Data/hora: 07/06/2024 01:43:12

Provedor de dados: | 42
País: | Brazil
Título: | Chlorophyll a spatial inference using artificial neural network from multispectral images and in situ measurements
Autores: | FERREIRA,MONIQUE S.; GALO,MARIA DE LOURDES B.T.
Data: | 2013-06-01
Ano: | 2013
Palavras-chave: | Remote sensing of water; Fluorescence; Chlorophyll a; Spatial inference; Artificial neural network.
Resumo: | Considering 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.
Tipo: | Info:eu-repo/semantics/article
Idioma: | Inglês
Identificador: | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652013000200519
Editor: | Academia Brasileira de Ciências
Relação: | 10.1590/S0001-37652013005000037
Formato: | text/html
Fonte: | Anais da Academia Brasileira de Ciências  v.85 n.2 2013
Fonte: | info:eu-repo/semantics/openAccess