A Summary of Detecting Air Pollution by Optical Satellite Images

Optical satellite images have been used in many fields but researching aspects and applications have only concentrated in terrain mapping without delving into studying and applying to establish thematic maps – one of remote sensing technology applied strong postures.

This article describes the study results about mapping air environment pollution based on processing of spectral and geometric Landsat ETM+ and SPOT images for 2 areas in Vietnam (Hanoi and Cam Pha). 1. INTRODUCTION Optical satellite data can be used to enhance current understanding in climate prediction caused by aerosols. A variety of numbers of optical satellites provides diversified products to supply many tasks in related fields and aerosol optical depth determination is one of those. With the spatial and spectral resolution varying from low, moderate to high, aerosol optical depth (AOD) can be specified in different conditions.

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For detecting air pollution, aerosols are considered as one of the major air pollutants responsible for human health problems related to the respiratory system (). The determination of aerosol optical depth (AOD) from satellite image data can be used as a tool for assessing air pollution in any area of interest (Kaskaoutis, Sifakis, Retalis, Kambezidis, 2010; Retalis, Sifakis, 2009) The monitoring of aerosol concentrations becomes a high environmental priority particularly in urban areas. The proposed algorithm has been developed to allow the quantification of the aerosol optical thickness (AOT) over land.

The algorithm compares multitemporal satellite data sets and evaluates radiometric alterations due to the optical atmospheric effects of aerosols (Sifakis. , Soulakellis , Paronis, 1998). This article will present the AOD determination by using optical satellite data for Hanoi and Cam Pha area in Vietnam. For the purpose of this study, both optical satellite data and field observing station data were used to assist mutually in mapping air pollution and in verifying the correctiveness. By using Landsat ETM+ has high spatial resolution 1 (Hutchison, 2003; Lorraine, Tanre, Kaufman). . DETECTING AIR POLLUTION BY OPTICAL SATELLITE IMAGE From the early of 70s, while American civil satellite called Landsat-1 was launched for monitoring Earth surface resources, scientists in industrialized countries mentioned about polluted air environment studying problems by satellite images data. Air pollution primarily occurs in troposphere that forming an atmospheric cloudy layer called aerosol layer. In several countries in EU group like Netherlands, Germany…, having updated daily air environment pollution level to websites by using remote sensing data. .

1. Methodology The entire methodology of this study is briefly presented as below: a/ Spectral processing of satellite images involves the following steps: at first, digital numbers (DN) values were converted to radiance values; then the radiance values were calibrated temporally (Jesen, 1996). b/ Geometric correction of satellite images and geo-reference of each image at the WGS-84 projection system was implemented. The affine transformation was used to do geometric correction with the Root Mean Square Error (RMSE) lower than 1 pixel. / The next radiometric processing is calculating ? TOA reflected image at the top of atmosphere, using following formula ( Sifakis. , Soulakellis , Paronis, 1998 ) ?TOA = G ( L; Eo? ; cos ? ; d ) where: L: radiance values were converted from DN values [W / (m 2 . Ster.? m )] E0 ? : Solar spectral irradiances [W / (m ? m)] 2 d: Astronomical distance from Earth to Sun ? : Sun elevation angle. d/ ? TOA image derived with 2 modules developed by our technical team, integrated into ENVI to detect air pollution concentration and air pollution components. / The final step is GIS spatial analysis to interpolate and describes as formal thematic maps. The applied interpolation model is Kriging with studied and suitable implementation parameters for each type of satellite images (for each Landsat and SPOT images). By interpolated images, this study also established 3-D models under TIN form 2 (Triangulated Irregular Network) for each area (Hanoi & Cam Pha areas) with pollution concentration values presented as elevation values ( Ung, et el. , 2001 ). 2. 2.

Data Input data for mapping air environment pollution contain: a/ Optical satellite images comprise of Landsat ETM+ (obtained in 29/09/2001, see Table 1) and SPOT 2(obtained in 17/10/2008, see Table 2). Table 1: Landsat ETM+ Scene Parameters for Cam Pha area Scence ID Date Instrument Preprocessing level Spectral mode Spectral band indicator Lmin (W/m2/sr/? m) Lmax (W/m2/sr/? m) Spatial resolution (m) Solar Spectral Irradiances (W/m2/sr/? m) Sun angles (degree) 126-045 29/09/2001 ETM+ 1G PAN + XS Band 1 Band 2 191. 600 -6. 200 28. 5 1969. 00 196. 500 -6. 400 28. 5 1840. 00 Band 3 152. 900 -5. 00 28. 5 1551. 00 Elevation: 56. 2178575 Band 4 241. 100 -5. 100 28. 5 1044. 00 Table 2: SPOT2 Scene Parameters for Hanoi area Scence ID Date Instrument Preprocessing level Spectral mode Spectral band indicator Absolute calibration gains (W/m2/sr/? m) Spatial resolution (m) Orientation angle (degree) Incidence angle (degree) Sun angles (degree) 269-308 17/10/2008 03:18:23 HRV 1 1A XS XS1 1. 45196 20 8. 6 R16. 5 Azimuth:143. 7 R XS2 1. 34939 20 XS3 1. 62855 20 Elevation: 53. 1 b/ Digital terrain maps for Cam Pha and Hanoi area. c/ Digital elevation model (DEM) for Cam Pha and Hanoi area. . THE RESULTS FOR MAPPING AIR POLLUTION 3 A major step in process of detecting air environment pollution is the spectral processing technique. In there, having 2 modules developed and integrated into ENVI: module “to detect pollution” and module to detect “pollution components”. Technological process for mapping air pollution by Landsat ETM+ satellite images (see fig 2). Figure 2: Technological process for mapping air environment pollution Radiance of raw images affected by two kinds of noises (errors) are noises caused by detector and noises by atmosphere.

