Characterizing the impact of non-stand replacing disturbances on LiDAR based forest structure using a Harmonized Landsat-Sentinel-2 time-series

Non-stand replacing disturbances (NSRs) are events that do not result in complete removal of forest stands and generally occur at a low intensity over an extended period (e.g., insect infestation), or at spatially variable intensities over shorter periods (e.g., windthrow). Forest structural change associated with NSRs can impact both timber supply and ecosystem services, necessitating the need for both detection of NSRs and characterization of their impact.  The increased accessibility of high frequency revisit, medium spatial resolution satellite imagery, has led to a subsequent increase in algorithms designed to detect sub-annual change across broad spatial scales. The Bayesian Estimator of Abrupt change, Seasonal change, and Trend (BEAST) has shown promise in both detecting NSRs on a sub-annual basis and estimating forest structure changes indicating the potential for continuous characterization of NSRs. This study assesses the impact of NSRs on forest structure across a dry interior forest in Western Canada with a specific case study on aspen leafminer (Phyllocnistis populiella) and two-year budworm (Choristoneura biennis). To do so, the BEAST algorithm was applied to a time-series of medium resolution optical satellite imagery for six disturbance-sensitive indices for the time period 2013-2021 to generate predictor variables capturing annual phenological variation (i.e., amplitude, slope, and trend). Three LiDAR derived forest attributes were modeled (i.e., canopy cover, height and height variability) using predictors variables as inputs (R² values between 0.5 - 0.7).  These models were then applied across the study areas, and changes in structure estimated over NSR impacted stands. Results showed changes in forest structure over the period of continued NSR events, including an 11% decline in canopy cover. This approach enables the structural change caused by NSRs to be more rapidly identified, providing forest practitioners with approaches to better identify areas in need of intervention.