Guía para la identificación de las áreas sembradas en palma de aceite, a partir del uso de la plataforma de Google Earth Engine (Estudio de caso: municipio de Maní – Casanare)

This document provides a comprehensive overview of the JavaScript code used within the Google Earth Engine (GEE) platform and serves as a Methodological Guide for the Identification of Oil Palm cultivation areas using supervised classification methods with Random Forest. The guide outlines detailed...

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Autores:: Mera Garzón, Diana Patricia

Tipo de recurso:: Trabajo de grado de pregrado

Fecha de publicación:: 2023

Institución:: Universidad Antonio Nariño

Repositorio:: Repositorio UAN

Idioma:: spa

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dc.title.es_ES.fl_str_mv	Guía para la identificación de las áreas sembradas en palma de aceite, a partir del uso de la plataforma de Google Earth Engine (Estudio de caso: municipio de Maní – Casanare)
title	Guía para la identificación de las áreas sembradas en palma de aceite, a partir del uso de la plataforma de Google Earth Engine (Estudio de caso: municipio de Maní – Casanare)
spellingShingle	Guía para la identificación de las áreas sembradas en palma de aceite, a partir del uso de la plataforma de Google Earth Engine (Estudio de caso: municipio de Maní – Casanare) JavaScript Random Forest Sentinel Planet Scope Inteligencia Artificial (IA) JavaScript Random Forest Sentinel Planet Scope Artificial Intelligence (AI)
title_short	Guía para la identificación de las áreas sembradas en palma de aceite, a partir del uso de la plataforma de Google Earth Engine (Estudio de caso: municipio de Maní – Casanare)
title_full	Guía para la identificación de las áreas sembradas en palma de aceite, a partir del uso de la plataforma de Google Earth Engine (Estudio de caso: municipio de Maní – Casanare)
title_fullStr	Guía para la identificación de las áreas sembradas en palma de aceite, a partir del uso de la plataforma de Google Earth Engine (Estudio de caso: municipio de Maní – Casanare)
title_full_unstemmed	Guía para la identificación de las áreas sembradas en palma de aceite, a partir del uso de la plataforma de Google Earth Engine (Estudio de caso: municipio de Maní – Casanare)
title_sort	Guía para la identificación de las áreas sembradas en palma de aceite, a partir del uso de la plataforma de Google Earth Engine (Estudio de caso: municipio de Maní – Casanare)
dc.creator.fl_str_mv	Mera Garzón, Diana Patricia
dc.contributor.advisor.spa.fl_str_mv	Carvajal Vanegas, Andrés Felipe
dc.contributor.author.spa.fl_str_mv	Mera Garzón, Diana Patricia
dc.subject.es_ES.fl_str_mv	JavaScript Random Forest Sentinel Planet Scope Inteligencia Artificial (IA)
topic	JavaScript Random Forest Sentinel Planet Scope Inteligencia Artificial (IA) JavaScript Random Forest Sentinel Planet Scope Artificial Intelligence (AI)
dc.subject.keyword.es_ES.fl_str_mv	JavaScript Random Forest Sentinel Planet Scope Artificial Intelligence (AI)
description	This document provides a comprehensive overview of the JavaScript code used within the Google Earth Engine (GEE) platform and serves as a Methodological Guide for the Identification of Oil Palm cultivation areas using supervised classification methods with Random Forest. The guide outlines detailed step-by-step procedures and recommends specific functions. Furthermore, the source code is made available to the public, simplifying access and enabling reproduction by other users of Geographic Information Systems (GIS).
