Autor(es): Isbaex, Crismeire ; Coelho, Ana Margarida ; Gonçalves, Ana Cristina ; Sousa, Adélia
Data: 2024
Identificador Persistente: http://hdl.handle.net/10174/37614
Origem: Repositório Científico da Universidade de Évora
Assunto(s): machine learning; supervised classification; mediterranean; random forest; k-nearest neighbor
Land use and land cover (LULC) studies, particularly those focused on mapping forest species using Sentinel-2 (S2A) data, face challenges in delineating and identifying areas of heterogeneous forest components with spectral similarity at the canopy level. In this context, the main objective of this study was to compare and analyze the feasibility of two classification algorithms, K-Nearest Neighbor (KNN) and Random Forest (RF), with S2A data for mapping forest cover in the southern regions of Portugal, using tools with a free, open-source, accessible, and easy-to-use interface. Sentinel-2A data from summer 2019 provided 26 independent variables at 10 m spatial resolution for the analysis. Nine object-based LULC categories were distinguished, including five forest species (Quercus suber, Quercus rotundifolia, Eucalyptus spp., Pinus pinaster, and Pinus pinea), and four non-forest classes. Orfeo ToolBox (OTB) proved to be a reliable and powerful tool for the classification process. The best results were achieved using the RF algorithm in all regions, where it reached the highest accuracy values in Alentejo Central region (OA = 92.16% and K = 0.91). The use of open-source tools has enabled high-resolution mapping of forest species in the Mediterranean, democratizing access to research and monitoring.
