Classification of NDVI Trends in the Yucatan Peninsula, Mexico, from 2014 to 2020

The Yucatan Peninsula (YP) is home to 32% of tropical forests of Mexico. Consequently, this area has a high cloudiness throughout the year, which represents a particular challenge for any mid- and long-term plant monitoring study based on satellite-image time series. This paper reports the results o...

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Autores: Tecuapetla-Gómez, Inder, Carbajal-Domínguez, Alfonso, Montesinos-Chica, Valeria
Tipo de documento: artigo
Estado:Versão publicada
Data de publicação:2022
País:México
Recursos:UNIVERSIDAD NACIONAL AUTÓNOMA DE MÉXICO
Repositório:Investigaciones Geográficas
Idioma:espanhol
OAI Identifier:oai:ojs.pkp.sfu.ca:article/60629
Acesso em linha:https://www.investigacionesgeograficas.unam.mx/index.php/rig/article/view/60629
Access Level:Acceso aberto
Palavra-chave:NVDI
trend analysis
time series
gapfill
abrupts estimation
NDVI
análisis de tendencias
estimación de cambios abruptos
series de tiempo
bfast01
Península de Yucatán
id MX_e45e2ea692e075da84bd5f0434be931e
oai_identifier_str oai:ojs.pkp.sfu.ca:article/60629
network_acronym_str MX
network_name_str México
repository_id_str
dc.title.none.fl_str_mv Classification of NDVI Trends in the Yucatan Peninsula, Mexico, from 2014 to 2020
Clasificación de tendencias de NDVI en la península de Yucatán, México, de 2014 a 2020
title Classification of NDVI Trends in the Yucatan Peninsula, Mexico, from 2014 to 2020
spellingShingle Classification of NDVI Trends in the Yucatan Peninsula, Mexico, from 2014 to 2020
Tecuapetla-Gómez, Inder
NVDI
trend analysis
time series
gapfill
abrupts estimation
NDVI
análisis de tendencias
estimación de cambios abruptos
series de tiempo
gapfill
bfast01
Península de Yucatán
title_short Classification of NDVI Trends in the Yucatan Peninsula, Mexico, from 2014 to 2020
title_full Classification of NDVI Trends in the Yucatan Peninsula, Mexico, from 2014 to 2020
title_fullStr Classification of NDVI Trends in the Yucatan Peninsula, Mexico, from 2014 to 2020
title_full_unstemmed Classification of NDVI Trends in the Yucatan Peninsula, Mexico, from 2014 to 2020
title_sort Classification of NDVI Trends in the Yucatan Peninsula, Mexico, from 2014 to 2020
dc.creator.none.fl_str_mv Tecuapetla-Gómez, Inder
Carbajal-Domínguez, Alfonso
Montesinos-Chica, Valeria
author Tecuapetla-Gómez, Inder
author_facet Tecuapetla-Gómez, Inder
Carbajal-Domínguez, Alfonso
Montesinos-Chica, Valeria
author_role author
author2 Carbajal-Domínguez, Alfonso
Montesinos-Chica, Valeria
author2_role author
author
dc.subject.none.fl_str_mv NVDI
trend analysis
time series
gapfill
abrupts estimation
NDVI
análisis de tendencias
estimación de cambios abruptos
series de tiempo
gapfill
bfast01
Península de Yucatán
topic NVDI
trend analysis
time series
gapfill
abrupts estimation
NDVI
análisis de tendencias
estimación de cambios abruptos
series de tiempo
gapfill
bfast01
Península de Yucatán
description The Yucatan Peninsula (YP) is home to 32% of tropical forests of Mexico. Consequently, this area has a high cloudiness throughout the year, which represents a particular challenge for any mid- and long-term plant monitoring study based on satellite-image time series. This paper reports the results of a trend classification analysis of a time series (11 Landsat-7 ETM+ and 150 Landsat 8 OLI images) of the Normalized Difference Vegetation Index (NDVI) by soil and vegetation types in the eastern region of Escarcega, Campeche (YP) from 2014 to 2020. We applied the bfast01 algorithm to classify pixels according to linear trends, either global (a line with a positive or negative slope through the study period) or local (two linear segments, each with a positive or negative slope). The analysis reveals that most of the study region has NDVI values with global linear trends (browning:  47%; greening:  15.39%) and, to a lesser degree, local linear trends (delayed browning: 20.66%;  browning to greening:  6.04%; delayed greening: 5.26%; greening to browning: 3.88%) We consider that generalized greening (which pools the greening, delayed greening, and browning to greening classes) and generalized browning (which pools the browning, delayed browning, and greening to browning classes) can be interpreted as dynamics with significant signs of recovery and degradation of the NDVI, respectively. These dynamics were identified mainly in the semi-evergreen medium tropical forest (generalized greening:  10.26%; generalized browning: 25.43%), semi-evergreen low thorny tropical forest (7.66 and 21.76) and the secondary tree vegetation of the medium tropical forest (3.26 and 10.93). The largest areas with any kind of linear local trend were identified in 2017 and 2018.
publishDate 2022
dc.date.none.fl_str_mv 2022-11-29
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://www.investigacionesgeograficas.unam.mx/index.php/rig/article/view/60629
10.14350/rig.60629
url https://www.investigacionesgeograficas.unam.mx/index.php/rig/article/view/60629
identifier_str_mv 10.14350/rig.60629
dc.language.none.fl_str_mv spa
language spa
dc.relation.none.fl_str_mv https://www.investigacionesgeograficas.unam.mx/index.php/rig/article/view/60629/54500
https://www.investigacionesgeograficas.unam.mx/index.php/rig/article/view/60629/54541
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
text/xml
dc.publisher.none.fl_str_mv Instituto de Geografía
publisher.none.fl_str_mv Instituto de Geografía
dc.source.none.fl_str_mv Investigaciones Geográficas; No. 109
