Attribution 4.0 International (CC BY 4.0)Carvajal Vanegas, Andrés FelipeMera Garzón, Diana Patricia2024-01-302024-01-302023-11-25http://repositorio.uan.edu.co/handle/123456789/9095This 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).spaAcceso abiertoJavaScriptRandom ForestSentinelPlanet ScopeInteligencia Artificial (IA)Trabajo de grado (Pregrado y/o Especialización)JavaScriptRandom ForestSentinelPlanet ScopeArtificial Intelligence (AI)info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Adepoju, 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/