Delineamento Amostral Em Reservatórios Utilizando Imagens Landsat-8/Oli: Um Estudo De Caso No Reservatório De Nova Avanhandava (Estado De São Paulo, Brasil)

Rodrigues, Thanan; Guimarães, Ulisses Silva; Rotta, Luiz; Watanabe, Fernanda; Alcântara, Enner; Imai, Nilton Nobuhiro

doi:10.1590/s1982-21702016000200017

Cited by 20 publications

(11 citation statements)

References 23 publications

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“…The data were collected from sampling stations selected according to variation of the water color [27]. Such sampling design built to reservoirs provides the maximum optical variability in collected samples.…”

Section: Study Area and Samplingmentioning

confidence: 99%

“…This acquisition geometry was adopted in order to minimize surface- Calibration and validation datasets were gathered in three field campaigns, conducted in BB, on 5-9 May (labeled BB1) and 13-16 October 2014 (labeled BB2), and in BA, on 15-18 August 2016. The data were collected from sampling stations selected according to variation of the water color [27]. Such sampling design built to reservoirs provides the maximum optical variability in collected samples.…”

Section: Remote Sensing Reflectance Rrsmentioning

confidence: 99%

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Estimation of Chlorophyll-a Concentration from Optimizing a Semi-Analytical Algorithm in Productive Inland Waters

et al. 2018

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Abstract:The high nutrient concentrations coming from non-point and point pollution have been linked to algae blooms, especially in hydroelectric plant reservoirs, due to higher residence time compared to rivers. The monitoring of algae is important to prevent risk of contamination by toxins in reservoirs used for drinking water supply. In this context, a physical model-based approach was adopted to retrieve chlorophyll-a (chl a) concentration, a photosynthetic pigment found in all phytoplankton species. We assumed that a semi-analytical algorithm parameterized to a eutrophic reservoir could also be applied to other eutrophic reservoirs, at least the specific inherent optical properties (SIOPs) are not similar. The parameterization was carried out based on Ocean and Land Color Instrument (OLCI) bands aboard Sentinel-3 spacecraft. In our study, the semi-analytical approach showed good performance in retrieving chl a content, with a normalized root mean square error (NRMSE) of 18.7%. The findings encourage the use of a unique semi-analytical algorithm in a reservoir cascade, where the impoundments present similar bio-optical status. The good performance of the algorithm indicates that this approach is rather useful in predicting trophic status in reservoirs.

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Section: Study Area and Samplingmentioning

confidence: 99%

Section: Remote Sensing Reflectance Rrsmentioning

confidence: 99%

Estimation of Chlorophyll-a Concentration from Optimizing a Semi-Analytical Algorithm in Productive Inland Waters

et al. 2018

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“…The first field trip (Nav 1) occurred in austral autumn (28 April to 2 May 2014), the second (Nav 2) in austral spring (23-26 September 2014), and the third (Nav 3) in austral autumn (7 and 14 of May 2016). Fifty-one samples were used in this study for model calibration, validation, and temporal comparison (Nav 1: 18 samples for model calibration; Nav 2: 14 samples for model validation; and Nav 3: 19 samples for temporal comparison) (see Figure 1 for sampling station locations) following the sampling strategy described by Rodrigues et al [31], which consisted of selecting samples randomly, taking into account the spectral differences of regions (stratum).…”

Section: Water Quality Datamentioning

confidence: 99%

Estimating the Optical Properties of Inorganic Matter-Dominated Oligo-to-Mesotrophic Inland Waters

Rodrigues

Mishra

Alcântara

et al. 2018

Water

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Many studies over the years have focused on bio-optical modeling of inland waters to monitor water quality. However, those studies have been conducted mainly in eutrophic and hyper-eutrophic environments dominated by phytoplankton. With the launch of the Ocean and Land Colour Instrument (OLCI)/Sentinel-3A in 2016, a range of bands became available including the 709 nm band recommended for scaling up these bio-optical models for productive inland waters. It was found that one category of existing bio-optical models, the quasi-analytical algorithms (QAAs), when applied to colored dissolved organic matter (CDOM) and detritus-dominated waters, produce large errors. Even after shifting the reference wavelength to 709 nm, the recently re-parameterized QAA versions could not accurately retrieve the inherent optical properties (IOPs) in waterbodies dominated by inorganic matter. In this study, three existing versions of QAA were implemented and proved inefficient for the study site. Therefore, several changes were incorporated into the QAA, starting with the re-parameterization of the empirical steps related to the total absorption coefficient retrieval. The re-parameterized QAA, QAA OMW showed a significant improvement in the mean absolute percentage error (MAPE). MAPE decreased from 58.05% for existing open ocean QAA (QAA Lv5 ) to 16.35% for QAA OMW . Considerable improvement was also observed in the estimation of the absorption coefficient of CDOM and detritus from a MAPE of 91.05% for QAAL v5 to 18.87% for QAA OMW . The retrieval of the absorption coefficient of phytoplankton (a φ ) using the native form of QAA proved to be inaccurate for the oligo-to-mesotrophic waterbody due to the low a φ returning negative predictions. Therefore, a novel approach based on the normalized a φ was adopted to maintain the spectral shape and retrieve positive values, resulting in an improvement of 119% in QAA OMW . Further tuning and scale-up of QAA OMW to OLCI bands will aid in monitoring water resources and associated watershed processes.

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“…Two field campaigns were conducted in each reservoir (see details in Table 1), and the sample geographic locations were selected following the design plan steps described by Rodrigues et al (2016) [28]. Measurements of radiometric quantities and water sample collection were taken at each sampling spot.…”

Section: Tietê River Cascade System Of Reservoirs (Tcsr)mentioning

confidence: 99%

Glint Removal Assessment to Estimate the Remote Sensing Reflectance in Inland Waters with Widely Differing Optical Properties

et al. 2018

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The quality control of remote sensing reflectance (R rs ) is a challenging task in remote sensing applications, mainly in the retrieval of accurate in situ measurements carried out in optically complex aquatic systems. One of the main challenges is related to glint effect into the in situ measurements. Our study evaluates four different methods to reduce the glint effect from the R rs spectra collected in cascade reservoirs with widely differing optical properties. The first (i) method adopts a constant coefficient for skylight correction (ρ) for any geometry viewing of in situ measurements and wind speed lower than 5 m·s −1 ; (ii) the second uses a look-up-table with variable ρ values accordingly to viewing geometry acquisition and wind speed; (iii) the third method is based on hyperspectral optimization to produce a spectral glint correction, and (iv) computes ρ as a function of wind speed. The glint effect corrected R rs spectra were assessed using HydroLight simulations. The results showed that using the glint correction with spectral ρ achieved the lowest errors, however, in a Colored Dissolved Organic Matter (CDOM) dominated environment with no remarkable chlorophyll-a concentrations, the best method was the second. Besides, the results with spectral glint correction reduced almost 30% of errors.

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Delineamento Amostral Em Reservatórios Utilizando Imagens Landsat-8/Oli: Um Estudo De Caso No Reservatório De Nova Avanhandava (Estado De São Paulo, Brasil)

Cited by 20 publications

References 23 publications

Estimation of Chlorophyll-a Concentration from Optimizing a Semi-Analytical Algorithm in Productive Inland Waters

Estimation of Chlorophyll-a Concentration from Optimizing a Semi-Analytical Algorithm in Productive Inland Waters

Estimating the Optical Properties of Inorganic Matter-Dominated Oligo-to-Mesotrophic Inland Waters

Glint Removal Assessment to Estimate the Remote Sensing Reflectance in Inland Waters with Widely Differing Optical Properties

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