Using satellite time-series data sets to analyze fire disturbance and forest recovery across Canada

Abstract

The boreal forest biome is one of the largest on Earth, covering more than 14% of the total land surface. Fire disturbance plays a dominant role in boreal ecosystems, altering forest succession, biogeochemical cycling, and carbon sequestration. We used two time-series data sets of Advanced Very High Resolution Radiometer (AVHRR) Normalized Differenced Vegetation Index (NDVI) imagery for North America to analyze vegetation recovery after fire. The Canadian Forest Service Large Fire Database was used to identify the location of fires and calculate scaled NDVI statistics from the Pathfinder AVHRR Land (PAL) and the Global Inventory Modeling and Mapping Studies (GIMMS) AVHRR data sets. Unburned areas were also identified, based on interannual variability metrics, in order to reduce the effects of factors other than fire on the temporal behavior of scaled NDVI. Burned and unburned areas were stratified by ecoregion to ensure the presence of comparable land cover types and account for influences of local environmental variability. Temporal anomalies in NDVI for burned and unburned areas show the impacts of fire and the recovery of the forest to pre-burn levels, and indicate changes in variability that might be associated with vegetation compositional changes consistent with early successional species. The rate of recovery varied in the three episodic fire years on which we focused our analysis (1981, 1989, and 1995), but were consistently shorter than previous studies that emphasized the most impacted areas within fires. Temporal variability in the time series, represented by the difference of burned and unburned area anomalies, increased beyond the observed post-fire recovery period. This indicates residual effects of fire disturbance over the regrowth period, perhaps associated with early successional vegetation and increased susceptibility to drought. Distinct differences were noted between the PAL and GIMMS data sets, with evidence for systematic data processing artifacts remaining in the PAL time series.