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{Alcântara2017a,
title = {Modeling the spatio-temporal dissolved organic carbon concentration in Barra Bonita reservoir using OLI/Landsat-8 images},
author = {Enner Herenio de Alcântara and Nariane Marselhe Ribeiro Bernardo and Thanan Walesza Pequeno Rodrigues and Fernanda Sayuri Yoshino Watanabe},
doi = {10.1007/s40808-017-0275-2},
issn = {2363-6211},
year = {2017},
date = {2017-01-31},
journal = {Modeling Earth Systems and Environment},
volume = {3},
number = {1},
pages = {11},
abstract = {Through exchange of heat between the water and the atmosphere inland waters affect climate at the regional scale and play an important role in the global carbon cycle. Therefore, there is a need to develop methods and models for mapping inland water carbon content to understand the role of lakes in the global carbon cycle. The colored dissolved organic matter (CDOM) has a strong correlation with dissolved organic carbon (DOC) and can be studied using remote sensed images. In this work, we developed an empirical model to estimate the DOC concentration by using the absorption coefficient of CDOM (a CDOM). The a CDOM was estimated through band ratio index and validated with in situ data. The empirically adjusted model to estimate the DOC was applied to a series of OLI/Landsat-8 images. The results showed a good relationship between the a CDOM at 412 nm (a CDOM412) and the ratio between OLI band 1 and OLI band 3, but the validation results showed a normalized root mean square error (NRMSE) of about 37.89%. The a CDOM412 obtained in laboratory was used to establish a relationship between a CDOM412 and DOC. The DOC spatial distribution was then obtained and the concentration varied from 22 to 52 mg.l-1 during the year of 2014.},
keywords = {Barra Bonita, dissolved organic carbon, landsat, reservoir, spatio-temporal},
pubstate = {published},
tppubtype = {article}
}

@article{BERNARDO20172335,
title = {Atmospheric correction issues for retrieving total suspended matter concentrations in inland waters using OLI/Landsat-8 image},
author = {Nariane Marselhe Ribeiro Bernardo and Fernanda Sayuri Yoshino Watanabe and Thanan Walesza Pequeno Rodrigues and Enner Herenio de Alcântara},
doi = {10.1016/j.asr.2017.02.017},
issn = {0273-1177},
year = {2017},
date = {2017-01-01},
journal = {Advances in Space Research},
volume = {59},
number = {9},
pages = {2335--2348},
abstract = {The atmospheric effects that influence on the signal registered by remote sensors might be minimized in order to provide reliable spectral information. In aquatic systems, the application of atmospheric correction aims to minimize such effects and avoid the under or overestimation of remote sensing reflectance (Rrs). Accurately Rrs provides better information about the state of aquatic system, it means, establishing the concentration of aquatic compounds more precisely. The aim of this study is to evaluate the outputs from several atmospheric correction methods (Dark Object Subtraction -- DOS; Quick Atmospheric Correction -- QUAC; Fast Line-of-sight Atmospheric Analysis of Hypercubes -- FLAASH; Atmospheric Correction for OLI `lite' -- ACOLITE, and Provisional Landsat-8 Surface Reflectance Algorithm -- L8SR) in order to investigate the suitability of Rrs for estimating total suspended matter concentrations (TSM) in the Barra Bonita Hydroelectrical Reservoir. To establish TSM concentrations via atmospherically corrected Operational Land Imager (OLI) scene, the TSM retrieval model was calibrated and validated with in situ data. Thereby, the achieved results from TSM retrieval model application demonstrated that L8SR is able to provide the most suitable Rrs values for green and red spectral bands, and consequently, the lowest TSM retrieval errors (Mean Absolute Percentage Error about 10},
keywords = {atmospheric correction, inland water, landsat, total suspended matter},
pubstate = {published},
tppubtype = {article}
}

