Análise derivativa de dados hiperespectrais medidos em nível de campo e orbital para caracterizar a composição de águas opticamente complexas na Amazônia

Rudorff, Conrado M.; Novo, Evlyn Márcia Leão de Moraes; Galvão, Lênio Soares; Filho, Waterloo Pereira

doi:10.1590/s0044-59672007000200014

Cited by 25 publications

(25 citation statements)

References 27 publications

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“…The objective of this work is to compile and organize a database and build a spectral library using field water reflectance spectra and limnological data collected by different researchers and campaigns in the Amazonian region (Barbosa 2005, Nóbrega 2005, Rudorff et al 2007). An iterative clustering procedure is developed and applied to define reference spectra representative of the major Amazonian water types based on the correspondence between their limnological and spectral reflectance attributes.…”

Section: Introductionmentioning

confidence: 99%

Reference spectra to classify Amazon water types

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Barbosa

et al. 2011

International Journal of Remote Sensing

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Reference spectra extracted from spectral libraries can distinguish different water types in images when associated with limnological information. In this study, we compiled available databases into a single spectral library, using field water reflectance spectra and limnological data collected by different researchers and campaigns in the Amazonian region. By using an iterative clustering procedure based on the combination of reflectance and optically active components (OACs), reference spectra representative of the major Amazonian water types were defined from this library. Differences between the resultant limnological classes were also evaluated by paired t-tests at significance level 0.05. Finally, reference spectra were tested for Spectral Angle Mapper (SAM) classification of waters in Hyperion/Earth Observing-One (EO-1) and Medium Resolution Imaging Spectrometer (MERIS)/Environment Satellite (Envisat) images acquired simultaneously as the field campaigns. Results showed highly variable concentrations of OACs due to the complexity of the Amazonian aquatic environments. Ten classes were defined to represent this complexity, broadly grouped into four limnological characteristics: clear waters with low concentrations of OACs (class 1); black waters rich in dissolved organic carbon (DOC) (class 2); waters with large concentrations of inorganic suspended solids (ISSs) (classes 3-7); and waters dominated by chlorophyll-a (chl-a) (classes 8-10). Using the ten reference spectra, SAM classification of the field water curves produced an overall accuracy of 86% with the highest values observed for classes 3, 4, 6 and 7 and the lowest accuracy for classes 1 and 2. The results of paired t-tests confirmed the class differences based on the concentrations of OACs. SAM classification of the Hyperion and MERIS images using ground truth information resulted in overall classification accuracies of 48% and 67%, respectively, with the highest errors associated with specific portions of the scenes that were not adequately represented in the spectral library.

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Section: Introductionmentioning

confidence: 99%

Reference spectra to classify Amazon water types

Lobo

Novo

Barbosa

et al. 2011

International Journal of Remote Sensing

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“…Hyperspectral imager sensors have the advantage of instantly composing images acquired in hundreds of spectra with a level of resolution closer to that obtained with field or laboratory spectroradiometer (RUDORFF et al, 2007). The Hyperion, which was launched aboard the Earth Observing One satellite (EO-1) in November 2000, is the first orbital hyperspectral sensor that allows the acquisition of images in 220 spectral bands (10 nm wide each), with a spatial resolution of 30 m. Covering the spectrum from 400 nm to 2,500 nm, the bands are positioned in the visible, near infrared and short wave infrared, allowing more precise analyses of the relationships between plant components and spectral bands.…”

Section: Introductionmentioning

confidence: 99%

“…The evaluation of the spectral profile can allow not only crop differentiation (TISOT et al, 2007), but also bring inferences about their conditions. At this point, the analysis of spectral profiles can identify differences in plants and provide important insights on the spatial distribution of postharvest damage, one of the critical steps of crop management.…”

Section: Introductionmentioning

confidence: 99%

Use of data mining and spectral profiles to differentiate condition after harvest of coffee plants

Lamparelli¹,

Johann²,

Santos

et al. 2012

Eng. Agríc.

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This study aimed at identifying different conditions of coffee plants after harvesting period, using data mining and spectral behavior profiles from Hyperion/EO1 sensor. The Hyperion image, with spatial resolution of 30 m, was acquired in August 28 th , 2008, at the end of the coffee harvest season in the studied area. For pre-processing imaging, atmospheric and signal/noise effect corrections were carried out using Flaash and MNF (Minimum Noise Fraction Transform) algorithms, respectively. Spectral behavior profiles (38) of different coffee varieties were generated from 150 Hyperion bands. The spectral behavior profiles were analyzed by ExpectationMaximization (EM) algorithm considering 2; 3; 4 and 5 clusters. T-test with 5% of significance was used to verify the similarity among the wavelength cluster means. The results demonstrated that it is possible to separate five different clusters, which were comprised by different coffee crop conditions making possible to improve future intervention actions.KEYWORDS: crop monitoring, spectral behavior, management, orbital remote sensing. Use of data mining and spectral profiles to differentiate condition after harvest of coffee plants DISCRIMINAÇÃO DE DIFERENTES ESTADOS DE PLANTIOS DE CAFÉ PÓS--COLHEITA, POR MEIO DA TÉCNICA DE MINERAÇÃO DE DADOS E PERFIS ESPECTRAIS RESUMO:Eng.

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“…O uso de Sensoriamento Remoto permite uma abordagem sinótica do problema visto que possibilitam não apenas identificar substâncias em suspensão na água como também, a partir da análise de séries temporais, determinar seu deslocamento ao longo do tempo (JENSEN, 2009;RUDORFF, 2007).…”

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Transparência Da Água Do Reservatório Passo Real E Fator De Reflectância Em Imagens Do Sensor Moderate Resolution Imaging Spectroradiometer – Modis

2015

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RESUMOO trabalho teve como objetivo avaliar o uso de imagens MODIS (banda 1) para estimar a profundidade da transparência da água medida pelo disco Secchi no reservatório Passo Real. Os dados de transparência foram obtidos com a utilização do disco de Secchi em 31 estações amostrais e em duas datas (04 de julho e 28 de outubro de 2009). As imagens MODIS (MOD09) com datas mais próximas ao trabalho de campo são provenientes do Catálogo de imagens da NASA. Os valores de reflectância da banda 1, com centro de banda no comprimento de onda de 648 nm, foram correlacionados com dados de transparência da água. A reflectância apresentou relação inversa com a transparência da água. As correlações foram significativas entre as variáveis no mês de julho, quando a transparência média foi de 1,16 metro e não foi significativa em outubro quando a transparência média foi 0,52 metro. As imagens Modis mostraram bom desempenho em condições específicas do ambiente aquático. Ao comparar dados de transparência estimada a partir da imagem de satélite com a transparência avaliada in loco, pode-se concluir que o Modis permite estimar transparências superiores a 0,5 metro. Palavras-chave:Reservatório. Transparência da água. Reflectância. Modis. Estimativa. ABSTRACT This work has the goal of evaluating the use of MODIS images (band 1) to estimate the depth of the water transparence measured by the Secchi disc in the reservoir of Passo Real -RS.The transparence data were acquired by using the Secchi disc at 31 sample stations and in two different dates (July, 4th and October,

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Análise derivativa de dados hiperespectrais medidos em nível de campo e orbital para caracterizar a composição de águas opticamente complexas na Amazônia

Cited by 25 publications

References 27 publications

Reference spectra to classify Amazon water types

Reference spectra to classify Amazon water types

Use of data mining and spectral profiles to differentiate condition after harvest of coffee plants

Transparência Da Água Do Reservatório Passo Real E Fator De Reflectância Em Imagens Do Sensor Moderate Resolution Imaging Spectroradiometer – Modis

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