Publicações

@article{Alcântara2017,
title = {Regional-scale algorithm to estimate the particulate organic carbon (POC) in inland waters using Landsat-5/TM images},
author = {Enner Herenio de Alcântara},
doi = {10.1007/s40808-017-0314-z},
issn = {2363-6211},
year = {2017},
date = {2017-06-01},
journal = {Modeling Earth Systems and Environment},
volume = {3},
number = {2},
pages = {831--837},
abstract = {A regional-scale algorithm was developed in order to test if the Landsat-5/TM can be used to estimate the particulate organic carbon (POC) in oligotrophic-to-mesotrophic inland water. To develop the POC algorithm two fieldworks were conducted, the first in May and the second in September 2009. The algorithm was calibrated using the dataset from September and validated using the dataset from May. The results showed that the best calibration was obtained using a polynomial fitting function (R2 of 0.80, p<0.0001). This model was validated with a normalized root mean square error (NRMSE) of 6.21%. The algorithm was then applied in two Landsat-5/TM images from April and July 2009. The spatial distribution of POC obtained from the satellite images reveals a strong dependence of POC concentrations with the weather conditions. These results allowed us to conclude that there is a great potential to study the temporal dynamics of POC in inland waters using Landsat-5/TM images.},
keywords = {algorithm, inland water, landsat, particulate organic carbon, poc},
pubstate = {published},
tppubtype = {article}
}

@article{RODRIGUES2017213,
title = {Retrieval of Secchi disk depth from a reservoir using a semi-analytical scheme},
author = {Thanan Walesza Pequeno Rodrigues and Enner Herenio de Alcântara and Fernanda Sayuri Yoshino Watanabe and Nilton Nobuhiro Imai},
doi = {10.1016/j.rse.2017.06.018},
issn = {0034-4257},
year = {2017},
date = {2017-01-01},
journal = {Remote Sensing of Environment},
volume = {198},
pages = {213--228},
abstract = {The mechanistic model reported in Lee et al. (2015) estimating the Secchi disk depth (ZSD) was applied to an oligo- to mesotrophic reservoir in Brazil. The model was originally validated with data covering lake, oceanic, and coastal waters; however, the model used the quasi-analytical algorithm (QAA) designed for optically deep waters as input and was applied to oceanic and coastal waters to derive absorption [a] and backscattering [bb] coefficients. The hypothesis is that the use of QAAv5 (http://www.ioccg.org/groups/Software_OCA/QAA_v5.pdf) to estimate both a and bb (step M1) to retrieve Kd (step M2) and ZSD (step M3) will lead to errors caused by M1 preventing an accurate estimate in oligo- to mesotrophic water. To test this hypothesis, data collected in three field trips were used to apply the mechanistic model based on the spectral bands from OLI/Landsat-8, (often applied to oceanic and coastal waters), and multispectral instrument (MSI)/Sentinel-2 bands (applied to QAA designed for very turbid inland water). The impact of step M1 over steps M2 and M3 was analyzed by the error analysis. The mean absolute percentage error (MAPE) for Kd using QAAv5 ranged between 10.35},
keywords = {algorithm, reservoir, Secchi disk, semi-analytical},
pubstate = {published},
tppubtype = {article}
}

