Detecting spatial and temporal patterns in NDVI time series using histograms

Autores
Gonzalez Loyarte, Maria Margarita
Año de publicación
2002
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The aim of this study was to analyse bimodal histogram patterns of monthly National Oceanic and Atmospheric Administration (NOAA) advanced very high resolution radiometer (AVHRR) normalized difference vegetation index (NDVI) global area coverage (GAC) data and their relation to vegetation dynamics and climatic conditions for the period 1982-1991 in Argentina. The proposed method was to split up bimodal histograms by the median criterion and to study each mode as a separate unimodal frequency distribution. Modes were analysed based on their histogram shape and statistical parameters, geographical distribution and dynamics, and climatic significance. For the latter, a multinomial statistical analysis was used. The split-up criterion yielded coherent results. Histogram shapes and statistical parameters changed according to season. For geographical dynamics, 84% of pixels remained in the same mode through the seasons, and 16% shifted temporarily to the other mode. Changes from low-NDVI mode to high-NDVI mode were caused by an improvement in water supply, rainfall or irrigation, and higher temperatures. Changes in the opposite direction were due to a reduction in vegetation cover produced by drought, harvest, or autumn effects. The low-NDVI mode was strongly related to the arid zone with 74.6% probability (α = 0.05), and the high-NDVI mode was related to humid (58.8%) and semiarid zones (38.4%). This contribution helps explain the dynamics of vegetation cover along the latitudinal range from 22° to 55°S, for nine growing cycles, with a simple methodology. Improving the knowledge of multimodal histograms may allow a better understanding of difficult classification results.
Fil: Gonzalez Loyarte, Maria Margarita. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Investigaciones de las Zonas Áridas. Provincia de Mendoza. Instituto Argentino de Investigaciones de las Zonas Áridas. Universidad Nacional de Cuyo. Instituto Argentino de Investigaciones de las Zonas Áridas; Argentina
Materia
TIME SERIES
NDVI
BIMODAL HISTOGRAM
DYNAMICS OF VEGETATION
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/177158
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/177158
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Detecting spatial and temporal patterns in NDVI time series using histogramsGonzalez Loyarte, Maria MargaritaTIME SERIESNDVIBIMODAL HISTOGRAMDYNAMICS OF VEGETATIONhttps://purl.org/becyt/ford/1.6https://purl.org/becyt/ford/1The aim of this study was to analyse bimodal histogram patterns of monthly National Oceanic and Atmospheric Administration (NOAA) advanced very high resolution radiometer (AVHRR) normalized difference vegetation index (NDVI) global area coverage (GAC) data and their relation to vegetation dynamics and climatic conditions for the period 1982-1991 in Argentina. The proposed method was to split up bimodal histograms by the median criterion and to study each mode as a separate unimodal frequency distribution. Modes were analysed based on their histogram shape and statistical parameters, geographical distribution and dynamics, and climatic significance. For the latter, a multinomial statistical analysis was used. The split-up criterion yielded coherent results. Histogram shapes and statistical parameters changed according to season. For geographical dynamics, 84% of pixels remained in the same mode through the seasons, and 16% shifted temporarily to the other mode. Changes from low-NDVI mode to high-NDVI mode were caused by an improvement in water supply, rainfall or irrigation, and higher temperatures. Changes in the opposite direction were due to a reduction in vegetation cover produced by drought, harvest, or autumn effects. The low-NDVI mode was strongly related to the arid zone with 74.6% probability (α = 0.05), and the high-NDVI mode was related to humid (58.8%) and semiarid zones (38.4%). This contribution helps explain the dynamics of vegetation cover along the latitudinal range from 22° to 55°S, for nine growing cycles, with a simple methodology. Improving the knowledge of multimodal histograms may allow a better understanding of difficult classification results.Fil: Gonzalez Loyarte, Maria Margarita. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Investigaciones de las Zonas Áridas. Provincia de Mendoza. Instituto Argentino de Investigaciones de las Zonas Áridas. Universidad Nacional de Cuyo. Instituto Argentino de Investigaciones de las Zonas Áridas; ArgentinaTaylor & Francis2002-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/177158Gonzalez Loyarte, Maria Margarita; Detecting spatial and temporal patterns in NDVI time series using histograms; Taylor & Francis; Canadian Journal Of Remote Sensing; 28; 2; 4-2002; 275-2900703-8992CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.tandfonline.com/doi/abs/10.5589/m02-027info:eu-repo/semantics/altIdentifier/doi/10.5589/m02-027info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T14:54:07Zoai:ri.conicet.gov.ar:11336/177158instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-15 14:54:07.365CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Detecting spatial and temporal patterns in NDVI time series using histograms
title Detecting spatial and temporal patterns in NDVI time series using histograms
spellingShingle Detecting spatial and temporal patterns in NDVI time series using histograms
Gonzalez Loyarte, Maria Margarita
