| Title | Automatic detection of fire smoke using artificial neural networks and threshold approaches applied to AVHRR imagery |
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| Author | Li, Z; Khananian, A; Fraser, R ; Cihlar, J |
| Source | IEEE Transactions on Geoscience and Remote Sensing (Institute of Electrical and Electronics Engineers) 39, 9, 2001 p. 1859-1870, https://doi.org/10.1109/36.951076 |
| Year | 2001 |
| Alt Series | Earth Sciences Sector, Contribution Series 20043039 |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| Document | serial |
| Lang. | English |
| Media | paper; on-line; digital |
| File format | pdf |
| Released | 2001 01 01 |
| Abstract | Satellite-based remote sensing techniques were developed for identifying smoke from forest fires. Both artificial neural networks (NN) and multithreshold techniques were explored for application with
imagery from the Advanced Very High Resolution Radiometer (AVHRR) aboard NOAA satellites. The NN was designed such that it does not only classify a scene into smoke, cloud, or clear background, but also generates continuous outputs representing the
mixture portions of these objects. While the NN approach offers many advantages, it is time consuming for application over large areas. A multithreshold algorithm was thus developed as well. The two approaches may be employed separately or in
combination depending on the size of an image and smoke conditions. The methods were evaluated in terms of Euclidean distance between the outputs of the NN classification, using error matrices, visual inspection, and comparisons of classified smoke
images with fire hot spots. They were applied to process daily AVHRR images acquired across Canada. The results obtained in the 1998 fire season were analyzed and compared with fire hot spots and TOMS-based aerosol index data. Reasonable
correspondence was found, but the signals of smoke detected by TOMS and AVHRR are quite different but complementary to each other. In general, AVHRR is most sensitive to low dense smoke plumes located near fires, whereas smoke detected by TOMS is
dispersed, thin, elevated, and further away from fires. |
| GEOSCAN ID | 219841 |
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