Effect of Baseline Definition on Post-Fire Resilience Metrics Derived from Landsat Time Series in Pinus halepensis.
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| Title: | Effect of Baseline Definition on Post-Fire Resilience Metrics Derived from Landsat Time Series in Pinus halepensis. |
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| 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] |
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| Database: | Engineering Source |
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| 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] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18091352 |
