Speaker
Description
Harvester data with accurate GNSS position provides a potentially valuable source of detailed information for forest inventory in first-thinning stands. In thinning operations performed with modern harvesters, all trees along strip roads are removed, forming a systematic line sample that represent approximately 15–20% of the stand. This study investigates how strip road trees can be identified from harvester production file (hpr) data and how strip road placement and within-stand spatial variation affect the estimation of pre-thinning forest attributes. Three different strip road tree identification methods were evaluated using data from seven first-thinning stands in western Finland: (1) a boom angle and length–based method, (2) a buffer-based method using reconstructed strip road centerlines in a global coordinate system, and (3) an angle-only sector method. The accuracy of the resulting diameter distributions, stem counts, and basal area estimates was assessed against detailed field reference data using Reynold’s error index and RMSE metrics. In addition, theoretical strip road networks were established and systematically shifted across each stand to quantify within-stand variation and sampling sensitivity. The results show that the buffer-based identification method consistently produced the most accurate estimates, while the angle-only method performed weakest. Offset strip road samples revealed substantial variation in stem count and basal area in some stands, indicating that strip road alignment and spatial heterogeneity can significantly influence representativeness. The findings demonstrate that harvester-based strip road sampling is a feasible and cost-efficient approach for estimating stand-level forest variables in first thinnings, if tree identification methods and spatial biases are carefully considered.
| Keywords | harvesterdata, systematic sampling, RTK |
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