Inland water's trophic status classification based on machine learning and remote sensing data
Autor(a) principal: | |
---|---|
Data de Publicação: | 2020 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1016/j.rsase.2020.100326 http://hdl.handle.net/11449/200465 |
Resumo: | In this work, we tested machine learning algorithms in classifying waters in a reservoir cascade with basis in trophic state. The classification was done through remote sensing reflectance (Rrs) measurements collected in situ. Chlorophyll-a (chla) content determined in the laboratory were used to define the trophic state in the sampling points distributed in four reservoirs (Barra Bonita, Bariri, Ibitinga and Nova Avanhandava), located at the Tietê River, Brazil. Those four impoundments exhibit widely differing optical properties from each other, which is rather evident in relation to chla concentration. From the dataset collected in the reservoir cascade, a trophic gradient is observed, decreasing from up-to downstream. To classify the trophic state, we tested three machine learning algorithms: Artificial Neural Network (ANN), Random Forest (RF) and Support Vector Machine (SVM). Results showed that ANN and RF algorithms exhibited the best performance in classifying the different trophic state in the cascade of reservoirs. Both approaches raised a global accuracy of 80.00% and average area under Receiver Operating Characteristics (ROC) curve (AUCROC) of 0.928 and 0.912, respectively. Comparing the machine learning approaches with a parametric algorithm, only SVM presented a slightly lower performance. The outcomes of this classification can be useful for trophic state mapping considering the large cascade of reservoirs or rivers. In addition, it can give a direction in bio-optical modeling studies, which have shown that a unique bio-optical algorithm is unable to accurately retrieving concentrations of optically active constituents in aquatic system with high optical variability. So that, it is possible to develop specific chla prediction models considering the optical characteristics of each stretch of river, since machine learning-based classifications (ANN and RF) indicate different optical regions. |
id |
UNSP_53093064d0ab8007fc03e930aa104e39 |
---|---|
oai_identifier_str |
oai:repositorio.unesp.br:11449/200465 |
network_acronym_str |
UNSP |
network_name_str |
Repositório Institucional da UNESP |
repository_id_str |
2946 |
spelling |
Inland water's trophic status classification based on machine learning and remote sensing dataArtificial neural networkMultispectral dataRandom forestRemote sensingSupport vector machineIn this work, we tested machine learning algorithms in classifying waters in a reservoir cascade with basis in trophic state. The classification was done through remote sensing reflectance (Rrs) measurements collected in situ. Chlorophyll-a (chla) content determined in the laboratory were used to define the trophic state in the sampling points distributed in four reservoirs (Barra Bonita, Bariri, Ibitinga and Nova Avanhandava), located at the Tietê River, Brazil. Those four impoundments exhibit widely differing optical properties from each other, which is rather evident in relation to chla concentration. From the dataset collected in the reservoir cascade, a trophic gradient is observed, decreasing from up-to downstream. To classify the trophic state, we tested three machine learning algorithms: Artificial Neural Network (ANN), Random Forest (RF) and Support Vector Machine (SVM). Results showed that ANN and RF algorithms exhibited the best performance in classifying the different trophic state in the cascade of reservoirs. Both approaches raised a global accuracy of 80.00% and average area under Receiver Operating Characteristics (ROC) curve (AUCROC) of 0.928 and 0.912, respectively. Comparing the machine learning approaches with a parametric algorithm, only SVM presented a slightly lower performance. The outcomes of this classification can be useful for trophic state mapping considering the large cascade of reservoirs or rivers. In addition, it can give a direction in bio-optical modeling studies, which have shown that a unique bio-optical algorithm is unable to accurately retrieving concentrations of optically active constituents in aquatic system with high optical variability. So that, it is possible to develop specific chla prediction models considering the optical characteristics of each stretch of river, since machine learning-based classifications (ANN and RF) indicate different optical regions.Universidade Estadual PaulistaConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Department of Cartography Faculty of Sciences and Technology São Paulo State University – UNESPFederal Institute for Education Science and Technology of Pará State – IFPADepartment of Environmental Engineering Institute of Science and Technology São Paulo State University – UNESPDepartment of Cartography Faculty of Sciences and Technology São Paulo State University – UNESPDepartment of Environmental Engineering Institute of Science and Technology São Paulo State University – UNESPCNPq: 151001/2019-7FAPESP: 2012/19821-1FAPESP: 2013/09045-7FAPESP: 2015/21586-9FAPESP: 2019/00259-0CNPq: 310660/2019-0CNPq: 400881/2013-6CNPq: 472131/2012-5CNPq: 482605/2013-8CNPq: 53854/2016-2CAPES: 88882.317841/2019-01Universidade Estadual Paulista (Unesp)Science and Technology of Pará State – IFPAWatanabe, Fernanda S.Y. [UNESP]Miyoshi, Gabriela T. [UNESP]Rodrigues, Thanan W.P.Bernardo, Nariane M.R. [UNESP]Rotta, Luiz H.S. [UNESP]Alcântara, Enner [UNESP]Imai, Nilton N. [UNESP]2020-12-12T02:07:23Z2020-12-12T02:07:23Z2020-08-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1016/j.rsase.2020.100326Remote Sensing Applications: Society and Environment, v. 19.2352-9385http://hdl.handle.net/11449/20046510.1016/j.rsase.2020.1003262-s2.0-8508517220166913103944104900000-0002-8077-2865Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengRemote Sensing Applications: Society and Environmentinfo:eu-repo/semantics/openAccess2024-06-18T15:01:08Zoai:repositorio.unesp.br:11449/200465Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-06-18T15:01:08Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Inland water's trophic status classification based on machine learning and remote sensing data |
