Quantifying the growing season dynamics and phenology of a boreal black spruce wildfire chronosequence: Coupling field measurements with MODIS

The boreal forest is the second largest forested biome and the vast area and large carbon stores in the soil makes these forests important to the global carbon, water and energy cycles. Analysis of global coverage, coarse resolution satellite Vegetation Index (VI) data have provided considerable information on the seasonal cycles of vegetation in the mid-to high-latitudes, including the boreal forest, with evidence of an increase in the magnitude of vegetation greenness and a lengthening of the active growing season, which has been attributed to climate warming. However, boreal forests are prone to extensive wildfire disturbance that influence canopy dynamics (i.e. species composition, LAI, and phenology) and separating the direct affect of warming from the indirect affect of increased wildfire frequency on the patterns of boreal phenology and seasonal greeness requires further analysis coupled to ground measurements. In this research we address the need for detailed information on the growing season dynamics and phenological patterns of boreal vegetation. We evaluate whether MODIS reflectance data can resolve small inter-annual variations in canopy phenology and growing season dynamics of boreal forests. We quantified the seasonality and inter-annual differences of the overstory and understory vegetation by optically measuring the LAI and light harvesting potential (FPAR) during the 2004-2006 growing seasons. An automated continuously operating system is used to monitor growing season PAR transmittance. We focused on a boreal wildfire chronosequence of sites comprising a range of forest ages (1-154 years since fire) to quantify the differences in vegetation dynamics and phenology between the deciduous/mixed and coniferous forests. The spatial and temporal characteristics of LAI / FPAR within the chronosequence were examined by comparing both the in situ measurements and the relevant MODIS products. A statistical curve fitting procedure is used to derive the key phenological transition periods of vegetation phenology across the chronosequence for both the in situ and MODIS time series data. In addition, we assessed the uncertainty in these estimates using Monte Carlo simulations to obtain 95% confidence intervals for each modeled transition date. This information is used to examine the effects of temporal aggregation, seasonal cloud and other residual atmospheric effects on determining phenological dates with MODIS. Collectively these data help discern the role warming and increased wildfire have in modifying boreal growing season dynamics while extending our ongoing long-term work to systematically link field measurements, remote sensing and ecosystem modeling to quantify the effects of global change on the carbon budget of boreal forests.