publishDate	2023
dc.date.issued.spa.fl_str_mv	2023-11-25
dc.date.accessioned.none.fl_str_mv	2024-01-30T14:33:05Z
dc.date.available.none.fl_str_mv	2024-01-30T14:33:05Z
dc.type.spa.fl_str_mv	Trabajo de grado (Pregrado y/o Especialización)
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dc.identifier.uri.none.fl_str_mv	http://repositorio.uan.edu.co/handle/123456789/9095
dc.identifier.bibliographicCitation.spa.fl_str_mv	Adepoju, K. A., & Adelabu, S. A. (2020). Improving accuracy evaluation of Landsat-8 OLI using image composite and multisource data with Google Earth Engine. Remote Sensing Letters, 11(2), 107–116. https://doi.org/10.1080/2150704X.2019.1690792 Amani, M., Ghorbanian, A., Ahmadi, S. A., Kakooei, M., Moghimi, A., Mirmazloumi, S. M., Moghaddam, S. H. A., Mahdavi, S., Ghahremanloo, M., Parsian, S., Wu, Q., & Brisco, B. (2020). Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 5326–5350. https://doi.org/10.1109/JSTARS.2020.3021052 Amani, M., Mahdavi, S., Afshar, M., Brisco, B., Huang, W., Mirzadeh, S. M. J., White, L., Banks, S., Montgomery, J., & Hopkinson, C. (2019). Canadian wetland inventory using Google Earth Engine: The first map and preliminary results. Remote Sensing, 11(7). https://doi.org/10.3390/RS11070842 Ang, Y., Shafri, H. Z. M., Lee, Y. P., Bakar, S. A., Abidin, H., Mohd Junaidi, M. U. U., Hashim, S. J., Che’Ya, N. N., Hassan, M. R., Lim, H. S., Abdullah, R., Yusup, Y., Muhammad, S. A., Teh, S. Y., & Samad, M. N. (2022). Oil palm yield prediction across blocks from multisource data using machine learning and deep learning. Earth Science Informatics, 15(4), 2349–2367. https://doi.org/10.1007/S12145-022-00882-9/METRICS Arias, A., Darghan, N. A. ;, Rivera, A. E. ; Beltran, C. ; Typology, J. A., Martínez-Arteaga, D., Atanasio, N., Darghan, A. E., Rivera, C., & Beltran, J. A. (2023). Typology of Irrigation Technology Adopters in Oil Palm Production: A Categorical Principal Components and Fuzzy Logic Approach. Sustainability 2023, Vol. 15, Page 9944, 15(13), 9944. https://doi.org/10.3390/SU15139944 Asming, M. A. A., Ibrahim, A. M., & Abir, I. M. (2022). Processing and classification of landsat and sentinel images for oil palm plantation detection. Remote Sensing Applications: Society and Environment, 26, 100747. https://doi.org/10.1016/J.RSASE.2022.100747 Azhar, B., Saadun, N., Prideaux, M., & Lindenmayer, D. B. (2017). The global palm oil sector must change to save biodiversity and improve food security in the tropics. In Journal of Environmental Management (Vol. 203, pp. 457–466). Academic Press. https://doi.org/10.1016/j.jenvman.2017.08.021 Basiron, Y. (2007). Palm oil production through sustainable plantations. European Journal of Lipid Science and Technology, 109(4), 289–295. https://doi.org/10.1002/ejlt.200600223 Belgiu, M., & Drăgu, L. (2016). Random forest in remote sensing: A review of applications and future directions. In ISPRS Journal of Photogrammetry and Remote Sensing (Vol. 114, pp. 24–31). Elsevier B.V. https://doi.org/10.1016/j.isprsjprs.2016.01.011 Breiman, L. (2001). Random Forests (Vol. 45) Carlson, K. M., Curran, L. M., Asner, G. P., Pittman, A. M. D., Trigg, S. N., & Marion Adeney, J. (2013). Carbon emissions from forest conversion by Kalimantan oil palm plantations. Nature Climate Change, 3(3), 283–287. https://doi.org/10.1038/nclimate1702
