Characterizing boreal forest wildfire with multi-temporal Landsat and LIDAR data

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Wildfire is an important disturbance agent in Canada's boreal forest. Optical remotely sensed imagery (e.g., Landsat TM/ETM+), is well suited for capturing horizontally distributed forest conditions, structure, and change, while Light Detection and Ranging (LIDAR) data are more appropriate for capturing vertically distributed elements of forest structure and change. The integration of optical remotely sensed imagery and LIDAR data provides improved opportunities to characterize post-fire conditions. The objective of this study is to compare changes in forest structure, as measured with a discrete return profiling LIDAR, to post-fire conditions, as measured with remotely sensed data. Our research is focused on a boreal forest fire that occurred in May 2002 in Alberta, Canada. The Normalized Burn Ratio (NBR), the differenced NBR (dNBR), and the relative dNBR (RdNBR) were calculated from two dates of Landsat data (August 2001 and September 2002). Forest structural attributes were derived from two spatially coincident discrete return LIDAR profiles acquired in September 1997 and 2002 respectively. Image segmentation was used to produce homogeneous spatial patches analogous to forest stands, with analysis conducted at this patch level. In this study area, which was relatively homogenous and dominated by open forest, no statistically significant relationships were found between pre-fire forest structure and post-fire conditions (r < 0.5; p > 0.05). Post-fire forest structure and absolute and relative changes in forest structure were strongly correlated to post-fire conditions (r ranging from - 0.507 to 0.712; p < 0.0001). Measures of vegetation fill (VF) (LIDAR capture of cross-sectional vegetation amount), post-fire and absolute change in crown closure (CC), and relative change in average canopy height, were most useful for characterizing post-fire conditions. Forest structural attributes generated from the post-fire LIDAR data were most strongly correlated to post-fire NBR, while dNBR and RdNBR had stronger correlations with absolute and relative changes in the forest structural attributes. Absolute and relative changes in VF and changes in CC had the strongest positive correlations with respect to dNBR and RdNBR, ranging from 0.514 to 0.715 (p < 0.05). Measures of average inter-tree distance and volume were not strongly correlated to post-fire NBR, dNBR, or RdNBR. No marked differences were found in the strength or significance of correlations between post-fire structure and the post-fire NBR, dNBR, RdNBR, indicating that for the conditions present in this study area all three burn severity indices captured post-fire conditions in a similar manner. Finally, the relationship between post-fire forest structure and post-fire condition was strongest for dense forests (> 60% crown closure) compared to open (26-60%) and sparse forests (10-25%). Forest structure information provided by LIDAR is useful for characterizing post-fire conditions and burn induced structural change, and will complement other attributes such as vegetation type and moisture, topography, and long-term weather patterns, all of which will also influence variations in post-fire conditions. Crown Copyright © 2009.

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Remote Sensing of Environment

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burn severity, disturbance, dNBR, fire, forest, forest structure, Landsat, LIDAR, NBR, post-fire effects, RdNBR, recovery, segmentation