The following stages need to be processed are: – Optical satellite images` spectral calibration in 29/09/2001. – Convert radiance images to reflectance image at the top of atmosphere. 4 – Using module “to detect” aerosol has been developed and integrated into the commercial software ENVI ver4. 2 through 2 intermediate stages for creating images those are “mean” images and “variance” images. Finally, from these stages, we derived an aerosol thickness image showing concentration or air quality, or pollution level in 29/09/2001 (see fig 4).

In practical, due to having no condition to approach to field surveying data of air environment pollution components in Cam Pha – Quang Ninh area, therefore, we only established the map of total components of air environment pollution for Cam Pha & Hanoi area and mapping pollution components only for Hanoi area (see fig 5 & fig 6). Figure 3: Landsat ETM+ raw image (29/09/2001) for Cam Pha area Figure 4: Landsat ETM+ aerosol image (29/09/2001) for Cam Pha area Figure 5: SPOT2 raw image (17/10/2008) for Hanoi area

Figure 6: SPOT2 aerosol image (17/10/2008) for Hanoi area 5 Figure 7: Air Pollution Map for Cam Pha area Figure 8: A part of Air Pollution Map for Cam Pha area Figure 9: Air Environment Pollution Map Using Spot Image For Hanoi area Figure 10: Air Pollution Component TSP Using Spot Image For Hanoi area Based on the air pollution map established above, we created a 3-D model with elevation values corresponding to pollution concentration values. 6 Figure 11: 3-D model (based on TIN) for Cam Pha area Figure 12: 3-D model (based on TIN) for Hanoi area 4. CONCLUSION

The practical results demonstrated the correctiveness of proposed algorithms with 2 modules developed and integrated into ENVI for detecting air environment pollution by using remote sensing data under Vietnam condition. Spectral processing and spatial analyzing results present air environment pollution condition in Cam Pha – Quang Ninh mine area via pollution concentration values and the map of pollution concentration. From the map established above, the main distribution areas can be seen clearly: – The polluted area focused on mine area; – The polluted area placed along traffic routes; – The polluted area focused on industrial area.

The above results confirm that under condition of current infrastructure so well as with technology level, we could totally apply remote sensing technology and GIS for supervising air environment pollution based on a period in urban areas, in export processing zones or industrial areas objectively; especially in exploit areas… In order to enhance remote sensing technology and GIS` applied effects in environment field, there is a need to be having an inter-branch alliance, particularly in the synchronization of observing field data about air environment pollution while satellites obtaining as well as distribution of field observing stations to be compatible to quantity and position. 7 REFERENCES 1. Jesen J. R. , 1996, Introduction Digital Image Orocessing, A Remote Sensing Perspective, Second Edition, Prentic Hall, New Jersey. 2. Hutchison Keith D. 2003, Applications of MODIS satellite data and products for monitoring air quality in the state of Texas. Atmospheric Environment 37 (2003) 2403–2412. 3. Kaskaoutis D. G. , Sifakis N. , Retalis A. , Kambezidis H. D. , 2010, Aerosol Monitoring over Athens Using Satellite and Ground-Based Measurements. Advances in Meteorology, Article ID 147910(2010), 12 p. , doi:10. 1155/2010/147910. 4. Lorraine A. Remer1, Didier Tanre, and Yoram J. Kaufman, ALGORITHM FOR REMOTE SENSING OF TROPOSPHERIC AEROSOL FROM MODIS: Collection 005, www. MOD04_MYD04_ATB_C005_rev1. pdf. 5. Retalis A. , Sifakis N. I. , 2009, Urban aerosol mapping over Athens using the differential textural analysis (DTA) algorithm on MERIS-ENVISAT data.

ISPRS Journal of Photogrammetry and Remote Sensing, 65(2010) 17-25. 6. Seema Gore Biday and Udhav Bhosle, 2010, Radiometric correction of multitemporal satellite imagery. Journal of Computer Science 6(9), pp. 940-949. 7. Sifakis N. , Soulakellis N. , Paronis D. , 1998, Quantitative mapping of air pollution density using Earth observations: A new processing method and application on an urban area. International Journal of Remote Sensing, 19(17), 3289-3300. 8. Ung A. , Rachin W. T. , Weber C. , Hirch J. , Perron G. , Kleinpeter J. , 2001, Satellite data for air pollution mapping over city – Virtual station. Procced. of 21th AERSeL symposium. Tokyo, pp. 147-151. 8