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identifier_str_mv	Adepoju, K. A., & Adelabu, S. A. (2020). Improving accuracy evaluation of Landsat-8 OLI using image composite and multisource data with Google Earth Engine. Remote Sensing Letters, 11(2), 107–116. https://doi.org/10.1080/2150704X.2019.1690792 Amani, M., Ghorbanian, A., Ahmadi, S. A., Kakooei, M., Moghimi, A., Mirmazloumi, S. M., Moghaddam, S. H. A., Mahdavi, S., Ghahremanloo, M., Parsian, S., Wu, Q., & Brisco, B. (2020). Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 5326–5350. https://doi.org/10.1109/JSTARS.2020.3021052 Amani, M., Mahdavi, S., Afshar, M., Brisco, B., Huang, W., Mirzadeh, S. M. J., White, L., Banks, S., Montgomery, J., & Hopkinson, C. (2019). Canadian wetland inventory using Google Earth Engine: The first map and preliminary results. Remote Sensing, 11(7). https://doi.org/10.3390/RS11070842 Ang, Y., Shafri, H. Z. M., Lee, Y. P., Bakar, S. A., Abidin, H., Mohd Junaidi, M. U. U., Hashim, S. J., Che’Ya, N. N., Hassan, M. R., Lim, H. S., Abdullah, R., Yusup, Y., Muhammad, S. A., Teh, S. Y., & Samad, M. N. (2022). Oil palm yield prediction across blocks from multisource data using machine learning and deep learning. Earth Science Informatics, 15(4), 2349–2367. https://doi.org/10.1007/S12145-022-00882-9/METRICS Arias, A., Darghan, N. A. ;, Rivera, A. E. ; Beltran, C. ; Typology, J. A., Martínez-Arteaga, D., Atanasio, N., Darghan, A. E., Rivera, C., & Beltran, J. A. (2023). Typology of Irrigation Technology Adopters in Oil Palm Production: A Categorical Principal Components and Fuzzy Logic Approach. Sustainability 2023, Vol. 15, Page 9944, 15(13), 9944. https://doi.org/10.3390/SU15139944 Asming, M. A. A., Ibrahim, A. M., & Abir, I. M. (2022). Processing and classification of landsat and sentinel images for oil palm plantation detection. Remote Sensing Applications: Society and Environment, 26, 100747. https://doi.org/10.1016/J.RSASE.2022.100747 Azhar, B., Saadun, N., Prideaux, M., & Lindenmayer, D. B. (2017). The global palm oil sector must change to save biodiversity and improve food security in the tropics. In Journal of Environmental Management (Vol. 203, pp. 457–466). Academic Press. https://doi.org/10.1016/j.jenvman.2017.08.021 Basiron, Y. (2007). Palm oil production through sustainable plantations. European Journal of Lipid Science and Technology, 109(4), 289–295. https://doi.org/10.1002/ejlt.200600223 Belgiu, M., & Drăgu, L. (2016). Random forest in remote sensing: A review of applications and future directions. In ISPRS Journal of Photogrammetry and Remote Sensing (Vol. 114, pp. 24–31). Elsevier B.V. https://doi.org/10.1016/j.isprsjprs.2016.01.011 Breiman, L. (2001). Random Forests (Vol. 45) Carlson, K. M., Curran, L. M., Asner, G. P., Pittman, A. M. D., Trigg, S. N., & Marion Adeney, J. (2013). Carbon emissions from forest conversion by Kalimantan oil palm plantations. Nature Climate Change, 3(3), 283–287. https://doi.org/10.1038/nclimate1702 instname:Universidad Antonio Nariño reponame:Repositorio Institucional UAN repourl:https://repositorio.uan.edu.co/
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dc.publisher.spa.fl_str_mv	Universidad Antonio Nariño
dc.publisher.program.spa.fl_str_mv	Especialización en Sistemas de Información Geográfica
dc.publisher.faculty.spa.fl_str_mv	Facultad de Ingeniería Ambiental
