View in EDS HTML Full Text PDF Full Text

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

Saved in:
Bibliographic Details
Title: Mapping Spatial Patterns and Recent Changes in Quercus pyrenaica (Willd.) Forests Using Remote Sensing and Machine Learning.
Authors: Passos, Isabel1,2 (AUTHOR) ipassos@student.fl.uc.pt, Vila-Viçosa, Carlos2,3,4,5,6 (AUTHOR), Ribeiro, Maria Margarida1,3,7,8 (AUTHOR), Figueiredo, Albano2,4 (AUTHOR), Gonçalves, João3,4,5,9 (AUTHOR)
Source: Remote Sensing. Apr2026, Vol. 18 Issue 8, p1208. 26p.
Subjects: Forest mapping, Machine learning, Oak, Fragmented landscapes, Forest restoration, Remote sensing
Geographic Terms: Portugal
Abstract: Highlights: What are the main findings? The total mapped area of Q. pyrenaica forests indicates a potential increasing trend. Area gains seem mainly driven by the emergence of small, spatially complex and loosely bonded patches, lacking connectivity among forest stands. What are the implications of the main findings? The results may suggest recovery or expansion, but the structural integrity of these forests may not necessarily be improving. The outputs support prioritizing connectivity, inner-forest conditions, and reducing excessive edge exposure in marginal Portuguese populations, as well as protecting and managing areas with a clear secondary succession drive. 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. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: egs
DbLabel: Engineering Source
An: 193435687
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Mapping Spatial Patterns and Recent Changes in Quercus pyrenaica (Willd.) Forests Using Remote Sensing and Machine Learning.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Passos%2C+Isabel%22">Passos, Isabel</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> ipassos@student.fl.uc.pt</i><br /><searchLink fieldCode="AR" term="%22Vila-Viçosa%2C+Carlos%22">Vila-Viçosa, Carlos</searchLink><relatesTo>2,3,4,5,6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ribeiro%2C+Maria+Margarida%22">Ribeiro, Maria Margarida</searchLink><relatesTo>1,3,7,8</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Figueiredo%2C+Albano%22">Figueiredo, Albano</searchLink><relatesTo>2,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Gonçalves%2C+João%22">Gonçalves, João</searchLink><relatesTo>3,4,5,9</relatesTo> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Apr2026, Vol. 18 Issue 8, p1208. 26p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Forest+mapping%22">Forest mapping</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Oak%22">Oak</searchLink><br /><searchLink fieldCode="DE" term="%22Fragmented+landscapes%22">Fragmented landscapes</searchLink><br /><searchLink fieldCode="DE" term="%22Forest+restoration%22">Forest restoration</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink>
– Name: SubjectGeographic
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Portugal%22">Portugal</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Highlights: What are the main findings? The total mapped area of Q. pyrenaica forests indicates a potential increasing trend. Area gains seem mainly driven by the emergence of small, spatially complex and loosely bonded patches, lacking connectivity among forest stands. What are the implications of the main findings? The results may suggest recovery or expansion, but the structural integrity of these forests may not necessarily be improving. The outputs support prioritizing connectivity, inner-forest conditions, and reducing excessive edge exposure in marginal Portuguese populations, as well as protecting and managing areas with a clear secondary succession drive. 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. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=193435687
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3390/rs18081208
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 26
        StartPage: 1208
    Subjects:
      – SubjectFull: Forest mapping
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Oak
        Type: general
      – SubjectFull: Fragmented landscapes
        Type: general
      – SubjectFull: Forest restoration
        Type: general
      – SubjectFull: Remote sensing
        Type: general
      – SubjectFull: Portugal
        Type: general
    Titles:
      – TitleFull: Mapping Spatial Patterns and Recent Changes in Quercus pyrenaica (Willd.) Forests Using Remote Sensing and Machine Learning.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Passos, Isabel
      – PersonEntity:
          Name:
            NameFull: Vila-Viçosa, Carlos
      – PersonEntity:
          Name:
            NameFull: Ribeiro, Maria Margarida
      – PersonEntity:
          Name:
            NameFull: Figueiredo, Albano
      – PersonEntity:
          Name:
            NameFull: Gonçalves, João
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 15
              M: 04
              Text: Apr2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 20724292
          Numbering:
            – Type: volume
              Value: 18
            – Type: issue
              Value: 8
          Titles:
            – TitleFull: Remote Sensing
              Type: main
ResultId 1