We estimated aboveground tree biomass and change in aboveground tree biomass using repeated airborne laser scanner (ALS) acquisitions and temporally coincident ground observations of forest biomass, for a relatively undisturbed period (2004–2007; ∆07–04), a contrasting period of disturbance (2007–2009; ∆09–07), and an integrated period (2004–2009; ∆09–04). A simple random sampling (SRS) estimator was used to estimate means and variances of biomass and biomass change for each measurement occasion and interval. For each year, linear regression models were used to predict mean total aboveground tree biomass for live, dead, and total biomass components from ALS-derived variables. These models predicted biomass with R2 = 0.68, 0.59, and 0.70 and RMSEs of 32.7, 30.5, and 31.7 Mg ha− 1 for 2004, 2007 and 2009, respectively. A model assisted indirect estimator was then used to estimate biomass and biomass change for comparison to the field-based SRS estimator. This model assisted indirect approach decreased standard errors of biomass estimation relative to the SRS estimator, but had larger variances for biomass change estimation. Linear regression models were also used to directly predict field-estimated biomass change using ALS Δ-variables, calculated as the difference between multi-temporal ALS variables, for the study area. Integrated over the 6 year period, these change models had R2 = 0.81, 0.72, and 0.68 with RMSEs of 2.0, 9.3, and 1.0 Mg ha− 1 yr− 1 for live, dead, and total aboveground tree biomass, respectively. A model assisted direct estimator reduced standard errors of change estimates by 100–200% compared to the field-based estimates. We discuss several potential advantages and limitations of the direct and indirect approaches. Our primary finding is that model assisted direct estimation of biomass change decreased estimation uncertainty relative to both field and model assisted indirect estimation.