Background： We explore the factors affecting the optimal plot design （size and type as well as the subsample tree selection strategies within a plot） and their relative importance in defining the optimal plot desig...Background： We explore the factors affecting the optimal plot design （size and type as well as the subsample tree selection strategies within a plot） and their relative importance in defining the optimal plot design in amultipurpose forest inventory. The factors include time used to lay out the plot and to make the tree measurements within the plot, the between-plot variation of each of the variables of interest in the area, and the measurement and model errors for the different variables. Methods： We simulate different plot types and sizes and subsample tree selection strategies on measuredtest areas from North Lapland. The plot types used are fixed-radius, concentric and relascope plots. Weselect the optimal type and size first at plot level using a cost-plus-loss approach and then at cluster level byminimizing the weighted standard error with fixed budget. Results： As relascope plots are ve~/efficient at the plot level for volume and basal area, and fixed-radius plots for stems per ha, the optimal plot type strongly depends on the relative importance of these variables. The concentric plot seems to be a good compromise between these two in many cases. The subsample tree selection strategy was more important in selecting optimal plot than many other factors. In cluster level, the most important factor is the transfer time between plots. Conclusions： While the optimal radius of plots and other parameters were sensitive to the measurement times and other cost factors, the concentric plot type was optimal in almost all studied cases. Subsample tree measurement strategies need further studies, as they were an important cost factor. However, their importance to the precision was not as clear.展开更多
文摘Background： We explore the factors affecting the optimal plot design （size and type as well as the subsample tree selection strategies within a plot） and their relative importance in defining the optimal plot design in amultipurpose forest inventory. The factors include time used to lay out the plot and to make the tree measurements within the plot, the between-plot variation of each of the variables of interest in the area, and the measurement and model errors for the different variables. Methods： We simulate different plot types and sizes and subsample tree selection strategies on measuredtest areas from North Lapland. The plot types used are fixed-radius, concentric and relascope plots. Weselect the optimal type and size first at plot level using a cost-plus-loss approach and then at cluster level byminimizing the weighted standard error with fixed budget. Results： As relascope plots are ve~/efficient at the plot level for volume and basal area, and fixed-radius plots for stems per ha, the optimal plot type strongly depends on the relative importance of these variables. The concentric plot seems to be a good compromise between these two in many cases. The subsample tree selection strategy was more important in selecting optimal plot than many other factors. In cluster level, the most important factor is the transfer time between plots. Conclusions： While the optimal radius of plots and other parameters were sensitive to the measurement times and other cost factors, the concentric plot type was optimal in almost all studied cases. Subsample tree measurement strategies need further studies, as they were an important cost factor. However, their importance to the precision was not as clear.
