High fire disturbance in forests leads to longer recovery, but varies by forest type

Across the world, millions of hectares of forest are burned by wildfires each year. Satellite remote sensing, particularly when used in time series, can describe complex disturbance-recovery processes, but is underutilized by ecologists. This study examines whether a greater disturbance magnitude equates to a longer recovery length, in the fire-adapted forests of south-east Australia. Using Landsat time series, spectral disturbance and recovery maps were first created, for 2.3 million hectares of forest, burned between 2002 and 2009. To construct these maps, a piecewise linear model was fitted to each pixel's Normalized Burn Ratio (NBR) temporal trajectory, and used to extract the disturbance magnitude (change in NBR) and the spectral recovery length (number of years for the NBR trajectory to return to its pre-fire state). Pearson's correlations between disturbance magnitude and spectral recovery length were then calculated at a state level, bioregion level and patch level (600 m × 600 m, or 36 hectares). Our results showed overall correlation at the state level to be inconclusive, due to confounding factors. At the bioregion level, correlations were predominantly positive (i.e. a greater disturbance equals a longer recovery). At the patch level, both positive and negative correlations occurred, with clear evidence of spatial patterns. This suggests that the relationship between disturbance magnitude and recovery length is dependent on forest type. This was further explored by investigating the major vegetation divisions within one bioregion, which provided further evidence that relationships varied by vegetation type. In Heathy Dry Forests, for example, a greater disturbance magnitude usually led to a longer recovery length, while in Tall Mist Forests, the opposite behaviour was evident. Results of the patch-level analysis were particularly promising, demonstrating the utility of satellite remote sensing in producing landscape scale information to inform policy and management.