Estimating the timber value of a forest property using geographically balanced samples and unoccupied aerial vehicle data

Abstract A common task in forestry is to determine the value of a forest property, and timber is the most valuable component of that property. Remotely sensed data collected by an unoccupied aerial vehicle (UAV) are suited for this purpose as most forest properties are of a size that permits the efficient collection of UAV data. These UAV data, when linked to a probability sample of field plots, enable the model-assisted (MA) estimation of the timber value and its associated uncertainty. Our objective was to estimate the value of timber (€/ha) in a 40-ha forest property in Finland. We used a systematic sample of field plots (n = 160) and 3D image point cloud data collected by an UAV. First, we studied the effects of spatial autocorrelation on the variance estimates associated with the timber value estimates produced using a field data-based simple expansion (EXP) estimator. The variance estimators compared were simple random sampling, Matérn, and a variant of the Grafström–Schelin estimator. Second, we compared the efficiencies of the EXP and MA estimators under different sampling intensities. The sampling intensity was varied by subsampling the systematic sample of 160 field plots. In the case of the EXP estimator, the simple random sampling variance estimator produced the largest variance estimates, whereas the Matérn estimator produced smaller variance estimates than the Grafström–Schelin estimator. The MA estimator was more efficient than the EXP estimator, which suggested that the reduction of sampling intensity from 160 to 60 plots is possible without deterioration in precision. The results suggest that the use of UAV data improves the precision of timber value estimates compared to the use of field data only. In practice, the proposed application improves the cost-efficiency of the design-based appraisal of a forest property because expensive field workload can be reduced by means of UAV data.