Land cover mapping of a tropical region by integrating multi-year data into an annual time series

Generating annual land cover maps in the tropics based on optical data is challenging because of the large amount of invalid observations resulting from the presence of clouds and haze or high moisture content in the atmosphere. This study proposes a strategy to build an annual time series from mult...

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Autores:
Tipo de recurso:
Fecha de publicación:
2015
Institución:
Universidad de Medellín
Repositorio:
Repositorio UDEM
Idioma:
eng
OAI Identifier:
oai:repository.udem.edu.co:11407/2867
Acceso en línea:
http://hdl.handle.net/11407/2867
Palabra clave:
Land cover
Quality assessment
Time series
Tree classifiers
Decision trees
Error statistics
Image reconstruction
Radiometers
Satellite imagery
Time series
Trees (mathematics)
Decision tree classification
High moisture contents
Integration approach
Land cover
Land cover classification
Moderate resolution imaging spectroradiometer
Quality assessment
Tree classifiers
Data integration
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restrictedAccess
License
http://purl.org/coar/access_right/c_16ec
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oai_identifier_str oai:repository.udem.edu.co:11407/2867
network_acronym_str REPOUDEM2
network_name_str Repositorio UDEM
repository_id_str
spelling 2016-10-28T16:44:54Z2016-10-28T16:44:54Z201520724292http://hdl.handle.net/11407/286710.3390/rs71215833Generating annual land cover maps in the tropics based on optical data is challenging because of the large amount of invalid observations resulting from the presence of clouds and haze or high moisture content in the atmosphere. This study proposes a strategy to build an annual time series from multi-year data to fill data gaps. The approach was tested using the Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation index and spectral bands as input for land cover classification of Colombia. In a second step, selected ancillary variables, such as elevation, L-band Radar, and precipitation were added to improve overall accuracy. Decision-tree classification was used for assigning eleven land cover classes using the International Geosphere-Biosphere Programme (IGBP) legend. Maps were assessed by their spatial confidence derived from the decision tree approach and conventional accuracy measures using reference data and statistics based on the error matrix. The multi-year data integration approach drastically decreased the area covered by invalid pixels. Overall accuracy of land cover maps significantly increased from 58.36% using only optical time series of 2011 filtered for low quality observations, to 68.79% when using data for 2011 ± 2 years. Adding elevation to the feature set resulted in 70.50% accuracy.engMDPI AGhttp://www.mdpi.com/2072-4292/7/12/15833Remote SensingScopusLand coverQuality assessmentTime seriesTree classifiersDecision treesError statisticsImage reconstructionRadiometersSatellite imageryTime seriesTrees (mathematics)Decision tree classificationHigh moisture contentsIntegration approachLand coverLand cover classificationModerate resolution imaging spectroradiometerQuality assessmentTree classifiersData integrationLand cover mapping of a tropical region by integrating multi-year data into an annual time seriesArticleinfo:eu-repo/semantics/conferenceObjecthttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_c94finfo:eu-repo/semantics/restrictedAccesshttp://purl.org/coar/access_right/c_16ecFacultad de Ingenierías, Universidad de Medellín, Carrera 87 Nro. 30-65, Medellín, ColombiaNational Commission for the Knowledge and Use of Biodiversity (CONABIO), Av. Liga Periférico-Insurgentes Sur 4903, Parques del Pedregal, Tlalpan, Ciudad de México, DF, MexicoFacultad de Ingenierías, Universidad de San Buenaventura, Carrera 56C Nro. 51-90, Medellín, ColombiaAnaya J.A.Colditz R.R.Valencia G.M.11407/2867oai:repository.udem.edu.co:11407/28672020-05-27 17:48:17.37Repositorio Institucional Universidad de Medellinrepositorio@udem.edu.co
dc.title.spa.fl_str_mv Land cover mapping of a tropical region by integrating multi-year data into an annual time series
title Land cover mapping of a tropical region by integrating multi-year data into an annual time series
spellingShingle Land cover mapping of a tropical region by integrating multi-year data into an annual time series
