Effect of data acquisition accuracy on timing of stand harvests and expected net present value

Abstract

Modern remote sensing provides cost-efficient spatial digital data that are more accurate than before. However, the influence of increased accuracy and cost-efficiency on simulations of forest management planning has not been evaluated. The aim of the present study was to analyse the effect of data acquisition accuracy on standwise forest inventory by comparing the accuracy and cost of traditional compartmentwise inventory methods with 2D and 3D measurements of digital aerial photographs and airborne laser scanning. Comparison was based on the expected net present value (NPV), i.e. economic losses that consisted of the inventory costs and incorrect timings of treatments. The reference data, totalling 700 ha, were measured from Central Park in the city of Helsinki, Finland. The data were simulated to final cut with a MOTTI simulator, which is a stand-level analysis tool that can be used to assess the effects of alternative forest management practices on growth and timber yield. The results showed that when inventory costs were not considered there were no significant differences between the expected NPV losses in 3D measurements of digital aerial photographs, laser scanning and the compartmentwise method. When inventory costs were taken into account, the compartmentwise method was still the most efficient inventory method in the study area. Forest inventories, however, are usually directed to larger areas when the costs per hectare of remote-sensing methods decrease. As a result of better accuracies, 3D and compartmentwise methods always produce better results than the 2D method when NPV losses are accounted. Simulations of this type are based on the accuracies and costs of the 3D data available today, assuming that the data can be used in tree-level measurements.

Uncertainty in forest simulators and forest planning systems

“…The errors observed in stand-level field inventory and area-level ALS estima-tion were characterised using three error properties: error trend, error distribution shape and correlations between the errors. In most previous studies, the errors have been assumed to be normally distributed and uncorrelated (Eid 2000, Holopainen & Talvitie 2006. According to Paper III, this assumption may be invalid.…”

“…The average inoptimality losses relative to stand-level NPV ranged from 4.5% to 6.4%, depending on the error simulation method. Holopainen & Talvitie (2006) reported inoptimality losses ranging from 4.2% to 11.4%, depending on the simulated forest inventory method. The inoptimality losses reported by Eid (2000) were considerably smaller than in this study (on average 0.92%) but the distributions of random errors were also narrower.…”

“…Real observed errors have been used, for example, by Eid et al (2004), Duvemo et al (2007), Juntunen (2006), Väisänen (2008) and simulated errors by Eid (2000), Holmström et al (2003), Holopainen & Talvitie (2006), Borders et al (2008), Islam et al (2009). One of the strengths of the case-study approach is that the observed errors are realistic and can be used as such.…”

Uncertainty in Forest Net Present Value Estimations

“…This aspect can be studied using cost-plus-loss analyses, in which the expected losses due to suboptimal decisions are added to the total forest inventory costs ( [16,17]). The cost-plus-loss approach was widely utilized in recent forest inventory-and planning-related research (e.g., [18][19][20][21][22][23][24]). …”

Analyzing the effects of inventory errors on holding-level forest plans: the case of measurement error in the basal area of the dominated tree species

“…For example, an error in the inventory data can lead to the early final cutting of a stand (Eid 2000). Holopainen and Talvitie (2006) also studied the effect of errors in inventory data on the expected net present value (NPV). They found that the NPV losses were different for different types of inventory methods.…”