||Lidar is currently the most accurate method for remote estimation of forest structure, but it has limited spatial and temporal coverage. Conversely, Landsat data are more widely available, but exhibit a weaker relationship with structure under medium to high leaf area conditions. One potentially valuable means of enhancing the relationship between Landsat reflectance and forest structure is to incorporate Landsat spectral trends prior to a date of interest. Because the condition of a forest stand at any point in time is linked to the stand's disturbance history, an approach that directly leverages the temporal information of Landsat time series should improve estimates of forest structure. The main objective of this study was to test and demonstrate the utility of disturbance and recovery metrics derived from spectral profiles of annual Landsat time series (LTS) to predict current forest structure attributes (as compared to more traditional approaches, including airborne, discrete return lidar and single-date Landsat). We estimated aboveground live biomass (AGBlive), dead woody biomass (AGBdead), basal area (live and dead), and Lorey's mean stand height for a mixed-conifer forest in eastern Oregon, USA, and compared the results with estimates from lidar and single, current-date Landsat imagery. Annual time-series stacks for the entire Landsat record (1972–2010) were obtained to characterize all long-term (insect, growth) and short-term (fire, harvest) vegetation changes that occurred during that period. This required the additional objective of integrating Landsat data from MSS and TM/ETM + sensors, and we describe here our approach. To extract spectral trajectories and change metrics associated with forest disturbances and recovery we applied a temporal segmentation to the calibrated time series. Lidar predicted forest structure of live trees most accurately (e.g. AGBlive: R2 = 0.88, RMSE = 35.3 Mg ha− 1). However, LTS metrics significantly improved model predictions (e.g. AGBlive: R2 = 0.80, RMSE = 46.9 Mg ha− 1) compared to single-date Landsat data (AGBlive, R2 = 0.58, RMSE = 65.1 Mg ha− 1). Conversely, distributions of AGBdead were more strongly associated with disturbance history than current structure of live trees. As a result, LTS models performed significantly better in estimating AGBdead (R2 = 0.73, RMSE = 31.0 Mg ha− 1), than lidar models (R2 = 0.21, RMSE = 43.8 Mg ha− 1); and single-date Landsat data failed completely (R2 = 0, RMSE = 47.8 Mg ha− 1). Further, LTS metrics that integrated disturbance and recovery history over the entire time series generally predicted AGBdead better than metrics describing single events only (e.g. the greatest disturbance). This study demonstrates the unique value of the long, historic Landsat record, and suggests new potentials for mapping current forest structure with Landsat.