Using hyperspectral imagery to estimate forest floor consumption from wildfire in boreal forests of Alaska, USA

Wildfire is a major forest disturbance in interior Alaska that can both directly and indirectly alter ecological processes. We used a combination of pre- and post-fire forest floor depths and post-fire ground cover assessments measured in the field, and high-resolution airborne hyperspectral imagery, to map forest floor conditions after the 2004 Taylor Complex in Alaska's boreal forest. We applied a linear spectral unmixing model with five endmembers representing green moss, non-photosynthetic moss, charred moss, ash and soil to reflectance data to produce fractional cover maps. Our study sites spanned low to moderately high burn severity, and both black and white spruce forest types; high cover of green or non-photosynthetic moss in the remotely sensed imagery indicated low consumption, whereas high cover of charred moss, ash or soil indicated higher consumption. Strong relationships (R-2 = 0.5 to 0.6) between green moss estimated from the imagery and both post-fire depth and percentage consumption suggest that potential burn severity may be predicted by a map of green (live) moss. Given that the depth of the post-fire forest floor is ecologically significant, the method of mapping the condition of the organic forest floor with hyperspectral imagery presented here may be a useful tool to assess the effect of future fires in the boreal region.