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REMOTE SENSING RETRIEVALS FROM SPOT MEASUREMENTS OF AEROSOL OVER PENANG ISLAND, MALAYSIA H. S. Lim, M. Z. MatJafri, K. Abdullah, K. C. Tan, C. J. Wong and N. Mohd. Saleh School of Physics, Universiti Sains Malaysia, 11800 Penang, Malaysia E-mail: [email protected], [email protected], [email protected], [email protected] Tel: +604-6533888, Fax: +604-6579150 Abstract Traditional air pollution monitoring by using ground based instruments cannot provides air pollution over a large spatial scale. Aerosol over Penang Island was mapped with SPOT satellite image in this study. The objective of this study is to evaluate the relationship between SPOT satellite observation and particulate matter of size less than 2.5 micron (PM2.5) parameter. It is based on detection of dark surface targets in the blue band. Only one visible wavelength band were used in this study. The surface reflectance values for the only one visible wavelength were determined based on the information given by using ATCOR2 image processing technique. The atmospheric components were then estimated from the image. A total of 25 ground truth PM2.5 data were collected simultaneously with the acquired satellite image by using a hand held DustTrak Meter and their locations were determined using a hand held Global Positioning System (GPS). The digital numbers of the corresponding in situ data were converted into irradiance and then reflectance. The atmospheric reflectance values was extracted from the satellite observation reflectance values subtrated by the amount given by the surface reflectance. The atmospheric reflectance values were later used for PM2.5 mapping using the calibrated algorithm. An algorithm was developed based on the atmospheric optical characteristic. The developed algorithm was used to correlate the digital signal and the PM2.5 concentration. A good linear correlation between the satellite signal and PM2.5 parameter was found in this study (R > 0.8). Finally, a PM2.5 map was generated using the proposed algorithm. This study indicates that the feasibility of using the visible band from SPOT for PM2.5 mapping. Introduction In the Earth’s atmosphere, natural and anthropogenic sources give rise to highly variable geographical and seasonal aerosol distributions, which are largely confined to the troposphere. Aerosols affect the atmospheric radiative transfer of sunlight directly due to scattering and absorption of radiation at particles, and indirectly by influencing the formation of clouds (Bojinski, et al., 2004). Aerosols in the atmosphere have several important environmental effects. They are a respiratory health hazard at the high concentrations found in urban environments. They scatter and absorb visible radiation, limiting visibility. They affect the Earth's climate both directly (by scattering and absorbing radiation) and indirectly (by serving as nuclei for cloud formation). They provide sites for surface chemistry and condensed-phase chemistry to take place in the atmosphere (Hashim, et al., (2004) and Jacob, 1999). Scattering of solar radiation by gases and atmospheric particulates can limit human visibility in the troposphere; this is the phenomenon known as haze (Hashim, et al., 2004).

Image and Signal Processing for Remote Sensing XIV, edited by Lorenzo Bruzzone, Claudia Notarnicola, Francesco Posa, Proc. of SPIE Vol. 7109 71090Y · © 2008 SPIE · CCC code: 0277-786X/08/$18 · doi: 10.1117/12.800166 Proc. of SPIE Vol. 7109 71090Y-1 2008 SPIE Digital Library -- Subscriber Archive Copy

Although accurate measurements of air quality parameters can be obtained with a conventional measuring technique, such a survey is time consuming and expensive. Consequently, investigators have become interested in the application of remotely sensed data to overcome these limitations. Our objective was to develop an algorithm to estimate the PM2.5 concentration over Penang Island, Malaysia by using remote sensing technique. In this study, we also tested its performance for obtaining PM2.5 data. Study Area The study area is the Penang Island, Malaysia within latitudes 5o 12’ N to 5o 30’ N and longitudes 100o 09’ E to 100o 26’ E. The map of the region is shown in Figure 1. The satellite image was acquired on 31 January 2006. The corresponding PM10 measurements were collected simultaneously during the satellite overpass.

Penang Potoni River

.

