume of acetone in ml (10 ml)

V is the volume of seawater in l (5l)

Image Processing

Image processing for SeaWiFS data in this study, was processed using an image processing software PCI Easi/Pace Version 8.0 and ENVI Version 3.6. The main operation in this study can be categorized onto four types of processing. There are geometric correction, radiometric correction, chlorophyll-a extraction and finally mapping chlorophyll-a concentration.

Chlorophyll-a Extraction

Based from curve fitting, a linear regression analysis was carried out between water parameters and spectral radiances were performed and their significant level examined. In this study, spectral attention was given to channel 2 (445nm), channel 3 (490nm) and channel 5 (555nm). Linear regression technique was applied between ratio of reflectance values and chlorophyll-a concentration samples from in-situ sampling. The best correlation coefficient (r2), would be used to extract the distribution of chlorophyll-a concentration.


Estimating Chlorophyll-a Concentration from Remotely Sensed Data in East Coast of Peninsular Malaysia

Results

Different algorithms were applied to the images. The in-situ data and the measured chlorophyll-a was highly correlated using empirical algorithm using level 1 data with r2 = 0.9472. This algorithm is however could be applied to that particular site. Table 1 shows the Correlation between In-situ chlorophyll-a and measured chlorophyll-a extracted from models. SeaBAM algorithm (NASA, 1997) had been modified to suit with the local condition and produced good correlation with in-situ data r2 =

0.924. Morel model (Morel, 1996) produced r2 = 0.8589 between In-situ chlorophyll-a and measured chlorophyll-a extracted from models.

Table 1: Correlation between In-situ
chlorophyll-a and measured
chlorophyll-a extracted from models.

Model
Empirical
(Level 1 Data)
Empirical
(Level 2 Data)
Morel
(Level 1 Data)
Seabam
(Level 1 Data)

R2
0.9472
0.798
0.8589
0.924

Discussion

The
results show that the empirical model has significantly highest
correlation to the in-situ data. SeaWiFS level 1 data gives
correlation of and level 2 data gives correlation of . The ratio
between channel 2, channel 3 and channel 5 is a good combination to
extract chlorophyll-a from SeaWiFS data. For SeaWiFS data, ratio
derived using blue channel (443nm), blue-green channel (490nm) and
green channel (555nm) was used to extract the chlorophyll-a
concentration from SeaWiFS data.

(a)


(b)


(C)

(d)

Figure 1(a) (b): Chlorophyll-a concentration map measured using Empirical model level 1 data on 24th August 2000 and 29thAugust 2000.
Figure 2(c) (d): Chlorophyll-a concentration map measured using SeaBAM model level 1 data on 24th August 2000 and 29thAugust 2000.

The ratio of ((443 – 555)/490) was used for implementing the empirical algorithm (linear regression) and Morel algorithm. For SeaBAM algorithm, the ratio of log10(443/555) was applied. The Morel and SeaBAM algorithms were modified to suit with the Tropical area. Details discussion can be found in Arnis (2001). Figure 1 (a) (b) and figure 2 (c) (d) show the chlorophyll-a concentration map.

From the map, the highest chlorophyll-a concentrations are found in the coastal waters of Terengganu and decreased to offshore. It can be concluded that a remote sensing technique with suitable extracting chlorophyll-a algorithm offers a useful technique for estimating of chlorophyll-a concentration.

Conclusion
The study proved that the remote sensing technique is a very useful tool for studying the distribution of chlorophyll-a concentration in a large water body area such as the Exclusive Economic Zone. In this work, channel 2, channel 3 and channel 5 of SeaWiFS data have been found to be the most suitable channel to extract the chlorophyll-a concentration from SeaWiFS data. Correlation analysis between remotely sensed data and chlorophyll-a in-situ data has indicated the possibility of mapping chlorophyll-a concentration with some degrees of success. The strong correlation of radiance ratio corresponding to above channel with in-situ data provided the basis for the development of equation and constant for the estimated chlorophyll-a concentration in South China Sea. However, the use of satellite remote sensing for mapping chlorophyll-a concentration in South China Sea is limited by the presence of cloud cover. Despite these advantages, satellite data are preferable to filed measurements if one aim is to follow the temporal of phytoplankton over large area.

References

  • Arnis, A.. Determination of chlorophyll-a concentration from SeaWIFS data in the South China Sea. M.S Thesis, Universiti Putra Malaysia, Malaysia (2001).
  • Harding, L.W., Itsweire, E.C., and Esaias, W.E.. Determination of phytoplankton chlorophyll concentration in the Chesapeake with Aircraft remote sensing, International Journal of Remote Sensing, 40: 79-100 (1992).
  • Hirawake, T., Satoh, H., Tsutomu, M., and Takashi, I.. In-water algorithms for estimation of chlorophyll-a and primary production in the Arabian Sea and the eastern Indian Ocean. SPIE. 2963: 29301. (1996).
  • Mansor, S.B, Tan Chu Knee., and M.I.H Ibrahim. Proceeding of 22nd Asian Conference on Remote Sensing. Satellite fish forecasting in South China Sea (2001).
  • Mayo, M., Gitelson, A., and Yacobi. Y.Z., Ben Avraham.. Chlorophyll distribution in Lake Kinneret determined from Landsat Thematic Mapper data. International Journal of Remote Sensing. (16) 1: 175-182 (1995).
  • Morel, A. Optical properties of oceanic case 1 water (revisited). Ocean Optic XIII. SPIE. 2963:108- 114 (1996).
  • NASA. Bio-Optical Algorithm Mini-Workshop (1997).