TIME SERIES
NDVI
BIMODAL HISTOGRAM
DYNAMICS OF VEGETATION
title_short Detecting spatial and temporal patterns in NDVI time series using histograms
title_full Detecting spatial and temporal patterns in NDVI time series using histograms
title_fullStr Detecting spatial and temporal patterns in NDVI time series using histograms
title_full_unstemmed Detecting spatial and temporal patterns in NDVI time series using histograms
title_sort Detecting spatial and temporal patterns in NDVI time series using histograms
dc.creator.none.fl_str_mv Gonzalez Loyarte, Maria Margarita
author Gonzalez Loyarte, Maria Margarita
author_facet Gonzalez Loyarte, Maria Margarita
author_role author
dc.subject.none.fl_str_mv TIME SERIES
NDVI
BIMODAL HISTOGRAM
DYNAMICS OF VEGETATION
topic TIME SERIES
NDVI
BIMODAL HISTOGRAM
DYNAMICS OF VEGETATION
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.6
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv The aim of this study was to analyse bimodal histogram patterns of monthly National Oceanic and Atmospheric Administration (NOAA) advanced very high resolution radiometer (AVHRR) normalized difference vegetation index (NDVI) global area coverage (GAC) data and their relation to vegetation dynamics and climatic conditions for the period 1982-1991 in Argentina. The proposed method was to split up bimodal histograms by the median criterion and to study each mode as a separate unimodal frequency distribution. Modes were analysed based on their histogram shape and statistical parameters, geographical distribution and dynamics, and climatic significance. For the latter, a multinomial statistical analysis was used. The split-up criterion yielded coherent results. Histogram shapes and statistical parameters changed according to season. For geographical dynamics, 84% of pixels remained in the same mode through the seasons, and 16% shifted temporarily to the other mode. Changes from low-NDVI mode to high-NDVI mode were caused by an improvement in water supply, rainfall or irrigation, and higher temperatures. Changes in the opposite direction were due to a reduction in vegetation cover produced by drought, harvest, or autumn effects. The low-NDVI mode was strongly related to the arid zone with 74.6% probability (α = 0.05), and the high-NDVI mode was related to humid (58.8%) and semiarid zones (38.4%). This contribution helps explain the dynamics of vegetation cover along the latitudinal range from 22° to 55°S, for nine growing cycles, with a simple methodology. Improving the knowledge of multimodal histograms may allow a better understanding of difficult classification results.
Fil: Gonzalez Loyarte, Maria Margarita. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Investigaciones de las Zonas Áridas. Provincia de Mendoza. Instituto Argentino de Investigaciones de las Zonas Áridas. Universidad Nacional de Cuyo. Instituto Argentino de Investigaciones de las Zonas Áridas; Argentina
description The aim of this study was to analyse bimodal histogram patterns of monthly National Oceanic and Atmospheric Administration (NOAA) advanced very high resolution radiometer (AVHRR) normalized difference vegetation index (NDVI) global area coverage (GAC) data and their relation to vegetation dynamics and climatic conditions for the period 1982-1991 in Argentina. The proposed method was to split up bimodal histograms by the median criterion and to study each mode as a separate unimodal frequency distribution. Modes were analysed based on their histogram shape and statistical parameters, geographical distribution and dynamics, and climatic significance. For the latter, a multinomial statistical analysis was used. The split-up criterion yielded coherent results. Histogram shapes and statistical parameters changed according to season. For geographical dynamics, 84% of pixels remained in the same mode through the seasons, and 16% shifted temporarily to the other mode. Changes from low-NDVI mode to high-NDVI mode were caused by an improvement in water supply, rainfall or irrigation, and higher temperatures. Changes in the opposite direction were due to a reduction in vegetation cover produced by drought, harvest, or autumn effects. The low-NDVI mode was strongly related to the arid zone with 74.6% probability (α = 0.05), and the high-NDVI mode was related to humid (58.8%) and semiarid zones (38.4%). This contribution helps explain the dynamics of vegetation cover along the latitudinal range from 22° to 55°S, for nine growing cycles, with a simple methodology. Improving the knowledge of multimodal histograms may allow a better understanding of difficult classification results.
publishDate 2002
dc.date.none.fl_str_mv 2002-04
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/177158
Gonzalez Loyarte, Maria Margarita; Detecting spatial and temporal patterns in NDVI time series using histograms; Taylor & Francis; Canadian Journal Of Remote Sensing; 28; 2; 4-2002; 275-290
0703-8992
CONICET Digital
CONICET
url http://hdl.handle.net/11336/177158
identifier_str_mv Gonzalez Loyarte, Maria Margarita; Detecting spatial and temporal patterns in NDVI time series using histograms; Taylor & Francis; Canadian Journal Of Remote Sensing; 28; 2; 4-2002; 275-290
0703-8992
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.tandfonline.com/doi/abs/10.5589/m02-027
info:eu-repo/semantics/altIdentifier/doi/10.5589/m02-027
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Taylor & Francis
publisher.none.fl_str_mv Taylor & Francis
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.22299

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