Remote sensing phenology of the brazilian Caatinga and its environmental drivers.
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
Texto Completo: | http://www.alice.cnptia.embrapa.br/alice/handle/doc/1144436 https://doi.org/10.3390/rs14112637 |
Resumo: | The Caatinga is the largest nucleus of Seasonally Dry Tropical Forests (SDTF) in the Neotropics. The leafing patterns of SDTF vegetation are adapted to the current environmental and climate variability, but the impacts of climate change tend to alter plants? phenology. Thus, it is necessary to characterise phenological parameters and evaluate the relationship between vegetation and environmental drivers. From this information, it is possible to identify the dominant forces in the environment that trigger the phenological dynamics of the Caatinga. In this way, remote sensing represents an essential tool to investigate the phenology of vegetation, particularly as it has a long series of vegetation monitoring and allows relationships with different environmental drivers. This study has two objectives: (i) estimate phenological parameters using an Enhanced Vegetation Index (EVI) time-series over 20 years, and (ii) characterise the relationship between phenologic dynamics and environmental drivers. TIMESAT software was used to determine four phenological parameters: Start Of Season (SOS), End Of Season (EOS), Length Of Season (LOS), and Amplitude (AMPL). Boxplots, Pearson?s, and partial correlation coefficients defined relationships between phenologic dynamics and environmental drivers. The non-parametric test of Fligner Killeen was used to test the interannual variability in SOS and EOS. Our results show that the seasonality of vegetation growth in the Caatinga was different in the three experimental sites. The SOS was the parameter that presented the greatest variability in the days of the year (DOY), reaching a variation of 117 days. The sites with the highest SOS variability are the same ones that showed the lowest EOS variation. In addition, the values of LOS and AMPL are directly linked to the annual distribution of rainfall, and the longer the rainy season, the greater their values are. The variability of the natural cycles of the environmental drivers that regulate the ecosystem?s phenology and the influence on the Caatinga?s natural dynamics indicated a greater sensitivity of the phenologic dynamics to water availability, with precipitation being the limiting factor of the phenologic dynamics. Highlights: The EVI time series was efficient in estimating phenological parameters. The high variability of the start of season (SOS) occurred in sites with low variability of end of the season (EOS) and vice versa. The precipitation and water deficit presented a higher correlation coefficient with phenological dynamics. Length of Season (LOS) and amplitude (AMPL) are directly linked to the annual distribution of rainfall. View Full-Text |
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Remote sensing phenology of the brazilian Caatinga and its environmental drivers.Fenologia da superfície terrestreÍndices de vegetaçãoSazonalmente secaSensoriamento RemotoFloresta TropicalVegetaçãoCaatingaMudança ClimáticaVegetationVegetation indexTropical forestsRemote sensingClimate changeThe Caatinga is the largest nucleus of Seasonally Dry Tropical Forests (SDTF) in the Neotropics. The leafing patterns of SDTF vegetation are adapted to the current environmental and climate variability, but the impacts of climate change tend to alter plants? phenology. Thus, it is necessary to characterise phenological parameters and evaluate the relationship between vegetation and environmental drivers. From this information, it is possible to identify the dominant forces in the environment that trigger the phenological dynamics of the Caatinga. In this way, remote sensing represents an essential tool to investigate the phenology of vegetation, particularly as it has a long series of vegetation monitoring and allows relationships with different environmental drivers. This study has two objectives: (i) estimate phenological parameters using an Enhanced Vegetation Index (EVI) time-series over 20 years, and (ii) characterise the relationship between phenologic dynamics and environmental drivers. TIMESAT software was used to determine four phenological