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.

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

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 10608, category Research article

Lennart Noordermeer, Erik Næsset, Terje Gobakken. (2022). Effects of harvester positioning errors on merchantable timber volume predicted and estimated from airborne laser scanner data in mature Norway spruce forests. Silva Fennica vol. 56 no. 1 article id 10608. https://doi.org/10.14214/sf.10608

Keywords: forest inventory; ALS; forest harvester; GNSS; precision forestry

Highlights: Timber volume was estimated using harvester and airborne laser scanner (ALS) data acquired with different scanners over eight years; The year of ALS acquisition did not have a significant effect on errors in timber volume estimates; Accuracies of timber volume estimates decreased significantly with increasing levels of positioning error; When using inaccurately positioned harvester data, larger grid cells are beneficial.

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

Newly developed positioning systems in cut-to-length harvesters enable georeferencing of individual trees with submeter accuracy. Together with detailed tree measurements recorded during processing of the tree, georeferenced harvester data are emerging as a valuable tool for forest inventory. Previous studies have shown that harvester data can be linked to airborne laser scanner (ALS) data to estimate a range of forest attributes. However, there is little empirical evidence of the benefits of improved positioning accuracy of harvester data. The two objectives of this study were to (1) assess the accuracy of timber volume estimation using harvester data and ALS data acquired with different scanners over multiple years and (2) assess how harvester positioning errors affect merchantable timber volume predicted and estimated from ALS data. We used harvester data from 33 commercial logging operations, comprising 93 731 harvested stems georeferenced with sub-meter accuracy, as plot-level training data in an enhanced area-based inventory approach. By randomly altering the tree positions in Monte Carlo simulations, we assessed how prediction and estimation errors were influenced by different combinations of simulated positioning errors and grid cell sizes. We simulated positioning errors of 1, 2, …, 15 m and used grid cells of 100, 200, 300 and 400 m². Values of root mean square errors obtained for cell-level predictions of timber volume differed significantly for the different grid cell sizes. The use of larger grid cells resulted in a greater accuracy of timber volume predictions, which were also less affected by positioning errors. Accuracies of timber volume estimates at logging operation level decreased significantly with increasing levels of positioning error. The results highlight the benefit of accurate positioning of harvester data in forest inventory applications. Further, the results indicate that when estimating timber volume from ALS data and inaccurately positioned harvester data, larger grid cells are beneficial.

Noordermeer, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, NMBU, P.O. Box 5003, NO-1432 Ås, Norway E-mail: lennart.noordermeer@nmbu.no
Næsset, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, NMBU, P.O. Box 5003, NO-1432 Ås, Norway E-mail: erik.naesset@nmbu.no
Gobakken, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, NMBU, P.O. Box 5003, NO-1432 Ås, Norway E-mail: terje.gobakken@nmbu.no

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