This project builds upon wildland fire remote sensing research by addressing novel questions through the analysis of pre-fire and post-fire light detection and ranging (lidar) data and fusion with optical remote sensing. Recent advances in remote sensing have improved top-down approaches for estimating carbon emissions through burned area; however, characterizing fuel consumption at scales finer than existing regional estimates remains a key problem for bottom-up analysis and for mechanistically grounding top-down estimates. Airborne light detection and ranging (lidar) has demonstrated effectiveness for estimating fuel components at landscape scale. Recent research in this project estimates fuel consumption for the 2012 Pole Creek Fire in Oregon and portions of the 2011 Las Conchas Fire in New Mexico, using multitemporal lidar and corresponding field data. Additionally, estimates for change in aboveground biomass (AGB) were compared to energy release estimates using fire radiative energy (FRE) observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite in order to mechanistically derive a combustion coefficient for top-down estimation. Results indicated that combining field data into a pooled model produced better landscape estimates, demonstrating that pre-fire field data may not be necessary for similar analysis if post-fire data captures the full range of forest conditions. Due to the unpredictable nature of wildfire, pre-fire data are rare. Therefore, these results are noteworthy for scientists and managers who have multiple lidar collections and wish to use a space-for-time approach to capture pre-fire condition. Past research efforts included the evaluation of lidar- and Landsat-derived burn severity indices, and fuel treatment and mountain pine beetle infestation effects on ensuing fire severity using multitemporal lidar data. This research is quantifying how pre-fire forest condition affects burn severity and how various remote sensing techniques can be used to explain fire patterns and improve modeling and remote sensing of wildland fire for natural resource management and forest ecology.
Location of the Pole Creek Fire in central Oregon and the overlap of viable light detection and ranging (lidar) and Landsat imagery.