parameters: Start Of Season (SOS), End Of Season (EOS), Length Of Season (LOS), and Amplitude (AMPL). Boxplots, Pearson?s, and partial correlation coefficients defined relationships between phenologic dynamics and environmental drivers. The non-parametric test of Fligner Killeen was used to test the interannual variability in SOS and EOS. Our results show that the seasonality of vegetation growth in the Caatinga was different in the three experimental sites. The SOS was the parameter that presented the greatest variability in the days of the year (DOY), reaching a variation of 117 days. The sites with the highest SOS variability are the same ones that showed the lowest EOS variation. In addition, the values of LOS and AMPL are directly linked to the annual distribution of rainfall, and the longer the rainy season, the greater their values are. The variability of the natural cycles of the environmental drivers that regulate the ecosystem?s phenology and the influence on the Caatinga?s natural dynamics indicated a greater sensitivity of the phenologic dynamics to water availability, with precipitation being the limiting factor of the phenologic dynamics. Highlights: The EVI time series was efficient in estimating phenological parameters. The high variability of the start of season (SOS) occurred in sites with low variability of end of the season (EOS) and vice versa. The precipitation and water deficit presented a higher correlation coefficient with phenological dynamics. Length of Season (LOS) and amplitude (AMPL) are directly linked to the annual distribution of rainfall. View Full-TextRODOLPHO MEDEIROS, UFCG; JOÃO ANDRADE, UFPE; DESIRÉE RAMOS, UNESP, Rio Claro, SP; MAGNA SOELMA BESERRA DE MOURA, CPATSA; ALDRIN MARTIN PÉREZ-MARIN, INSA; CARLOS A. C. DOS SANTOS, UFCG; BERNARDO BARBOSA DA SILVA, UFCG; JOHN CUNHA, UFCG.MEDEIROS, R.ANDRADE, J.RAMOS, D.MOURA, M. S. B. dePÉREZ-MARIN, a. m.SANTOS, C. A. C. dosSILVA, B. B. daCUNGA, J.2022-07-04T16:19:22Z2022-07-04T16:19:22Z2022-07-042022info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleRemote Sensing, v. 14, n. 11, 2637, 2022.http://www.alice.cnptia.embrapa.br/alice/handle/doc/1144436https://doi.org/10.3390/rs14112637enginfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)instacron:EMBRAPA2022-07-04T16:19:31Zoai:www.alice.cnptia.embrapa.br:doc/1144436Repositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestopendoar:21542022-07-04T16:19:31falseRepositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestcg-riaa@embrapa.bropendoar:21542022-07-04T16:19:31Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)false |
dc.title.none.fl_str_mv |
Remote sensing phenology of the brazilian Caatinga and its environmental drivers. |
title |
Remote sensing phenology of the brazilian Caatinga and its environmental drivers. |
spellingShingle |
Remote sensing phenology of the brazilian Caatinga and its environmental drivers. MEDEIROS, R. Fenologia da superfície terrestre Índices de vegetação Sazonalmente seca Sensoriamento Remoto Floresta Tropical Vegetação Caatinga Mudança Climática Vegetation Vegetation index Tropical forests Remote sensing Climate change |
title_short |
Remote sensing phenology of the brazilian Caatinga and its environmental drivers. |
title_full |
Remote sensing phenology of the brazilian Caatinga and its environmental drivers. |
title_fullStr |
Remote sensing phenology of the brazilian Caatinga and its environmental drivers. |
title_full_unstemmed |
Remote sensing phenology of the brazilian Caatinga and its environmental drivers. |
title_sort |
Remote sensing phenology of the brazilian Caatinga and its environmental drivers. |
author |
MEDEIROS, R. |
author_facet |
MEDEIROS, R. ANDRADE, J. RAMOS, D. MOURA, M. S. B. de PÉREZ-MARIN, a. m. SANTOS, C. A. C. dos SILVA, B. B. da CUNGA, J. |
author_role |
author |
author2 |
ANDRADE, J. RAMOS, D. MOURA, M. S. B. de PÉREZ-MARIN, a. m. SANTOS, C. A. C. dos SILVA, B. B. da CUNGA, J. |
author2_role |
author author author author author author author |
dc.contributor.none.fl_str_mv |
RODOLPHO MEDEIROS, UFCG; JOÃO ANDRADE, UFPE; DESIRÉE RAMOS, UNESP, Rio Claro, SP; MAGNA SOELMA BESERRA DE MOURA, CPATSA; ALDRIN MARTIN PÉREZ-MARIN, INSA; CARLOS A. C. DOS SANTOS, UFCG; BERNARDO BARBOSA DA SILVA, UFCG; JOHN CUNHA, UFCG. |
dc.contributor.author.fl_str_mv |