Land cover
Quality assessment
Time series
Tree classifiers
Decision trees
Error statistics
Image reconstruction
Radiometers
Satellite imagery
Time series
Trees (mathematics)
Decision tree classification
High moisture contents
Integration approach
Land cover
Land cover classification
Moderate resolution imaging spectroradiometer
Quality assessment
Tree classifiers
Data integration
title_short Land cover mapping of a tropical region by integrating multi-year data into an annual time series
title_full Land cover mapping of a tropical region by integrating multi-year data into an annual time series
title_fullStr Land cover mapping of a tropical region by integrating multi-year data into an annual time series
title_full_unstemmed Land cover mapping of a tropical region by integrating multi-year data into an annual time series
title_sort Land cover mapping of a tropical region by integrating multi-year data into an annual time series
dc.contributor.affiliation.spa.fl_str_mv Facultad de Ingenierías, Universidad de Medellín, Carrera 87 Nro. 30-65, Medellín, Colombia
National Commission for the Knowledge and Use of Biodiversity (CONABIO), Av. Liga Periférico-Insurgentes Sur 4903, Parques del Pedregal, Tlalpan, Ciudad de México, DF, Mexico
Facultad de Ingenierías, Universidad de San Buenaventura, Carrera 56C Nro. 51-90, Medellín, Colombia
dc.subject.spa.fl_str_mv Land cover
Quality assessment
Time series
Tree classifiers
topic Land cover
Quality assessment
Time series
Tree classifiers
Decision trees
Error statistics
Image reconstruction
Radiometers
Satellite imagery
Time series
Trees (mathematics)
Decision tree classification
High moisture contents
Integration approach
Land cover
Land cover classification
Moderate resolution imaging spectroradiometer
Quality assessment
Tree classifiers
Data integration
dc.subject.keyword.eng.fl_str_mv Decision trees
Error statistics
Image reconstruction
Radiometers
Satellite imagery
Time series
Trees (mathematics)
Decision tree classification
High moisture contents
Integration approach
Land cover
Land cover classification
Moderate resolution imaging spectroradiometer
Quality assessment
Tree classifiers
Data integration
description Generating annual land cover maps in the tropics based on optical data is challenging because of the large amount of invalid observations resulting from the presence of clouds and haze or high moisture content in the atmosphere. This study proposes a strategy to build an annual time series from multi-year data to fill data gaps. The approach was tested using the Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation index and spectral bands as input for land cover classification of Colombia. In a second step, selected ancillary variables, such as elevation, L-band Radar, and precipitation were added to improve overall accuracy. Decision-tree classification was used for assigning eleven land cover classes using the International Geosphere-Biosphere Programme (IGBP) legend. Maps were assessed by their spatial confidence derived from the decision tree approach and conventional accuracy measures using reference data and statistics based on the error matrix. The multi-year data integration approach drastically decreased the area covered by invalid pixels. Overall accuracy of land cover maps significantly increased from 58.36% using only optical time series of 2011 filtered for low quality observations, to 68.79% when using data for 2011 ± 2 years. Adding elevation to the feature set resulted in 70.50% accuracy.
publishDate 2015
dc.date.created.none.fl_str_mv 2015
dc.date.accessioned.none.fl_str_mv 2016-10-28T16:44:54Z
dc.date.available.none.fl_str_mv 2016-10-28T16:44:54Z
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_c94f
dc.type.driver.none.fl_str_mv info:eu-repo/semantics/conferenceObject
dc.identifier.issn.none.fl_str_mv 20724292
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/11407/2867
dc.identifier.doi.none.fl_str_mv 10.3390/rs71215833
identifier_str_mv 20724292
10.3390/rs71215833
url http://hdl.handle.net/11407/2867
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.isversionof.spa.fl_str_mv http://www.mdpi.com/2072-4292/7/12/15833
dc.relation.ispartofes.spa.fl_str_mv Remote Sensing
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.spa.fl_str_mv MDPI AG
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
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