Comparison of in-Situ Hyperspectral and Landsat ETM+ Data for Chlorophyll-a Mapping in Case-II Water (Krishnarajapuram Lake, Bangalore)

Murugan, Pallikonda Rajasekaran; Sivakumar, R.; Pandiyan, R.; Annadurai, M.

doi:10.1007/s12524-015-0531-8

Cited by 15 publications

(7 citation statements)

References 35 publications

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“…Studies indicate that the spectral response obtained in the field is the collective response of Chl-a and other components/minerals of lake water. Because minerals and other components vary from lake to lake, the spectral bands suitable for Chl-a estimation also vary and should be selected for a model by comparing the estimated values with the measured values obtained from laboratory analysis [22,23].…”

Section: Introductionmentioning

confidence: 99%

An Alternative Method of Spatial Autocorrelation for Chlorophyll Detection in Water Bodies Using Remote Sensing

et al. 2017

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Additional measures of in situ water quality monitoring in natural environments can be obtained through remote sensing because certain elements in water modify its spectral behavior. One of the indicators of water quality is the presence of algae, and the aim of this study was to propose an alternative method for the quantification of chlorophyll in water by correlating spectral data, infrared images, and limnology data. The object of study was an artificial lake located at Unisinos University, São Leopoldo/RS, Brazil. The area has been mapped with a modified NGB (near infrared (N), green (G) and blue (B)) camera coupled to an unmanned aerial vehicle (UAV). From the orthorectified and georeferenced images, a modified normalized difference vegetation index (NDVImod) image has been generated. Additionally, 20 sampling points have been established on the lake. At these points, in situ spectral analysis with a spectroradiometer has been performed, and water samples have been collected for laboratory determination of chlorophyll concentrations. The correlation resulted in two models. The first model, based on the multivariate analysis of spectral data, and the second model, based on polynomial equations from NDVI, had coefficients of determination (R 2 ) of 0.86 and 0.51, respectively. This study confirmed the applicability of remote sensing for water resource management using UAVs, which can be characterized as a quick and easy methodology.

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Section: Introductionmentioning

confidence: 99%

An Alternative Method of Spatial Autocorrelation for Chlorophyll Detection in Water Bodies Using Remote Sensing

et al. 2017

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“…Gitelson (1992) argumenta que a magnitude e a posição desses picos podem ser usadas como indicadores precisos do conteúdo de fitoplâncton, e por conseguinte, da concentração de clorofila-a. Ainda assim, a presença desse pico, para Guimarães et al (2017), está associado à baixa absorção pelo pigmento, bem como ao alto espalhamento pela matéria particulada (Murugan et al 2016), de modo que, associando a feição às concentrações da Tabela 1, observa-se que o ponto 3 apresenta menor concentração de clorofila-a e maior reflectância nesse pico. Portanto, o deslocamento do pico para 580 nm, bem como sua magnitude nos espectros, está associado à baixa concentração do pigmento nos pontos amostrais.…”

Section: Ponto Amostralunclassified

Espectrorradiometria de Campo e Dados Sentinel-2 Aplicado ao Estudo da Clorofila-A em Corpos Hídricos de Reservatórios

et al. 2021

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ResumoO monitoramento da qualidade de água em corpos hídricos é fundamental para a conservação destes, por isso metodologias de monitoramento em larga escala são cada vez mais imprescindíveis para monitorar ações antrópicas e naturais que alteram a qualidade destes ambientes. Partindo dessa premissa, este trabalho propôs utilizar dados radiométricos obtidos in situ e multiespectrais do sensor MSI (Multispectral Instrument) a bordo do satélite Sentinel-2 para estudar o comportamento da clorofila-a como parâmetro de qualidade de água, em reservatório no curso do rio Juliana, na Área de Preservação Ambiental (APA) do Pratigi, Bahia, Brasil. A espectrorradiometria de campo foi empregada para estudar o comportamento ultraespectral do corpo hídrico, visando identificar feições da presença do pigmento. Com auxílio de técnicas de extração de informações, foi possível identificar a presença da clorofila na região do espectro vermelho e infravermelho próximo, possibilitando a escolha de razões de bandas do MSI. Dentre as relações matemáticas de bandas do MSI escolhidas, as que apresentaram melhor ajuste às concentrações de clorofila-a foram as razões Verde-Vermelho e Infravermelho próximo-Vermelho, com r² de 0,771 e 0,895, respectivamente, mostrando que, mesmo não tendo sido desenvolvido com a finalidade de monitorar ambientes aquáticos, os resultados demonstram um potencial de uso dos dados deste sensor para monitoramento em larga escala.

