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...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
AIMS Press
2008-09-01
|
| Series: | Mathematical Biosciences and Engineering |
| Subjects: | |
| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2008.5.691 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832590193235853312 |
|---|---|
| author | Graciela Canziani Rosana Ferrati Claudia Marinelli Federico Dukatz |
| author_facet | Graciela Canziani Rosana Ferrati Claudia Marinelli Federico Dukatz |
| author_sort | Graciela Canziani |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-790a780a95e341e8b3aaa7aa591c45b0 |
| institution | Kabale University |
| issn | 1551-0018 |
| language | English |
| publishDate | 2008-09-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj-art-790a780a95e341e8b3aaa7aa591c45b02025-01-24T01:58:42ZengAIMS PressMathematical Biosciences and Engineering1551-00182008-09-015469171110.3934/mbe.2008.5.691Artificial neural networks and remote sensing in the analysis of the highly variable Pampean shallow lakesGraciela Canziani0Rosana Ferrati1Claudia Marinelli2Federico Dukatz3Multidisciplinary Institute on Ecosystems and Sustainable Development, Universidad Nacional del Centro de la Provincia de Buenos Aires, Pinto 399, 7000 TandilMultidisciplinary Institute on Ecosystems and Sustainable Development, Universidad Nacional del Centro de la Provincia de Buenos Aires, Pinto 399, 7000 TandilMultidisciplinary Institute on Ecosystems and Sustainable Development, Universidad Nacional del Centro de la Provincia de Buenos Aires, Pinto 399, 7000 TandilMultidisciplinary Institute on Ecosystems and Sustainable Development, Universidad Nacional del Centro de la Provincia de Buenos Aires, Pinto 399, 7000 TandilSuspended 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.https://www.aimspress.com/article/doi/10.3934/mbe.2008.5.691artificial neural networkremote sensingeutrophicshallow lakes |
| spellingShingle | Graciela Canziani Rosana Ferrati Claudia Marinelli Federico Dukatz Artificial neural networks and remote sensing in the analysis of the highly variable Pampean shallow lakes Mathematical Biosciences and Engineering artificial neural network remote sensing eutrophicshallow lakes |
| title | Artificial neural networks and remote sensing in the analysis of the highly variable Pampean shallow lakes |
| title_full | Artificial neural networks and remote sensing in the analysis of the highly variable Pampean shallow lakes |
| title_fullStr | Artificial neural networks and remote sensing in the analysis of the highly variable Pampean shallow lakes |
| title_full_unstemmed | Artificial neural networks and remote sensing in the analysis of the highly variable Pampean shallow lakes |
| title_short | Artificial neural networks and remote sensing in the analysis of the highly variable Pampean shallow lakes |
| title_sort | artificial neural networks and remote sensing in the analysis of the highly variable pampean shallow lakes |
| topic | artificial neural network remote sensing eutrophicshallow lakes |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2008.5.691 |
| work_keys_str_mv | AT gracielacanziani artificialneuralnetworksandremotesensingintheanalysisofthehighlyvariablepampeanshallowlakes AT rosanaferrati artificialneuralnetworksandremotesensingintheanalysisofthehighlyvariablepampeanshallowlakes AT claudiamarinelli artificialneuralnetworksandremotesensingintheanalysisofthehighlyvariablepampeanshallowlakes AT federicodukatz artificialneuralnetworksandremotesensingintheanalysisofthehighlyvariablepampeanshallowlakes |