Aboveground biomass assessment in Colombia: A remote sensing approach

This paper presents a method to increase the level of detail of aboveground biomass estimates at a regional scale. Methods are based on empirical relationships while materials are based on MODIS products and field measurements; the area covers from 4° south up to 12° north of the Equator with a tota...

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Tipo de recurso:
Fecha de publicación:
2009
Institución:
Universidad de Medellín
Repositorio:
Repositorio UDEM
Idioma:
eng
OAI Identifier:
oai:repository.udem.edu.co:11407/1405
Acceso en línea:
http://hdl.handle.net/11407/1405
Palabra clave:
Biomass
EVI
MODIS
Tropics
VCF
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restrictedAccess
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http://purl.org/coar/access_right/c_16ec
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spelling 2015-10-09T13:18:26Z2015-10-09T13:18:26Z20093781127http://hdl.handle.net/11407/140510.1016/j.foreco.2008.11.016This paper presents a method to increase the level of detail of aboveground biomass estimates at a regional scale. Methods are based on empirical relationships while materials are based on MODIS products and field measurements; the area covers from 4° south up to 12° north of the Equator with a total of 1,139,012 km 2 corresponding to the continental area of Colombia. Vegetation was classified in three broad classes: grasslands, secondary forests and primary forests which have been proved to enhance biomass estimates. MOD44 vegetation continuous fields (VCFs) was used as an explanatory variable for primary and secondary forests following an exponential relationship, while MOD13A1 enhanced vegetation index (EVI) was used as explanatory variable for grasslands following a linear relationship; biomass for this vegetation class was estimated every 16 days given its large variation throughout the year. EVI-biomass relationships were established from 2001 to 2006. Vegetation maps were used to separate primary forests from secondary forest, since the latter has shown lower biomass levels. Confidence intervals of the exponential regression are larger as the biomass values increases, for this reason the uncertainty is quite high ranging from 3.7 to 25.2 millions of Mg with a mean of 16.2 million of Mg. Despite the uncertainty our biomass results are within the estimates of previous studies. © 2008 Elsevier B.V. All rights reserved.enghttp://www.sciencedirect.com/science/article/pii/S0378112708008426Forest Ecology and Management, febrero de 2009, volume 257, issue 4, pp 1237-1246ScopusAboveground biomass assessment in Colombia: A remote sensing approachAboveground biomass assessment in Colombia: A remote sensing approachArticleinfo:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1info:eu-repo/semantics/restrictedAccesshttp://purl.org/coar/access_right/c_16ecIngeniería AmbientalFacultad de IngenieríasIngeniería Ambiental, Universidad de Medellín, Carrera 87 N 30 - 65, Medellín, ColombiaDepartamento de Geografía, Universidad de Alcalá, Colegios 2, 28801 Alcalá de Henares, SpainDepartamento de Silvopascicultura, E.T.S.I Montes, Universidad Politécnica de Madrid, SpainAnaya J.A.Chuvieco E.Palacios-Orueta A.BiomassEVIMODISTropicsVCFTHUMBNAILportada.JPGportada.JPGimage/jpeg16011http://repository.udem.edu.co/bitstream/11407/1405/1/portada.JPG4ccca470e9fcc0fddd78446043b093ffMD5111407/1405oai:repository.udem.edu.co:11407/14052020-05-27 16:35:52.774Repositorio Institucional Universidad de Medellinrepositorio@udem.edu.co
dc.title.eng.fl_str_mv Aboveground biomass assessment in Colombia: A remote sensing approach
dc.title.english.eng.fl_str_mv Aboveground biomass assessment in Colombia: A remote sensing approach
title Aboveground biomass assessment in Colombia: A remote sensing approach
spellingShingle Aboveground biomass assessment in Colombia: A remote sensing approach
Biomass
EVI
MODIS
Tropics
VCF
title_short Aboveground biomass assessment in Colombia: A remote sensing approach
title_full Aboveground biomass assessment in Colombia: A remote sensing approach
title_fullStr Aboveground biomass assessment in Colombia: A remote sensing approach
title_full_unstemmed Aboveground biomass assessment in Colombia: A remote sensing approach
title_sort Aboveground biomass assessment in Colombia: A remote sensing approach
dc.contributor.affiliation.spa.fl_str_mv Ingeniería Ambiental, Universidad de Medellín, Carrera 87 N 30 - 65, Medellín, Colombia
Departamento de Geografía, Universidad de Alcalá, Colegios 2, 28801 Alcalá de Henares, Spain
Departamento de Silvopascicultura, E.T.S.I Montes, Universidad Politécnica de Madrid, Spain
dc.subject.keyword.eng.fl_str_mv Biomass
EVI
MODIS
Tropics
VCF
topic Biomass
EVI
MODIS
Tropics
VCF
description This paper presents a method to increase the level of detail of aboveground biomass estimates at a regional scale. Methods are based on empirical relationships while materials are based on MODIS products and field measurements; the area covers from 4° south up to 12° north of the Equator with a total of 1,139,012 km 2 corresponding to the continental area of Colombia. Vegetation was classified in three broad classes: grasslands, secondary forests and primary forests which have been proved to enhance biomass estimates. MOD44 vegetation continuous fields (VCFs) was used as an explanatory variable for primary and secondary forests following an exponential relationship, while MOD13A1 enhanced vegetation index (EVI) was used as explanatory variable for grasslands following a linear relationship; biomass for this vegetation class was estimated every 16 days given its large variation throughout the year. EVI-biomass relationships were established from 2001 to 2006. Vegetation maps were used to separate primary forests from secondary forest, since the latter has shown lower biomass levels. Confidence intervals of the exponential regression are larger as the biomass values increases, for this reason the uncertainty is quite high ranging from 3.7 to 25.2 millions of Mg with a mean of 16.2 million of Mg. Despite the uncertainty our biomass results are within the estimates of previous studies. © 2008 Elsevier B.V. All rights reserved.
publishDate 2009
dc.date.created.none.fl_str_mv 2009
dc.date.accessioned.none.fl_str_mv 2015-10-09T13:18:26Z
dc.date.available.none.fl_str_mv 2015-10-09T13:18:26Z
dc.type.eng.fl_str_mv Article
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http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.driver.none.fl_str_mv info:eu-repo/semantics/article
dc.identifier.issn.none.fl_str_mv 3781127
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/11407/1405
dc.identifier.doi.none.fl_str_mv 10.1016/j.foreco.2008.11.016
identifier_str_mv 3781127
10.1016/j.foreco.2008.11.016
url http://hdl.handle.net/11407/1405
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.isversionof.spa.fl_str_mv http://www.sciencedirect.com/science/article/pii/S0378112708008426
dc.relation.ispartofen.eng.fl_str_mv Forest Ecology and Management, febrero de 2009, volume 257, issue 4, pp 1237-1246
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_16ec
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dc.publisher.program.spa.fl_str_mv Ingeniería Ambiental
dc.publisher.faculty.spa.fl_str_mv Facultad de Ingenierías
dc.source.spa.fl_str_mv Scopus
institution Universidad de Medellín
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