A fast spectral recovery does not necessarily indicate post-fire forest recovery

Background

Climate change has increased wildfire activity in the western USA and limited the capacity for forests to recover post-fire, especially in areas burned at high severity. Land managers urgently need a better understanding of the spatiotemporal variability in natural post-fire forest recovery to plan and implement active recovery projects. In burned areas, post-fire “spectral recovery”, determined by examining the trajectory of multispectral indices (e.g., normalized burn ratio) over time, generally corresponds with recovery of multiple post-fire vegetation types, including trees and shrubs. Field data are essential for deciphering the vegetation types reflected by spectral recovery, yet few studies validate spectral recovery metrics with field data or incorporate spectral recovery into spatial models of post-fire vegetation recovery. We investigated relationships between spectral recovery and field measurements of post-fire recovery (16 to 27 years post-fire) from 99 plots in mixed conifer forests of the Blue Mountains, USA. Additionally, using generalized linear mixed effects models, we assessed the relative capacities of multispectral, climatic, and topographic data to predict field measurements of post-fire recovery.

Results

We found that a fast spectral recovery did not necessarily coincide with field measurements of forest recovery (e.g., density of regenerating seedlings, saplings, and young trees and % juvenile conifer cover). Instead, fast spectral recovery often coincided with increases in % shrub cover. We primarily attributed this relationship to the response of snowbrush ceanothus, an evergreen shrub that vigorously resprouts post-fire. However, in non-trailing edge forests—where it was cooler and wetter and fast-growing conifers were more common—rapid spectral recovery coincided with both increases in % shrub cover and forest recovery. Otherwise, spectral recovery showed potential to identify transitions to grasslands, as grass-dominated sites showcased distinctly slow spectral trajectories. Lastly, field measurements of post-fire forest recovery were best predicted when including post-fire climate and multispectral data in predictive models.

Conclusions

Despite a disconnect between a fast spectral recovery and forest recovery, our results suggest that including multispectral data improved models predicting the likelihood of post-fire forest recovery. Improving predictive models would aid land managers in identifying sites to implement active reforestation projects.