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, Víctor A1 Santamarta, Mónica A1 Fernández Manso, Alfonso A1 Quintano Pastor, Carmen A1 Marcos Porras, Elena María A1 Calvo Galván, María Leonor A2 Ecologia K1 Ecología. Medio ambiente K1 Composite Burn Index K1 dNBR-EVI K1 Fire severity K1 Pine forest K1 Spectral index K1 Mediterranean K1 Climatic conditions 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 2018-03-15 LK http://hdl.handle.net/10612/7481 UL http://hdl.handle.net/10612/7481 NO Remote sensing of environment, 2018, vol. 206 NO P. 205-217 DS BULERIA. Repositorio Institucional de la Universidad de León RD 28-mar-2024