DEVELOPMENT OF MULTISPECTRAL ALGORITHM AND REMOTE SENSING TECHNIQUE FOR AIR QUALITY MEASUREMENTS OVER MAKKAH, MINA AND ARAFAH
| Presenter | : | NADZRI BIN OTHMAN |
| : | - | |
| Date | : | 26 / 7 / 2010 |
| Time | : | 3.45 PM |
| Venue | : | PHYSICS MEETING ROOM LEVEL 2, SCHOOL OF PHYSICS |
| Sypnosis | : | The air quality indicator approximated by satellite measurements is known as an atmospheric particulate loading, which is evaluated in terms of the columnar optical thickness of aerosol scattering. The effect brought by particulate pollution has gained interest among researchers to study aerosol and particulate matter. This study presents the potentiality of retrieving concentrations of particles with diameters less than ten micrometer (PM10) and aerosol optical thickness (AOT) in the atmosphere using the Landsat 7 ETM+ satellite imageries over Makkah, Mina and Arafah. A multispectral algorithm was developed by assuming that surface condition of study area was lambertian and homogeneous. It also neglected atmospheric effect due to Rayleigh scattering. In-situ PM10 measurements were collected using DustTrak aerosol monitor 8520 while AOT data was measured using FieldSpec handheld spectroradiometer and their locations were determined by a handheld global positioning system (GPS). The Beer Lambert law was used to calculate AOT from transmittance of atmospheric measured using the FieldSpec handheld spectroradiometer. The digital number (DN) recorded by satellite imageries were converted to top of the atmosphere (TOA) reflectance which is the sum of the ground reflectance and atmospheric reflectance. Then, the atmospheric correction (ATCOR2) method was used to retrieve the surface reflectance. The reflectance measured from the satellite at the top of the atmosphere (TOA) was subtracted from the amount given by the surface reflectance to obtain the atmospheric reflectance. Measured PM10 and AOT were correlated with atmospheric reflectance value using regression technique. Various types of regression algorithms were then examined by comparing the correlation coefficient (R) values and the root-mean-square error (RMSE) values. The three band model of algorithm was selected based on the highest R value and the lowest RMSE value. The proposed algorithms were used to generate PM10 and AOT maps for the study areas. Various types of filters and windows size were used like average, median and mode were applied to Landsat 7 ETM+ satellite imageries in order to increase the accuracy and to minimize the noise effect of the PM10 and AOT maps over study area. The proposed algorithm also validated using multi temporal data and aerosol product from the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) and the Multiangle Imaging SpectroRadiometer (MISR). |
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