Using cost-plus-loss analysis to define optimal forest inventory interval and forest inventory accuracy

Abstract

, M. 2012. Using cost-plus-loss analysis to define optimal forest inventory interval and forest inventory accuracy. Silva Fennica 46(2): 211-226.In recent years, optimal inventory accuracy has been analyzed with a cost-plus-loss methodology, where the total costs of inventory include both the measurement costs and the losses from the decisions based on the collected information. Losses occur, when the inaccuracies in the data lead to sub-optimal decisions. In almost all cases, it has been assumed that the accuracy of the once collected data remains the same throughout the planning period, and the period has been from 10 up to 100 years. In reality, the quality of the data deteriorates in time, due to errors in the predicted growth. In this study, we carried out a cost-plus-loss analysis accounting for the errors in (stand-level) growth predictions of basal area and dominant height. In addition, we included the inventory errors of these two variables with several different levels of accuracy, and costs of inventory with several different assumptions of cost structure. Using the methodology presented in this study, we could calculate the optimal inventory interval (life-span of data) minimizing the total costs of inventory and losses through the 30-year planning period. When the inventory costs only to a small extent depended on the accuracy, the optimal inventory period was 5 years and optimal accuracy RMSE 0%. When the costs more and more heavily depended on the accuracy, the optimal interval turned out to be either 10 or 15 years, and the optimal accuracy reduced from RMSE 0% to RMSE 20%. By increasing the accuracy of the growth models, it was possible to reduce the inventory accuracy or lengthen the interval, i.e. obtain clear savings in inventory costs.

Reliability of the predicted stand structure for clear-cut stands using optional methods: airborne laser scanning-based methods, smartphone-based forest inventory application Trestima and pre-harvest measurement tool EMO

“…What is the threshold value of accuracy for making decisions resulting in more profitable wood allocation and conversion solutions? The final judgment of the accuracy required has to be based on analytical comparisons of costs and revenues related to these decisions (Eid et al 2004;Mäkinen et al 2012).…”

Comparing 30-year diameter censuses and tree-ring chronologies in natural old-growth Cryptomeria japonica forest on Yakushima Island, Japan

“…Furthermore, a shorter measurement interval will require a large amount of time, effort and cost in conducting the census more frequently. Mäkinen et al (2012) suggested an optimal forest inventory interval of 10-15 years to minimize the overall costs, to minimize measurements, and to reduce economic losses caused by inaccurate growth prediction related to a harvesting plan. Considering the various aspects of the problems mentioned above, the currently adopted measurement interval of our plots, 10-19 years, could be appropriate.…”

Review. Assessing uncertainty and risk in forest planning and decision support systems: review of classical methods and introduction of new approaches

“…Inoptimality losses due to growth prediction errors have been so far studied using cost-plus-loss analysis only by Pietilä et al (2010) and by Mäkinen et al (2012). Both studies concluded that the inoptimality losses varied between 3.3% and 11.6% of the stand-level NPV and depended on the inventory interval.…”