Macrophytes’ abundance changes in eutrophicated tropical reservoirs exemplified by Salto Grande (Brazil): Trends and temporal analysis exploiting Landsat remotely sensed data

Detalhes bibliográficos
Autor(a) principal: Coladello, Leandro Fernandes [UNESP]
Data de Publicação: 2020
Outros Autores: de Lourdes Bueno Trindade Galo, Maria [UNESP], Shimabukuro, Milton Hirokazu [UNESP], Ivánová, Ivana, Awange, Joseph
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1016/j.apgeog.2020.102242
http://hdl.handle.net/11449/201883
Resumo: River damming for electric power production generally triggers a set of anthropic activities that strongly impact on aquatic ecosystem, especially in small reservoirs located in urbanized and industrialized areas. Among the possible adverse effects is the over-abundance of aquatic macrophytes resulting from the input of high concentration of nutrients in the ecosystem that can affect the health of the ecosystem. In these situations, macrophytes are treated as weeds that need to be continuously monitored and analysed over time. Historically, remote sensing has played a prominent role in change detection studies and, nowadays, considering the open data sources of multi-temporal images and the high computational performance that allows for larger volumes of historical images to be mined, water monitoring is a recurrent object of analysis. The Salto Grande reservoir is a small water body located in the metropolitan region of Campinas, São Paulo, Brazil, characterized by high rates of urbanization and industrialization. The intense anthropic occupation around the reservoir triggered the degradation of the landscape and the decrease of water quality. This study explored the potential of image-attributes’ time series to monitor the spatio-temporal behavior of aquatic macrophytes in the Salto Grande Reservoir. Our assumption was that the combination of techniques for analyzing large multi-temporal datasets enables us to understand the trends and changes in the macrophytes occurrence in this small reservoir. To achieve this, quarterly Normalized Difference Vegetation Index (NDVI) time series based on Landsat data imagery from 1984 to 2017 were built to analyze the occurrence and persistence of these aquatic plants in the reservoir. A principal component analysis (PCA) was applied to the NDVI time series, which allowed us to identify typical years in the abundance of macrophytes and twelve regions of greater and lesser temporal variability in its abundance, by a K-means aggregation of the first principal component scores. For these regions, the Breaks for Additive and Seasonality Trend (BFAST) algorithm was used to analyze the trend, cyclic behaviour, and changes in the time series of the average NDVI. BFAST was able to detect gradual and abrupt changes for each of the twelve areas by searching for breakpoints in the temporal series. It was observed that the regions near the dam and where the conditions of the river are still maintained are most affected by the occurrence of macrophytes, characterized by an average NDVI greater than 0.4. Although subject to more subtle seasonal variations, all these regions defined at least one breakpoint, suggesting abrupt changes such as sharp interventions to control the overabundance of macrophytes at specific time. The regions located in the middle of the reservoir, with a more lacustrine influence, had lower average NDVI and small variations over time. Thus, it was possible to identify the critical regions of the studied reservoir with excess of growing macrophytes through the applied method, which also can be applied to similar areas.