dc.publisher.campus.spa.fl_str_mv	Bogotá - Federmán
institution	Universidad Antonio Nariño
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spelling	Attribution 4.0 International (CC BY 4.0)Acceso abiertohttps://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Carvajal Vanegas, Andrés FelipeMera Garzón, Diana Patricia117923147972024-01-30T14:33:05Z2024-01-30T14:33:05Z2023-11-25http://repositorio.uan.edu.co/handle/123456789/9095Adepoju, K. A., & Adelabu, S. A. (2020). Improving accuracy evaluation of Landsat-8 OLI using image composite and multisource data with Google Earth Engine. Remote Sensing Letters, 11(2), 107–116. https://doi.org/10.1080/2150704X.2019.1690792Amani, M., Ghorbanian, A., Ahmadi, S. A., Kakooei, M., Moghimi, A., Mirmazloumi, S. M., Moghaddam, S. H. A., Mahdavi, S., Ghahremanloo, M., Parsian, S., Wu, Q., & Brisco, B. (2020). Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 5326–5350. https://doi.org/10.1109/JSTARS.2020.3021052Amani, M., Mahdavi, S., Afshar, M., Brisco, B., Huang, W., Mirzadeh, S. M. J., White, L., Banks, S., Montgomery, J., & Hopkinson, C. (2019). Canadian wetland inventory using Google Earth Engine: The first map and preliminary results. Remote Sensing, 11(7). https://doi.org/10.3390/RS11070842Ang, Y., Shafri, H. Z. M., Lee, Y. P., Bakar, S. A., Abidin, H., Mohd Junaidi, M. U. U., Hashim, S. J., Che’Ya, N. N., Hassan, M. R., Lim, H. S., Abdullah, R., Yusup, Y., Muhammad, S. A., Teh, S. Y., & Samad, M. N. (2022). Oil palm yield prediction across blocks from multisource data using machine learning and deep learning. Earth Science Informatics, 15(4), 2349–2367. https://doi.org/10.1007/S12145-022-00882-9/METRICSArias, A., Darghan, N. A. ;, Rivera, A. E. ; Beltran, C. ; Typology, J. A., Martínez-Arteaga, D., Atanasio, N., Darghan, A. E., Rivera, C., & Beltran, J. A. (2023). Typology of Irrigation Technology Adopters in Oil Palm Production: A Categorical Principal Components and Fuzzy Logic Approach. Sustainability 2023, Vol. 15, Page 9944, 15(13), 9944. https://doi.org/10.3390/SU15139944Asming, M. A. A., Ibrahim, A. M., & Abir, I. M. (2022). Processing and classification of landsat and sentinel images for oil palm plantation detection. Remote Sensing Applications: Society and Environment, 26, 100747. https://doi.org/10.1016/J.RSASE.2022.100747Azhar, B., Saadun, N., Prideaux, M., & Lindenmayer, D. B. (2017). The global palm oil sector must change to save biodiversity and improve food security in the tropics. In Journal of Environmental Management (Vol. 203, pp. 457–466). Academic Press. https://doi.org/10.1016/j.jenvman.2017.08.021Basiron, Y. (2007). Palm oil production through sustainable plantations. European Journal of Lipid Science and Technology, 109(4), 289–295. https://doi.org/10.1002/ejlt.200600223 Belgiu, M., & Drăgu, L. (2016). Random forest in remote sensing: A review of applications and future directions. In ISPRS Journal of Photogrammetry and Remote Sensing (Vol. 114, pp. 24–31). Elsevier B.V. https://doi.org/10.1016/j.isprsjprs.2016.01.011Breiman, L. (2001). Random Forests (Vol. 45)Carlson, K. M., Curran, L. M., Asner, G. P., Pittman, A. M. D., Trigg, S. N., & Marion Adeney, J. (2013). Carbon emissions from forest conversion by Kalimantan oil palm plantations. Nature Climate Change, 3(3), 283–287. https://doi.org/10.1038/nclimate1702instname:Universidad Antonio Nariñoreponame:Repositorio Institucional UANrepourl:https://repositorio.uan.edu.co/This