Structure area curves in Eastern Hardwoods: implications for minimum plot sizes to capture spatially explicit structure indices

Abstract

Key message Sampling needs differ by forest type for timber inventory and structural complexity metrics. We demonstrate in a typical mixed Eastern Hardwoods forest that optimal sampling of timber inventory metrics and spatially explicit structure indices may be achieved in one large plot plus a cruise for large diameter trees, but accurately capturing inventory metrics may not be possible with sparse large-scale sampling.• Context Managing forest stand structures for multiple objectives require accurate and precise estimates of structural features that may be best estimated at different scales.• Aims We document minimum necessary plot sizes for structural metrics and spatially explicit indices to characterize structure in a mature North American Eastern hardwoods forest.• Methods Metrics and indices (Index of Aggregation, Diameter Differentiation Index, Dissimilarity Coefficient, Structural Complexity Index) were calculated within 0.05-1.75-ha plots for 1000 iterations of random placement in two 2.0-ha macroplots. Estimation adequacy required (1) precision (varied < 10% among plots) and ( 2) accuracy (within 10% of the 2.0-ha value at 5th and 95th percentiles).• Results Minimum single plot sizes to achieve estimation adequacy were 0.25-0.75 ha for spatially explicit indices and 0.5-2 ha for stand metrics. A minimum of five 0.10-ha subplots would be needed for most indices and 6-25 for most metrics, but an untenable 375+ for the density of large diameter trees.• Conclusion Estimation adequacy for structural complexity requires no greater sampling intensity than for timber metrics, except for density of large trees. A single large plot may be most cost-effective. National inventories in Eastern hardwoods may not estimate structural complexity well due to inadequate sampling intensity.

Assessing scale‐dependent effects on Forest biomass productivity based on machine learning

“…Admittedly, a larger sampling area will lead to higher accuracy, but at the same time undoubtedly increase the manpower, material, financial resources consumed. Thus, sampling design is often faced with a dilemma (Peck & Zenner, 2021) that the proper sampling plot scale to assess forest productivity must take productivity variability and spatial distribution into consideration; however, it is difficult to predict such variables before an inventory is conducted. Hence, our results provide potential guidance for future sampling schemes.…”