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 471, category Research article

Michael Vohland, Johannes Stoffels, Christina Hau, Gebhard Schüler. (2007). Remote sensing techniques for forest parameter assessment: multispectral classification and linear spectral mixture analysis. Silva Fennica vol. 41 no. 3 article id 471. https://doi.org/10.14214/sf.471

Keywords: Picea abies; remote sensing; stand variables; stem number; multispectral classification; Linear Spectral Mixture Analysis

Abstract | View details | Full text in PDF | Author Info

One of the most common applications of remote sensing in forestry is the production of thematic maps, depicting e.g. tree species or stand age, by means of image classification. Nevertheless, the absolute quantification of stand variables is even more essential for forest inventories. For both issues, satellite data are attractive for their large-area and up-to-date mapping capacities. This study followed two steps, and at first a supervised parametric classification was performed for a German test site based on a radiometrically corrected Landsat-5 TM scene. There, eight forest classes were identified with an overall accuracy of 87.5%. In the following, the study focused on the estimation of one key stand variable, the stem number per hectare (SN), which was carried out for a number of Norway spruce stands that had been clearly identified in the multispectral classification. For the estimation of SN, the approach of Linear Spectral Mixture Analysis (LSMA) was found to be clearly more effective than spectral indices. LSMA is based on the premise that measured reflectances can be linearly modelled from a set of so-called endmember spectra. In this study, the endmember sets were held variable to decompose pixel values to abundances of a vegetation, a background (soil, litter, bark) and a shade fraction. Forest structure determines the visible portions of these fractions, and therefore, a multiple regression using them as predictor variables provided the best SN estimates. LSMA allows a pixel-by-pixel quantification of SN for complete satellite images. This opens the view to exploit these data for an improved calibration of large-scale multi-parameter assessment strategies (e.g. statistical modelling or the kNN method for satellite data interpretation).

Vohland, University of Trier, Faculty of Geography and Geosciences, Remote Sensing Department, Trier, Germany E-mail: mv@nn.de
Stoffels, University of Trier, Faculty of Geography and Geosciences, Remote Sensing Department, Trier, Germany E-mail: js@nn.de
Hau, University of Trier, Faculty of Geography and Geosciences, Remote Sensing Department, Trier, Germany E-mail: ch@nn.de
Schüler, Research Institution for Forest Ecology and Forestry (FAWF), Department of Forest Growth and Silviculture, Trippstadt, Germany E-mail: gs@nn.de

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