George Town

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Figure 1 Study area Algorithm Model An algorithm was developed for PM10 determination. The independent variables are the TM visible wavelengths reflectance and thermal infrared band signals. The equation is given below. A = e0 + e1 Ratm1 + e 2 R atm 2 (1) where A = Particle concentration (PM10) Ratmi= atmopsheric reflectance, i = 1 and 2 are the band number ej= algorithm coefficients, j = 0, 1 and, 2are then empirically determined. The atmospheric reflectance due to molecule, Rr, is given by (Liu, et al., 1996)

Proc. of SPIE Vol. 7109 71090Y-2

Rr =

τ r Pr (Θ) 4µ s µ v

(2)

where τr = Aerosol optical thickness (Molecule) Pr( Θ ) = Rayleigh scattering phase function µv = Cosine of viewing angle µs = Cosine of solar zenith angle We assume that the atmospheric reflectance due to particle, Ra, is also linear with the τa [King, et al., (1999) and Fukushima, et al., (2000)]. This assumption is reasonable because Liu, et al., (1996) also found the linear relationship between both aerosol and molecule scattering. τ P ( Θ) (3) Ra = a a 4µ s µ v where τa = Aerosol optical thickness (aerosol) Pa( Θ ) = Aerosol scattering phase function Atmospheric reflectance is the sum of the particle reflectance and molecule reflectance, Ratm, (Vermote, et al., 1997). Ratm=Ra+Rr

(4)

where Ratm=atmospheric reflectance Rp=particle reflectance Rr=molecule reflectance ⎡τ P (Θ) τ r Pr (Θ) ⎤ + R atm = ⎢ a a ⎥ 4 µ µ 4µ s µ v ⎦ s v ⎣ 1 [τ a Pa (Θ) + τ r Pr (Θ)] R atm = 4µ s µ v

(5)

The optical depth is given by Camagni and Sandroni, (1983), as in equation (6). From the equation, we rewrite the optical depth for particle and molecule as equation (7)

τ = σρs

(6)

where τ = optical depth σ = absorption s= finite path


τ = τ

a

+ τ

r

(Camagni and Sandroni, 1983).

τ r = σ r ρr s τ p =σ pρps

(7a) (7b)

Equations (7) are substituted into equation (5). The result was extended to a three bands algorithm as equation (8) Form the equation; we found that PM10 was linearly related to the reflectance for band 1 and band 2. This algorithm was generated based on the linear relationship between τ and reflectance. Retalis et al., (2003) also found that the PM10 was linearly related to the τ and the correlation coefficient for linear was better that exponential in their study (overall). This means that reflectance is linear with the PM10. 1

[σ a ρ a sPa (Θ) + σ r ρ r sPr (Θ)] 4µ s µ v s R atm = [σ a ρ a Pa (Θ) + σ r ρ r Pr (Θ)] 4µ s µ v s [σ a (λ1 ) PPa (Θ, λ1 ) + σ r (λ1 )GPr (Θ, λ1 )] R atm (λ1 ) = 4µ s µ v s [σ a (λ 2 ) PPa (Θ, λ 2 ) + σ r (λ 2 )GPr (Θ, λ 2 )] R atm (λ 2 ) = 4µ s µ v

R atm =

(8)

P = a 0 R atm ( λ 1 ) + a 1 R atm ( λ 2 )

where P = Particle concentration (PM10) Ratmi= atmopsheric reflectance, i = 1 and 3 are the band number ej= algorithm coefficients, j = 0, 1, 2, … are then empirically determined. Data Analysis and Results Recently most the works on air quality using satellite data have been done using MODIS data. In this study, data from satellite SPOT 5 was used because of its high spatial resolution of 20 m for visible bands. The Landsat TM satellite image was rectified using the second order polynomial coordinates transformation to relate groumd control points in the map to their equivalent row and column positions in the TM scences. A nearest neighbour geometric correction method was applied to the acquired satellite image to ensure that the digital numbers of the image remained the same. The raw satellite image was used for retrieval of surface reflectance. The digital numbers were extracted corresponding to the in-situ data for all the three visible bands and thermal band. Then the digital numbers for the all three visible bands were converted to radiance and reflectance values. It should be noted that the relfectance values at the top of atmosphere was the sum of


