Theses and Dissertations Collection

Assessing post-fire burn severity is important to identify potential post-fire hazards,opportunities to revegetate affected areas, potential trends, biomass lost, and whether pre-fire treatments affect the burn severity of subsequent fires. Post-fire burn severity has been commonly examined using optical remote sensing systems, i.e., using the Normalized Burn Ratio (NBR) and differenced NBR (pre-fire NBR - post-fire NBR), which sense spectral changes in vegetation greenness but do not measure actual change in vegetation structure (e.g., biomass lost). Much of this work has compared satellite estimates of burn severity with on the ground visual estimates of burn severity, including the Composite Burn Index (CBI). These comparisons, again, do not typically quantify measurable changes in vegetation. Measuring vegetation structure using 3-dimensional terrestrial lidar offers an opportunity to begin to reconcile remotely sensed and ground measurements of burn severity. In this dissertation, there were three overall objectives including 1) to build models that relate remotely sensed (i.e., Landsat or Sentinel satellite) NBR and dNBR estimates of burn severity with CBI ground estimates at different scales for the conterminous US; 2) to determine relationships between CBI and terrestrial lidar-derived vegetation structure variables to explain variation in burn severity; and 3) to develop models that explain the relationship terrestrial lidar-derived vegetation structure with remotely sensed data that can be applied at the landscape scale. I found that NBR and dNBR were related to CBI at some scales, and subsequently developed regression relationships that could be applied following decision tree logic that examined the goodness of fit for each model ranging from narrow vegetation classifications to the conterminous United States (CONUS) scale. Once relationships were developed between CBI and remotely sensed estimates of burn severity, I then developed methodologies to convert 30 m terrestrial lidar plot point clouds for the 2017 Legion Lake Fire (Custer, SD USA) to vegetation structural metrics, including AGB, canopy base height, canopy area, canopy radius, crown length, crown volume, and diameter at breast height, and determined each vegetation structure variables relationship with CBI. Many vegetation structural estimates were related to CBI, suggesting that CBI could be defined in terms of physical changes in vegetation structure. Finally, I explored the relationship between vegetation structural metrics and change in vegetation structural metrics with post-fire only satellite derived bands, pre- and post-fire satellite-derived bands, or dNBR imagery using machine learning algorithms. I found that burn severity was found to be related to specific vegetation structure variables, and that modeling them presents a way to extend them to the landscape level. The results of this work suggest that remotely sensed burn severity can be defined in terms of vegetation structure and that models applying the relationships between vegetation structure and remotely satellite data can be applied at the landscape scale.