RT info:eu-repo/semantics/article
T1 Burn severity metrics in fire-prone pine ecosystems along a climatic gradient using Landsat imagery
A1 Fernández García, Victor
A1 Santamarta, Mónica
A1 Fernández Manso, Alfonso
A1 Quintano Pastor, María del Carmen
A1 Marcos, Elena
A1 Calvo, Leonor
K1 Composite burn index
K1 dNBR-EVI
K1 Fire severity
K1 Mediterranean-Transition-Oceanic climatic conditions
K1 Pine forest
K1 Spectral index
AB Multispectral imagery is a widely used source of information to address post-fire ecosystem management. Theaim of this study is to evaluate the ability of remotely sensed indices derived from Landsat 8 OLI/TIRS to assessinitial burn severity (overall, on vegetation and on soil) in fire-prone pine forests along the Mediterranean-Transition-Oceanic climatic gradient in the Mediterranean Basin. We selected four large wildfires which affected pineforests in a climatic gradient within the Iberian Peninsula. In each wildfire we established CBI plots to obtain fieldvalues of three burn severity metrics: site, vegetation and soil burn severity. The ability of 13 spectral indices tomatch these three field burn severity metrics was compared and their transferability along the climatic gradientassessed using linear regression models. Specifically, we analysed the performance of 12 indices previously usedfor burn severity assessments (8 reflective, 2 thermal, 2 mixed) and a new reflective index (dNBR-EVI). The resultsshowed that Landsat spectral indices have a greater ability to determine site and vegetation burn severitythan soil burn severity. We found large differences in indices performances among the three different climaticregions, since most indices performed better in the Mediterranean and Transition regions than in the Oceanicone. In general, the dNBR-EVI showed the best fit to site, vegetation and soil burn severity in the three regions,demonstrating broad transferability along the entire climatic gradient.
PB Elsevier
YR 2018
FD 2018
LK https://uvadoc.uva.es/handle/10324/67735
UL https://uvadoc.uva.es/handle/10324/67735
LA spa
NO Remote Sensing of Environment, Marzo 2018, vol. 206, p. 205- 217.
DS UVaDOC
RD 22-dic-2024