Mapa de coberturas del suelo utilizando imágenes satelitales Sentinel-2 y Landsat-8 del municipio de Covarachía – Colombia

Autores:: León León, Jose
Medina, Ruben Javier
Ovalle, Diana Marcela

Tipo de recurso:: Article of journal

Fecha de publicación:: 2025

Institución:: Corporación Unificada Nacional de Educación Superior

Repositorio:: Repositorio Corporación Unificada Nacional de Educación Superior

Idioma:

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dc.title.spa.fl_str_mv	Mapa de coberturas del suelo utilizando imágenes satelitales Sentinel-2 y Landsat-8 del municipio de Covarachía – Colombia
title	Mapa de coberturas del suelo utilizando imágenes satelitales Sentinel-2 y Landsat-8 del municipio de Covarachía – Colombia
spellingShingle	Mapa de coberturas del suelo utilizando imágenes satelitales Sentinel-2 y Landsat-8 del municipio de Covarachía – Colombia clasificadores no supervisados, cobertura del suelo imágenes satelitales unsupervised classifiers ground cover satellite images
title_short	Mapa de coberturas del suelo utilizando imágenes satelitales Sentinel-2 y Landsat-8 del municipio de Covarachía – Colombia
title_full	Mapa de coberturas del suelo utilizando imágenes satelitales Sentinel-2 y Landsat-8 del municipio de Covarachía – Colombia
title_fullStr	Mapa de coberturas del suelo utilizando imágenes satelitales Sentinel-2 y Landsat-8 del municipio de Covarachía – Colombia
title_full_unstemmed	Mapa de coberturas del suelo utilizando imágenes satelitales Sentinel-2 y Landsat-8 del municipio de Covarachía – Colombia
title_sort	Mapa de coberturas del suelo utilizando imágenes satelitales Sentinel-2 y Landsat-8 del municipio de Covarachía – Colombia
dc.creator.fl_str_mv	León León, Jose Medina, Ruben Javier Ovalle, Diana Marcela
dc.contributor.author.spa.fl_str_mv	León León, Jose Medina, Ruben Javier Ovalle, Diana Marcela
dc.subject.none.fl_str_mv	clasificadores no supervisados, cobertura del suelo imágenes satelitales unsupervised classifiers ground cover satellite images
topic	clasificadores no supervisados, cobertura del suelo imágenes satelitales unsupervised classifiers ground cover satellite images
publishDate	2025
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dc.date.accessioned.none.fl_str_mv	2021-12-12 00:00:00 2025-11-05T14:59:15Z
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dc.relation.citationedition.spa.fl_str_mv	Núm. 19 , Año 2021 : Revista Hashtag 2021B
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dc.relation.references.none.fl_str_mv	Abbas, A., Minallh, N., Ahmad, N., Rehman, S., y Khan, M. (2016). K-Means and ISODATA Clustering Algorithms for Landcover Classification Using Remote Sensing. ResearchGate, 48, 315-318. https://www.researchgate.net/publication/303971825_K-Means_and_ISODATA_Clustering_Algorithms_for_Landcover_Classification_Using_Remote_Sensing Fisterra. (2020). Medidas de concordancia: El índice Kappa. https://www.fisterra.com/formacion/metodologia-investigacion/medidas-concordancia-indice-kappa/ García, D., Camacho, M., y Paegelow, M. (2019). Sensitivity of a common Land Use Cover Change (LUCC) model to the Minimum Mapping Unit (MMU) and Minimum Mapping Width (MMW) of input maps. Computers, Environment and Urban Systems, 78, 101389. https://doi.org/10.1016/j.compenvurbsys.2019.101389 Gisadminbeers. (26 de marzo de 2017). Combinaciones RGB de imágenes satélite Landsat y Sentinel. Gis&Beers. http://www.gisandbeers.com/combinacion-de-imagenes-satelite-landsat-sentinel-rgb/ He, Y., Lee, E., y Warner, T. A. (2017). A time series of annual land use and land cover maps of China from 1982 to 2013 generated using AVHRR GIMMS NDVI3g data. Remote Sensing of Environment, 199, 201-217. https://doi.org/10.1016/j.rse.2017.07.010 Kiswanto, Tsuyuki, S., Mardiany, y Sumaryono. (2018). Completing yearly land cover maps for accurately describing annual changes of tropical landscapes. Global Ecology and Conservation, 13. https://doi.org/10.1016/j.gecco.2018.e00384 NV5 Geospatial. (2020). K-Means. https://www.harrisgeospatial.com/docs/KMeansClassification.html Li, X., Ling, F., Foody, G., Ge, Y., Zhang, Y., y Du, Y. (2017). Generating a series of fine spatial and temporal resolution land cover maps by fusing coarse spatial resolution remotely sensed images and fine spatial resolution land cover maps. Remote Sensing of Environment, 196, 293-311. https://doi.org/10.1016/j.rse.2017.05.011 Misra, M., Kumar, D., y Shekhar, S. (2020). Assessing