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.

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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

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article

Mapping Spatial Patterns and Recent Changes in Quercus pyrenaica (Willd.) Forests Using Remote Sensing and Machine Learning

Isabel Passos, Albano Figueiredo, Carlos Vila-Viçosa, M Ribeiro et al.
Remote Sensing
Remote Sensing in Agriculture
article

Mapping Spatial Patterns and Recent Changes in Quercus pyrenaica (Willd.) Forests Using Remote Sensing and Machine Learning

Isabel Passos, Albano Figueiredo, Carlos Vila-Viçosa, M Ribeiro, João Gonçalves
article en

Abstract

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.

Remote SensingVol. 18(8)
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)
Fundação para a Ciência e a Tecnologia
Climate action
Openalex Percentile: Top 19%
Remote Sensing in Agriculture
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