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...
- Autores:
- 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
- Rights
- restrictedAccess
- License
- http://purl.org/coar/access_right/c_16ec
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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 |
dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_6501 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 |
dc.rights.accessrights.none.fl_str_mv |
info:eu-repo/semantics/restrictedAccess |
eu_rights_str_mv |
restrictedAccess |
rights_invalid_str_mv |
http://purl.org/coar/access_right/c_16ec |
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 |
bitstream.url.fl_str_mv |
http://repository.udem.edu.co/bitstream/11407/1405/1/portada.JPG |
bitstream.checksum.fl_str_mv |
4ccca470e9fcc0fddd78446043b093ff |
bitstream.checksumAlgorithm.fl_str_mv |
MD5 |
repository.name.fl_str_mv |
Repositorio Institucional Universidad de Medellin |
repository.mail.fl_str_mv |
repositorio@udem.edu.co |
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1814159153982078976 |