Chlorophyll a spatial inference using artificial neural network from multispectral images and in situ measurements

dc.contributorUniversidade Estadual Paulista (UNESP)
dc.creatorFerreira, Monique S.
dc.creatorGalo, Maria de Lourdes B.T.
dc.date2014-05-27T11:28:48Z
dc.date2016-10-25T18:46:35Z
dc.date2014-05-27T11:28:48Z
dc.date2016-10-25T18:46:35Z
dc.date2013-04-01
dc.date.accessioned2017-04-06T02:18:56Z
dc.date.available2017-04-06T02:18:56Z
dc.identifierAnais da Academia Brasileira de Ciencias, v. 85, n. 2, p. 519-532, 2013.
dc.identifier0001-3765
dc.identifier1678-2690
dc.identifierhttp://hdl.handle.net/11449/74997
dc.identifierhttp://acervodigital.unesp.br/handle/11449/74997
dc.identifier10.1590/S0001-37652013005000037
dc.identifierS0001-37652013005000037
dc.identifierWOS:000321395300007
dc.identifier2-s2.0-84879580128.pdf
dc.identifier2-s2.0-84879580128
dc.identifierhttp://dx.doi.org/10.1590/S0001-37652013005000037
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/895755
dc.descriptionConsidering the importance of monitoring the water quality parameters, remote sensing is a practicable alternative to limnological variables detection, which interacts with electromagnetic radiation, called optically active components (OAC). Among these, the phytoplankton pigment chlorophyll a is the most representative pigment of photosynthetic activity in all classes of algae. In this sense, this work aims to develop a method of spatial inference of chlorophyll a concentration using Artificial Neural Networks (ANN). To achieve this purpose, a multispectral image and fluorometric measurements were used as input data. The multispectral image was processed and the net training and validation dataset were carefully chosen. From this, the neural net architecture and its parameters were defined to model the variable of interest. In the end of training phase, the trained network was applied to the image and a qualitative analysis was done. Thus, it was noticed that the integration of fluorometric and multispectral data provided good results in the chlorophyll a inference, when combined in a structure of artificial neural networks.
dc.languageeng
dc.relationAnais da Academia Brasileira de Ciências
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectArtificial neural network
dc.subjectChlorophyll a
dc.subjectFluorescence
dc.subjectRemote sensing of water
dc.subjectSpatial inference
dc.titleChlorophyll a spatial inference using artificial neural network from multispectral images and in situ measurements
dc.typeOtro


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