Machine Learning Based Supervised Classifiers For Built-Up Impervious Surface Area Extraction From Sentinel-2 Images. Urban Forestry & Urban Greening, 53. https://doi.org/10.1016/j.ufug.2020.126714 Nuestro municipio - Alcaldía Municipal de Covarachía en Boyacá. (2020). http://www.covarachia-boyaca.gov.co/municipio/nuestro-municipio Pérez, A., Udías, F., y Rembold, F. (2020). Integrating Multiple Land Cover Maps through a Multi-Criteria Analysis to Improve Agricultural Monitoring in Africa. International Journal of Applied Earth Observation and Geoinformation, 88. https://doi.org/10.1016/j.jag.2020.102064 Rega, C., Short, C., Pérez, M., y Paracchini, M. (2020). A classification of European agricultural land using an energy-based intensity indicator and detailed crop description. Landscape and Urban Planning, 198. https://doi.org/10.1016/j.landurbplan.2020.103793 Renza, D., Martinez, E., Molina, I., y Ballesteros, D. (2017). Unsupervised change detection in a particular vegetation land cover type using spectral angle mapper. Advances in Space Research, 59(8), 2019-2031. https://doi.org/10.1016/j.asr.2017.01.027 Saah, D., Tenneson, K., Poortinga, A., Nguyen, Q., Chishtie, F., … Ganz, D. (2020). Primitives as building blocks for constructing land cover maps. International Journal of Applied Earth Observation and Geoinformation, 85. https://doi.org/10.1016/j.jag.2019.101979 Satélite Sentinel-2. Flota de satélites europeos de vigilancia medioambiental del programa Copernicus Satélite Landsat-8. Satélite estadounidense para estudios cartográficos y de características de temperatura de la superficie Stéphane, D., Laurence, D., Raffaele, G., Valérie, A., y Eloise, R. Land Cover Maps of Antananarivo (Capital of Madagascar) Produced by Processing Multisource Satellite Imagery and Geospatial Reference Data. Data in Brief, 31. https://doi.org/10.1016/j.dib.2020.105952 Humboldt State University. (2019). Supervised Classification. http://gsp.humboldt.edu/olm/Courses/GSP_216/lessons/Classification/supervised.html Szantoi, Z., Geller, G., Tsendbazar, N., See, L., Griffiths, P., Fritz, S., Gong, P., Herold, M., Mora, B., y Obregón, A. (2020). Addressing the need for improved land cover map products for policy support. Environmental Science & Policy, 112, 28-35. https://doi.org/10.1016/j.envsci.2020.04.005 Vilar, L., Garrido, J., Echavarría, P., Martínez, J., y Martín, M. (2019). Comparative analysis of CORINE and climate change initiative land cover maps in Europe: Implications for wildfire occurrence estimation at regional and local scales. International Journal of Applied Earth Observation and Geoinformation, 78, 102-117. https://doi.org/10.1016/j.jag.2019.01.019
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spelling	León León, JoseMedina, Ruben JavierOvalle, Diana Marcela2021-12-12 00:00:002025-11-05T14:59:15Z2021-12-12 00:00:00%0-%12-%12https://repositorio.cun.edu.co/handle/cun/1086210.52143/2346139X.9302346-139Xhttps://doi.org/10.52143/2346139X.930application/pdfFondo Editorial CUN#ashtag - 2021https://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2https://revistas.cun.edu.co/index.php/hashtag/article/view/930clasificadores no supervisados,cobertura del sueloimágenes satelitalesunsupervised classifiersground coversatellite imagesMapa de coberturas del suelo utilizando imágenes satelitales Sentinel-2 y Landsat-8 del municipio de Covarachía – ColombiaArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_970fb48d4fbd8a85Textinfo:eu-repo/semantics/articleJournal articleinfo:eu-repo/semantics/publishedVersionhttps://revistas.cun.edu.co/index.php/hashtag/article/download/930/634Núm. 19 , Año 2021 : Revista Hashtag 2021B271982#ashtagAbbas, A., Minallh, N., Ahmad, N., Rehman, S., y Khan, M. (2016). K-Means and ISODATA Clustering Algorithms for Landcover Classification Using Remote Sensing. ResearchGate, 48, 315-318. https://www.researchgate.net/publication/303971825_K-Means_and_ISODATA_Clustering_Algorithms_for_Landcover_Classification_Using_Remote_SensingFisterra. (2020). Medidas de concordancia: El índice Kappa. https://www.fisterra.com/formacion/metodologia-investigacion/medidas-concordancia-indice-kappa/García, D., Camacho, M., y Paegelow, M. (2019). Sensitivity of a common Land Use Cover Change (LUCC) model to the Minimum Mapping Unit (MMU) and Minimum Mapping Width (MMW) of input maps. Computers, Environment and Urban Systems, 78, 101389. https://doi.org/10.1016/j.compenvurbsys.2019.101389Gisadminbeers. (26 