the surface reflectance and atmospheric relfectance. In this study, we used ATCOR2 image correction software in the PCI Geomatica 10.1 image processing software for creating a surface reflectance image. Then the reflectance measured from the satellite [reflectance at the top of atmospheric, ρ(TOA)] was subtracted by the amount given by the surface reflectance to obtain the atmospheric reflectance. The atmospheric reflectance was then related to the PM10 using the regression algorithm analysis. In this study, SPOT 5 signals were used as independent variables in our calibration regression analysis. A good result was produced by the proposed model, which achieved the correlation coefficient of about 0.9288. The PM10 map was generated using the proposed algorithm. The generated map was colour-coded for visual interpretation (Figure 2).


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A Figure 2: Map of PM2.5 around Penang Island, Malaysia (Blue < 40 µg/m3, Green = (40-80) µg/m3, Yellow = (80-120) µg/m3, Orange = (120-160) µg/m3, Red = (>160) µg/m3 and Black = Water and cloud area) Conclusions The SPOT 5 satellite image could be used to generate PM10 map over Penang Islands, Malaysia. This technique provided a high spatial resolution PM10 map with promising accuracy. The algorithm developed from optical model of atmosphere can be used for air analysis study. Further research will be carried out to validate the proposed technique. Finally, the proposed technique can monitor the air quality in Penang Island effectively.


Acknowledgements This project was carried out using the Science Fund and USM short term grants. We would like to thank the technical staff that participated in this project. Thanks are extended to USM for support and encouragement.

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References Bojinski, S., Schlapeer, D., Schaepman. M., Keller, J. and Itten, K., “Aerosol mapping over land with imaging spectroscopy using spectral autocorrelation,” International Journal of Remote Sensing, 25(22), 5025–5047 (2004). Camagni, P. and Sandroni, S., “Optical Remote sensing of air pollution,” Joint Research Centre, Ispra, Italy, Elsevier Science Publishing Company Inc (1983). Hashim, M. Kanniah, K.D., Ahmad, A., Rasib, A. W., and Ibrahim, A. I., “The use of AVHRR data to determine the concentration of visible and invisible troposheric pollutants originating from a 1997 forest fire in southeast Asia,” International Journal of Remote Sensing, 25(21), 4781-4794 (2004). Jacob, D. J., “ Introduction to Atmospheric Chemistry,” Princeton University Press Princeton, New Jersey (1999). Fukushima, H., Toratani, M., Yamamiya, S. and Mitomi, Y., “Atmospheric correction algorithm for ADEOS/OCTS acean color data: performance comparison based on ship and buoy measurements,” Adv. Space Res, 25(5), 10151024 (2000). King, M. D., Kaufman, Y. J., Tanre, D. dan Nakajima, T., “Remote sensing of tropospheric aerosold form space: past, present and future,” Bulletin of the American Meteorological society, 2229-2259 (1999). Liu, C. H., Chen, A. J. and Liu, G. R., “An image-based retrieval algorithm of aerosol characteristics and surface reflectance for satellite images,” International Journal Of Remote Sensing,” 17 (17), 3477-3500 (1996). Retalis, A., Sifakis, N., Grosso, N., Paronis, D. and Sarigiannis, D., “Aerosol optical thickness retrieval from AVHRR images over the Athens urban area,” [Online] available: http://sat2.space.noa.gr/rsensing/documents/IGARSS2003_AVHRR_Retalisetal_ web.pdf (2003). Vermote, E., Tanre, D., Deuze, J. L., Herman, M. and Morcrette, J. J., “Second Simulation of the satellite signal in the solar spectrum (6S),” [Online] available: http://www.geog.tamu.edu/klein/geog661/handouts/6s/6smanv2.0_P1.pdf (1997).
