Attributing a Causal Agent and Assessing the Severity of Non-Stand Replacing Disturbances in a Northern Hardwood Forest using Landsat-Derived Vegetation Indices

Abstract

Non-stand-replacing disturbances are major drivers of northern hardwood forest dynamics, but are more challenging to characterize using satellite imagery than stand-replacing events. This study proposes a hurdle approach in which disturbance causal agents are first attributed to permanent sample plots that were either partially harvested, had sustained damage from an ice storm or remained undisturbed during the observation period, reaching an overall accuracy of 82.9%. Ordinary least square regression was then used to develop disturbance-specific models to assess the severity of partial harvests and damage from ice storms, with r-squared values of 0.57 and 0.59, respectively. The disturbance-specific models included a different set of predictors, confirming the importance of attributing a causal agent to a disturbance before assessing its severity. The sequence of models was implemented regionally to produce severity maps for two disturbance events, revealing within-stand variability in the severity that could be useful for the planning of future silvicultural actions. Although the proposed models offer acceptable performance, more research is needed to include additional disturbance agents and develop models that better capture the small variations in the spectral reflectance caused by low-severity disturbances, especially in the case of low-intensity partial harvests.