Assessing the Uncertainty of Traditional Sample-Based Forest Inventories in Mixed and Single Species Conifer Systems Using a Digital Forest Twin

Abstract

Forest managers need regular accurate assessments of forest conditions to make informed decisions associated with harvest schedules, growth projections, merchandising, investment, and overall management planning. Traditionally, this is achieved through field-based sampling (i.e., timber cruising) a subset of the trees within a desired area (e.g., 1%–2%) through stratification of the landscape to group similar vegetation structures and apply a grid within each stratum where fixed- or variable-radius sample locations (i.e., plots) are installed to gather information used to estimate trees throughout the unmeasured remainder of the area. These traditional approaches are often limited in their assessment of uncertainty until trees are harvested and processed. However, the increasing availability of airborne laser scanning datasets in commercial forestry processed into Digital Inventories® enables the ability to non-destructively assess the accuracy of these field-based surveys, which are commonly referred to as cruises. In this study, we assess the uncertainty of common field sampling-based estimation methods by comparing them to a population of individual trees developed using established and validated methods and in operational use on the University of Idaho Experimental Forest (UIEF) and a commercial conifer plantation in Louisiana, USA (PLLP). A series of repeated sampling experiments, representing over 90 million simulations, were conducted under industry-standard cruise specifications, and the resulting estimates are compared against the population values. The analysis reveals key limitations in current sampling approaches, highlighting biases and inefficiencies inherent in certain specifications. Specifically, methods applied to handle edge plots (i.e., measurements conducted on or near the boundary of a sampling stratum), and stratum delineation contributes most significantly to systematic bias in estimates of the mean and variance around the mean. The study also shows that conventional estimators, designed for perfectly randomized experiments, are highly sensitive to plot location strategies in field settings, leading to potential inaccurate estimations of BAA and TPA. Overall, the study highlights the challenges and limitations of traditional forest sampling and impacts specific sampling design decisions can have on the reliability of key statistical estimates.