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Título
Environmental drivers of fire severity in extreme fire events that affect Mediterranean pine forest ecosystems
Autor
Año del Documento
2019
Documento Fuente
Forest Ecology and Management, Febrero 2019, vol. 433, p. 24 - 32.
Abstract
The increasing occurrence of large and severe fires in Mediterranean forest ecosystems produces major ecological
and socio-economic damage. In this study, we aim to identify the main environmental factors driving fire
severity in extreme fire events in Pinus fire prone ecosystems, providing management recommendations for
reducing fire effects. The study case was a megafire (11,891 ha) that occurred in a Mediterranean ecosystem
dominated by Pinus pinaster Aiton in NW Spain. Fire severity was estimated on the basis of the differenced
Normalized Burn Ratio from Landsat 7 ETM +, validated by the field Composite Burn Index. Model predictors
included pre-fire vegetation greenness (normalized difference vegetation index and normalized difference water
index), pre-fire vegetation structure (canopy cover and vertical complexity estimated from LiDAR), weather
conditions (spring cumulative rainfall and mean temperature in August), fire history (fire-free interval) and
physical variables (topographic complexity, actual evapotranspiration and water deficit). We applied the
Random Forest machine learning algorithm to assess the influence of these environmental factors on fire severity.
Models explained 42% of the variance using a parsimonious set of five predictors: NDWI, NDVI, time
since the last fire, spring cumulative rainfall, and pre-fire vegetation vertical complexity. The results indicated
that fire severity was mostly influenced by pre-fire vegetation greenness. Nevertheless, the effect of pre-fire
vegetation greenness was strongly dependent on interactions with the pre-fire vertical structural arrangement of
vegetation, fire history and weather conditions (i.e. cumulative rainfall over spring season). Models using only
physical variables exhibited a notable association with fire severity. However, results suggested that the control
exerted by the physical properties may be partially overcome by the availability and structural characteristics of
fuel biomass. Furthermore, our findings highlighted the potential of low-density LiDAR for evaluating fuel
structure throughout the coefficient of variation of heights. This study provides relevant keys for decisionmaking
on pre-fire management such as fuel treatment, which help to reduce fire severity.
ISSN
0378-1127
Revisión por pares
SI
Propietario de los Derechos
Elsevier
Idioma
spa
Tipo de versión
info:eu-repo/semantics/acceptedVersion
Derechos
restrictedAccess
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