@article{Martins2016,
title = {Support Vector Machine algorithm optimal parameterization for change detection mapping in Funil Hydroelectric Reservoir (Rio de Janeiro State, Brazil)},
author = {Sarah Cristina Araujo Martins and Nariane Marselhe Ribeiro Bernardo and Igor Ogashawara and Enner Herenio de Alcântara},
doi = {10.1007/s40808-016-0190-y},
issn = {2363-6211},
year = {2016},
date = {2016-07-21},
journal = {Modeling Earth Systems and Environment},
volume = {2},
number = {3},
pages = {138},
abstract = {Change detection in Land Use and Land Cover (LULC) using Support Vector Machines (SVM) to mapping a geographic area is a way that has been studded and improved because of its advantages as low costs in terms of computing, field research and staff team. To use SVM, it is needed firstly to define the most efficient function to be used (linear, polynomial, and radial base function---RBF) and secondly to establish the most appropriate input parameters of them, based on the study area, which was the main challenge of using SVM algorithm. The main goal of this work was to test the performance of polynomial function and RBF, and to identify which input parameters combination are the best to use SVM algorithm for Funil Hydroelectric Reservoir (FHR) sub-watershed LULC mapping, using TM/Landsat-5 time-series images. After several tests and based on the obtained results, the RBF was identified as the best SVM's function, which was used to classify the time-series images. The referred SVM function has the following parameters to be defined: the error tolerance (ξ or C), the pyramid depths (P), the radial basis function parameter (γ), and the threshold. The most effective combination of input parameters to RBF was C = 100; P = 2, γ = 0.1},
keywords = {algorithm, change detection, Funil, parametrization, reservoir, Rio de Janeiro, Support vector machine, SVM},
pubstate = {published},
tppubtype = {article}
}

@article{Alcântara2016a,
title = {The variability of particle backscattering coefficient in an oligo-to-hypertrophic cascading reservoir system: implications to TSM bio-optical model development},
author = {Enner Herenio de Alcântara and Fernanda Sayuri Yoshino Watanabe and Nariane Marselhe Ribeiro Bernardo and Thanan Walesza Pequeno Rodrigues},
doi = {10.1007/s40808-016-0146-2},
issn = {2363-6211},
year = {2016},
date = {2016-06-06},
journal = {Modeling Earth Systems and Environment},
volume = {2},
number = {2},
pages = {84},
abstract = {The particle backscattering coefficient (b bp ) has been obtained either by equipment or semi-analytically based on relations between b bp and the remote sensing reflectance (R rs ). Correlation between b bp and R rs can be significantly high allowing the development of bio-optical model to estimate the total suspended matter concentration [TSM] on water surface from satellite images. The development of such model to monitor cascading reservoir systems can be challenging since this type of water resources changes their biogeochemical composition from upstream to downstream; the water in such system can range from hypertrophic to oligotrophic state. The scientific question raised in this letter is that: in an oligo-to-hypertrophic water system the models based on b bp will keep their good agreement or the influence of organic matter (e.g. chlorophyll-a) can affect this relationship? The aim of this letter was to analyze the b bp variability in a cascading reservoir system and search for empirical models that can capture the relationship between the b bp and [TSM]. The results showed that there are not only differences in the biogeochemical concentrations but also in the b bp from upstream to downstream. In addition there is an influence of chlorophyll-a concentration [Chl-a] on the relationship between b bp and [TSM] which prevents the bio-optical model development.},
keywords = {algorithm, backscattering, bio-optical, cascading, coefficient, hypertrophic, model, oligotrophic, particle, reservoir, total suspended matter, TSM},
pubstate = {published},
tppubtype = {article}
}

@article{ROTTA2016158,
title = {Atmospheric correction assessment of SPOT-6 image and its influence on models to estimate water column transparency in tropical reservoir},
author = {Luiz Henrique da Silva Rotta and Enner Herenio de Alcântara and Fernanda Sayuri Yoshino Watanabe and Thanan Walesza Pequeno Rodrigues and Nilton Nobuhiro Imai},
doi = {10.1016/j.rsase.2016.09.001},
issn = {2352-9385},
year = {2016},
date = {2016-01-01},
journal = {Remote Sensing Applications: Society and Environment},
volume = {4},
pages = {158--166},
abstract = {Remote sensing images have been increasingly used by its ability to collect data from extensive areas in a short time and with relatively low cost. Studies conducted in aquatic environments require great attention in relation to atmospheric correction, since the signal leaving water bodies is strongly attenuated. The present work aimed to assess the atmospheric correction of SPOT-6 image based on the variation of initial visibility parameter in FLAASH and analyze its influence on models to estimate Secchi depth (SD) and diffuse attenuation coefficient (Kd). The study was carried out in Nova Avanhandava Reservoir, which belongs to the chain of the Tietê River reservoirs (São Paulo, Brazil). The models calibration was based on remote sensing reflectance (Rrs) of simulated SPOT bands from data collected in the field. The best models were obtained using the band ratio Rrs(560nm)/Rrs(660nm) for SD (R2=92},
keywords = {algorithm, atmospheric correction, imagens, models, reservoir, spot, transparency, tropical reservoir, water column},
pubstate = {published},
tppubtype = {article}
}

