Designing plots for precise estimation of forest attributes in landscapes and forests of varying heterogeneity

• Published in: Canadian journal of forest research Vol. 51; no. 10; pp. 1569 - 1578
• Main Authors: Lister, Andrew J, Leites, Laura P
• Format: Journal Article
• Language: English
• Published: 1840 Woodward Drive, Suite 1, Ottawa, ON K2C 0P7 NRC Research Press 01.10.2021; Canadian Science Publishing NRC Research Press
• Subjects: cluster plot design; Clusters; Coefficient of variation; configuration des grappes de parcelles; Configurations; Distribution; efficacité de l’inventaire forestier; Environmental aspects; Factorial experiments; forest inventory; forest inventory design optimization; forest inventory efficiency; forest pattern simulation; forest sampling simulation; Forests; Forests and forestry; Heterogeneity; Landscape; landscapes; optimisation de la conception de l’inventaire forestier; simulation de patrons forestiers; simulation d’échantillonnage forestier; Spatial heterogeneity; spatial variation; statistical models; Trees; United States
• ISSN: 0045-5067; 1208-6037; 1208-6037
• DOI: 10.1139/cjfr-2020-0508

Models of relationships among forest inventory sampling efficiency and cluster plot configuration variables inform decisions by inventory planners. However, relationships vary under different spatial heterogeneity scenarios. To improve understanding of how spatial patterns of forests affect these relationships, we implemented a factorial experiment by simulating forest pattern at both the landscape and stand scales. We sampled these simulated forests with a variety of cluster plot configurations, calculated coefficient of variation (CV) of trees per hectare for each replicate, and tested the relationships among CV and the heterogeneity and cluster plot configuration factors within a linear mixed model framework. Both landscape- and stand-scale pattern aggregation had a significant relationship with CV. Changing cluster plot configuration factors did little to change the overall CV when using larger subplots but had some important effects when using smaller subplots. These impacts were stronger in the more uniform landscapes. Results were opposite for stand-scale heterogeneity; changing plot configuration in areas with aggregated patterns had a stronger impact than it did in areas with more uniform patterns. Results of this study reveal the importance of accounting for spatial pattern at multiple scales when making cluster configuration choices if the goal is statistical efficiency.