Predicting commercial tree quality by means of airborne laser scanning

Karjalainen, Tomi

doi:10.14214/df.307

Cited by 2 publications

(2 citation statements)

References 101 publications

(184 reference statements)

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“…Thus tree size distribution models can convert information obtained at the stand level into tree-level data (Maltamo et al 2018). It should be noted, however, that all the predictions involved in the previous steps introduce some measure of uncertainty (Holopainen et al 2010;Karjalainen 2020).…”

Section: Airborne Laser Scanning (Als)-based Forest Inventory Methodsmentioning

confidence: 99%

Methods for supporting digital timber trade

Sanz¹

2022

Diss. For.

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Information on timber assortment recovery and wood quality is crucial for wood procurement planning, as the various tree species and wood dimensions and qualities may be utilized and refined in separate mills. The aim of this thesis is to improve our understanding of the timber trade in digital environments in order to support the planning of harvesting operations.The work for the thesis was carried out in three areas, two of which (discussed in Papers I and II) were located in Eastern Finland and one (Paper III) in Southern Finland. The field data comprised tree characteristics obtained from 79, 99 and 665 sample plots (Papers I, II and III, respectively), 249 harvested stands (Paper III) and a stem quality database (Papers I and III), whereas the remote sensing material consisted of aerial imagery (Papers I and III) and airborne laser scanning (ALS) data (Papers I, II and III) covering all the sites.With the stated overarching aim, we set out in Papers I and III to estimate timber assortment volumes, economic values and wood paying capabilities (WPC) for plots (Paper I) or stands (Paper III) with different bucking scenarios, and used the resulting timber assortment estimates to assess logging recoveries. The alternative bucking scenarios investigated were (1) bucking-to-value using maximum sawlog and pulpwood volumes but excluding quality (theoretical maximum), and (2) bucking-to-value using sawlog lengths at 30 cm intervals for Scots pine (Pinus sylvestris L., Papers I and III) and Norway spruce (Picea abies (L.) H.Karst, Paper III) and veneer logs of lengths 4.7 m, 5.0 m, 6.0 m and 6.7 m for birch (Betula spp., Paper III), either excluding or including wood quality indicators. The first approach resembled the state-of-the-art in Nordic forestry business circles and the second approach went beyond that. The commercial value of timber stands is substantially affected by the quantity of understorey trees, and pre-harvest clearing is typically needed when forest stands have an understorey vegetation that hinders harvesting operations. We therefore proposed a method in Paper II for estimating this need for the pre-harvest clearing of small trees (diameters at breast height < 7 cm).The results showed that use of the methods developed in this thesis could support wood procurement practices by (1) locating valuable stands with the desired timber assortment distributions (Papers I and III), (2) identifying understorey vegetation that needs to be removed before harvesting (Paper II), and (3) reducing costs, as the number of field visits needed before harvesting will diminish (Papers I, II and III).In conclusion, the present findings may make timber markets more competent, since the methods developed here provide detailed pre-harvest information that can be used as a decision support tool by either buyers or sellers of timber in traditional and digital marketplaces.

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Section: Airborne Laser Scanning (Als)-based Forest Inventory Methodsmentioning

confidence: 99%

Methods for supporting digital timber trade

Sanz¹

2022

Diss. For.

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“…Thus, tree size distribution models can convert information obtained at the stand level into tree-level data [15]. It should be noted, however, that all the predictions involved in the previous steps introduce some measure of uncertainty [16,17]. Timber assortments can be estimated from species-specific diameter-height distributions [3] and, as cut-to-length harvesters that record data such as tree species and diameters at different intervals along the stems are commonly used in Nordic countries, these timber assortments can be calculated employing cut-to-length harvesting methods such as the nonparametric k most similar neighbour (k-MSN) technique [18] or bucking optimizations and tree stem simulations [4,19,20].…”

Section: Introductionmentioning

confidence: 99%

Integrating Detailed Timber Assortments into Airborne Laser Scanning (ALS)-Based Assessments of Logging Recoveries

Sanz

Malinen

Sirparanta

et al. 2021

Forests

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The methodology presented here can assist in making timber markets more efficient when assessing the value of harvestable timber stands and the amounts of timber assortments during the planning of harvesting operations. Information on wood quality and timber assortments is essential for wood valuation and procurement planning as varying wood dimensions and qualities may be utilized and refined in different places, including sawmills, plywood mills, pulp mills, heating plants or combined heat and power plants. We investigate here alternative approaches for generating detailed timber assortments for Norway spruce (Picea abies (L.) H.Karst.), Scots pine (Pinus sylvestris L.) and birch (Betula spp.) from airborne laser scanning (ALS) data, aerial images, harvester data and field data. For this purpose, we used 665 circular plots, and logging recovery information recorded from 249 clear-cut stands using cut-to-length harvesters. We estimated timber assortment volumes, economic values and wood paying capabilities (WPC) for each stand in different bucking scenarios, and used the resulting timber assortment estimates to assess logging recoveries. The bucking scenarios were (1) bucking-to-value using maximum sawlog and pulpwood volumes excluding quality (theoretical maximum), and (2) bucking-to-value using sawlog lengths at 30 cm intervals for Norway spruce and Scots pine and veneer logs of lengths 4.7 m, 5.0 m, 6.0 m and 6.7 m for birch, either excluding quality (the usual business practice) or including quality (a novel business practice). The results showed that our procedure can assist in locating stands that are likely to be more valuable and have the desired timber assortment distributions. We conclude that the method can estimate WPC with root mean square errors of 28.7%, 66.0% and 45.7% in Norway spruce, Scots pine and birch, respectively, for sawlogs and 19.3%, 63.7% and 29.5% for pulpwood.

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Predicting commercial tree quality by means of airborne laser scanning

Cited by 2 publications

References 101 publications

Methods for supporting digital timber trade

Methods for supporting digital timber trade

Integrating Detailed Timber Assortments into Airborne Laser Scanning (ALS)-Based Assessments of Logging Recoveries

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