@Article{CuradoMuCuRoPeNoSa:2016:MoRePh,
author = "Curado, Leone F. A. and Musis, Carlos R. de and Cunha, Cristiano
R. da and Rodrigues, Thiago R. and Pereira, Vinicius M{\'a}rcio
Rodrigues and Nogueira, Jos{\'e} S. and Sanches, Luciana",
affiliation = "{Universidade Federal do Mato Grosso (UFMT)} and {Universidade
Federal do Mato Grosso (UFMT)} and {Universidade Federal do Mato
Grosso (UFMT)} and {Universidade Federal do Mato Grosso (UFMT)}
and {Instituto Nacional de Pesquisas Espaciais (INPE)} and
{Universidade Federal do Mato Grosso (UFMT)} and {Universidade
Federal do Mato Grosso (UFMT)}",
title = "Modeling the reflection of Photosynthetically active radiation in
a monodominant floodable forest in the Pantanal of Mato Grosso
State using multivariate statistics and neural networks",
journal = "Anais da Academia Brasileira de Ci{\^e}ncias",
year = "2016",
volume = "88",
number = "3",
pages = "1387--1395",
month = "Sept.",
keywords = "Pantanal, photosynthetically active radiation (PAR), Cambarazal,
Albedo.",
abstract = "The study of radiation entrance and exit dynamics and energy
consumption in a system is important for understanding the
environmental processes that rule the biosphere-atmosphere
interactions of all ecosystems. This study provides an analysis of
the interaction of energy in the form of photosynthetically active
radiation (PAR) in the Pantanal, a Brazilian wetland forest, by
studying the variation of PAR reflectance and its interaction with
local rainfall. The study site is located in Private Reserve of
Natural Heritage, Mato Grosso State, Brazil, where the vegetation
is a monodominant forest of Vochysia divergens Phol. The results
showed a high correlation between the reflection of visible
radiation and rainfall; however, the behavior was not the same at
the three heights studied. An analysis of the hourly variation of
the reflected waves also showed the seasonality of these phenomena
in relation to the dry and rainy seasons. A predictive model for
PAR was developed with a neural network that has a hidden layer,
and it showed a determination coefficient of 0.938. This model
showed that the Julian day and time of measurements had an inverse
association with the wind profile and a direct association with
the relative humidity profile.",
doi = "10.1590/0001-3765201620150176",
url = "http://dx.doi.org/10.1590/0001-3765201620150176",
issn = "0001-3765",
language = "en",
targetfile = "curado_modeling.pdf",
urlaccessdate = "27 abr. 2024"
}