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Plot intensity and cycle-length effects on growth and removals estimates from forest inventories

Formally Refereed

Abstract

Continuous forest inventory planners can allocate the budget to more plots per acre or a shorter remeasurement cycle. A higher plot intensity benefits small area estimation and allows for more precision in current status estimates. Shorter cycles may provide better estimates of growth, removals and mortality. On a fixed budget, the planner can't have both greater plot intensity and shorter cycles. Therefore, it is important to understand the trade-offs involved. Growth over removals ratios are important indicators of sustainability, and can be adversely affected by changes in cycle length. However, it might be possible to ameliorate negative impacts of longer cycle lengths with judicious use of aerial imagery. Increasing the cycle length reduces the value of an inventory for monitoring, but reducing the number of plots increases the variance of both current status estimates and trend estimates. There may be no optimal statistical solution to this quandary, but the best solution will depend on policy and management objectives. Continuous forest inventories use permanent plots that are remeasured to provide information on growth, removals and mortality. Typically, all plots are remeasured within a narrow time span, but the USDA Forest Service has popularized a variant referred to as an annual forest inventory where a percentage of the permanent plots are remeasured every year. We discuss trade-offs between number of field plots and cycle length and provide some insight with example applications showing how these decisions impact growth and removals estimates. We also discuss a variant of the traditional growth over removals ratio estimator that limits degradation in estimate quality as cycle lengths increase.

Keywords

Forest inventory, Remote Sensing, Remeasurement Cycle, FIA data

Citation

Van Deusen, Paul C.; Roesch, Francis A. 2015. Plot intensity and cycle-length effects on growth and removals estimates from forest inventories. Mathematical and Computational Forestry & Natural-Resource Science 7(1):32-37 6 p.
https://www.fs.usda.gov/research/treesearch/48416