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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Crismeire Isbaex & Ana Margarida Coelho & Ana Cristina Gonçalves & Adélia M. O. Sousa, 2024. "Mapping of Forest Species Using Sentinel-2A Images in the Alentejo and Algarve Regions, Portugal," Land, MDPI, vol. 13(12), pages 1-21, December.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:12:p:2184-:d:1543784
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    References listed on IDEAS

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    1. Sergio Vélez & Raquel Martínez-Peña & David Castrillo, 2023. "Beyond Vegetation: A Review Unveiling Additional Insights into Agriculture and Forestry through the Application of Vegetation Indices," J, MDPI, vol. 6(3), pages 1-16, July.
    2. Chen, Yun & Guerschman, Juan P & Cheng, Zhibo & Guo, Longzhu, 2019. "Remote sensing for vegetation monitoring in carbon capture storage regions: A review," Applied Energy, Elsevier, vol. 240(C), pages 312-326.
    3. Martínez-Chico, M. & Batlles, F.J. & Bosch, J.L., 2011. "Cloud classification in a mediterranean location using radiation data and sky images," Energy, Elsevier, vol. 36(7), pages 4055-4062.
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