Characterizing changes in forest condition associated with non-stand replacing disturbances within a continuous forest inventory context

Non-stand-replacing disturbances (NSRD), such as insect outbreaks or windthrow, are major drivers of forest dynamics. Unlike stand-replacing events, NSRD alter stand condition through changes in canopy structure and composition without causing complete mortality, leading to subtle but marked impacts across a range of forest attributes. Detecting the impact of NSRD on forest condition at operational scales remains challenging, as ground surveys and aerial photography are limited in spatiotemporal coverage and subject to interpreter variability. We evaluated a workflow for characterizing changes in forest attributes using a continuous forest inventory (CFI) framework and stand-level matching. CFI data integrated a Harmonized Landsat and Sentinel-2 time series with light detection and ranging (lidar)-derived forest attributes. Stands were matched to a set of three similar candidates based on CFI attribute trajectories. Stands with NSRD later in the time series showed marked changes in stand condition compared to undisturbed controls, specifically declines in a range of forest attributes (e.g. aboveground biomass), with the strongest effects in the lower (25th percentile) portions of the stand-level distribution. By leveraging these spatiotemporally robust datasets, this approach enables quantifiable, spatially continuous, near-real-time updates to forest condition, improving the capacity for forest monitoring under changing environmental conditions.