A century of fire suppression in western North America has created large, contiguous patches of homogeneous forests that raise questions about these forests’ resilience (ability to recover structure and function after disturbance), particularly in the face of increased fire frequency. Currently, there is a need to better characterize and understand burn mosaic patterns created by current fire regimes and how these patterns contribute to forest resilience. In this study, we investigate whether current fire regimes create forest structures that are characteristic of resilient forests (heterogeneity in gaps and openings). Specifically, we ask: for a given pre-fire forest structure and burn severity classification, what are the dominant pathways of post-fire forest structure change? What biophysical attributes are these pathways associated with? We used 40 cm resolution stereo Digital Aerial Photogrammetry (DAP) data from one month pre- and two and four years post- three 2015 fires in Colville National Forest (northeastern Washington State) to analyze changes in forest canopy patterns across a gradient of burn severities. We created pre- and post-fire digital surface models (DSM) from DAP data, then used DSMs to calculate canopy cover and canopy fragmentation as measured by fractal area index to identify spatial scales of fire-resilient forest structures.
Results/Conclusions
The analysis showed that before the fires, most of the forests in the study area contained dense continuous canopy cover. The three fires in our study burned primarily with high severity (Stickpin), moderate severity (Renner), and low severity (Graves Mountain). We found multiple pathways to post-fire forest structure per burn severity class. Low-severity fire created fine-scale patterns of enclosed canopy gaps. Moderate-severity fire fragmented continuous canopy into multiple fine- and meso-scale clumps with openings of various size. Contiguous openings interspersed with smaller patches of live canopy were present in high-severity patches. Future work will address the biophysical conditions (including forest type, topography, and pre-fire structure variation) associated with the above dominant pathways. This novel pre- and post-fire photogrammetry-derived dataset allows for a unique opportunity to use low-cost, high-fidelity data to assess fire-caused change to forest structure across broad spatial scales