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. The aim of this study is to evaluate the ability of remotely sensed indices derived from Landsat 8 OLI/TIRS to assess initial 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 pine forests in a climatic gradient within the Iberian Peninsula. In each wildfire we established CBI plots to obtain field values of three burn severity metrics: site, vegetation and soil burn severity. The ability of 13 spectral indices to match these three field burn severity metrics was compared and their transferability along the climatic gradient assessed using linear regression models. Specifically, we analysed the performance of 12 indices previously used for burn severity assessments (8 reflective, 2 thermal, 2 mixed) and a new reflective index (dNBR-EVI). The results showed that Landsat spectral indices have a greater ability to determine site and vegetation burn severity than soil burn severity. We found large differences in indices performances among the three different climatic regions, since most indices performed better in the Mediterranean and Transition regions than in the Oceanic one. 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/10324/72413
UL https://uvadoc.uva.es/handle/10324/72413
LA eng
NO Remote Sensing of Environment, 2018, vol. 206, p. 205- 217.
NO Producción Científica
DS UVaDOC
RD 22-dic-2024