@article{RODRIGUES2016,
title = {DELINEAMENTO AMOSTRAL EM RESERVATÓRIOS UTILIZANDO IMAGENS LANDSAT-8/OLI: UM ESTUDO DE CASO NO RESERVATÓRIO DE NOVA AVANHANDAVA (ESTADO DE SÃO PAULO, BRASIL)},
author = {Thanan Walesza Pequeno Rodrigues and Ulisses Silva Guimarães and Luiz Henrique da Silva Rotta and Fernanda Sayuri Yoshino Watanabe and Enner Herenio de Alcântara and Nilton Nobuhiro Imai},
issn = {1982-2170},
year = {2016},
date = {2016-06-01},
journal = {Boletim de Cià GeodÃ},
volume = {22},
pages = {303--323},
publisher = {scielo},
keywords = {delineamento amostral, imagens, landsat, nova avanhadava, reservoir, São Paulo},
pubstate = {published},
tppubtype = {article}
}

@article{doi:10.1080/2150704X.2016.1177242,
title = {Estimating the CDOM absorption coefficient in tropical inland waters using OLI/Landsat-8 images},
author = {Enner Herenio de Alcântara and Nariane Marselhe Ribeiro Bernardo and Fernanda Sayuri Yoshino Watanabe and Thanan Walesza Pequeno Rodrigues and Luiz Henrique da Silva Rotta and Alisson Fernando Coelho do Carmo and Milton Hirokazu Shimabukuro and Stela Rosa Amaral Gonçalves and Nilton Nobuhiro Imai},
doi = {10.1080/2150704X.2016.1177242},
year = {2016},
date = {2016-01-01},
journal = {Remote Sensing Letters},
volume = {7},
number = {7},
pages = {661--670},
abstract = {ABSTRACTColoured dissolved organic matter (CDOM) is the most abundant dissolved organic matter (DOM) in many natural waters and can affect the water quality, such as the light penetration and the thermal properties of water system. So the objective of this letter was to estimate the CDOM absorption coefficient at 440 nm, aCDOM(440), in Barra Bonita Reservoir (São Paulo State, Brazil) using operational land imager (OLI)/Landsat-8 images. For this two field campaigns were conducted in May and October 2014. During the field campaigns remote sensing reflectance (Rrs) were measured using a TriOS hyperspectral radiometer. Water samples were collected and analysed to obtain the aCDOM(440). To predict the aCDOM(440) from Rrs at two key wavelengths (650 and 480 nm) were regressed against laboratory-derived aCDOM(440) values. The validation using in situ data of aCDOM(440) algorithm indicated a goodness of fit},
keywords = {absorption, cdom, coefficient, imagens, inland water, landsat, tropical reservoir},
pubstate = {published},
tppubtype = {article}
}

@article{BERNARDO201668,
title = {Evaluation of the suitability of MODIS, OLCI and OLI for mapping the distribution of total suspended matter in the Barra Bonita Reservoir (Tietê River, Brazil)},
author = {Nariane Marselhe Ribeiro Bernardo and Fernanda Sayuri Yoshino Watanabe and Thanan Walesza Pequeno Rodrigues and Enner Herenio de Alcântara},
doi = {10.1016/j.rsase.2016.06.002},
issn = {2352-9385},
year = {2016},
date = {2016-01-01},
journal = {Remote Sensing Applications: Society and Environment},
volume = {4},
pages = {68--82},
abstract = {The objective of this work was to evaluate the suitability of three remote sensors, namely, the Moderate Resolution Imaging Spectroradiometer (MODIS), the Operational Land Imager (OLI), and the Ocean Land Color Instrument (OLCI), for estimating total suspended matter (TSM) concentrations in the Barra Bonita reservoir. Although remote sensors have been widely explored for ocean and inland water applications in Brazilian reservoirs, a thorough comparison of sensors as a TSM monitoring tool has yet to be conducted. OLI data have been used for inland waters, but few studies on Brazilian aquatic systems have been performed. MODIS data were investigated due to their daily coverage, and OLCI data (scheduled for launch in December 2015) were analyzed because of their spatial (better than MODIS) and temporal (lower than OLI) resolution. In situ hyperspectral measurements were used as input to simulate MODIS, OLI and OLCI spectral bands while considering the spectral response function for each sensor. Simulated data and TSM concentrations were tuned to generate regional models using linear and non-linear regressions. The models were assessed using the coefficient of determination (R2), which had a range of between 0 keywords = {Barra Bonita, landsat, MODIS, OLCI, OLI, reservoir, sentinel, Tietê, total suspended matter, TSM},
pubstate = {published},
tppubtype = {article}
}

