Using multi-temporal Landsat imagery and linear mixed models for assessing water quality parameters in Río Tercero reservoir (Argentina)

The application of remote sensing technology to water quality monitoring has special significance for lake management at regional scales. Many studies have proposed algorithms between Landsat data and in-situ water quality parameters using classical regression models. The novelty of this paper is th...

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Autores principales: Bonansea, M., Rodriguez, M.C., Pinotti, L., Ferrero, S.
Formato: JOUR
Materias:
Algorithms
Landsat
Linear mixed models
Remote sensing
Reservoir
Water quality
Atmospheric temperature
Lakes
Nuclear reactors
Parameter estimation
Regression analysis
Chlorophyll-a concentration
LANDSAT
Remote sensing technology
Spatial correlation structures
Water quality monitoring
Water quality parameters
Water surface temperature
Reservoirs (water)
algorithm
chlorophyll a
data set
numerical model
satellite imagery
seasonality
surface temperature
transparency
water quality
Argentina
Cordoba [Argentina]
Tercero River
Acceso en línea:http://hdl.handle.net/20.500.12110/paper_00344257_v158_n_p28_Bonansea
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id todo:paper_00344257_v158_n_p28_Bonansea
record_format dspace
spelling todo:paper_00344257_v158_n_p28_Bonansea2023-10-03T14:45:51Z Using multi-temporal Landsat imagery and linear mixed models for assessing water quality parameters in Río Tercero reservoir (Argentina) Bonansea, M. Rodriguez, M.C. Pinotti, L. Ferrero, S. Algorithms Landsat Linear mixed models Remote sensing Reservoir Water quality Algorithms Atmospheric temperature Lakes Nuclear reactors Parameter estimation Regression analysis Remote sensing Water quality Chlorophyll-a concentration LANDSAT Linear mixed models Remote sensing technology Spatial correlation structures Water quality monitoring Water quality parameters Water surface temperature Reservoirs (water) algorithm chlorophyll a data set Landsat numerical model satellite imagery seasonality surface temperature transparency water quality Argentina Cordoba [Argentina] Tercero River The application of remote sensing technology to water quality monitoring has special significance for lake management at regional scales. Many studies have proposed algorithms between Landsat data and in-situ water quality parameters using classical regression models. The novelty of this paper is that we developed algorithms to determine log-transformed chlorophyll-a concentration (Chl-a) and Secchi disk transparency (SDT) in Río Tercero reservoir using Landsat TM and ETM. + imagery, ancillary environmental factors and linear mixed models (LMM), obtaining an increase in the accuracy of the estimates. The validation results showed that LMM with spatial correlation structure that take into account water surface temperature (WST) and rainfall were the most suitable method for estimating these parameters. WST derived from the Landsat thermal band was also validated. The algorithms were used to generate quantitative maps providing spatially and temporally rich information on patterns of water quality throughout the reservoir. Water quality features related to the hydrogeomorphology of the reservoir, typical seasonality and influx from the cooling system of a local nuclear reactor were identified in the time series maps. © 2014 Elsevier Inc. JOUR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_00344257_v158_n_p28_Bonansea
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic Algorithms
Landsat
Linear mixed models
Remote sensing
Reservoir
Water quality
Algorithms
Atmospheric temperature
Lakes
Nuclear reactors
Parameter estimation
Regression analysis
Remote sensing
Water quality
Chlorophyll-a concentration
LANDSAT
Linear mixed models
Remote sensing technology
Spatial correlation structures
Water quality monitoring
Water quality parameters
Water surface temperature
Reservoirs (water)
algorithm
chlorophyll a
data set
Landsat
numerical model
satellite imagery
seasonality
surface temperature
transparency
water quality
Argentina
Cordoba [Argentina]
Tercero River
spellingShingle Algorithms
Landsat
Linear mixed models
Remote sensing
Reservoir
Water quality
Algorithms
Atmospheric temperature
Lakes
Nuclear reactors
Parameter estimation
Regression analysis
Remote sensing
Water quality
Chlorophyll-a concentration
LANDSAT
Linear mixed models
Remote sensing technology
Spatial correlation structures
Water quality monitoring
Water quality parameters
Water surface temperature
Reservoirs (water)
algorithm
chlorophyll a
data set
Landsat
numerical model
satellite imagery
seasonality
surface temperature
transparency
water quality
Argentina
Cordoba [Argentina]
Tercero River
Bonansea, M.
Rodriguez, M.C.
Pinotti, L.
Ferrero, S.
Using multi-temporal Landsat imagery and linear mixed models for assessing water quality parameters in Río Tercero reservoir (Argentina)
topic_facet Algorithms
Landsat
Linear mixed models
Remote sensing
Reservoir
Water quality
Algorithms
Atmospheric temperature
Lakes
Nuclear reactors
Parameter estimation
Regression analysis
Remote sensing
Water quality
Chlorophyll-a concentration
LANDSAT
Linear mixed models
Remote sensing technology
Spatial correlation structures
Water quality monitoring
Water quality parameters
Water surface temperature
Reservoirs (water)
algorithm
chlorophyll a
data set
Landsat
numerical model
satellite imagery
seasonality
surface temperature
transparency
water quality
Argentina
Cordoba [Argentina]
Tercero River
description The application of remote sensing technology to water quality monitoring has special significance for lake management at regional scales. Many studies have proposed algorithms between Landsat data and in-situ water quality parameters using classical regression models. The novelty of this paper is that we developed algorithms to determine log-transformed chlorophyll-a concentration (Chl-a) and Secchi disk transparency (SDT) in Río Tercero reservoir using Landsat TM and ETM. + imagery, ancillary environmental factors and linear mixed models (LMM), obtaining an increase in the accuracy of the estimates. The validation results showed that LMM with spatial correlation structure that take into account water surface temperature (WST) and rainfall were the most suitable method for estimating these parameters. WST derived from the Landsat thermal band was also validated. The algorithms were used to generate quantitative maps providing spatially and temporally rich information on patterns of water quality throughout the reservoir. Water quality features related to the hydrogeomorphology of the reservoir, typical seasonality and influx from the cooling system of a local nuclear reactor were identified in the time series maps. © 2014 Elsevier Inc.
format JOUR
author Bonansea, M.
Rodriguez, M.C.
Pinotti, L.
Ferrero, S.
author_facet Bonansea, M.
Rodriguez, M.C.
Pinotti, L.
Ferrero, S.
author_sort Bonansea, M.
title Using multi-temporal Landsat imagery and linear mixed models for assessing water quality parameters in Río Tercero reservoir (Argentina)
title_short Using multi-temporal Landsat imagery and linear mixed models for assessing water quality parameters in Río Tercero reservoir (Argentina)
title_full Using multi-temporal Landsat imagery and linear mixed models for assessing water quality parameters in Río Tercero reservoir (Argentina)
title_fullStr Using multi-temporal Landsat imagery and linear mixed models for assessing water quality parameters in Río Tercero reservoir (Argentina)
title_full_unstemmed Using multi-temporal Landsat imagery and linear mixed models for assessing water quality parameters in Río Tercero reservoir (Argentina)
title_sort using multi-temporal landsat imagery and linear mixed models for assessing water quality parameters in río tercero reservoir (argentina)
url http://hdl.handle.net/20.500.12110/paper_00344257_v158_n_p28_Bonansea
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AT pinottil usingmultitemporallandsatimageryandlinearmixedmodelsforassessingwaterqualityparametersinrioterceroreservoirargentina
AT ferreros usingmultitemporallandsatimageryandlinearmixedmodelsforassessingwaterqualityparametersinrioterceroreservoirargentina
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