Propagating the errors of initial forest variables through stand- and tree-level growth simulators

Abstract

Developments in the field of remote sensing have led to various cost-efficient forest inventory methods at different levels of detail. Remote-sensing techniques such as airborne laser scanning (ALS) and digital photogrammetry are becoming feasible alternatives for providing data for forest planning. Forest-planning systems are used to determine the future harvests and silvicultural operations. Input data errors affect the forest growth projections and these effects are dependent on the magnitude of the error. Our objective in this study was to determine how the errors typical to different inventory methods affect forest growth projections at individual stand level during a planning period of 30 years. Another objective was to examine how the errors in input data behave when different types of growth simulators are used. The inventory methods we compared in this study were stand-wise field inventory and single-tree ALS. To study the differences between growth models, we compared two forest simulators consisting of either distance-independent tree-level models or stand-level models. The data in this study covered a 2,000-ha forest area in southern Finland, including 240 sample plots with individually measured trees. The analysis was conducted with Monte Carlo simulations. The results show that the tree-level simulator is less sensitive to errors in the input data and that by using single-tree ALS data, more precise growth projections can be obtained than using stand-wise field inventory data.

Effects of Individual Tree Detection Error Sources on Forest Management Planning Calculations

“…The effects of the errors in the ITD were analyzed with Monte Carlo (MC) simulations (e.g., [30][31][32][33][34]). With the MC method the calculation procedure is run dozens or hundreds of times, the results of which are used to determine the final predicted value error statistics.…”

Uncertainty in Forest Net Present Value Estimations

“…The relative importance of the three sources of uncertainty in forest NPV computations was determined by simulating each stand within each forest property repeatedly with the MC method (e.g., [25,26,29,39]). In MC methodology, confidence estimates are obtained by generating an error term from the model's error distribution for each output estimate.…”

“…These studies have mainly focused on the influence of various uncertainty components in growth model functioning. However, Mäkinen et al [25] and Mäkinen [30] showed that instead of analyzing individual models, the model chains implemented by the simulators should be scrutinized as a whole.…”

“…The degree of uncertainty is dependent on the functioning of individual models and the interaction between them. Models applied for simulating forest growth form a complex entity that often complicates the analysis of individual model uncertainty ( [25,26]). …”

“…Since tree growth is difficult to measure directly, it must be estimated by models based on other measurable tree characteristics. Growth models can be divided into tree-and stand-level models (e.g., [25]). …”