Investigaciones Geográficas; Núm. 109
2448-7279
0188-4611
reponame:Investigaciones Geográficas
instname:UNIVERSIDAD NACIONAL AUTÓNOMA DE MÉXICO
instacron:UNAM
instname_str UNIVERSIDAD NACIONAL AUTÓNOMA DE MÉXICO
instacron_str UNAM
institution UNAM
reponame_str Investigaciones Geográficas
collection Investigaciones Geográficas
repository.name.fl_str_mv
repository.mail.fl_str_mv
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spelling Classification of NDVI Trends in the Yucatan Peninsula, Mexico, from 2014 to 2020Clasificación de tendencias de NDVI en la península de Yucatán, México, de 2014 a 2020Tecuapetla-Gómez, InderCarbajal-Domínguez, AlfonsoMontesinos-Chica, ValeriaNVDItrend analysistime seriesgapfillabrupts estimationNDVIanálisis de tendenciasestimación de cambios abruptosseries de tiempogapfillbfast01Península de YucatánThe Yucatan Peninsula (YP) is home to 32% of tropical forests of Mexico. Consequently, this area has a high cloudiness throughout the year, which represents a particular challenge for any mid- and long-term plant monitoring study based on satellite-image time series. This paper reports the results of a trend classification analysis of a time series (11 Landsat-7 ETM+ and 150 Landsat 8 OLI images) of the Normalized Difference Vegetation Index (NDVI) by soil and vegetation types in the eastern region of Escarcega, Campeche (YP) from 2014 to 2020. We applied the bfast01 algorithm to classify pixels according to linear trends, either global (a line with a positive or negative slope through the study period) or local (two linear segments, each with a positive or negative slope). The analysis reveals that most of the study region has NDVI values with global linear trends (browning:  47%; greening:  15.39%) and, to a lesser degree, local linear trends (delayed browning: 20.66%;  browning to greening:  6.04%; delayed greening: 5.26%; greening to browning: 3.88%) We consider that generalized greening (which pools the greening, delayed greening, and browning to greening classes) and generalized browning (which pools the browning, delayed browning, and greening to browning classes) can be interpreted as dynamics with significant signs of recovery and degradation of the NDVI, respectively. These dynamics were identified mainly in the semi-evergreen medium tropical forest (generalized greening:  10.26%; generalized browning: 25.43%), semi-evergreen low thorny tropical forest (7.66 and 21.76) and the secondary tree vegetation of the medium tropical forest (3.26 and 10.93). The largest areas with any kind of linear local trend were identified in 2017 and 2018.La península de Yucatán (LPY) alberga 32% de las selvas tropicales de México. Por ello esta zona presenta una alta densidad de nubes a lo largo del año, característica que resulta un reto particular para cualquier estudio de monitoreo de la vegetación a mediano y largo plazo basado en series de tiempo de imágenes satelitales. En este artículo presentamos los resultados de un análisis de clasificación de tendencias de una serie de tiempo (11 imágenes Landsat-7 ETM+ y 150 Landsat 8 OLI) del Índice de Vegetación de Diferencia Normalizada (NDVI, por sus siglas en inglés) por tipo de suelo y vegetación en la región oriental de Escárcega, Campeche, en LPY de 2014 a 2020. Aplicamos el algoritmo bfast01 para clasificar pixeles por tendencias lineales, globales (una línea con pendiente positiva o negativa a lo largo del periodo de estudio) o locales (dos segmentos de línea con pendiente positiva o negativa en cada segmento). A partir del análisis se deduce que la mayor parte de la región estudiada tiene NDVI con tendencias lineales globales (pardeamiento: 47%; enverdecimiento: 15.39%) y en menor grado tendencias lineales locales (pardeamiento demorado: 20.66%; pardeamiento a enverdecimiento: 6.04%; enverdecimiento demorado: 5.26%; enverdecimiento a pardeamiento: 3.88%). Consideramos que el enverdecimiento generalizado (el agregado de las clases enverdecimiento, enverdecimiento demorado y pardeamiento a enverdecimiento) y el pardeamiento generalizado (el agregado de las clases pardeamiento, pardeamiento demorado y enverdecimiento a pardeamiento) pueden interpretarse como dinámicas con indicios significativos de recuperación y degradación del NDVI, respectivamente. Dichas dinámicas fueron identificadas principalmente en la selva mediana subperennifolia (enverdecimiento generalizado: 10.26%; pardeamiento generalizado: 25.43%), selva baja espinosa subperennifolia (7.66 y 21.76) y en la vegetación secundaria arbórea de selva mediana (3.26 y 10.93). En 2017 y 2018 se identificaron las áreas más grandes con alguna clase de tendencia local lineal.Instituto de Geografía2022-11-29info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdftext/xmlhttps://www.investigacionesgeograficas.unam.mx/index.php/rig/article/view/6062910.14350/rig.60629Investigaciones Geográficas; No. 109Investigaciones Geográficas; Núm. 1092448-72790188-4611reponame:Investigaciones Geográficasinstname:UNIVERSIDAD NACIONAL AUTÓNOMA DE MÉXICOinstacron:UNAMspahttps://www.investigacionesgeograficas.unam.mx/index.php/rig/article/view/60629/54500https://www.investigacionesgeograficas.unam.mx/index.php/rig/article/view/60629/54541Derechos de autor 2022 Inder Tecuapetla-Gómez, * Autor de correspondencia, Alfonso Carbajal-Domínguez, Valeria Montesinos-Chicainfo:eu-repo/semantics/openAccessoai:ojs.pkp.sfu.ca:article/606292024-11-05T15:52:31Z
score 15,811543

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