Extracting spatial data from satellite sensor to support air pollution determination using remote sensing technique

Nowadays, air quality is a major concern in many countries whether in the developed or the developing countries. Due to the high cost and limited number of air pollutant stations in each area, they cannot provide a good spatial distribution of the air pollutant readings over a city. Satellite observations can give a high spatial distribution of air pollution. The objective of this study was to map air quality concentration in Penang Island, Malaysia using our proposed developed algorithm from Landsat TM data. The algorithm was developed base on the aerosol characteristics in the atmosphere. PM10 measurements were collected simultaneously with the image acquisition using a DustTrak Aerosol Monitor 8520. The station locations of the PM10 measurements were determined using a handheld GPS. The retrieval of surface reflectance is important to obtain the atmospheric reflectance in remotely sensed data and later used for algorithm calibration. For each visible band, the dark target surface reflectance was estimated from that of the mid-infrared band. The reflectance measured from the satellite [reflectance at the top of atmospheric, p(TOA)] was subtracted by the amount given by the surface reflectance to obtain the atmospheric reflectance. Then the atmospheric reflectance was related to the AOT using the regression algorithm. Similarly, the atmospheric reflectance was related to the measured PM10 values. In this study, the atmospheric reflectance derived from Landsat TM signals were used as independent variables in our calibration regression analyses. The newly developed algorithm produced a high degree of accuracy. The generated PM10 map was also colour coded for visual interpretation and smoothed using an average filter to minimize random noise. This study indicated that the Landsat TM can be a useful tool for air quality study.