Militino, Ana F.Goyena Baroja, HarkaitzPérez Goya, UnaiUgarte Martínez, María Dolores2024-04-032024-04-032024Militino, A. F., Goyena, H., Pérez-Goya, U., & Ugarte, M. D. (2024). Logistic regression versus XGBoost for detecting burned areas using satellite images. Environmental and Ecological Statistics, 31(1), 57-77. https://doi.org/10.1007/s10651-023-00590-71352-850510.1007/s10651-023-00590-7https://academica-e.unavarra.es/handle/2454/47827Classical statistical methods prove advantageous for small datasets, whereas machine learning algorithms can excel with larger datasets. Our paper challenges this conventional wisdom by addressing a highly significant problem: the identification of burned areas through satellite imagery, that is a clear example of imbalanced data. The methods are illustrated in the North-Central Portugal and the North-West of Spain in October 2017 within a multi-temporal setting of satellite imagery. Daily satellite images are taken from Moderate Resolution Imaging Spectroradiometer (MODIS) products. Our analysis shows that a classical Logistic regression (LR) model competes on par, if not surpasses, a widely employed machine learning algorithm called the extreme gradient boosting algorithm (XGBoost) within this particular domain.application/pdfeng© The Author(s) 2024. This article is licensed under a Creative Commons Attribution 4.0 International License.Commission errorLRMachine learningMODISOmission errorSpectral indicesVIIRSXGBoostinfo:eu-repo/semantics/article2024-04-03Acceso abierto / Sarbide irekiainfo:eu-repo/semantics/openAccess