Effects of diameter distribution errors on stand management decisions according to a simulated individual tree detection

Abstract

& Key Message Tree-level forest inventory data are becoming increasingly available, which motivates the use of these data for decision-making. However, airborne inventories carried out tree-by-tree typically include systematic errors, which can propagate to objective function variables used to determine optimal forest management. Effects of under-detection focused on the smallest trees on predicted immediate harvest profits and future expectation values were assessed assuming different sites and interest rates. Management decisions based on the erroneous information caused losses of 0-17% of the total immediate and future expected income of Scots pine stands. & Context Optimal decisions on how to manage forest stands can depend on the absence or presence of intermediate and understory trees. Yet, these tree strata are likely prone to inventory errors. & Aims The aim of this study is to examine implications of making stand management decisions based on data that include systematic errors resembling those typically observed in airborne inventories carried out tree-by-tree. & Methods Stand management instructions were developed based on theoretical diameter distribution functions simulated to have different shape, scale, and frequency parameters corresponding to various degrees of under-detection focused on the smallest trees. Immediate harvest income and future expectation value were derived based on various management alternatives simulated. & Results Errors in diameter distributions affected the predicted harvest profits and future expectation values differently between the simulated alternatives and depending on site type and interest rate assumptions. As a result, different alternatives were considered as optimal management compared to the use of the error-free reference distributions. In particular, the use of no management or most intensive management alternatives became preferred over alternatives with intermediate harvesting intensities. Certain harvesting types such as thinning from below became preferred more often than what was optimal. The errors did not affect the selection of the management alternative in 71% of the simulations, whereas in the remaining proportion, relying on the erroneous information would have caused losing 2%, on average, and 17% at maximum, of the total immediate and future expected income. & Conclusion The effects above might not have been discovered, if the results were validated for inventory totals instead of separately considering the immediate and future income and losses produced by the erroneous decisions. It is recommended not to separate but to integrate the inventory and planning systems for well-informed decisions.

Provoking a Cultural Shift in Data Quality

“…Communicating data quality steps and detecting gaps in data quality is difficult, especially in large, transdisciplinary teams. The consequences of such errors include erroneous conclusions (Morrison 2016 ), lack of reproducibility (Peng 2011 , Powers and Hampton 2019 ), retraction (Evaristo and McDonnell 2020 ), and effects on management decisions (Vauhkonen 2020 ). A comprehensive data quality approach is needed to adequately represent both technological and cultural aspects of producing and maintaining high quality ecological data.…”

“…Such breakdowns can also increase the risk of ill-conceived data-driven management decisions. For instance, Vauhkonen ( 2020 ) found that tree-level inventories derived from airborne methods underdetect small trees and, therefore, underpredict harvest profits, resulting in misleading future profit expectations for managers. Similarly, Brunialti and colleagues ( 2012 ) demonstrated limited comparability of lichen diversity estimates because of variability in protocol interpretation, data collector skill sets, and training procedures, which resulted in a restricted ability to monitor changes in lichen biodiversity in response to ecological drivers that would inform management.…”

Prediction of diameter distributions in boreal forests using remotely sensed data

“…Moreover, a group of trees can be difficult to detect as distinct trees (Packalen et al 2013). The errors related to the detection of suppressed trees may not have a critical effect on the errors associated with predicted volumes but are surely critical shortcomings from the point of view of diameter distributions (Persson et al 2002;Vauhkonen 2020).…”

Exploring new frontiers in forecasting forest growth, yield and wood property variation

“…Airborne inventories are also becoming more routine and have great potential to be used to generate even tree-level data for forest modelling and decision support. Vauhkonen (2020), however, warns of the incorporation of systemic errors by this method in some forest types. As a result, under-detection of smaller classes of trees may lead to sub-optimal management decisions and losses of as much as 17% in future income.…”