Version 1
: Received: 13 September 2024 / Approved: 14 September 2024 / Online: 14 September 2024 (07:38:55 CEST)
How to cite:
Cheng, T. Y.; Duarte, L.; Teodoro, A. C. Comparative Study of Random Forest and SVM for Land Cover Classification and Post-Wildfire Change Detection. Preprints2024, 2024091107. https://doi.org/10.20944/preprints202409.1107.v1
Cheng, T. Y.; Duarte, L.; Teodoro, A. C. Comparative Study of Random Forest and SVM for Land Cover Classification and Post-Wildfire Change Detection. Preprints 2024, 2024091107. https://doi.org/10.20944/preprints202409.1107.v1
Cheng, T. Y.; Duarte, L.; Teodoro, A. C. Comparative Study of Random Forest and SVM for Land Cover Classification and Post-Wildfire Change Detection. Preprints2024, 2024091107. https://doi.org/10.20944/preprints202409.1107.v1
APA Style
Cheng, T. Y., Duarte, L., & Teodoro, A. C. (2024). Comparative Study of Random Forest and SVM for Land Cover Classification and Post-Wildfire Change Detection. Preprints. https://doi.org/10.20944/preprints202409.1107.v1
Chicago/Turabian Style
Cheng, T. Y., Lia Duarte and Ana Cláudia Teodoro. 2024 "Comparative Study of Random Forest and SVM for Land Cover Classification and Post-Wildfire Change Detection" Preprints. https://doi.org/10.20944/preprints202409.1107.v1
Abstract
The land use land cover (LULC) map is extensively employed for different purposes. The LULC maps provided by the Portuguese National Geographic Information System (SNIG) are not ideal for continuous assessment and regular scrutiny. Machine learning (ML) algorithms applied in remote sensing (RS) data has 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 achieved high accuracy for land classification. 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 land cover classification of a burned area in the Serra da Estrela Natural Park (PNSE), Portugal. This aimed to detect the land cover change and closely observe the burned 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.
Keywords
Machine Learning; Land cover classification; Object-based image analysis; Pixel-based image analysis; Random Forest; Support Vector Machine
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
