The Profitability of Cross-Cutting Practices in Butt-Rotten Picea abies Final-Felling Stands

Kärhä, Kalle; Räsänen, Marita; Palander, Teijo

doi:10.3390/f10100874

Cited by 5 publications

(6 citation statements)

References 52 publications

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“…The waste pieces of decayed wood are not included in the harvester data (cf. Kärhä et al 2019) although they are included in ALS-based estimates. The same trend has been observed also in earlier studies.…”

Section: Discussionmentioning

confidence: 99%

Evaluating the accuracy of ALS-based removal estimates against actual logging data

Vähä-Konka

Maltamo

Pukkala

et al. 2020

Annals of Forest Science

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Key message We examined the accuracy of the stand attribute data based on airborne laser scanning (ALS) provided by the Finnish Forest Centre. The precision of forest inventory data was compared for the first time with operative logging data measured by the harvester. Context Airborne laser scanning (ALS) is increasingly used together with models to predict the stand attributes of boreal forests. The information is updated by growth models. Information produced by remote sensing, model prediction, and growth simulation needs field verification. The data collected by harvesters on logging sites provide a means to evaluate and verify the accuracy of the ALS-based data. Aims This study investigated the accuracy of ALS-based forest inventory data provided by the Finnish Forest Centre at the stand level, using harvester data as the reference. Special interest was on timber assortment volumes where the quality reductions of sawlog are model predictions in ALS-based data and true realized reductions in the logging data. Methods We examined the accuracy of total volume and timber assortment volumes by comparing ALS-based data and operative logging data measured by a harvester. This was done both for clear cuttings and thinning sites. Accuracy of the identification of the dominant tree species of the stand was examined using the Kappa coefficient. Results In clear-felling sites, the total harvest removals based on ALS and model prediction had a RMSE% of 26.0%. In thinning, the corresponding difference in the total harvested removal was 42.4%. Compared to logged volume, ALS-based prediction overestimated sawlog removals in clear cuttings and underestimated pulpwood removals. Conclusion The study provided valuable information on the accuracy of ALS-based stand attribute data. Our results showed that ALS-based data need better methods to predict the technical quality of harvested trees, to avoid systematic overestimates of sawlog volume. We also found that the ALS-based estimates do not accurately predict the volume of trees removed in actual thinnings.

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Section: Discussionmentioning

confidence: 99%

Evaluating the accuracy of ALS-based removal estimates against actual logging data

Vähä-Konka

Maltamo

Pukkala

et al. 2020

Annals of Forest Science

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“…Other factors may also have caused errors. For example, retention trees and waste pieces of decayed wood were not included in the harvester data (Kärhä et al 2019), although they are included in the Trestima-based estimates.…”

Section: Discussionmentioning

confidence: 99%

“…All merchantable stem pieces cut in the forest stand, except waste pieces of decayed wood and treetops, were included in the harvester data (Kärhä et al 2019). Sawlog lengths were mainly 3.7-5.5 m at 0.3 m increments.…”

Section: Harvested Datamentioning

confidence: 99%

Estimating the accuracy of smartphone app-based removal estimates against actual wood-harvesting data from clear cuttings

Vähä-Konka,

Korhonen,

Kärhä

et al. 2024

iForest

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is a computer vision-based smartphone application that utilises relascope theory to obtain estimates of forest attributes from smartphone photographs. The aim of this study was to investigate the accuracy of Trestima estimation and evaluate whether it is sufficiently accurate for operational use in forestry. Our data consisted of 37 forest stands, encompassing 73.5 ha in southeastern Finland, where Trestima estimates were obtained by forestry professionals during their work. The results were compared with harvester data obtained from clear-cut stands. The number of photographs taken per stand ranged between 1-29 (average: 7.3; standard deviation: 5.0). The total amount of industrial roundwood harvested from the stands was 21,531 m 3 and the average harvest removal per hectare was 282 m 3 . The accuracy of Trestima estimation was relatively good when ≥ 10 photographs per stand were taken. In this case, the root mean square error percent (RMSE%) value associated with roundwood volume was 17.7%. When the number of photographs per stand was < 10, the accuracy of Trestima was much weaker (RMSE% 22.7-55.3%). On average, Trestima underestimated harvested volumes in Scots pine (Pinus sylvestris L.) stands (Bias% 11.4-89.2), although the bias was smaller (Bias% -12.7-12.4) with Norway spruce (Picea abies [L.] Karst.) stands. The Trestima smartphone application is a possible option for traditional field measurements in operational forestry, provided that its usage instructions are strictly followed, which is not always the case in practice.

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“…This is especially the case when the quality of cutting is also considered to avoid cutting damage (see Inberg et al 2002) and when additional cuts are also needed. It is also the case when processing rotten parts of the stem (see Kärhä et al 2019). The aim of both is to produce high-quality products.…”

Section: Introductionmentioning

confidence: 99%

“…Nurminen et al (2006) studied the time consumption for different CTL harvester working elements and showed the average work phase times as a proportion of the total effective time, with felling contributing to 17% and processing 45% of the total in final felling 18% and 27% in thinning. Kärhä et al (2019) mentioned an average share of 61% of processing time (incl. delimbing, cross-cutting, sounding) of total stem processing time in Norway spruce stems.…”

Section: Introductionmentioning

confidence: 99%

Cutting duration and performance parameters of a harvester’s sawing unit under real working conditions

2020

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Amongst all the working elements of single grip harvesters, the working elements of felling and processing play an important role within the cut-to-length (CTL) harvester’s working phases. This includes the felling cutting and cross-cutting of stems within the felling and processing operation. The detailed investigation of such individual machine activities may help to analyse and improve the performance of forest machines. The objective of this study was to investigate the cutting duration and other performance parameters, including the fuel consumption, of a CTL harvester’s sawing unit under real working conditions. Detailed information on the felling cutting and cross-cutting performance was collected at short intervals using CAN bus data of two single grip harvesters in final felling from two different sites in Eastern Finland. As a result, models for effective time consumption in the work phase of cutting as a function of stem size were developed, both for felling cutting and cross-cutting. Felling cutting and cross-cutting durations were somewhat identical until the cutting diameters of 400–450 mm, depending on the site. Thereafter, the cutting time difference increased and was higher in felling cutting. At the site with large diameters of 550–650 mm, the difference varied between 15% and 28%, between the comparisons of formulae. In addition, other performance parameters, including the respective fuel consumption of this working phase, were part of the study. The study revealed a higher hourly based fuel consumption for the entire guide bar movement time compared to the pure cutting time in cross-cutting with stem diameters below 400 mm. Detailed knowledge of the performance of the sawing unit’s activities might help the planning process of future studies, as well as support the future development of efficient and intelligent machinery.

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The Profitability of Cross-Cutting Practices in Butt-Rotten Picea abies Final-Felling Stands

Cited by 5 publications

References 52 publications

Evaluating the accuracy of ALS-based removal estimates against actual logging data

Evaluating the accuracy of ALS-based removal estimates against actual logging data

Estimating the accuracy of smartphone app-based removal estimates against actual wood-harvesting data from clear cuttings

Cutting duration and performance parameters of a harvester’s sawing unit under real working conditions

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