000165279 001__ 165279
000165279 005__ 20251219174252.0
000165279 0247_ $$2doi$$a10.18172/cig.6769
000165279 0248_ $$2sideral$$a146740
000165279 037__ $$aART-2025-146740
000165279 041__ $$aeng
000165279 100__ $$0(orcid)0009-0008-6156-3110$$aMartín Ortiz, Pedro$$uUniversidad de Zaragoza
000165279 245__ $$aConsequences of fire on vegetation composition and its influence on Leaf Area Index (LAI) distribution using multi-resolution images
000165279 260__ $$c2025
000165279 5060_ $$aAccess copy available to the general public$$fUnrestricted
000165279 5203_ $$aIn recent decades, wildfires have become one of the main disturbances affecting Mediterranean forest ecosystems. Understanding how fire-affected formations recover is crucial for assessing their resilience and effectively managing potential hydrological-forest restoration measures. This study analyzes vegetation regeneration in burned areas representative of the landscape diversity of Aragón (NE Iberian Peninsula) considering (i) the type of colonizing vegetation in relation to the pre-existing one and (ii) the impact of the colonizing vegetation type on the spatial distribution of the Leaf Area Index (LAI), which is used as a proxy for the eco-physiological functionality of the affected formations. High-spatial-resolution GeoSAT-2 images andSentinel-2 L2A collections were used to generate maps of current vegetation distribution and multitemporal LAI composites, respectively. Contingency tables derived from diachronic comparisons of dominant vegetation type (before the fire and at present) and Random Forest (RF) predictive models were employed. The RF models also determined the importance of different natural factors in the spatial distribution of colonizing vegetation formations. The results highlighted the strong dependence between pre-fire and colonizing vegetation formations (χ² = 10.067) and the role of regenerative trajectories in the spatial distribution of LAI (p < 0.05). Greater regeneration was observed in areas dominated by species with active reproductive strategies (resprouting and serotiny). Additionally, in the Random Forest modeling (OOB = 21%), pre-existing vegetation emerged as the most determining factor (MDG = 600) in predicting current vegetation, surpassing fire severity and the regenerative trend of the Normalized Difference Vegetation Index (MDG ≈ 250), whose effects vary depending on the type of vegetation formation.
000165279 536__ $$9info:eu-repo/grantAgreement/ES/MCIN-AEI/PID2020-118886RB-I00-AEI-10.13039-501100011033$$9info:eu-repo/grantAgreement/ES/MICIU/CGL2016-80783-R
000165279 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttps://creativecommons.org/licenses/by/4.0/deed.es
000165279 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000165279 700__ $$0(orcid)0000-0003-4831-4060$$aPérez-Cabello, Fernando$$uUniversidad de Zaragoza
000165279 700__ $$0(orcid)0000-0002-3359-6213$$aIranzo Cubel, Cristian$$uUniversidad de Zaragoza
000165279 700__ $$0(orcid)0000-0001-7403-1764$$aMontorio Lloveria, Raquel$$uUniversidad de Zaragoza
000165279 7102_ $$13006$$2010$$aUniversidad de Zaragoza$$bDpto. Geograf. Ordenac.Territ.$$cÁrea Análisis Geográfico Regi.
000165279 773__ $$g51 (2025), [24 pp.]$$pCuad. investig. geogr.$$tGeographical Research Letters$$x0211-6820
000165279 8564_ $$s7805581$$uhttps://zaguan.unizar.es/record/165279/files/texto_completo.pdf$$yVersión publicada
000165279 8564_ $$s2768837$$uhttps://zaguan.unizar.es/record/165279/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000165279 909CO $$ooai:zaguan.unizar.es:165279$$particulos$$pdriver
000165279 951__ $$a2025-12-19-14:44:18
000165279 980__ $$aARTICLE