RT info:eu-repo/semantics/article
T1 Environmental drivers of fire severity in extreme fire events that affect Mediterranean pine forest ecosystems
A1 Garcia-Llamas, Paula
A1 Suarez-Seoane, Susana
A1 Taboada, Angela
A1 Fernández-Manso, Alfonso
A1 Quintano, Carmen
A1 Fernandez-García, Victor
A1 Fernandez-Guisuraga, Jose Manuel
A1 Marcos, Elena
A1 Calvo, Leonor
A1 García-Llamas, Paula
A1 Suárez-Seoane, Susana
A1 Taboada, Angela
A1 Fernández-García, Víctor
A1 Fernández-Guisuraga, José Manuel
K1 Vegetation structure
K1 LiDAR
K1 Physical properties
K1 Fire history
K1 Weather conditions
K1 Landsat
K1 CBI
AB The increasing occurrence of large and severe fires in Mediterranean forest ecosystems produces major ecologicaland socio-economic damage. In this study, we aim to identify the main environmental factors driving fireseverity in extreme fire events in Pinus fire prone ecosystems, providing management recommendations forreducing fire effects. The study case was a megafire (11,891 ha) that occurred in a Mediterranean ecosystemdominated by Pinus pinaster Aiton in NW Spain. Fire severity was estimated on the basis of the differencedNormalized Burn Ratio from Landsat 7 ETM +, validated by the field Composite Burn Index. Model predictorsincluded pre-fire vegetation greenness (normalized difference vegetation index and normalized difference waterindex), pre-fire vegetation structure (canopy cover and vertical complexity estimated from LiDAR), weatherconditions (spring cumulative rainfall and mean temperature in August), fire history (fire-free interval) andphysical variables (topographic complexity, actual evapotranspiration and water deficit). We applied theRandom 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, timesince the last fire, spring cumulative rainfall, and pre-fire vegetation vertical complexity. The results indicatedthat fire severity was mostly influenced by pre-fire vegetation greenness. Nevertheless, the effect of pre-firevegetation greenness was strongly dependent on interactions with the pre-fire vertical structural arrangement ofvegetation, fire history and weather conditions (i.e. cumulative rainfall over spring season). Models using onlyphysical variables exhibited a notable association with fire severity. However, results suggested that the controlexerted by the physical properties may be partially overcome by the availability and structural characteristics offuel biomass. Furthermore, our findings highlighted the potential of low-density LiDAR for evaluating fuelstructure throughout the coefficient of variation of heights. This study provides relevant keys for decisionmakingon pre-fire management such as fuel treatment, which help to reduce fire severity.
SN 0378-1127
YR 2019
FD 2019
LK https://uvadoc.uva.es/handle/10324/67707
UL https://uvadoc.uva.es/handle/10324/67707
LA spa
NO Forest Ecology and Management, Febrero 2019, vol. 433, p. 24 - 32.
DS UVaDOC
RD 28-nov-2024