document provides a comprehensive overview of the JavaScript code used within the Google Earth Engine (GEE) platform and serves as a Methodological Guide for the Identification of Oil Palm cultivation areas using supervised classification methods with Random Forest. The guide outlines detailed step-by-step procedures and recommends specific functions. Furthermore, the source code is made available to the public, simplifying access and enabling reproduction by other users of Geographic Information Systems (GIS).Este documento ofrece una visión general del código JavaScript utilizado en la plataforma Google Earth Engine (GEE) y funciona como una Guía Metodológica para la Identificación de áreas de cultivo de Palma Aceitera mediante métodos de clasificación supervisada con Random Forest. La guía detalla los procedimientos paso a paso y recomienda funciones específicas. Además, el código fuente se encuentra disponible al público, facilitando así su acceso y la posibilidad de reproducción por parte de otros usuarios de Sistemas de Información Geográfica (SIG).Especialista en Sistemas de Información GeográficaEspecializaciónPresencialMonografíaspaUniversidad Antonio NariñoEspecialización en Sistemas de Información GeográficaFacultad de Ingeniería AmbientalBogotá - FedermánJavaScriptRandom ForestSentinelPlanet ScopeInteligencia Artificial (IA)JavaScriptRandom ForestSentinelPlanet ScopeArtificial Intelligence (AI)Guía para la identificación de las áreas sembradas en palma de aceite, a partir del uso de la plataforma de Google Earth Engine (Estudio de caso: municipio de Maní – Casanare)Trabajo de grado (Pregrado y/o Especialización)http://purl.org/coar/resource_type/c_7a1fhttp://purl.org/coar/version/c_970fb48d4fbd8a85EspecializadaORIGINAL2023_DianaPatriciaMeraGarzón.pdf2023_DianaPatriciaMeraGarzón.pdfTrabajo de gradoapplication/pdf3664720https://repositorio.uan.edu.co/bitstreams/f864f051-75bb-4038-ba14-e0bdc392af6d/download5f2f9672e79604c3a8036afd1413ccbbMD512023_DianaPatriciaMeraGarzón_Anexo.pdf2023_DianaPatriciaMeraGarzón_Anexo.pdfArtículo anexoapplication/pdf2918273https://repositorio.uan.edu.co/bitstreams/1a227c5e-5564-46b6-b130-8bf945997639/download0837d7b54d5ca1169836ebe5db1fc56dMD522023_DianaPatriciaMeraGarzón_Autorizacion.pdf2023_DianaPatriciaMeraGarzón_Autorizacion.pdfAutorización de uso para consulta, publicación y reproducción electrónicaapplication/pdf1040437https://repositorio.uan.edu.co/bitstreams/1ef43578-ed5f-47c9-b8ba-5c5b4f87915e/download8886071b3ba96ae782ba52414d4ce07dMD532023_DianaPatriciaMeraGarzón_Acta.pdf2023_DianaPatriciaMeraGarzón_Acta.pdfActa de sustentaciónapplication/pdf254775https://repositorio.uan.edu.co/bitstreams/836c9468-d66c-4e94-aea2-47c5ebd26d86/download33dc8c9923fa48e073248fb0e4fbb813MD54CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8914https://repositorio.uan.edu.co/bitstreams/e76378cc-cdb9-42ca-99e4-601950058254/download2b2ab6ec8a6a222739b9c0e57c635c2eMD55123456789/9095oai:repositorio.uan.edu.co:123456789/90952024-10-09 22:51:09.779https://creativecommons.org/licenses/by/4.0/Acceso abiertoopen.accesshttps://repositorio.uan.edu.coRepositorio Institucional UANalertas.repositorio@uan.edu.co

Guía para la identificación de las áreas sembradas en palma de aceite, a partir del uso de la plataforma de Google Earth Engine (Estudio de caso: municipio de Maní – Casanare)

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