de marzo de 2017). Combinaciones RGB de imágenes satélite Landsat y Sentinel. Gis&Beers. http://www.gisandbeers.com/combinacion-de-imagenes-satelite-landsat-sentinel-rgb/He, Y., Lee, E., y Warner, T. A. (2017). A time series of annual land use and land cover maps of China from 1982 to 2013 generated using AVHRR GIMMS NDVI3g data. Remote Sensing of Environment, 199, 201-217. https://doi.org/10.1016/j.rse.2017.07.010Kiswanto, Tsuyuki, S., Mardiany, y Sumaryono. (2018). Completing yearly land cover maps for accurately describing annual changes of tropical landscapes. Global Ecology and Conservation, 13. https://doi.org/10.1016/j.gecco.2018.e00384 NV5 Geospatial. (2020). K-Means. https://www.harrisgeospatial.com/docs/KMeansClassification.htmlLi, X., Ling, F., Foody, G., Ge, Y., Zhang, Y., y Du, Y. (2017). Generating a series of fine spatial and temporal resolution land cover maps by fusing coarse spatial resolution remotely sensed images and fine spatial resolution land cover maps. Remote Sensing of Environment, 196, 293-311. https://doi.org/10.1016/j.rse.2017.05.011Misra, M., Kumar, D., y Shekhar, S. (2020). Assessing Machine Learning Based Supervised Classifiers For Built-Up Impervious Surface Area Extraction From Sentinel-2 Images. Urban Forestry & Urban Greening, 53. https://doi.org/10.1016/j.ufug.2020.126714Nuestro municipio - Alcaldía Municipal de Covarachía en Boyacá. (2020). http://www.covarachia-boyaca.gov.co/municipio/nuestro-municipioPérez, A., Udías, F., y Rembold, F. (2020). Integrating Multiple Land Cover Maps through a Multi-Criteria Analysis to Improve Agricultural Monitoring in Africa. International Journal of Applied Earth Observation and Geoinformation, 88. https://doi.org/10.1016/j.jag.2020.102064Rega, C., Short, C., Pérez, M., y Paracchini, M. (2020). A classification of European agricultural land using an energy-based intensity indicator and detailed crop description. Landscape and Urban Planning, 198. https://doi.org/10.1016/j.landurbplan.2020.103793Renza, D., Martinez, E., Molina, I., y Ballesteros, D. (2017). Unsupervised change detection in a particular vegetation land cover type using spectral angle mapper. Advances in Space Research, 59(8), 2019-2031. https://doi.org/10.1016/j.asr.2017.01.027Saah, D., Tenneson, K., Poortinga, A., Nguyen, Q., Chishtie, F., … Ganz, D. (2020). Primitives as building blocks for constructing land cover maps. International Journal of Applied Earth Observation and Geoinformation, 85. https://doi.org/10.1016/j.jag.2019.101979Satélite Sentinel-2. Flota de satélites europeos de vigilancia medioambiental del programa CopernicusSatélite Landsat-8. Satélite estadounidense para estudios cartográficos y de características de temperatura de la superficieStéphane, D., Laurence, D., Raffaele, G., Valérie, A., y Eloise, R. Land Cover Maps of Antananarivo (Capital of Madagascar) Produced by Processing Multisource Satellite Imagery and Geospatial Reference Data. Data in Brief, 31. https://doi.org/10.1016/j.dib.2020.105952 Humboldt State University. (2019). Supervised Classification. http://gsp.humboldt.edu/olm/Courses/GSP_216/lessons/Classification/supervised.htmlSzantoi, Z., Geller, G., Tsendbazar, N., See, L., Griffiths, P., Fritz, S., Gong, P., Herold, M., Mora, B., y Obregón, A. (2020). Addressing the need for improved land cover map products for policy support. Environmental Science & Policy, 112, 28-35. https://doi.org/10.1016/j.envsci.2020.04.005Vilar, L., Garrido, J., Echavarría, P., Martínez, J., y Martín, M. (2019). Comparative analysis of CORINE and climate change initiative land cover maps in Europe: Implications for wildfire occurrence estimation at regional and local scales. International Journal of Applied Earth Observation and Geoinformation, 78, 102-117. https://doi.org/10.1016/j.jag.2019.01.019PublicationOREORE.xmltext/xml2681https://repositorio.cun.edu.co/bitstreams/db5c7761-1078-4c69-96eb-058263b638cb/download46933c91b4ad9869918d119dfa9dc79fMD51falseAnonymousREADcun/10862oai:repositorio.cun.edu.co:cun/108622025-11-05 09:59:16.169https://creativecommons.org/licenses/by-nc-sa/4.0/#ashtag - 2021metadata.onlyhttps://repositorio.cun.edu.coRepositorio Digital Corporación Unificada Nacional de Educación Superiorbdigital@metabiblioteca.com

Mapa de coberturas del suelo utilizando imágenes satelitales Sentinel-2 y Landsat-8 del municipio de Covarachía – Colombia

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