Comparative Study of Random Forest and Support Vector Machine for Land Cover Classification and Post-Wildfire Change Detection
The land use land cover (LULC) map is extensively employed for different purposes. Machine learning (ML) algorithms applied in remote sensing (RS) data have been proven effective in image classification, object detection, and semantic segmentation. Previous studies have shown that random forest (RF)...
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MDPI AG
2024-11-01
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| author | Yan-Cheng Tan Lia Duarte Ana Cláudia Teodoro |
| author_facet | Yan-Cheng Tan Lia Duarte Ana Cláudia Teodoro |
| author_sort | Yan-Cheng Tan |
| collection | DOAJ |
| description | The land use land cover (LULC) map is extensively employed for different purposes. Machine learning (ML) algorithms applied in remote sensing (RS) data have been proven effective in image classification, object detection, and semantic segmentation. Previous studies have shown that random forest (RF) and support vector machine (SVM) consistently achieve high accuracy for land classification. Considering the important role of Portugal’s Serra da Estrela Natural Park (PNSE) in biodiversity and nature conversation at an international scale, the availability of timely data on the PNSE for emergency evaluation and periodic assessment is crucial. In this study, the application of RF and SVM classifiers, and object-based (OBIA) and pixel-based (PBIA) approaches, with Sentinel-2A imagery was evaluated using Google Earth Engine (GEE) platform for the land cover classification of a burnt area in the PNSE. This aimed to detect the land cover change and closely observe the burnt area and vegetation recovery after the 2022 wildfire. The combination of RF and OBIA achieved the highest accuracy in all evaluation metrics. At the same time, a comparison with the Normalized Difference Vegetation Index (NDVI) map and Conjunctural Land Occupation Map (COSc) of 2023 year indicated that the SVM and PBIA map resembled the maps better. |
| format | Article |
| id | doaj-art-2128031cb9874339803f16d0dc55cbe7 |
| institution | DOAJ |
| issn | 2073-445X |
| language | English |
| publishDate | 2024-11-01 |
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| record_format | Article |
| series | Land |
| spelling | doaj-art-2128031cb9874339803f16d0dc55cbe72025-08-20T02:47:59ZengMDPI AGLand2073-445X2024-11-011311187810.3390/land13111878Comparative Study of Random Forest and Support Vector Machine for Land Cover Classification and Post-Wildfire Change DetectionYan-Cheng Tan0Lia Duarte1Ana Cláudia Teodoro2Department of Geography, Faculty of Arts and Humanities, University of Porto, Via Panorâmica, 4150-564 Porto, PortugalDepartment of Geosciences, Environment and Land Planning, Faculty of Sciences, University of Porto, Rua Campo Alegre, 4169-007 Porto, PortugalDepartment of Geosciences, Environment and Land Planning, Faculty of Sciences, University of Porto, Rua Campo Alegre, 4169-007 Porto, PortugalThe land use land cover (LULC) map is extensively employed for different purposes. Machine learning (ML) algorithms applied in remote sensing (RS) data have been proven effective in image classification, object detection, and semantic segmentation. Previous studies have shown that random forest (RF) and support vector machine (SVM) consistently achieve high accuracy for land classification. Considering the important role of Portugal’s Serra da Estrela Natural Park (PNSE) in biodiversity and nature conversation at an international scale, the availability of timely data on the PNSE for emergency evaluation and periodic assessment is crucial. In this study, the application of RF and SVM classifiers, and object-based (OBIA) and pixel-based (PBIA) approaches, with Sentinel-2A imagery was evaluated using Google Earth Engine (GEE) platform for the land cover classification of a burnt area in the PNSE. This aimed to detect the land cover change and closely observe the burnt area and vegetation recovery after the 2022 wildfire. The combination of RF and OBIA achieved the highest accuracy in all evaluation metrics. At the same time, a comparison with the Normalized Difference Vegetation Index (NDVI) map and Conjunctural Land Occupation Map (COSc) of 2023 year indicated that the SVM and PBIA map resembled the maps better.https://www.mdpi.com/2073-445X/13/11/1878machine learningland cover classificationobject-based image analysispixel-based image analysisrandom forestsupport vector machine |
| spellingShingle | Yan-Cheng Tan Lia Duarte Ana Cláudia Teodoro Comparative Study of Random Forest and Support Vector Machine for Land Cover Classification and Post-Wildfire Change Detection Land machine learning land cover classification object-based image analysis pixel-based image analysis random forest support vector machine |
| title | Comparative Study of Random Forest and Support Vector Machine for Land Cover Classification and Post-Wildfire Change Detection |
| title_full | Comparative Study of Random Forest and Support Vector Machine for Land Cover Classification and Post-Wildfire Change Detection |
| title_fullStr | Comparative Study of Random Forest and Support Vector Machine for Land Cover Classification and Post-Wildfire Change Detection |
| title_full_unstemmed | Comparative Study of Random Forest and Support Vector Machine for Land Cover Classification and Post-Wildfire Change Detection |
| title_short | Comparative Study of Random Forest and Support Vector Machine for Land Cover Classification and Post-Wildfire Change Detection |
| title_sort | comparative study of random forest and support vector machine for land cover classification and post wildfire change detection |
| topic | machine learning land cover classification object-based image analysis pixel-based image analysis random forest support vector machine |
| url | https://www.mdpi.com/2073-445X/13/11/1878 |
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