RT info:eu-repo/semantics/article
T1 Comparison of physical-based models to measure forest resilience to fire as a function of burn severity
A1 Fernández Guisuraga, José Manuel
A1 Suarez Seoane, Susana
A1 Quintano Pastor, María del Carmen
A1 Fernández Manso, Alfonso
A1 Calvo, Leonor
K1 Fractional vegetation cover
K1 Spectral mixture analysis
K1 Espectroscopia
K1 PROSAIL
K1 Forest fires
K1 Bosques - Incendios - Prevención y control
K1 Wildfires
K1 Incendio forestal
K1 Environmental degradation
K1 Deterioro del medio ambiente
K1 Forests and forestry
K1 Bosques y silvicultura
K1 3307 Tecnología Electrónica
K1 3308 Ingeniería y Tecnología del Medio Ambiente
K1 3106.08 Silvicultura
AB We aimed to compare the potential of physical-based models (radiative transfer and pixel unmixing models) for evaluating the short-term resilience to fire of several shrubland communities as a function of their regenerative strategy and burn severity. The study site was located within the perimeter of a wildfire that occurred in summer 2017 in the northwestern Iberian Peninsula. A pre- and post-fire time series of Sentinel-2 satellite imagery was acquired to estimate fractional vegetation cover (FVC) from the (i) PROSAIL-D radiative transfer model inversion using the random forest algorithm, and (ii) multiple endmember spectral mixture analysis (MESMA). The FVC retrieval was validated throughout the time series by means of field data stratified by plant community type (i.e., regenerative strategy). The inversion of PROSAIL-D featured the highest overall fit for the entire time series (R2 > 0.75), followed by MESMA (R2 > 0.64). We estimated the resilience of shrubland communities in terms of FVC recovery using an impact-normalized resilience index and a linear model. High burn severity negatively influenced the short-term resilience of shrublands dominated by facultative seeder species. In contrast, shrublands dominated by resprouters reached pre-fire FVC values regardless of burn severity.
PB MDPI
SN 2072-4292
YR 2022
FD 2022
LK https://uvadoc.uva.es/handle/10324/61584
UL https://uvadoc.uva.es/handle/10324/61584
LA eng
NO Remote Sensing, 2022, Vol. 14, Nº. 20,  5138
NO Producción Científica
DS UVaDOC
RD 18-nov-2024