Determination of Haze Using NOAA-14 AVHRR Satellite Data
Seven sets of NOAA-14 AVHRR satellite data during the 1997 thick haze episode in Malaysia were used in order to determine haze quantitatively. In-situ measurements of corresponding Air Pollution Index (API) which were carried out at five selected air pollution stations in Peninsular Malaysia are pa...
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| Format: | Article |
| Language: | English |
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Asian Association on Remote Sensing
2002
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| Online Access: | http://eprints.utm.my/2155/1/050.pdf |
| _version_ | 1825909192396898304 |
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| author | Ahmad, Asmala Hashim, Mazlan |
| author_facet | Ahmad, Asmala Hashim, Mazlan |
| author_sort | Ahmad, Asmala |
| collection | ePrints |
| description | Seven sets of NOAA-14 AVHRR satellite data during the 1997 thick haze episode in Malaysia were used in order to determine haze quantitatively. In-situ measurements of
corresponding Air Pollution Index (API) which were carried out at five selected air pollution stations in Peninsular Malaysia are particulate matter sizing less than 10 micron (PM10), sulphur dioxide (SO2), nitrogen dioxide (NO2),carbon monoxide (CO), and ozone (O3). These
in-situ measurements were then used as inputs in deriving the haze information from NOAA-14 AVHRR data. Bands 1 and 2 of NOAA-14 AVHRR data were converted from digital
numbers (DNs) to reflectance before being pre-processed for atmospheric and geometric correction. The relationship between the reflectance and the corresponding API for all five pollutants were carried out using regression analysis. Eight regression models analysed were
linear, logarithmic, quadratic, cubic, power, S-curve, exponential, and multiple linear. The best models were chosen based on the highest coefficient of determination, R2 for each haze constituent examined. The obtained model were then used to map the distributions and concentrations of five haze components during the seven acquisition dates of NOAA-14 data under study. Multi -temporal analysis was also carried out at each air pollution station to examine the variation of haze intensity for all major haze components respectively. The results of this study indicate that remote sensing technique of bands 1 and 2 NOAA-14 AVHRR data was capable of mapping haze intensity spatially. RMS errors in API of 33 were btained for the best PM10 model, 18 for SO2, 11 for NO2, 13 for CO, and 10 for O3. |
| first_indexed | 2024-03-05T17:58:30Z |
| format | Article |
| id | utm.eprints-2155 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T17:58:30Z |
| publishDate | 2002 |
| publisher | Asian Association on Remote Sensing |
| record_format | dspace |
| spelling | utm.eprints-21552010-06-01T03:01:19Z http://eprints.utm.my/2155/ Determination of Haze Using NOAA-14 AVHRR Satellite Data Ahmad, Asmala Hashim, Mazlan Q Science (General) GE Environmental Sciences Seven sets of NOAA-14 AVHRR satellite data during the 1997 thick haze episode in Malaysia were used in order to determine haze quantitatively. In-situ measurements of corresponding Air Pollution Index (API) which were carried out at five selected air pollution stations in Peninsular Malaysia are particulate matter sizing less than 10 micron (PM10), sulphur dioxide (SO2), nitrogen dioxide (NO2),carbon monoxide (CO), and ozone (O3). These in-situ measurements were then used as inputs in deriving the haze information from NOAA-14 AVHRR data. Bands 1 and 2 of NOAA-14 AVHRR data were converted from digital numbers (DNs) to reflectance before being pre-processed for atmospheric and geometric correction. The relationship between the reflectance and the corresponding API for all five pollutants were carried out using regression analysis. Eight regression models analysed were linear, logarithmic, quadratic, cubic, power, S-curve, exponential, and multiple linear. The best models were chosen based on the highest coefficient of determination, R2 for each haze constituent examined. The obtained model were then used to map the distributions and concentrations of five haze components during the seven acquisition dates of NOAA-14 data under study. Multi -temporal analysis was also carried out at each air pollution station to examine the variation of haze intensity for all major haze components respectively. The results of this study indicate that remote sensing technique of bands 1 and 2 NOAA-14 AVHRR data was capable of mapping haze intensity spatially. RMS errors in API of 33 were btained for the best PM10 model, 18 for SO2, 11 for NO2, 13 for CO, and 10 for O3. Asian Association on Remote Sensing 2002-11-25 Article NonPeerReviewed application/pdf en http://eprints.utm.my/2155/1/050.pdf Ahmad, Asmala and Hashim, Mazlan (2002) Determination of Haze Using NOAA-14 AVHRR Satellite Data. Proceedings on The 23rd Asian Conference on Remote Sensing 2002, Kathmandu, Nepal . |
| spellingShingle | Q Science (General) GE Environmental Sciences Ahmad, Asmala Hashim, Mazlan Determination of Haze Using NOAA-14 AVHRR Satellite Data |
| title | Determination of Haze Using NOAA-14 AVHRR Satellite Data |
| title_full | Determination of Haze Using NOAA-14 AVHRR Satellite Data |
| title_fullStr | Determination of Haze Using NOAA-14 AVHRR Satellite Data |
| title_full_unstemmed | Determination of Haze Using NOAA-14 AVHRR Satellite Data |
| title_short | Determination of Haze Using NOAA-14 AVHRR Satellite Data |
| title_sort | determination of haze using noaa 14 avhrr satellite data |
| topic | Q Science (General) GE Environmental Sciences |
| url | http://eprints.utm.my/2155/1/050.pdf |
| work_keys_str_mv | AT ahmadasmala determinationofhazeusingnoaa14avhrrsatellitedata AT hashimmazlan determinationofhazeusingnoaa14avhrrsatellitedata |