@article{doi:10.1080/23729333.2016.1179864,
title = {Spatiotemporal total suspended matter estimation in Itumbiara reservoir with Landsat-8/OLI images},
author = {Enner Herenio de Alcântara and Marcelo Curtarelli and Milton Kampel and José Stech},
doi = {10.1080/23729333.2016.1179864},
year = {2016},
date = {2016-01-01},
journal = {International Journal of Cartography},
volume = {2},
number = {2},
pages = {148--165},
abstract = {ABSTRACTThe transparency of water is affected by the amount of sunlight available, suspended particles and dissolved solids such as colored dissolved organic material present in the water column. High concentrations of total suspended matter (TSM) reduce water clarity, which can affect photosynthesis of submerged aquatic vegetation, thereby affecting oxygen production which is essential to aquatic organisms at upper levels in the food chain. The aim of this work is to evaluate the use of Landsat-8 Operational Land Imager (OLI) sensor to estimate TSM concentrations in the Itumbiara hydroelectric reservoir, Midwest Brazil (1825' S, 4906' W). Concurrent proximal remote-sensing and limnological data were collected in May and September 2009, acquired between 10:00 and 14:00 (Brazil time UTC-3) to provide representative daily readings. In situ above-water radiometric data were used to simulate remote-sensing reflectance (Rrs) for the Landsat-8/OLI spectral bands. TSM empirical models were derived from Landsat-8/OLI simulated spectral bands. The data set acquired in September 2009 was used to derive the models and the data collected in May 2009 was used for validation. To assess the similarities and differences between measured and model derived TSM concentrations, two statistical indicators were calculated. The model with lowest error was applied to selected Landsat-8/OLI images. Preliminary results showed that the model with lowest error was calibrated using Rrs from bands 2 and 3 as index. The results obtained here show that Landsat-8/OLI sensor has enough sensibility to estimate TSM concentrations in inland waters in Brazil.},
keywords = {imagens, Itumbiara, landsat, spatio-temporal, total suspended matter, TSM},
pubstate = {published},
tppubtype = {article}
}

@article{ijerph120910391,
title = {Estimation of Chlorophyll-a Concentration and the Trophic State of the Barra Bonita Hydroelectric Reservoir Using OLI/Landsat-8 Images},
author = {Fernanda Sayuri Yoshino Watanabe and Enner Herenio de Alcântara and Thanan Walesza Pequeno Rodrigues and Nilton Nobuhiro Imai and Cláudio Clemente Faria Barbosa and Luiz Henrique da Silva Rotta},
doi = {10.3390/ijerph120910391},
issn = {1660-4601},
year = {2015},
date = {2015-01-01},
journal = {International Journal of Environmental Research and Public Health},
volume = {12},
number = {9},
pages = {10391--10417},
abstract = {Reservoirs are artificial environments built by humans, and the impacts of these environments are not completely known. Retention time and high nutrient availability in the water increases the eutrophic level. Eutrophication is directly correlated to primary productivity by phytoplankton. These organisms have an important role in the environment. However, high concentrations of determined species can lead to public health problems. Species of cyanobacteria produce toxins that in determined concentrations can cause serious diseases in the liver and nervous system, which could lead to death. Phytoplankton has photoactive pigments that can be used to identify these toxins. Thus, remote sensing data is a viable alternative for mapping these pigments, and consequently, the trophic. Chlorophyll-a (Chl-a) is present in all phytoplankton species. Therefore, the aim of this work was to evaluate the performance of images of the sensor Operational Land Imager (OLI) onboard the Landsat-8 satellite in determining Chl-a concentrations and estimating the trophic level in a tropical reservoir. Empirical models were fitted using data from two field surveys conducted in May and October 2014 (Austral Autumn and Austral Spring, respectively). Models were applied in a temporal series of OLI images from May 2013 to October 2014. The estimated Chl-a concentration was used to classify the trophic level from a trophic state index that adopted the concentration of this pigment-like parameter. The models of Chl-a concentration showed reasonable results, but their performance was likely impaired by the atmospheric correction. Consequently, the trophic level classification also did not obtain better results.},
keywords = {Barra Bonita, clorophyl, concentration, imagens, landsat, OLI, trophic state},
pubstate = {published},
tppubtype = {article}
}