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spelling Macrophytes’ abundance changes in eutrophicated tropical reservoirs exemplified by Salto Grande (Brazil): Trends and temporal analysis exploiting Landsat remotely sensed dataAbrupt changesBFASTMacrophytesNDVI time SeriesReservoirSeasonalityRiver damming for electric power production generally triggers a set of anthropic activities that strongly impact on aquatic ecosystem, especially in small reservoirs located in urbanized and industrialized areas. Among the possible adverse effects is the over-abundance of aquatic macrophytes resulting from the input of high concentration of nutrients in the ecosystem that can affect the health of the ecosystem. In these situations, macrophytes are treated as weeds that need to be continuously monitored and analysed over time. Historically, remote sensing has played a prominent role in change detection studies and, nowadays, considering the open data sources of multi-temporal images and the high computational performance that allows for larger volumes of historical images to be mined, water monitoring is a recurrent object of analysis. The Salto Grande reservoir is a small water body located in the metropolitan region of Campinas, São Paulo, Brazil, characterized by high rates of urbanization and industrialization. The intense anthropic occupation around the reservoir triggered the degradation of the landscape and the decrease of water quality. This study explored the potential of image-attributes’ time series to monitor the spatio-temporal behavior of aquatic macrophytes in the Salto Grande Reservoir. Our assumption was that the combination of techniques for analyzing large multi-temporal datasets enables us to understand the trends and changes in the macrophytes occurrence in this small reservoir. To achieve this, quarterly Normalized Difference Vegetation Index (NDVI) time series based on Landsat data imagery from 1984 to 2017 were built to analyze the occurrence and persistence of these aquatic plants in the reservoir. A principal component analysis (PCA) was applied to the NDVI time series, which allowed us to identify typical years in the abundance of macrophytes and twelve regions of greater and lesser temporal variability in its abundance, by a K-means aggregation of the first principal component scores. For these regions, the Breaks for Additive and Seasonality Trend (BFAST) algorithm was used to analyze the trend, cyclic behaviour, and changes in the time series of the average NDVI. BFAST was able to detect gradual and abrupt changes for each of the twelve areas by searching for breakpoints in the temporal series. It was observed that the regions near the dam and where the conditions of the river are still maintained are most affected by the occurrence of macrophytes, characterized by an average NDVI greater than 0.4. Although subject to more subtle seasonal variations, all these regions defined at least one breakpoint, suggesting abrupt changes such as sharp interventions to control the overabundance of macrophytes at specific time. The regions located in the middle of the reservoir, with a more lacustrine influence, had lower average NDVI and small variations over time. Thus, it was possible to identify the critical regions of the studied reservoir with excess of growing macrophytes through the applied method, which also can be applied to similar areas.Karlsruhe Institute of TechnologyAlexander von Humboldt-StiftungCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)School of Technology and Sciences São Paulo State University (Unesp)School of Earth and Planetary Sciences (EPS) Curtin UniversityGeodetic Institute Karlsruhe Institute of Technology, Engler-Strasse 7School of Technology and Sciences São Paulo State University (Unesp)CAPES: 88882.433950/2019-01Universidade Estadual Paulista (Unesp)Curtin UniversityKarlsruhe Institute of TechnologyColadello, Leandro Fernandes [UNESP]de Lourdes Bueno Trindade Galo, Maria [UNESP]Shimabukuro, Milton Hirokazu [UNESP]Ivánová, IvanaAwange, Joseph2020-12-12T02:44:17Z2020-12-12T02:44:17Z2020-08-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1016/j.apgeog.2020.102242Applied Geography, v. 121.0143-6228http://hdl.handle.net/11449/20188310.1016/j.apgeog.2020.1022422-s2.0-850865869591184195536814806Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengApplied Geographyinfo:eu-repo/semantics/openAccess2024-06-19T14:32:06Zoai:repositorio.unesp.br:11449/201883Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T22:41:40.807839Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Macrophytes’ abundance changes in eutrophicated tropical reservoirs exemplified by Salto Grande (Brazil): Trends and temporal analysis exploiting Landsat remotely sensed data
title Macrophytes’ abundance changes in eutrophicated tropical reservoirs exemplified by Salto Grande (Brazil): Trends and temporal analysis exploiting Landsat remotely sensed data
spellingShingle Macrophytes’ abundance changes in eutrophicated tropical reservoirs exemplified by Salto Grande (Brazil): Trends and temporal analysis exploiting Landsat remotely sensed data
Coladello, Leandro Fernandes [UNESP]
Abrupt changes
BFAST
Macrophytes
NDVI time Series
Reservoir
Seasonality
title_short Macrophytes’ abundance changes in eutrophicated tropical reservoirs exemplified by Salto Grande (Brazil): Trends and temporal analysis exploiting Landsat remotely sensed data
