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...
- 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
- Rights
- restrictedAccess
- License
- http://purl.org/coar/access_right/c_16ec
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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 |
_version_ |
1814159184037412864 |