Bayesian Evaluation of Smartphone Applications for Forest Inventories in Small Forest Holdings

Abstract

There are increasingly advanced mobile applications for forest inventories on the market. Small enterprises and nonprofessionals may find it difficult to opt for a more sophisticated application without comparing it to an established standard. In a small private forest holding (19 ha, 4 stands, 61 standing points), we compared TRESTIMA, a computer vision-based mobile application for stand inventories, to MOTI, a smartphone-based relascope, in measuring the number of stems (N) and stand basal area (G). Using a Bayesian approach, we (1) weighted evidence for the hypothesis of no difference in N and G between TRESTIMA and MOTI relative to the hypothesis of difference, and (2) weighted evidence for the hypothesis of overestimating versus underestimating N and G when using TRESTIMA compared to MOTI. The results of the Bayesian tests were then compared to the results of frequentist tests after the p-values of paired sample t-tests were calibrated to make both approaches comparable. TRESTIMA consistently returned higher N and G, with a mean difference of +305.8 stems/ha and +5.8 m2/ha. However, Bayes factors (BF10) suggest there is only moderate evidence for the difference in N (BF10 = 4.061) and anecdotal evidence for the difference in G (BF10 = 1.372). The frequentist tests returned inconclusive results, with p-values ranging from 0.03 to 0.13. After calibration of the p-values, the frequentist tests suggested rather small odds for the differences between the applications. Conversely, the odds of overestimating versus underestimating N and G were extremely high for TRESTIMA compared to MOTI. In a small forest holding, Bayesian evaluation of differences in stand parameters can be more helpful than frequentist analysis, as Bayesian statistics do not rely on asymptotics and can answer more specific hypotheses.