Quantitative assessments of post-fire effects are key to improving our understanding of ecosystem resilience. While remote sensing technology has allowed us to assess post-fire landscape effects, we are often limited by the lack of information related to pre-fire forest attributes. As a result, our ability to interpret fire effects in relation to landscape-scale canopy fuel distributions is severely inhibited. We used discrete-return multi-temporal Light Detection and Ranging (LiDAR) to quantify pre-fire basal area, basal area mortality, and post-fire basal area. Observed pre-fire basal area values were reconstructed from field measurements taken 2-years after fire. We modeled pre-fire basal area using a log-linear model, whereas, basal area mortality was modeled with beta regression and change estimation. Model performance was compared using bias, RMSE, RMSPE, AIC, and BIC. We also modeled basal area mortality using a combined approach, where we included RdNBR within the selection process. Intensity values were not used in combined models. In general, LiDAR models outperformed combined models (RMSPE of 0.1293 vs. 0.1347 with 3 and 4 variables, respectively) when quantifying basal area mortality. Intensity metrics improved pre-fire basal area models (reduction in AIC BIC values ≈ 10-20; not shown). Lastly, we provide multiple examples of practical applications for renewed perspectives by clearly defining fire effects, directly quantifying, and calibrating remotely sensed LiDAR information to field observations.