Riku Tarvainen, Kirsi Riekki, Heikki Ovaskainen, Asko Poikela, Kalle Kärhä, Jukka Malinen. (2025). Estimating stand variables in first-thinnings using harvester data. Silva Fennica vol. 59 no. 3 article id 25014. https://doi.org/10.14214/sf.25014

Keywords: forest inventory; diameter distribution; stand variables; harvester data; accurate GNSS positioning; harvesting quality; Reynold’s error index

Highlights: The idea was to identify the trees removed from the strip roads from the harvester data and use them as a sample of the initial growing stock of the first thinning stand; The goal was also to estimate the number of stems, basal area and diameter distribution of the growing stock after thinning; The sampling method based on identifying strip road trees is applicable to monitoring the density of the remaining growing stock at first thinning stands.

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First thinning is an important operation in forest management because it determines the further growth of the whole stand. Harvester operators open strip roads to the thinning stand and select which trees are removed and which are left to grow. Modern cut-to-length harvesters produce precise information about the dimensions and positions of the cut trees. The location of the harvester is already commonly recorded to harvester production (hpr) files, according to the StanForD 2010 standard, and recording the position of the harvester head is also becoming more common. The aim of this study was to develop a method to estimate the stem count and basal area of the remaining growing stock using this novel harvester data. Precision hpr data from Komatsu harvesters and reference field measurements were gathered from seven stands in western Finland in the summer of 2024. In the method used, the strip road trees were identified and used as samples representing the initial growing stock. The remaining growing stock was estimated using diameter distributions, and by subtracting the harvested trees from the initial growing stock. The results were evaluated using Reynold’s error index, in addition to a visual interpretation of the diameter distributions with respect to the reference data. We found that the method had the potential to determine the basal area and diameter distribution of the remaining growing stock. In the future, this method can be automated, which will allow automated reporting and quality management in first-thinning operations.

Tarvainen, Metsäteho Oy, Vernissakatu 1, FI-01300 Vantaa, Finland https://orcid.org/0009-0001-2336-3125 E-mail: riku.tarvainen@metsateho.fi
Riekki, Metsäteho Oy, Vernissakatu 1, FI-01300 Vantaa, Finland https://orcid.org/0009-0007-8366-3297 E-mail: kirsi.riekki@metsateho.fi
Ovaskainen, Metsäteho Oy, Vernissakatu 1, FI-01300 Vantaa, Finland https://orcid.org/0000-0001-5063-6662 E-mail: heikki.ovaskainen@metsateho.fi
Poikela, Metsäteho Oy, Vernissakatu 1, FI-01300 Vantaa, Finland E-mail: asko.poikela@metsateho.fi
Kärhä, University of Eastern Finland, School of Forest Sciences, Faculty of Science, Forestry and Technology, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: kalle.karha@uef.fi
Malinen, Metsäteho Oy, Vernissakatu 1, FI-01300 Vantaa, Finland https://orcid.org/0000-0002-5023-1056 E-mail: jukka.malinen@metsateho.fi

article id 23023, category Research article

Lennart Noordermeer, Hans Ole Ørka, Terje Gobakken. (2023). Imputing stem frequency distributions using harvester and airborne laser scanner data: a comparison of inventory approaches. Silva Fennica vol. 57 no. 3 article id 23023. https://doi.org/10.14214/sf.23023

Keywords: forest inventory; airborne laser scanning; harvester data; inventory approaches

Highlights: We imputed stem frequency distributions using harvester reference data and predictor variables computed from airborne laser scanner data.; Stand-level distributions of stem diameter, tree height, volume, and sawn wood volume; (Enhanced) area-based and semi-individual tree crown approaches outperformed the individual tree crown method.

Abstract | Full text in HTML | Full text in PDF | Author Info

Stem frequency distributions provide useful information for pre-harvest planning. We compared four inventory approaches for imputing stem frequency distributions using harvester data as reference data and predictor variables computed from airborne laser scanner (ALS) data. We imputed distributions and stand mean values of stem diameter, tree height, volume, and sawn wood volume using the k-nearest neighbor technique. We compared the inventory approaches: (1) individual tree crown (ITC), semi-ITC, area-based (ABA) and enhanced ABA (EABA). We assessed the accuracies of imputed distributions using a variant of the Reynold’s error index, obtaining the best mean accuracies of 0.13, 0.13, 0.10 and 0.10 for distributions of stem diameter, tree height, volume and sawn wood volume, respectively. Accuracies obtained using the semi-ITC, ABA and EABA inventory approaches were significantly better than accuracies obtained using the ITC approach. The forest attribute, inventory approach, stand size and the laser pulse density had significant effects on the accuracies of imputed frequency distributions, however the ALS delay and percentage of deciduous trees did not. This study highlights the utility of harvester and ALS data for imputing stem frequency distributions in pre-harvest inventories.

Noordermeer, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway https://orcid.org/0000-0002-8840-0345 E-mail: lennart.noordermeer@nmbu.no
Ørka, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway https://orcid.org/0000-0002-7492-8608 E-mail: hans-ole.orka@nmbu.no
Gobakken, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway E-mail: terje.gobakken@nmbu.no

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