title_full Macrophytes’ abundance changes in eutrophicated tropical reservoirs exemplified by Salto Grande (Brazil): Trends and temporal analysis exploiting Landsat remotely sensed data
title_fullStr Macrophytes’ abundance changes in eutrophicated tropical reservoirs exemplified by Salto Grande (Brazil): Trends and temporal analysis exploiting Landsat remotely sensed data
title_full_unstemmed Macrophytes’ abundance changes in eutrophicated tropical reservoirs exemplified by Salto Grande (Brazil): Trends and temporal analysis exploiting Landsat remotely sensed data
title_sort Macrophytes’ abundance changes in eutrophicated tropical reservoirs exemplified by Salto Grande (Brazil): Trends and temporal analysis exploiting Landsat remotely sensed data
author Coladello, Leandro Fernandes [UNESP]
author_facet Coladello, Leandro Fernandes [UNESP]
de Lourdes Bueno Trindade Galo, Maria [UNESP]
Shimabukuro, Milton Hirokazu [UNESP]
Ivánová, Ivana
Awange, Joseph
author_role author
author2 de Lourdes Bueno Trindade Galo, Maria [UNESP]
Shimabukuro, Milton Hirokazu [UNESP]
Ivánová, Ivana
Awange, Joseph
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
Curtin University
Karlsruhe Institute of Technology
dc.contributor.author.fl_str_mv Coladello, Leandro Fernandes [UNESP]
de Lourdes Bueno Trindade Galo, Maria [UNESP]
Shimabukuro, Milton Hirokazu [UNESP]
Ivánová, Ivana
Awange, Joseph
dc.subject.por.fl_str_mv Abrupt changes
BFAST
Macrophytes
NDVI time Series
Reservoir
Seasonality
topic Abrupt changes
BFAST
Macrophytes
NDVI time Series
Reservoir
Seasonality
description River damming for electric power production generally triggers a set of anthropic activities that strongly impact on aquatic ecosystem, especially in small reservoirs located in urbanized and industrialized areas. Among the possible adverse effects is the over-abundance of aquatic macrophytes resulting from the input of high concentration of nutrients in the ecosystem that can affect the health of the ecosystem. In these situations, macrophytes are treated as weeds that need to be continuously monitored and analysed over time. Historically, remote sensing has played a prominent role in change detection studies and, nowadays, considering the open data sources of multi-temporal images and the high computational performance that allows for larger volumes of historical images to be mined, water monitoring is a recurrent object of analysis. The Salto Grande reservoir is a small water body located in the metropolitan region of Campinas, São Paulo, Brazil, characterized by high rates of urbanization and industrialization. The intense anthropic occupation around the reservoir triggered the degradation of the landscape and the decrease of water quality. This study explored the potential of image-attributes’ time series to monitor the spatio-temporal behavior of aquatic macrophytes in the Salto Grande Reservoir. Our assumption was that the combination of techniques for analyzing large multi-temporal datasets enables us to understand the trends and changes in the macrophytes occurrence in this small reservoir. To achieve this, quarterly Normalized Difference Vegetation Index (NDVI) time series based on Landsat data imagery from 1984 to 2017 were built to analyze the occurrence and persistence of these aquatic plants in the reservoir. A principal component analysis (PCA) was applied to the NDVI time series, which allowed us to identify typical years in the abundance of macrophytes and twelve regions of greater and lesser temporal variability in its abundance, by a K-means aggregation of the first principal component scores. For these regions, the Breaks for Additive and Seasonality Trend (BFAST) algorithm was used to analyze the trend, cyclic behaviour, and changes in the time series of the average NDVI. BFAST was able to detect gradual and abrupt changes for each of the twelve areas by searching for breakpoints in the temporal series. It was observed that the regions near the dam and where the conditions of the river are still maintained are most affected by the occurrence of macrophytes, characterized by an average NDVI greater than 0.4. Although subject to more subtle seasonal variations, all these regions defined at least one breakpoint, suggesting abrupt changes such as sharp interventions to control the overabundance of macrophytes at specific time. The regions located in the middle of the reservoir, with a more lacustrine influence, had lower average NDVI and small variations over time. Thus, it was possible to identify the critical regions of the studied reservoir with excess of growing macrophytes through the applied method, which also can be applied to similar areas.
publishDate 2020
dc.date.none.fl_str_mv 2020-12-12T02:44:17Z
2020-12-12T02:44:17Z
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.apgeog.2020.102242
Applied Geography, v. 121.
0143-6228
http://hdl.handle.net/11449/201883
10.1016/j.apgeog.2020.102242
2-s2.0-85086586959
1184195536814806
url http://dx.doi.org/10.1016/j.apgeog.2020.102242
http://hdl.handle.net/11449/201883
identifier_str_mv Applied Geography, v. 121.
0143-6228
10.1016/j.apgeog.2020.102242
2-s2.0-85086586959
1184195536814806
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Applied Geography
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)
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