Artificial neural networks and remote sensing in the analysis of the highly variable Pampean shallow lakes
Suspended organic and inorganic particles, resulting from the interactionsamong biological, physical, and chemical variables, modify the opticalproperties of water bodies and condition the trophic chain. The analysis oftheir optic properties through the spectral signatures obtained fromsatellite ima...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
AIMS Press
2008-09-01
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| Series: | Mathematical Biosciences and Engineering |
| Subjects: | |
| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2008.5.691 |
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| Summary: | Suspended organic and inorganic particles, resulting from the interactionsamong biological, physical, and chemical variables, modify the opticalproperties of water bodies and condition the trophic chain. The analysis oftheir optic properties through the spectral signatures obtained fromsatellite images allows us to infer the trophic state of the shallow lakesand generate a real time tool for studying the dynamics of shallow lakes.Field data (chlorophyll-a, total solids, and Secchi disk depth) allow us todefine levels of turbidity and to characterize the shallow lakes understudy. Using bands 2 and 4 of LandSat 5 TM and LandSat 7 ETM+ images andconstructing adequate artificial neural network models (ANN), aclassification of shallow lakes according to their turbidity is obtained.ANN models are also used to determine chlorophyll-a and total suspendedsolids concentrations from satellite image data. The results arestatistically significant. The integration of field and remote sensors datamakes it possible to retrieve information on shallow lake systems at broadspatial and temporal scales. This is necessary to understanding themechanisms that affect the trophic structure of these ecosystems. |
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| ISSN: | 1551-0018 |