Mapping of Forest Species Using Sentinel-2A Images in the Alentejo and Algarve Regions, Portugal

Crismeire Isbaex (), Ana Margarida Coelho, Ana Cristina Gonçalves and Adélia M. O. Sousa
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Crismeire Isbaex: MED—Mediterranean Institute for Agriculture, Environment and Development & CHANGE—Global Change and Sustainability Institute, Institute for Advanced Research and Training, University of Évora, P.O. Box 94, 7002-544 Évora, Portugal
Ana Margarida Coelho: ICT—Institute of Earth Sciences, Institute for Advanced Research and Training, Colégio Luis António Verney, Rua Romão Ramalho, University of Évora, 59, 7002-554 Évora, Portugal
Ana Cristina Gonçalves: MED—Mediterranean Institute for Agriculture, Environment and Development & CHANGE—Global Change and Sustainability Institute, Institute for Advanced Research and Training, Rural Engineering Department, School of Science and Technology, University of Évora, P.O. Box 94, 7002-544 Évora, Portugal
Adélia M. O. Sousa: MED—Mediterranean Institute for Agriculture, Environment and Development & CHANGE—Global Change and Sustainability Institute, Institute for Advanced Research and Training, Remote Sensing Laboratory—EaRSLab, Rural Engineering Department, School of Science and Technology, University of Évora, P.O. Box 94, 7002-544 Évora, Portugal

Land, 2024, vol. 13, issue 12, 1-21

Abstract: 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.

Keywords: machine learning; supervised classification; mediterranean; random forest; k-nearest neighbor (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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