title |
Inland water's trophic status classification based on machine learning and remote sensing data |
spellingShingle |
Inland water's trophic status classification based on machine learning and remote sensing data Watanabe, Fernanda S.Y. [UNESP] Artificial neural network Multispectral data Random forest Remote sensing Support vector machine |
title_short |
Inland water's trophic status classification based on machine learning and remote sensing data |
title_full |
Inland water's trophic status classification based on machine learning and remote sensing data |
title_fullStr |
Inland water's trophic status classification based on machine learning and remote sensing data |
title_full_unstemmed |
Inland water's trophic status classification based on machine learning and remote sensing data |
title_sort |
Inland water's trophic status classification based on machine learning and remote sensing data |
author |
Watanabe, Fernanda S.Y. [UNESP] |
author_facet |
Watanabe, Fernanda S.Y. [UNESP] Miyoshi, Gabriela T. [UNESP] Rodrigues, Thanan W.P. Bernardo, Nariane M.R. [UNESP] Rotta, Luiz H.S. [UNESP] Alcântara, Enner [UNESP] Imai, Nilton N. [UNESP] |
author_role |
author |
author2 |
Miyoshi, Gabriela T. [UNESP] Rodrigues, Thanan W.P. Bernardo, Nariane M.R. [UNESP] Rotta, Luiz H.S. [UNESP] Alcântara, Enner [UNESP] Imai, Nilton N. [UNESP] |
author2_role |
author author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) Science and Technology of Pará State – IFPA |
dc.contributor.author.fl_str_mv |
Watanabe, Fernanda S.Y. [UNESP] Miyoshi, Gabriela T. [UNESP] Rodrigues, Thanan W.P. Bernardo, Nariane M.R. [UNESP] Rotta, Luiz H.S. [UNESP] Alcântara, Enner [UNESP] Imai, Nilton N. [UNESP] |
dc.subject.por.fl_str_mv |
Artificial neural network Multispectral data Random forest Remote sensing Support vector machine |
topic |
Artificial neural network Multispectral data Random forest Remote sensing Support vector machine |
description |
In this work, we tested machine learning algorithms in classifying waters in a reservoir cascade with basis in trophic state. The classification was done through remote sensing reflectance (Rrs) measurements collected in situ. Chlorophyll-a (chla) content determined in the laboratory were used to define the trophic state in the sampling points distributed in four reservoirs (Barra Bonita, Bariri, Ibitinga and Nova Avanhandava), located at the Tietê River, Brazil. Those four impoundments exhibit widely differing optical properties from each other, which is rather evident in relation to chla concentration. From the dataset collected in the reservoir cascade, a trophic gradient is observed, decreasing from up-to downstream. To classify the trophic state, we tested three machine learning algorithms: Artificial Neural Network (ANN), Random Forest (RF) and Support Vector Machine (SVM). Results showed that ANN and RF algorithms exhibited the best performance in classifying the different trophic state in the cascade of reservoirs. Both approaches raised a global accuracy of 80.00% and average area under Receiver Operating Characteristics (ROC) curve (AUCROC) of 0.928 and 0.912, respectively. Comparing the machine learning approaches with a parametric algorithm, only SVM presented a slightly lower performance. The outcomes of this classification can be useful for trophic state mapping considering the large cascade of reservoirs or rivers. In addition, it can give a direction in bio-optical modeling studies, which have shown that a unique bio-optical algorithm is unable to accurately retrieving concentrations of optically active constituents in aquatic system with high optical variability. So that, it is possible to develop specific chla prediction models considering the optical characteristics of each stretch of river, since machine learning-based classifications (ANN and RF) indicate different optical regions. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-12-12T02:07:23Z 2020-12-12T02:07:23Z 2020-08-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1016/j.rsase.2020.100326 Remote Sensing Applications: Society and Environment, v. 19. 2352-9385 http://hdl.handle.net/11449/200465 10.1016/j.rsase.2020.100326 2-s2.0-85085172201 6691310394410490 0000-0002-8077-2865 |
url |
http://dx.doi.org/10.1016/j.rsase.2020.100326 http://hdl.handle.net/11449/200465 |
identifier_str_mv |
Remote Sensing Applications: Society and Environment, v. 19. 2352-9385 10.1016/j.rsase.2020.100326 2-s2.0-85085172201 6691310394410490 0000-0002-8077-2865 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Remote Sensing Applications: Society and Environment |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1803045250956001280 |