Iskandar, Iskhaq and Affandi, Azhar Kholiq and Setiabudidaya, Dedi and Irfan, Muhammad and Mardiansyah, Wijaya (2012) Identifying Patterns of Satellite Imagery using an Artificial Neural Network. International Journal of Remote Sensing and Earth Sciences, 9 (1). pp. 35-40. ISSN 2549-516X
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Abstract
An artificial neural network analysis based on the self-organizing map (SOM) was used to examine patterns of satellite imagery. This study used 3 × 4 SOM array to extract patterns of satellite-observed chlorophyll-a (chl-a) along the southern coast of the Lesser Sunda Islands from 1998 to 2006. The analyses indicated two characteristic spatial patterns, namely the northwest and the southeast monsoon patterns. The northwest monsoon pattern was characterized by a low chl-a concentration. In contrast, the southeast monsoon pattern was indicated by a high chl-a distributed along the southern coast of the Lesser Sunda Islands. Furthermore, this study demonstrated that the seasonal variations of those two patterns were related to the variations of winds and sea surface temperature (SST). The winds were predominantly southeasterly (northwesterly) during southeast (northwest) monsoon, drived offshore (onshore) Ekman transport and produced upwelling (downwelling) along the southern coasts of the Lesser Sunda Islands. Consequently, upwelling reduced SST and helped replenish the surface water nutrients, thus supporting high chl-a concentration. Finally, this study demonstrated that the SOM method was very useful for the identifications of patterns in various satellite imageries.
| Item Type: | Article |
|---|---|
| Subjects: | Q Science > QC Physics > QC980-999 Climatology and weather |
| Divisions: | 08-Faculty of Mathematics and Natural Science > 45201-Physics (S1) |
| Depositing User: | Dr. Muhammad Irfan |
| Date Deposited: | 28 Dec 2022 03:27 |
| Last Modified: | 28 Dec 2022 03:27 |
| URI: | http://repository.unsri.ac.id/id/eprint/84712 |
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