Sentinel-2 Satellite Image Processing using Machine Learning Algorithms of the Manombo Nature Reserve
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 e...
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| Format: | Article |
| Language: | English |
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Universitas Gadjah Mada
2025-01-01
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| Series: | Journal of Geospatial Information Science and Engineering |
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| Online Access: | https://jurnal.ugm.ac.id/jgise/article/view/94152 |
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| _version_ | 1846097196423839744 |
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| author | Valerien Eugene Tsaramanana |
| author_facet | Valerien Eugene Tsaramanana |
| author_sort | Valerien Eugene Tsaramanana |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c46cd74e496844a3880b7e97f6fe4a7f |
| institution | Kabale University |
| issn | 2623-1182 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Universitas Gadjah Mada |
| record_format | Article |
| series | Journal of Geospatial Information Science and Engineering |
| spelling | doaj-art-c46cd74e496844a3880b7e97f6fe4a7f2025-01-02T04:29:05ZengUniversitas Gadjah MadaJournal of Geospatial Information Science and Engineering2623-11822025-01-017212713210.22146/jgise.9415236981Sentinel-2 Satellite Image Processing using Machine Learning Algorithms of the Manombo Nature ReserveValerien Eugene Tsaramanana0University Fianarantsoa MadagascarThis 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.https://jurnal.ugm.ac.id/jgise/article/view/94152land usesentinel2supervised classificationmachine learningsatellite images. |
| spellingShingle | Valerien Eugene Tsaramanana Sentinel-2 Satellite Image Processing using Machine Learning Algorithms of the Manombo Nature Reserve Journal of Geospatial Information Science and Engineering land use sentinel2 supervised classification machine learning satellite images. |
| title | Sentinel-2 Satellite Image Processing using Machine Learning Algorithms of the Manombo Nature Reserve |
| title_full | Sentinel-2 Satellite Image Processing using Machine Learning Algorithms of the Manombo Nature Reserve |
| title_fullStr | Sentinel-2 Satellite Image Processing using Machine Learning Algorithms of the Manombo Nature Reserve |
| title_full_unstemmed | Sentinel-2 Satellite Image Processing using Machine Learning Algorithms of the Manombo Nature Reserve |
| title_short | Sentinel-2 Satellite Image Processing using Machine Learning Algorithms of the Manombo Nature Reserve |
| title_sort | sentinel 2 satellite image processing using machine learning algorithms of the manombo nature reserve |
| topic | land use sentinel2 supervised classification machine learning satellite images. |
| url | https://jurnal.ugm.ac.id/jgise/article/view/94152 |
| work_keys_str_mv | AT valerieneugenetsaramanana sentinel2satelliteimageprocessingusingmachinelearningalgorithmsofthemanombonaturereserve |