Assessments of postfire recovery outcomes for the chamise chaparral shrublands of southern California provide a basis for land managers and ecologists to identify long-term changes in this sensitive ecosystem. Postfire vegetation recovery assessments based on field-plot vegetation sampling and aerial image analysis have proven to be limited in coverage and inefficient for large areas of this landscape type. This study evaluates the potential of remotely sensed regrowth trajectories based on multi-temporal Landsat 4, 5, 7, and 8 satellite image observations for the postfire recovery assessment of chamise. Methods included: 1) an a priori determination of postfire shrub fractional cover changes based on multi-date high spatial resolution orthoimagery, 2) statistical testing to assess the sensitivity of regrowth trajectories based on several spectral vegetation indices and applied metrics to the recovery outcomes, and 3) an examination of regrowth trajectories which extend 19-29 years postfire relative to field-based measurements from other studies. Results provide a basis for interpretations about the sensitivities of the postfire regrowth trajectories derived from Landsat surface reflectance data to changes in the shrub matrix at various spatial and temporal scales. A primary finding was that several measures, including the Regeneration Index and another proposed here which is termed the Scaled Recovery Metric, enhanced the signals of postfire recovery derived from the multi-temporal trajectories and increased their comparability. Findings indicate that several of the spectral vegetation indices (NDVI, NDMI, NBR, and NBR2) were sensitive to long-term postfire changes in chamise, and that these same indices were statistically significant indicators of postfire recovery outcomes when certain metrics were applied. This study provides an overview of some advantages, limitations, and technical considerations of deriving postfire regrowth trajectories from Landsat imagery to assess postfire recovery outcomes of chamise.