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Assessing tree crown fire damage integrating linear spectral mixture analysis and supervised machine learning on Sentinel-2 imagery

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International Journal of Digital Earth 2023年第1期16卷 3162-3198页

作者： Giandomenico De Luca Giuseppe Modica Joao M.N.Silva Salvatore Praticò JoséM.C.Pereira Dipartimento di Agrari1 Universitàdegli Studi Mediterranea di Reggio Calabri1Reggio Calabri1Italy Dipartimento di Scienze veterinarie Universitàdegli Studi di Messin1Messin1Italy Forest Research Centre School of AgricultureUniversity of LisbonLisbonPortugal

Crown fire damage is a mixture of three principal fire-related components:charred material,scorched foliage,and unaltered green *** study estimated the abundance of these physical alterations in two immediate post-fire Mediterranean forest contexts(Portugal and Italy)by applying linear spectral mixture analysis(LSMA)on Sentinel-2 *** tree crowns fire damage was subsequently mapped,integrating fractional abundance information in a random forest(RF)algorithm,comparing the accuracy resulting from the adoption of generic or image spectral libraries as the primary investigative *** image-derived endmembers resulted in more effectiveness in terms of fire-related components abundance quantification(LMSAderived RMSEendmembers or image endmembers derived information was *** environmental heterogeneity of the two study areas affected the fire severity gradients,with a prevalence of the charred(PT)(45–46%)and green class(IT)(44–53%).Post-fire temporal monitoring was initialized by applying the proposed strategies,and the preliminary results showed a positive recovery trend in forest vegetation from the first year following the fire event,with a reduced charcoal predominance and an increasing proportion of green components.

关键词： Post-fire assessment fire severity post-fire vegetation recovery random forest(RF) scikit-learn fraction image extraction spectral unmixing endmembers crown fire damage mapping fully constrained least squares pixel purity index

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