000170952 001__ 170952
000170952 005__ 20260430151735.0
000170952 0247_ $$2doi$$a10.3390/rs18091352
000170952 0248_ $$2sideral$$a149071
000170952 037__ $$aART-2026-149071
000170952 041__ $$aeng
000170952 100__ $$0(orcid)0009-0008-6156-3110$$aMartín-Ortiz, Pedro$$uUniversidad de Zaragoza
000170952 245__ $$aEffect of Baseline Definition on Post-Fire Resilience Metrics Derived from Landsat Time Series in Pinus halepensis
000170952 260__ $$c2026
000170952 5060_ $$aAccess copy available to the general public$$fUnrestricted
000170952 5203_ $$aWildfires 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.
000170952 536__ $$9info:eu-repo/grantAgreement/ES/MCIN-AEI/PID2020-118886RB-I00-AEI-10.13039-501100011033$$9info:eu-repo/grantAgreement/ES/MCIN-AEI/PID2024-160889OA-I00-AEI-10.13039-501100011033
000170952 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttps://creativecommons.org/licenses/by/4.0/deed.es
000170952 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000170952 700__ $$0(orcid)0000-0002-3359-6213$$aIranzo, Cristian$$uUniversidad de Zaragoza
000170952 700__ $$aAlves, Daniel Borini
000170952 700__ $$0(orcid)0000-0001-7403-1764$$aMontorio, Raquel$$uUniversidad de Zaragoza
000170952 700__ $$0(orcid)0000-0003-4831-4060$$aPérez-Cabello, Fernando$$uUniversidad de Zaragoza
000170952 7102_ $$13006$$2010$$aUniversidad de Zaragoza$$bDpto. Geograf. Ordenac.Territ.$$cÁrea Análisis Geográfico Regi.
000170952 773__ $$g18, 9 (2026), 1352 [31 pp.]$$pRemote sens. (Basel)$$tRemote Sensing$$x2072-4292
000170952 8564_ $$s15466844$$uhttps://zaguan.unizar.es/record/170952/files/texto_completo.pdf$$yVersión publicada
000170952 8564_ $$s2332945$$uhttps://zaguan.unizar.es/record/170952/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000170952 909CO $$ooai:zaguan.unizar.es:170952$$particulos$$pdriver
000170952 951__ $$a2026-04-30-13:57:46
000170952 980__ $$aARTICLE