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“…Substances in the atmosphere such as water vapor, aerosol and cloud particles are bound to exert a certain influence on the reflectance spectrum of ground objects, resulting in a difference between the spectrum received by the sensor and the spectral information of the ground objects themselves [6,42]. Therefore, atmospheric correction should be carried out on the images to remove the effects of the atmosphere, so as to obtain real physical parameters such as reflectance of ground objects and surface temperature.…”

Section: (B) Flaash Atmospheric Correctionmentioning

confidence: 99%

Temporal and Spatial Variations of Chlorophyll a Concentration and Eutrophication Assessment (1987–2018) of Donghu Lake in Wuhan Using Landsat Images

Yang

Jiang

Deng

et al. 2020

Water

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Chlorophyll a (Chl-a) concentration, which reflects the biomass and primary productivity of phytoplankton in water, is an important water quality parameter to assess the eutrophication status of water. The band combinations shown in the images of Donghu Lake (Wuhan City, China) captured by Landsat satellites from 1987 to 2018 were analyzed. The (B4 − B3)/(B4 + B3) [(Green − Red)/(Green + Red)] band combination was employed to construct linear, power, exponential, logarithmic and cubic polynomial models based on Chl-a values in Donghu Lake in April 2016. The correlation coefficient (R2), the relative error (RE) and the root mean square error (RMSE) of the cubic model were 0.859, 9.175% and 11.194 μg/L, respectively and those of the validation model were 0.831, 6.509% and 19.846μg/L, respectively. Remote sensing images from 1987 to 2018 were applied to the model and the spatial distribution of Chl-a concentrations in spring and autumn of these years was obtained. At the same time, the eutrophication status of Donghu Lake was monitored and evaluated based on the comprehensive trophic level index (TLI). The results showed that the TLI (∑) of Donghu Lake in April 2016 was 63.49 and the historical data on Chl-a concentration showed that Donghu Lake had been eutrophic. The distribution of Chl-a concentration in Donghu Lake was affected by factors such as construction of bridges and dams, commercial activities and enclosure culture in the lake. The overall distribution of Chl-a concentration in each sub-lake was higher than that in the main lake region and Chl-a concentration was highest in summer, followed by spring, autumn and winter. Based on the data of three long-term (2005–2018) monitoring points in Donghu Lake, the matching patterns between meteorological data and Chl-a concentration were analyzed. It revealed that the Chl-a concentration was relatively high in warmer years or rainy years. The long-term measured data also verified the accuracy of the cubic model for Chl-a concentration. The R2, RE and RMSE of the validation model were 0.641, 2.518% and 22.606 μg/L, respectively, which indicated that it was feasible to use Landsat images to retrieve long-term Chl-a concentrations. Based on longitudinal remote sensing data from 1987 to 2018, long-term and large-scale dynamic monitoring of Chl-a concentrations in Donghu Lake was carried out in this study, providing reference and guidance for lake water quality management in the future.

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Comparison of in-Situ Hyperspectral and Landsat ETM+ Data for Chlorophyll-a Mapping in Case-II Water (Krishnarajapuram Lake, Bangalore)

Cited by 15 publications

References 35 publications

An Alternative Method of Spatial Autocorrelation for Chlorophyll Detection in Water Bodies Using Remote Sensing

An Alternative Method of Spatial Autocorrelation for Chlorophyll Detection in Water Bodies Using Remote Sensing

Espectrorradiometria de Campo e Dados Sentinel-2 Aplicado ao Estudo da Clorofila-A em Corpos Hídricos de Reservatórios

Temporal and Spatial Variations of Chlorophyll a Concentration and Eutrophication Assessment (1987–2018) of Donghu Lake in Wuhan Using Landsat Images

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