MEDEIROS, R. ANDRADE, J. RAMOS, D. MOURA, M. S. B. de PÉREZ-MARIN, a. m. SANTOS, C. A. C. dos SILVA, B. B. da CUNGA, J. |
dc.subject.por.fl_str_mv |
Fenologia da superfície terrestre Índices de vegetação Sazonalmente seca Sensoriamento Remoto Floresta Tropical Vegetação Caatinga Mudança Climática Vegetation Vegetation index Tropical forests Remote sensing Climate change |
topic |
Fenologia da superfície terrestre Índices de vegetação Sazonalmente seca Sensoriamento Remoto Floresta Tropical Vegetação Caatinga Mudança Climática Vegetation Vegetation index Tropical forests Remote sensing Climate change |
description |
The Caatinga is the largest nucleus of Seasonally Dry Tropical Forests (SDTF) in the Neotropics. The leafing patterns of SDTF vegetation are adapted to the current environmental and climate variability, but the impacts of climate change tend to alter plants? phenology. Thus, it is necessary to characterise phenological parameters and evaluate the relationship between vegetation and environmental drivers. From this information, it is possible to identify the dominant forces in the environment that trigger the phenological dynamics of the Caatinga. In this way, remote sensing represents an essential tool to investigate the phenology of vegetation, particularly as it has a long series of vegetation monitoring and allows relationships with different environmental drivers. This study has two objectives: (i) estimate phenological parameters using an Enhanced Vegetation Index (EVI) time-series over 20 years, and (ii) characterise the relationship between phenologic dynamics and environmental drivers. TIMESAT software was used to determine four phenological parameters: Start Of Season (SOS), End Of Season (EOS), Length Of Season (LOS), and Amplitude (AMPL). Boxplots, Pearson?s, and partial correlation coefficients defined relationships between phenologic dynamics and environmental drivers. The non-parametric test of Fligner Killeen was used to test the interannual variability in SOS and EOS. Our results show that the seasonality of vegetation growth in the Caatinga was different in the three experimental sites. The SOS was the parameter that presented the greatest variability in the days of the year (DOY), reaching a variation of 117 days. The sites with the highest SOS variability are the same ones that showed the lowest EOS variation. In addition, the values of LOS and AMPL are directly linked to the annual distribution of rainfall, and the longer the rainy season, the greater their values are. The variability of the natural cycles of the environmental drivers that regulate the ecosystem?s phenology and the influence on the Caatinga?s natural dynamics indicated a greater sensitivity of the phenologic dynamics to water availability, with precipitation being the limiting factor of the phenologic dynamics. Highlights: The EVI time series was efficient in estimating phenological parameters. The high variability of the start of season (SOS) occurred in sites with low variability of end of the season (EOS) and vice versa. The precipitation and water deficit presented a higher correlation coefficient with phenological dynamics. Length of Season (LOS) and amplitude (AMPL) are directly linked to the annual distribution of rainfall. View Full-Text |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-07-04T16:19:22Z 2022-07-04T16:19:22Z 2022-07-04 2022 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
Remote Sensing, v. 14, n. 11, 2637, 2022. http://www.alice.cnptia.embrapa.br/alice/handle/doc/1144436 https://doi.org/10.3390/rs14112637 |
identifier_str_mv |
Remote Sensing, v. 14, n. 11, 2637, 2022. |
url |
http://www.alice.cnptia.embrapa.br/alice/handle/doc/1144436 https://doi.org/10.3390/rs14112637 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa) instacron:EMBRAPA |
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Empresa Brasileira de Pesquisa Agropecuária (Embrapa) |
instacron_str |
EMBRAPA |
institution |
EMBRAPA |
reponame_str |
Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
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Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
repository.name.fl_str_mv |
Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa) |
repository.mail.fl_str_mv |
cg-riaa@embrapa.br |
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1794503525545279488 |