RT info:eu-repo/semantics/article
T1 Vegetation and soil fire damage analysis based on species distribution modeling trained with multispectral satellite data
A1 Quintano Pastor, María del Carmen
A1 Fernández Manso, Alfonso
A1 Calvo, Leonor
A1 Roberts, Dar A.
AB Forest managers demand reliable tools to evaluate post-fire vegetation and soil damage. In this study, we quantify wildfire damage to vegetation and soil based on the analysis of burn severity, using multitemporal and multispectral satellite data and species distribution models, particularly maximum entropy (MaxEnt). We studied a mega-wildfire (9000 ha burned) in North-Western Spain, which occurred from 21 to 27 August 2017. Burn severity was measured in the field using the composite burn index (CBI). Burn severity of vegetation and soil layers (CBIveg and CBIsoil) was also differentiated. MaxEnt provided the relative contribution of each pre-fire and post-fire input variable on low, moderate and high burn severity levels, as well as on all severity levels combined (burned area). In addition, it built continuous suitability surfaces from which the burned surface area and burn severity maps were built. The burned area map achieved a high accuracy level (κ = 0.85), but slightly lower accuracy when differentiating the three burn severity classes (κ = 0.81). When the burn severity map was validated using field CBIveg and CBIsoil values we reached lower κ statistic values (0.76 and 0.63, respectively). This study revealed the effectiveness of the proposed multi-temporal MaxEnt based method to map fire damage accurately in Mediterranean ecosystems, providing key information to forest managers.
PB MDPI
SN 2072-4292
YR 2019
FD 2019
LK https://uvadoc.uva.es/handle/10324/55854
UL https://uvadoc.uva.es/handle/10324/55854
LA eng
NO Remote Sensing, 2019, vol. 11, n. 15, 1832
NO Producción Científica
DS UVaDOC
RD 24-nov-2024