Classification of homogeneous regions of vegetation cover in the State of Rio Grande do Sul, Brazil and its temporal dynamics, using AVHRR GIMMS and MODIS data sets
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Anais da Academia Brasileira de Ciências (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652021000501006 |
Resumo: | Abstract This study aimed to classify the homogeneous regions of vegetation cover, which occur in Rio Grande do Sul, formed by clustering of pixels with same pattern of temporal variability of the Normalized Difference Vegetation Index (NDVI) of AVHRR GIMMS and MODIS series and to compare their temporal dynamics. We use K means cluster analysis for defining homogeneous regions, based on the temporal variability of GIMMS (8 km spatial resolution) and MODIS (1 km spatial resolution) NDVI data sets, using monthly images mean from 2000 to 2008 (overlapping period); and we analyzed the annual pattern of NDVI. Accuracy assessment was done with Landsat images. The results show that the temporal variability of GIMMS and MODIS NDVI allows to delimit similar homogeneous regions in order to mapping the main vegetation cover. MODIS series shows a greater detail in the definition of the regions, but with compatibility with those generated by GIMMS. The temporal dynamics show a typical seasonal pattern, with variations of NDVI amplitude between the groups, that allow to monitor phenological changes. The deviations from calibration between times series are linear, which would facilitate a correction in order to construct a long synthetic time series for studies of land cover change. |
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Anais da Academia Brasileira de Ciências (Online) |
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Classification of homogeneous regions of vegetation cover in the State of Rio Grande do Sul, Brazil and its temporal dynamics, using AVHRR GIMMS and MODIS data setsagricultureclassificationclusteringforestgrasslandkmeansAbstract This study aimed to classify the homogeneous regions of vegetation cover, which occur in Rio Grande do Sul, formed by clustering of pixels with same pattern of temporal variability of the Normalized Difference Vegetation Index (NDVI) of AVHRR GIMMS and MODIS series and to compare their temporal dynamics. We use K means cluster analysis for defining homogeneous regions, based on the temporal variability of GIMMS (8 km spatial resolution) and MODIS (1 km spatial resolution) NDVI data sets, using monthly images mean from 2000 to 2008 (overlapping period); and we analyzed the annual pattern of NDVI. Accuracy assessment was done with Landsat images. The results show that the temporal variability of GIMMS and MODIS NDVI allows to delimit similar homogeneous regions in order to mapping the main vegetation cover. MODIS series shows a greater detail in the definition of the regions, but with compatibility with those generated by GIMMS. The temporal dynamics show a typical seasonal pattern, with variations of NDVI amplitude between the groups, that allow to monitor phenological changes. The deviations from calibration between times series are linear, which would facilitate a correction in order to construct a long synthetic time series for studies of land cover change.Academia Brasileira de Ciências2021-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652021000501006Anais da Academia Brasileira de Ciências v.93 n.3 2021reponame:Anais da Academia Brasileira de Ciências (Online)instname:Academia Brasileira de Ciências (ABC)instacron:ABC10.1590/0001-3765202120201278info:eu-repo/semantics/openAccessCORDEIRO,ANA PAULA A.ALVES,RITA DE CÁSSIA M.STEFFLER,ANA PAULA L.W.MENGUE,VAGNER P.FONTANA,DENISE C.ROGLIO,VINICIUS S.GUASSELLI,LAURINDO A.eng2021-06-11T00:00:00Zoai:scielo:S0001-37652021000501006Revistahttp://www.scielo.br/aabchttps://old.scielo.br/oai/scielo-oai.php||aabc@abc.org.br1678-26900001-3765opendoar:2021-06-11T00:00Anais da Academia Brasileira de Ciências (Online) - Academia Brasileira de Ciências (ABC)false |
dc.title.none.fl_str_mv |
Classification of homogeneous regions of vegetation cover in the State of Rio Grande do Sul, Brazil and its temporal dynamics, using AVHRR GIMMS and MODIS data sets |
title |
Classification of homogeneous regions of vegetation cover in the State of Rio Grande do Sul, Brazil and its temporal dynamics, using AVHRR GIMMS and MODIS data sets |
