Land cover maps at different resolutions and mapping extents contribute to modeling and support decision making processes. Because land cover affects and is affected by climate change, it is listed among the 13 terrestrial essential climate variables. This paper describes the generation of a land co...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2013
- Institución:
- Universidad de Medellín
- Repositorio:
- Repositorio UDEM
- Idioma:
- eng
- OAI Identifier:
- oai:repository.udem.edu.co:11407/1401
- Acceso en línea:
- http://hdl.handle.net/11407/1401
- Palabra clave:
- Class memberships
Decision trees
GOFC-GOLD
Land cover classification
Latin America
MODIS
RedLaTIF network
SERENA project
- Rights
- restrictedAccess
- License
- http://purl.org/coar/access_right/c_16ec
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2015-10-09T13:18:26Z2015-10-09T13:18:26Z2013344257http://hdl.handle.net/11407/140110.1016/j.rse.2012.12.025Land cover maps at different resolutions and mapping extents contribute to modeling and support decision making processes. Because land cover affects and is affected by climate change, it is listed among the 13 terrestrial essential climate variables. This paper describes the generation of a land cover map for Latin America and the Caribbean (LAC) for the year 2008. It was developed in the framework of the project Latin American Network for Monitoring and Studying of Natural Resources (SERENA), which has been developed within the GOFC-GOLD Latin American network of remote sensing and forest fires (RedLaTIF). The SERENA land cover map for LAC integrates: 1) the local expertise of SERENA network members to generate the training and validation data, 2) a methodology for land cover mapping based on decision trees using MODIS time series, and 3) class membership estimates to account for pixel heterogeneity issues. The discrete SERENA land cover product, derived from class memberships, yields an overall accuracy of 84% and includes an additional layer representing the estimated per-pixel confidence. The study demonstrates in detail the use of class memberships to better estimate the area of scarce classes with a scattered spatial distribution. The land cover map is already available as a printed wall map and will be released in digital format in the near future. The SERENA land cover map was produced with a legend and classification strategy similar to that used by the North American Land Change Monitoring System (NALCMS) to generate a land cover map of the North American continent, that will allow to combine both maps to generate consistent data across America facilitating continental monitoring and modeling. © 2012 Elsevier Inc.enghttp://www.sciencedirect.com/science/article/pii/S0034425713000035Remote Sensing of Environment, 15 de mayo de 2013, volume 132, pp 13-31ScopusArticleinfo: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_16ecNational Patagonian Center-Argentinean National Research Council, Terrestrial Ecology Unit, U9120ACD Puerto Madryn, Chubut, ArgentinaNational Commission for the Knowledge and Use of Biodiversity (CONABIO), Av. Liga Periférico-Insurgentes Sur 4903, Parques del Pedregal, Tlalpan 14010, Mexico City, D.F., MexicoDepartment of Forestry, School of Agronomy, Technical University of Lisbon, Tapada da Ajuda, 1349-017 Lisbon, PortugalClimate and Water Institute-INTA, B1712WAA Castelar, Buenos Aires, ArgentinaCenter of Scientific Research, Technological Transfer to Productivity-Argentinean National Research Council, CP 3105 Diamante, Entre Ríos, ArgentinaAutonomous University of Entre Ríos, CP 3100 Paraná, Entre Ríos, ArgentinaUniversity of Concepción, Department of Electronic Engineering, 4070386 Concepción, ChileUniversity of Magallanes, Science and Agricultural Technology School, 621-0427 Punta Arenas, ChileCenter for Weather Forecasting and Climate Studies, INPE, SP 12227-010 Sao José dos Campos, BrazilUniversity of Medellín, Faculty of Engineering, Medellín, ColombiaNational University of Luján, 6700 Luján, Buenos Aires, ArgentinaNational Agrarian University - La Molina (UNALM), Lima, PeruBlanco P.D.Colditz R.R.Lopez Saldana G.Hardtke L.A.Llamas R.M.Mari N.A.Fischer A.Caride C.Acenolaza P.G.del Valle H.F.Lillo-Saavedra M.Coronato F.Opazo S.A.Morelli F.Anaya J.A.Sione W.F.Zamboni P.Arroyo V.B.Class membershipsDecision treesGOFC-GOLDLand cover classificationLatin AmericaMODISRedLaTIF networkSERENA projectA land cover map of Latin America and the Caribbean in the framework of the SERENA project11407/1401oai:repository.udem.edu.co:11407/14012020-05-27 18:31:34.749Repositorio Institucional Universidad de Medellinrepositorio@udem.edu.co |
