AVHRR detection of red tides with neural networks

International Journal of Remote Sensing

Volumn 24, Issue 10, 2003, Pages 1991-1996

  Huang, W G ^a   Lou, X L ^a  

^a Chongqing Institute of Geology and Mineral Resources   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL NEURAL NETWORK; AVHRR; DETECTION METHOD; RED TIDE; REMOTE SENSING;

EID: 0038445151     PISSN: 01431161     EISSN: None     Source Type: Journal    
DOI: 10.1080/0143116031000068213     Document Type: Article

References (12)

1. ANDERSON, D. M., 1994, Red tides. Scientific American, 271, 52-58.
2. ANDERSON, D. M., 1995, Toxic red tides and harmful algal blooms: a practical challenge in coastal oceanography. Reviews of Geophysics, Supplement, 1189-1200.
3. CULLEN, J. J., CIOTTI, A. M., DAVIS, R. F., and LEWIS, M. R., 1997, Optical detection and assessment of algal blooms. Limnology and Oceanography, 42, 1223-1239.
4. GOWER, J. F. R., 1994, Red tide monitoring using AVHRR HRPT imagery from a local receiver. Remote Sensing of Environment, 48, 309-318.
5. GREPMA, G., 1988, Satellite (AVHRR/NOAA-9) and ship studies of a coccolithophorid bloom in the western English Channel. Marine Nature, 1(1), 1-14.
6. HUANG, W. G., and MAO, X. M., 1998, A Procedure for forecasting of red tides. In Proceedings of the 4th Pacific Ocean Remote Sensing Conference, Qingdao, China, (Beijing: Beijing Fortune Quick Printing Corporation Ltd.), pp. 573-575.
7. HUANG, W. G. et al., 1998, Detection and real-time forecast of red tides using remotely sensed data. Marine Forecasts [in Chinese], 15, 111-115.
8. KAHRU, M., 1997, Using satellites to monitor large-scale environmental changes: a case study of cyanobacteria blooms in the Baltic Sea. In Monitoring Algal Blooms: New Techniques for Detecting Large-scale Environmental Change, edited by M. Kahru and C. W. Brown (TX, USA: Springer-Verlag and Landes Biosciencepp), pp. 43-61.
9. KAHRU, M., LEPPANEN, J., and RUD, O., 1993, Cyanobacterial blooms cause heating of the sea surface. Marine Ecology Progress Series, 101, 1-7.
10. KEINER, L. E., and BROWN, C. W., 1999, Estimating oceanic chlorophyll concentrations with neural networks. International Journal of Remote Sensing, 20, 189-194.
11. SCHILLER, H., and DOERFFER, R., 1999, Neural network for emulation of an inverse model-operational derivation of Case II water properties from MERIS data. International Journal of Remote Sensing, 20, 1735-1746.
12. STUMPF, R. G., 2001, Applications of satellite ocean colour sensors for monitoring and predicting harmful algal blooms. Human and Ecological Risk Assessment, 7, 1363-1368.