View in EDS HTML Full Text PDF Full Text

Effect of Baseline Definition on Post-Fire Resilience Metrics Derived from Landsat Time Series in Pinus halepensis.

Saved in:
Bibliographic Details
Title: Effect of Baseline Definition on Post-Fire Resilience Metrics Derived from Landsat Time Series in Pinus halepensis.
Authors: Martín-Ortiz, Pedro1,2 (AUTHOR) pedro.martin@unizar.es, Iranzo, Cristian1,2 (AUTHOR), Alves, Daniel Borini3 (AUTHOR), Montorio, Raquel1,2 (AUTHOR), Pérez-Cabello, Fernando1,2 (AUTHOR)
Source: Remote Sensing. May2026, Vol. 18 Issue 9, p1352. 31p.
Subjects: Aleppo pine, Normalized difference vegetation index, Image segmentation, Ecosystems, Forest management, Ecological resilience, Wildfire risk
Geographic Terms: Southern Europe
Abstract: Highlights: GEOBIA-based segmentation delineates homogeneous ecological units, reducing uncertainty in baseline selection for post-fire resilience assessment. NDVI time series combined with the spectral probability of belonging to Pinus halepensis allows distinguishing early greenness recovery from actual pine canopy recovery. NDVI alone may overestimate resilience in early post-fire stages, as shrub species dominate before the pine canopy fully recovers. What are the main findings? NDVI in Pinus halepensis communities recovers within approximately seven years after wildfire, whereas structural pine canopy recovery requires more than 15 years. NDVI alone overestimates resilience, as early recovery stages are dominated by shrub species such as Quercus coccifera. What are the implications of the main findings? Baseline selection critically influences the accuracy of remote sensing-based post-fire recovery assessments. Remote sensing assessments based solely on NDVI may misinterpret forest resilience in Mediterranean pine ecosystems. Wildfires have historically shaped Mediterranean ecosystems, fostering the adaptation of fire-resilient species such as Pinus halepensis Mill. Assessing post-fire resilience is essential to understand landscape recovery and guide forest management. This requires evaluating the speed, intensity, and trajectory of vegetation recovery relative to a defined baseline, although the influence of control point selection and baseline configuration remains unclear, despite its critical role in shaping the interpretation of recovery dynamics. This study proposes a methodological framework to assess the resilience of P. halepensis using 14-year Landsat time series following wildfire events, combined with image segmentation algorithms and Object-Based Image Analysis (GEOBIA). The analysis integrates two complementary vectors: (i) temporal evolution of NDVI and (ii) spectral probability of assignment to P. halepensis. Results indicate that NDVI suggests an average vegetation recovery time of seven years; however, spectral probability remains below 40% during this period, indicating slower tree cover recovery. Field inventories confirm that full recovery requires more than 15 years, with early stages dominated by shrublands, mainly Quercus coccifera. These findings show that NDVI alone overestimates resilience and that control selection and baseline configuration strongly influence assessments. GEOBIA enhances the ecological precision of resilience evaluation. [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: 193715383
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Effect of Baseline Definition on Post-Fire Resilience Metrics Derived from Landsat Time Series in Pinus halepensis.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Martín-Ortiz%2C+Pedro%22">Martín-Ortiz, Pedro</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> pedro.martin@unizar.es</i><br /><searchLink fieldCode="AR" term="%22Iranzo%2C+Cristian%22">Iranzo, Cristian</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Alves%2C+Daniel+Borini%22">Alves, Daniel Borini</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Montorio%2C+Raquel%22">Montorio, Raquel</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Pérez-Cabello%2C+Fernando%22">Pérez-Cabello, Fernando</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. May2026, Vol. 18 Issue 9, p1352. 31p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Aleppo+pine%22">Aleppo pine</searchLink><br /><searchLink fieldCode="DE" term="%22Normalized+difference+vegetation+index%22">Normalized difference vegetation index</searchLink><br /><searchLink fieldCode="DE" term="%22Image+segmentation%22">Image segmentation</searchLink><br /><searchLink fieldCode="DE" term="%22Ecosystems%22">Ecosystems</searchLink><br /><searchLink fieldCode="DE" term="%22Forest+management%22">Forest management</searchLink><br /><searchLink fieldCode="DE" term="%22Ecological+resilience%22">Ecological resilience</searchLink><br /><searchLink fieldCode="DE" term="%22Wildfire+risk%22">Wildfire risk</searchLink>