@article{WATANABE201628,
title = {Parametrization and calibration of a quasi-analytical algorithm for tropical eutrophic waters},
author = {Fernanda Sayuri Yoshino Watanabe and Deepak R Mishra and Ike Astuti and Thanan Walesza Pequeno Rodrigues and Enner Herenio de Alcântara and Nilton Nobuhiro Imai and Cláudio Barbosa},
doi = {10.1016/j.isprsjprs.2016.08.009},
issn = {0924-2716},
year = {2016},
date = {2016-01-01},
journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
volume = {121},
pages = {28--47},
abstract = {Quasi-analytical algorithm (QAA) was designed to derive the inherent optical properties (IOPs) of water bodies from above-surface remote sensing reflectance (Rrs). Several variants of QAA have been developed for environments with different bio-optical characteristics. However, most variants of QAA suffer from moderate to high negative IOP prediction when applied to tropical eutrophic waters. This research is aimed at parametrizing a QAA for tropical eutrophic water dominated by cyanobacteria. The alterations proposed in the algorithm yielded accurate absorption coefficients and chlorophyll-a (Chl-a) concentration. The main changes accomplished were the selection of wavelengths representative of the optically relevant constituents (ORCs) and calibration of values directly associated with the pigments and detritus plus colored dissolved organic material (CDM) absorption coefficients. The re-parametrized QAA eliminated the retrieval of negative values, commonly identified in other variants of QAA. The calibrated model generated a normalized root mean square error (NRMSE) of 21.88},
keywords = {algorithm, calibration, parametrization, quasi-analytical, tropical reservoir},
pubstate = {published},
tppubtype = {article}
}

@article{OGASHAWARA2016128,
title = {Re-parameterization of a quasi-analytical algorithm for colored dissolved organic matter dominant inland waters},
author = {Igor Ogashawara and Deepak R Mishra and Renata F F Nascimento and Enner Herenio de Alcântara and Milton Kampel and Jose L Stech},
doi = {10.1016/j.jag.2016.09.001},
issn = {0303-2434},
year = {2016},
date = {2016-01-01},
journal = {International Journal of Applied Earth Observation and Geoinformation},
volume = {53},
pages = {128--145},
abstract = {Quasi-Analytical Algorithms (QAAs) are based on radiative transfer equations and have been used to derive inherent optical properties (IOPs) from the above surface remote sensing reflectance (Rrs) in aquatic systems in which phytoplankton is the dominant optically active constituents (OACs). However, Colored Dissolved Organic Matter (CDOM) and Non Algal Particles (NAP) can also be dominant OACs in water bodies and till now a QAA has not been parametrized for these aquatic systems. In this study, we compared the performance of three widely used QAAs in two CDOM dominated aquatic systems which were unsuccessful in retrieving the spectral shape of IOPS and produced minimum errors of 350},
keywords = {algorithm, cdom, inland water, parametrization, quasi-analytical},
pubstate = {published},
tppubtype = {article}
}

@article{bernardo2015bio,
title = {Bio-optical model tuning for retrieving the total suspended matter concentration in Barra Bonita Reservoir},
author = {Nariane Marselhe Ribeiro Bernardo and Enner Herenio de Alcântara and Fernanda Sayuri Yoshino Watanabe and Thanan Walesza Pequeno Rodrigues and Nilton Nobuhiro Imai and Marcelo Curtarelli and Claudio Barbosa},
year = {2015},
date = {2015-01-01},
journal = {Revista Brasileira de Cartografia},
volume = {67},
number = {7},
keywords = {algorithm, Barra Bonita, bio-optical, concentration, models, total suspended matter, TSM},
pubstate = {published},
tppubtype = {article}
}