RT info:eu-repo/semantics/article
T1 Improving fire severity analysis in Mediterranean environments: A comparative study of eeMETRIC and SSEBop Landsat-based evapotranspiration models
A1 Quintano Pastor, María del Carmen
A1 Fernández Manso, Alfonso
A1 Fernández Guisuraga, José Manuel
A1 Roberts, Dar A.
K1 Evapotranspiration
K1 Evaporación
K1 Meteorology
K1 Climatology
K1 Fire severity
K1 Forest fires
K1 Incendio forestal
K1 Mediterranean Region
K1 Mediterráneo, Región del - Clima
K1 Forests and forestry
K1 Bosques - Incendios - Mediterráneo, Región del
K1 Landsat satellites
K1 Satelites artificiales
K1 Remote sensing
K1 Teledetección
K1 2502 Climatología
K1 2509 Meteorología
K1 3106 Ciencia Forestal
K1 tele
AB Wildfires represent a significant threat to both ecosystems and human assets in Mediterranean countries, where fire occurrence is frequent and often devastating. Accurate assessments of the initial fire severity are required for management and mitigation efforts of the negative impacts of fire. Evapotranspiration (ET) is a crucial hydrological process that links vegetation health and water availability, making it a valuable indicator for understanding fire dynamics and ecosystem recovery after wildfires. This study uses the Mapping Evapotranspiration at High Resolution with Internalized Calibration (eeMETRIC) and Operational Simplified Surface Energy Balance (SSEBop) ET models based on Landsat imagery to estimate fire severity in five large forest fires that occurred in Spain and Portugal in 2022 from two perspectives: uni- and bi-temporal (post/pre-fire ratio). Using-fine-spatial resolution ET is particularly relevant for heterogeneous Mediterranean landscapes with different vegetation types and water availability. ET was significantly affected by fire severity according to eeMETRIC (F > 431.35; p-value < 0.001) and SSEBop (F > 373.83; p-value < 0.001) metrics, with reductions of 61.46% and 63.92%, respectively, after the wildfire event. A Random Forest machine learning algorithm was used to predict fire severity. We achieved higher accuracy (0.60 < Kappa < 0.67) when employing both ET models (eeMETRIC and SSEBop) as predictors compared to utilizing the conventional differenced Normalized Burn Ratio (dNBR) index, which resulted in a Kappa value of 0.46. We conclude that both fine resolution ET models are valid to be used as indicators of fire severity in Mediterranean countries. This research highlights the importance of Landsat-based ET models as accurate tools to improve the initial analysis of fire severity in Mediterranean countries.
PB MDPI
SN 2072-4292
YR 2024
FD 2024
LK https://uvadoc.uva.es/handle/10324/67290
UL https://uvadoc.uva.es/handle/10324/67290
LA eng
NO Remote Sensing, 2024, Vol. 16, Nº. 2, 361
NO Producción Científica
DS UVaDOC
RD 22-dic-2024