spellingShingle |
Classification of homogeneous regions of vegetation cover in the State of Rio Grande do Sul, Brazil and its temporal dynamics, using AVHRR GIMMS and MODIS data sets CORDEIRO,ANA PAULA A. agriculture classification clustering forest grassland kmeans |
title_short |
Classification of homogeneous regions of vegetation cover in the State of Rio Grande do Sul, Brazil and its temporal dynamics, using AVHRR GIMMS and MODIS data sets |
title_full |
Classification of homogeneous regions of vegetation cover in the State of Rio Grande do Sul, Brazil and its temporal dynamics, using AVHRR GIMMS and MODIS data sets |
title_fullStr |
Classification of homogeneous regions of vegetation cover in the State of Rio Grande do Sul, Brazil and its temporal dynamics, using AVHRR GIMMS and MODIS data sets |
title_full_unstemmed |
Classification of homogeneous regions of vegetation cover in the State of Rio Grande do Sul, Brazil and its temporal dynamics, using AVHRR GIMMS and MODIS data sets |
title_sort |
Classification of homogeneous regions of vegetation cover in the State of Rio Grande do Sul, Brazil and its temporal dynamics, using AVHRR GIMMS and MODIS data sets |
author |
CORDEIRO,ANA PAULA A. |
author_facet |
CORDEIRO,ANA PAULA A. ALVES,RITA DE CÁSSIA M. STEFFLER,ANA PAULA L.W. MENGUE,VAGNER P. FONTANA,DENISE C. ROGLIO,VINICIUS S. GUASSELLI,LAURINDO A. |
author_role |
author |
author2 |
ALVES,RITA DE CÁSSIA M. STEFFLER,ANA PAULA L.W. MENGUE,VAGNER P. FONTANA,DENISE C. ROGLIO,VINICIUS S. GUASSELLI,LAURINDO A. |
author2_role |
author author author author author author |
dc.contributor.author.fl_str_mv |
CORDEIRO,ANA PAULA A. ALVES,RITA DE CÁSSIA M. STEFFLER,ANA PAULA L.W. MENGUE,VAGNER P. FONTANA,DENISE C. ROGLIO,VINICIUS S. GUASSELLI,LAURINDO A. |
dc.subject.por.fl_str_mv |
agriculture classification clustering forest grassland kmeans |
topic |
agriculture classification clustering forest grassland kmeans |
description |
Abstract This study aimed to classify the homogeneous regions of vegetation cover, which occur in Rio Grande do Sul, formed by clustering of pixels with same pattern of temporal variability of the Normalized Difference Vegetation Index (NDVI) of AVHRR GIMMS and MODIS series and to compare their temporal dynamics. We use K means cluster analysis for defining homogeneous regions, based on the temporal variability of GIMMS (8 km spatial resolution) and MODIS (1 km spatial resolution) NDVI data sets, using monthly images mean from 2000 to 2008 (overlapping period); and we analyzed the annual pattern of NDVI. Accuracy assessment was done with Landsat images. The results show that the temporal variability of GIMMS and MODIS NDVI allows to delimit similar homogeneous regions in order to mapping the main vegetation cover. MODIS series shows a greater detail in the definition of the regions, but with compatibility with those generated by GIMMS. The temporal dynamics show a typical seasonal pattern, with variations of NDVI amplitude between the groups, that allow to monitor phenological changes. The deviations from calibration between times series are linear, which would facilitate a correction in order to construct a long synthetic time series for studies of land cover change. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-01-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652021000501006 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652021000501006 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/0001-3765202120201278 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Academia Brasileira de Ciências |
publisher.none.fl_str_mv |
Academia Brasileira de Ciências |
dc.source.none.fl_str_mv |
Anais da Academia Brasileira de Ciências v.93 n.3 2021 reponame:Anais da Academia Brasileira de Ciências (Online) instname:Academia Brasileira de Ciências (ABC) instacron:ABC |
instname_str |
Academia Brasileira de Ciências (ABC) |
instacron_str |
ABC |
institution |
ABC |
reponame_str |
Anais da Academia Brasileira de Ciências (Online) |
collection |
Anais da Academia Brasileira de Ciências (Online) |
repository.name.fl_str_mv |
Anais da Academia Brasileira de Ciências (Online) - Academia Brasileira de Ciências (ABC) |
repository.mail.fl_str_mv |
||aabc@abc.org.br |
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1754302870453223424 |