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Open AccessArticle

A Multivariate Analysis Framework to Detect Key Environmental Factors Affecting Spatiotemporal Variability of Chlorophyll-a in a Tropical Productive Estuarine-Lagoon System

1
Department of Civil Engineering, Federal University of Pernambuco, 50670-901 Recife, Brazil
2
Géosciences Environnement Toulouse (GET), Unité Mixte de Recherche 5563, IRD/CNRS/Université Toulouse III, 31400 Toulouse, France
3
Hydraulic Research Institute, Federal University of Rio Grande do Sul, CP 15029 Porto Alegre, Brazil
4
Institute of Geography, Federal University of Alagoas, 57072-970 Maceió, Brazil
5
Center for Technology, Federal University of Alagoas, 57072-970 Maceió, Brazil
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(6), 853; https://doi.org/10.3390/rs10060853
Received: 3 May 2018 / Revised: 22 May 2018 / Accepted: 28 May 2018 / Published: 1 June 2018
(This article belongs to the Special Issue Remote Sensing of Inland Waters and Their Catchments)
Here, we demonstrate how a combination of three multivariate statistic techniques can identify key environmental factors affecting the seasonal and spatial variability of chlorophyll-a (Chl-a) in a productive tropical estuarine-lagoon system. Remote estimation of Chl-a was carried out using a NIR-Red model based on MODIS bands, which is highly consistent with the in situ measurement of Chl-a with root mean square error (RMSE) of 15.24 mg m−3 and 13.43 mg m−3 for two independent datasets used for the model’s calibration and validation, respectively. Our findings suggest that the river discharges and hydraulic residence time of the lagoons promote a stronger effect on the spatial variability of Chl-a in the coastal lagoons, while wind, solar radiation and temperature have a secondary importance. The results also indicate a slight seasonal variability of Chl-a in Mundaú lagoon, which are different the from Manguaba lagoon. The multivariate approach was able to fully understand the relative importance of key environmental factors on the spatiotemporal variability of Chl-a of the aquatic ecosystem, providing a powerful tool for reducing dimensionality and analyzing large amounts of satellite-derived Chl-a data. View Full-Text
Keywords: MODIS; Múndaú lagoon; Manguaba lagoon MODIS; Múndaú lagoon; Manguaba lagoon
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MDPI and ACS Style

Lins, R.C.; Martinez, J.-M.; Motta Marques, D.D.; Cirilo, J.A.; Medeiros, P.R.P.; Fragoso Júnior, C.R. A Multivariate Analysis Framework to Detect Key Environmental Factors Affecting Spatiotemporal Variability of Chlorophyll-a in a Tropical Productive Estuarine-Lagoon System. Remote Sens. 2018, 10, 853. https://doi.org/10.3390/rs10060853

AMA Style

Lins RC, Martinez J-M, Motta Marques DD, Cirilo JA, Medeiros PRP, Fragoso Júnior CR. A Multivariate Analysis Framework to Detect Key Environmental Factors Affecting Spatiotemporal Variability of Chlorophyll-a in a Tropical Productive Estuarine-Lagoon System. Remote Sensing. 2018; 10(6):853. https://doi.org/10.3390/rs10060853

Chicago/Turabian Style

Lins, Regina C.; Martinez, Jean-Michel; Motta Marques, David D.; Cirilo, José A.; Medeiros, Paulo R.P.; Fragoso Júnior, Carlos R. 2018. "A Multivariate Analysis Framework to Detect Key Environmental Factors Affecting Spatiotemporal Variability of Chlorophyll-a in a Tropical Productive Estuarine-Lagoon System" Remote Sens. 10, no. 6: 853. https://doi.org/10.3390/rs10060853

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