Such large-scale natural disturbances have critical implications for both short-term and long-term ecosystem dynamics.  They can significantly change forest species composition and structure, soil and soil nutrient retention, and biogeochemical cycling at a landscape scale. These effects can persist for many decades.Among the most important disturbance attributes is severity, that is, the combined function of the disturbance intensity and the relative susceptibility of ecosystem elements such as vegetation and soils.

Developing an understanding of the effects of fire on soil carbon (C), nitrogen (N), and mercury (Hg) pools is particularly important to inform management approaches and policies that consider the effects of fire on forest ecosystems.

   

What makes the Pagami Creek Fire a truly exceptional research opportunity is the highly detailed spatial data on pre-fire forest composition, structure, and disturbance history, including unique data such as NASA hyperspectral imagery, forest plot and biogeochemical cycling data, and spruce budworm disturbance assessments---see, for example, Figure 2 (a, from  Wolter and Townsend 2011; b, from Wolter et al. 2009). This set of pre-fire data has been assembled by our research team over the past decade through a series of remote sensing and disturbance ecology research projects within the region.  However, many of the indicators of fire effects, such as needle and leaf coloration and soil chemistry, are ephemeral and therefore must be assessed immediately following the disturbance.  The timing of the Pagami Creek Fire (autumn) therefore required a rapid assessment of fire effects before the onset of winter.

 

Our Research

The key questions that are typically difficult to address once the immediate effects of fire have disappeared are

To what extent do remotely-sensed (hyperspectral) fire severity estimates reflect field-based severity indices in both the overstory and the understory?

How do overstory and understory fire severities interact to influence soil carbon (C), nitrogen (N), and mercury (Hg) loss immediately after a fire in comparison with those in the first growing season after the fire?

To address these questions we collected on-the-ground burn severity, soil, and vegetation measurements in late fall of 2011.  One hundred plots were established across a range of fire severities and forest types to measure overstory and forest floor fire severity and collect soil samples.  We produced an initial estimate of fire severity using the relative difference normalized burn ratio applied to pre-burn and post-burn Landsat TM images to optimize our sampling effort (Figure 3).   

 

We also acquired remotely sensed images from coordinated over-flights of NASA’s Airborne Visible/Infrared Imaging Spectrometer (AVIRIS).  The AVIRIS sensor is used for imaging spectroscopy – known as hyperspectral imagery because of the high number and resolution of the “bands” it detects within the light reflectance profile (Figure 4). Such resolution makes hyperspectral imagery ideal for mapping the chemical composition of the canopy and exposed soil.  By comparison, Landsat TM applies multispectral imaging with fewer bands, each with lower resolution (indicated by the bars in figure 4).  We will use the 6-m resolution AVIRIS images taken post burn (see left insets, figure 4) in combination with our field data to precisely model and map fire burn severity for the complete extent of the Pagami Creek Fire.

Overstory fire severity was estimated visually by the relative proportions of green, brown, and consumed foliage.  Understory fire severity was estimated using visual indicators of forest floor consumption and soil impacts (for example, Figure 5).  We supplemented empirical measures of fire severity with a regular sequence of both color and infrared photos (Figure 6 and 7).  

We will revisit the burn area in early summer 2012 and repeat our fall 2011 soil measurements. This will allow us to assess  immediate after-fire losses of C, N, and Hg from the system, as well as the considerable losses that occur in the winter through spring months immediately after the fire.  Both field and image data will be analyzed with respect to the extensive pre-fire data that we have previously mapped for the study area.

Expected Outcomes

Last Modified: 12/12/2016