| dc.title.english.eng.fl_str_mv |
A land cover map of Latin America and the Caribbean in the framework of the SERENA project |
| dc.contributor.affiliation.spa.fl_str_mv |
National Patagonian Center-Argentinean National Research Council, Terrestrial Ecology Unit, U9120ACD Puerto Madryn, Chubut, Argentina National Commission for the Knowledge and Use of Biodiversity (CONABIO), Av. Liga Periférico-Insurgentes Sur 4903, Parques del Pedregal, Tlalpan 14010, Mexico City, D.F., Mexico Department of Forestry, School of Agronomy, Technical University of Lisbon, Tapada da Ajuda, 1349-017 Lisbon, Portugal Climate and Water Institute-INTA, B1712WAA Castelar, Buenos Aires, Argentina Center of Scientific Research, Technological Transfer to Productivity-Argentinean National Research Council, CP 3105 Diamante, Entre Ríos, Argentina Autonomous University of Entre Ríos, CP 3100 Paraná, Entre Ríos, Argentina University of Concepción, Department of Electronic Engineering, 4070386 Concepción, Chile University of Magallanes, Science and Agricultural Technology School, 621-0427 Punta Arenas, Chile Center for Weather Forecasting and Climate Studies, INPE, SP 12227-010 Sao José dos Campos, Brazil University of Medellín, Faculty of Engineering, Medellín, Colombia National University of Luján, 6700 Luján, Buenos Aires, Argentina National Agrarian University - La Molina (UNALM), Lima, Peru |
| dc.subject.keyword.eng.fl_str_mv |
Class memberships Decision trees GOFC-GOLD Land cover classification Latin America MODIS RedLaTIF network SERENA project |
| topic |
Class memberships Decision trees GOFC-GOLD Land cover classification Latin America MODIS RedLaTIF network SERENA project |
| spellingShingle |
Class memberships Decision trees GOFC-GOLD Land cover classification Latin America MODIS RedLaTIF network SERENA project |
| description |
Land cover maps at different resolutions and mapping extents contribute to modeling and support decision making processes. Because land cover affects and is affected by climate change, it is listed among the 13 terrestrial essential climate variables. This paper describes the generation of a land cover map for Latin America and the Caribbean (LAC) for the year 2008. It was developed in the framework of the project Latin American Network for Monitoring and Studying of Natural Resources (SERENA), which has been developed within the GOFC-GOLD Latin American network of remote sensing and forest fires (RedLaTIF). The SERENA land cover map for LAC integrates: 1) the local expertise of SERENA network members to generate the training and validation data, 2) a methodology for land cover mapping based on decision trees using MODIS time series, and 3) class membership estimates to account for pixel heterogeneity issues. The discrete SERENA land cover product, derived from class memberships, yields an overall accuracy of 84% and includes an additional layer representing the estimated per-pixel confidence. The study demonstrates in detail the use of class memberships to better estimate the area of scarce classes with a scattered spatial distribution. The land cover map is already available as a printed wall map and will be released in digital format in the near future. The SERENA land cover map was produced with a legend and classification strategy similar to that used by the North American Land Change Monitoring System (NALCMS) to generate a land cover map of the North American continent, that will allow to combine both maps to generate consistent data across America facilitating continental monitoring and modeling. © 2012 Elsevier Inc. |
| publishDate |
2013 |
| dc.date.created.none.fl_str_mv |
2013 |
| 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 |
344257 |
| dc.identifier.uri.none.fl_str_mv |
http://hdl.handle.net/11407/1401 |
| dc.identifier.doi.none.fl_str_mv |
10.1016/j.rse.2012.12.025 |
| identifier_str_mv |
344257 10.1016/j.rse.2012.12.025 |
| url |
http://hdl.handle.net/11407/1401 |
| dc.language.iso.none.fl_str_mv |
eng |
| language |
eng |
| dc.relation.isversionof.spa.fl_str_mv |
http://www.sciencedirect.com/science/article/pii/S0034425713000035 |
| dc.relation.ispartofen.eng.fl_str_mv |
Remote Sensing of Environment, 15 de mayo de 2013, volume 132, pp 13-31 |
| 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.source.spa.fl_str_mv |
Scopus |
| institution |
Universidad de Medellín |
| repository.name.fl_str_mv |
Repositorio Institucional Universidad de Medellin |
| repository.mail.fl_str_mv |
repositorio@udem.edu.co |
| _version_ |
1814159224592138240 |