– Name: SubjectGeographic
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Southern+Europe%22">Southern Europe</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Highlights: GEOBIA-based segmentation delineates homogeneous ecological units, reducing uncertainty in baseline selection for post-fire resilience assessment. NDVI time series combined with the spectral probability of belonging to Pinus halepensis allows distinguishing early greenness recovery from actual pine canopy recovery. NDVI alone may overestimate resilience in early post-fire stages, as shrub species dominate before the pine canopy fully recovers. What are the main findings? NDVI in Pinus halepensis communities recovers within approximately seven years after wildfire, whereas structural pine canopy recovery requires more than 15 years. NDVI alone overestimates resilience, as early recovery stages are dominated by shrub species such as Quercus coccifera. What are the implications of the main findings? Baseline selection critically influences the accuracy of remote sensing-based post-fire recovery assessments. Remote sensing assessments based solely on NDVI may misinterpret forest resilience in Mediterranean pine ecosystems. Wildfires have historically shaped Mediterranean ecosystems, fostering the adaptation of fire-resilient species such as Pinus halepensis Mill. Assessing post-fire resilience is essential to understand landscape recovery and guide forest management. This requires evaluating the speed, intensity, and trajectory of vegetation recovery relative to a defined baseline, although the influence of control point selection and baseline configuration remains unclear, despite its critical role in shaping the interpretation of recovery dynamics. This study proposes a methodological framework to assess the resilience of P. halepensis using 14-year Landsat time series following wildfire events, combined with image segmentation algorithms and Object-Based Image Analysis (GEOBIA). The analysis integrates two complementary vectors: (i) temporal evolution of NDVI and (ii) spectral probability of assignment to P. halepensis. Results indicate that NDVI suggests an average vegetation recovery time of seven years; however, spectral probability remains below 40% during this period, indicating slower tree cover recovery. Field inventories confirm that full recovery requires more than 15 years, with early stages dominated by shrublands, mainly Quercus coccifera. These findings show that NDVI alone overestimates resilience and that control selection and baseline configuration strongly influence assessments. GEOBIA enhances the ecological precision of resilience evaluation. [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=193715383
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3390/rs18091352
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 31
        StartPage: 1352
    Subjects:
      – SubjectFull: Aleppo pine
        Type: general
      – SubjectFull: Normalized difference vegetation index
        Type: general
      – SubjectFull: Image segmentation
        Type: general
      – SubjectFull: Ecosystems
        Type: general
      – SubjectFull: Forest management
        Type: general
      – SubjectFull: Ecological resilience
        Type: general
      – SubjectFull: Wildfire risk
        Type: general
      – SubjectFull: Southern Europe
        Type: general
    Titles:
      – TitleFull: Effect of Baseline Definition on Post-Fire Resilience Metrics Derived from Landsat Time Series in Pinus halepensis.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Martín-Ortiz, Pedro
      – PersonEntity:
          Name:
            NameFull: Iranzo, Cristian
      – PersonEntity:
          Name:
            NameFull: Alves, Daniel Borini
      – PersonEntity:
          Name:
            NameFull: Montorio, Raquel
      – PersonEntity:
          Name:
            NameFull: Pérez-Cabello, Fernando
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 05
              Text: May2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 20724292
          Numbering:
            – Type: volume
              Value: 18
            – Type: issue
              Value: 9
          Titles:
            – TitleFull: Remote Sensing
              Type: main
ResultId 1