Mapping Spatial Patterns and Recent Changes in Quercus pyrenaica (Willd.) Forests Using Remote Sensing and Machine Learning
Quercus pyrenaica (Willd.), a sub-Mediterranean oak, is expected to experience substantial distribution shifts under climate change, with some populations in Portugal at risk. Beyond climate-driven pressures, long-standing anthropogenic pressures have likely contributed to the species’ current vulnerability. This work aims to characterize the current status of closed-canopy Q. pyrenaica forests by providing a spatio-temporal assessment of forest fragmentation and its recent evolution. Using multispectral bands from Sentinel-2 time-series data, vegetation indices, embedding vectors generated by Google’s AlphaEarth foundational model, and topographic variables, we applied a machine learning Random Forest classifier to map Q. pyrenaica forests in 2019 and 2024 and to analyze their spatial configuration patterns. The findings indicate robust predictive performance (spatial cross-validation OA of 95.1%, Kappa of 83.7%, and F1 of 86.9%) and reveal the prominent role of AlphaEarth embedding features in the RF classifier, suggesting that these features are well-suited for classifying forest habitats of conservation importance. Quercus pyrenaica occurs predominantly at mid-elevations (~820 m a.s.l.), on gentle slopes (~9°), topographically neutral terrain, and northwestern-facing aspects, consistently across both years. Between 2019 and 2024, the Q. pyrenaica forest area showed an increasing signal. However, the results point to a landscape in an initial phase of forest recovery, constrained by land-use legacies, with cover increasing predominantly through the sprawl of small, geometrically complex, and poorly connected patches. Together, these results provide a baseline to track recent changes in Q. pyrenaica distribution and fragmentation, highlighting a contrast between apparent area expansion and declining overall structural integrity. In the future, patch connectivity and full recovery of secondary succession should be a priority for policymakers and forest owners.
Authors
- Isabel Passos (ORCID: https://orcid.org/0000-0003-3226-0977)
- Albano Figueiredo (ORCID: https://orcid.org/0000-0003-0142-4764)
- Carlos Vila-Viçosa (ORCID: https://orcid.org/0000-0003-4285-8008)
- M Ribeiro
- João Gonçalves (ORCID: https://orcid.org/0000-0002-6615-0218)
Institutions
- University of Lisbon (PT)
- Universidade Politécnica de Castelo Branco (PT)
- Universidade do Porto (PT)
- Universidade Politécnica de Viana do Castelo (PT)
- Institute for Biodiversity (DE)
- Centro de Estudos em Geografia e Ordenamento do Territorio (PT)
Publication Details
- Journal
- Remote Sensing
- Published
- 2026-04-17
- DOI
- https://doi.org/10.3390/rs18081208
- Primary Topic
- Remote Sensing in Agriculture
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Fundação para a Ciência e a Tecnologia