[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119207-en":3,"doc-seo-119207-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119207,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Sentinel-2 Satellite Image Processing Using Machine Learning Algorithms of the Manombo Nature Reserve","Satellite image processing and analysis using Sentinel-2 data with machine learning in Google Earth Engine supports studying land cover evolution in the Manombo nature reserve in Madagascar. The study aims to identify land elements occupying the reserve and compares CART, Random Forest, Naive Bayes, and SVM classifiers to select the best-performing algorithm for Sentinel-2 inputs. A proposed workflow addresses data treatment and classification, and results show that Random Forest achieves the highest accuracy for correct land-cover classification.","JGISE Vol. 7 No. 2 (2024), pp. 127-132 | [https://doi.org/10.22146/jgise.94152](https://doi.org/10.22146/jgise.94152)  \nJGISE  \nJournal of Geospatial Information Science and Engineering ISSN: 2623-1182 | [https://jurnal.ugm.ac.id/jgise](https://jurnal.ugm.ac.id/jgise)  \n\n| Sentinel-2 satellite image processing using machine learning algorithms ofthe\u003Cbr>Manombo nature reserve\u003Cbr>Valérien Eugène Tsaramanana 1,2,3, Arisetra Razafinimaro1,2,3, Aimé Richard Hajalalaina 1,2,3,\u003Cbr>1 School of Management and Technological Innovation, University of Fianarantsoa, Madagascar\u003Cbr>2 Laboratory of Computer Science and Mathematics Applied to Development, University of Fianarantsoa, Madagascar\u003Cbr>3 Computer Science, Geomatics, Mathematics and Applications, Host Team, Fianarantsoa, Madagascar\u003Cbr>Corresponding : Valérien Eugè[ne Tsaramanana](ne Tsaramanana | Email : zoherval@gmail.com)[ | Email :](ne Tsaramanana | Email : zoherval@gmail.com)[ zoherval@gmail.com](ne Tsaramanana | Email : zoherval@gmail.com)\u003Cbr>Diterima (Received): 18/Feb/2024 Direvisi (Revised): 15/Dec/2024 Diterima untuk Publikasi (Accepted): 16/Dec/2024 |\n| --- |\n| ABSTRACT\u003Cbr>This paper is based on the fields of satellite image processing and analysis using Sentinel-2 satellite images with machine learning algorithms under Google Earth Engine for the study of land cover evolution in the Manombo Madagascar, nature reserve. The objectives of the study are to identify the elements that occupy the land in the reserve. During our experiments, we compared the best machine learning algorithm using CART, Random Forest, Naive Bayes, SVM to determine the best machine learning algorithm for our Sentinel-2 data. So, we have proposed a methodology to do the treatment and, in the end, we have treatment results. From our treatments, we can conclude that the use of Random Forest classifier gave the most accuracy on the correct classification.\u003Cbr>Keywords: Land use, Sentinel2, Supervised Classification, machine learning, satellite images. |\n\n© Author(s) 2024. This is an open access article under the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0).  \n1. Introduction  \nThis work is part of the promotion of the use of Sentinel- 2 satellite images for the study and monitoring of the evolution of land use. Researchers are interested in this type of image for the study of land cover in their respective countries, like (Inglada, 2016), which proposes the land cover map of France in 2016,(Akodéwou et al, 2019) which carries out the study of land cover in and around the Togodo protected area in Togo,(Delalay et al., 2019) who map the land use and land cover of a mountainous region of Nepal, (Ayoubi, 2017) who maps the land cover of Reunion Island, and finally (CIRAD, 2018) who maps the land cover of the Antananarivo Madagascar agglomeration using Sentinnel-2 with Landsat8 . This leads us to reflect on the valorization of the potential of Sentinel-2 images for the monitoring study of the land cover of the Manombo nature reserve in Madagascar. In our processing approach, we have focused on the use of different classification algorithms based on supervised machine learning, which are still little exploited in Madagascar. Algorithms based on this technique use a variety of sources of inspiration,  \nranging from probability theory to geometric intuitions and heuristic approaches (Djaloul, 2017). The machine learning technique makes it possible to learn automatically from past data and experiences, and it seeks to best solve a given problem (Ah-Pine, 2019) . These algorithms compromise supervised classifiers: CART (Classification And Regression Trees) (Breiman et al., 1984), Random Forest (Breiman, 2001), Naive Bayes (Rish, 2001), SVM (Weston and Watkins, 1998) .  \n2. Data, methods and tools  \n2.1. Presentation of methods and techniques by machine learning  \nMachine learning refers to the development, analysis, and implementation of methods that allow a machine to evolve thro","cbCaipb32ueOk4zy","https://ap.wps.com/l/cbCaipb32ueOk4zy","pdf",773303,1,6,"English","en",105,"# Abstract\n# Introduction\n# Data, methods and tools\n## Presentation of methods and techniques by machine learning\n## Study area\n## Presentation of data used","[{\"question\":\"What is the main goal of the study on the Manombo nature reserve?\",\"answer\":\"To process and analyze Sentinel-2 satellite images with machine learning to identify the elements occupying the reserve and support land cover evolution assessment.\"},{\"question\":\"Which supervised machine learning algorithms are compared in the experiments?\",\"answer\":\"CART, Random Forest, Naive Bayes, and SVM are compared to determine the best algorithm for Sentinel-2 data classification.\"},{\"question\":\"Which classifier provides the best accuracy for correct classification?\",\"answer\":\"The Random Forest classifier delivers the most accurate land-cover classification results.\"}]","Sentinel-2 Satellite Image Processing Using Machine Learning Algorithms of the Manombo Nature Reserve | 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