Linking crown fire likelihood with post-fire spectral variability in Mediterranean fire-prone ecosystems

Abstract

Background. Fire behaviour assessments of past wildfire events have major implications for anticipating post-fire ecosystem responses and fuel treatments to mitigate extreme fire behaviour of subsequent wildfires. Aims. This study evaluates for the first time the potential of remote sensing techniques to provide explicit estimates of fire type (surface fire, intermittent crown fire, and continuous crown fire) in Mediterranean ecosystems. Methods. Random Forest classification was used to assess the capability of spectral indices and multiple endmember spectral mixture analysis (MESMA) image fractions (char, photosynthetic vegetation, non- photosynthetic vegetation) retrieved from Sentinel-2 data to predict fire type across four large wildfires Key results. MESMA fraction images procured more accurate fire type estimates in broadleaf and conifer forests than spectral indices, without remarkable confusion among fire types. High crown fire likelihood in conifer and broadleaf forests was linked to a post-fire MESMA char fractional cover of about 0.8, providing a direct physical interpretation. Conclusions. Intrinsic biophysical characteristics such as the fractional cover of char retrieved from sub- pixel techniques with physical basis are accurate to assess fire type given the direct physical interpretation. Implications. MESMA may be leveraged by land managers to determine fire type across large areas, but further validation with field data is advised.