Original scientific paper
https://doi.org/10.5552/crojfe.2022.1596
Stem-Level Bucking Pattern Optimization in Chainsaw Bucking Based on Terrestrial Laser Scanning Data
Gernot Erber
; University of Natural Resources and Life Sciences, Vienna Department of Forest and Soil Sciences Institute of Forest Engineering Peter Jordan Strasse 82 1190, Vienna AUSTRIA
Christoph Gollob
; University of Natural Resources and Life Sciences, Vienna Department of Forest and Soil Sciences Institute of Forest Growth Peter Jordan Strasse 82 1190, Vienna AUSTRIA
Ralf Krassnitzer
; University of Natural Resources and Life Sciences, Vienna Department of Forest and Soil Sciences Institute of Forest Growth Peter Jordan Strasse 82 1190, Vienna AUSTRIA
Arne Nothdurft
; University of Natural Resources and Life Sciences, Vienna Department of Forest and Soil Sciences Institute of Forest Growth Peter Jordan Strasse 82 1190, Vienna AUSTRIA
Karl Stampfer
; University of Natural Resources and Life Sciences, Vienna Department of Forest and Soil Sciences Institute of Forest Growth Peter Jordan Strasse 82 1190, Vienna AUSTRIA
Abstract
Cross-cutting of a tree into a set of assortments (»bucking pattern«) presents a large potential
for optimizing the volume and value recovery; therefore, bucking pattern optimization has been
studied extensively in the past. However, it has not seen widespread adoption in chainsaw bucking,
where time consuming and costly manual measurement of input parameters is required for
taper curve estimation. The present study investigated an alternative approach, where taper
curves are fit based on terrestrial laser scanning data (TLS), and how deviations from observed
taper curves (REF) affect the result of bucking pattern optimization. In addition, performance
of TLS was compared to a traditional, segmental taper curve estimation approach (APP) and
an experienced chainsaw operator’s solution (CHA).
A mature Norway Spruce stand was surveyed by stationary terrestrial laser scanning. In TLS,
taper curves were fit by a mixed-effects B-spline regression approach to stem diameters extracted
from 3D point cloud data. A network analysis technique algorithm was used for bucking
pattern optimization during harvesting. Stem diameter profiles and the chainsaw operator’s
bucking pattern were obtained by manual measurement. The former was used for post-operation
fit of REF taper curves by the same approach as in TLS. APP taper curves were fit based on
part of the data. For 35 trees, TLS and APP taper curves were compared to REF on tree, trunk
and crown section level. REF and APP bucking patterns were optimized with the same algorithm
as in TLS. For 30 trees, TLS, APP and CHA bucking patterns were compared to REF on
operation and tree level.
Taper curves were estimated with high accuracy and precision (underestimated by 0.2 cm on
average (SD=1.5 cm); RMSE=1.5 cm) in TLS and the fit outperformed APP. Volume and value
recovery were marginally higher in TLS (0.6%; 0.9%) than in REF on operation level, while
substantial differences were observed for APP (–6.1%; –4.1%). Except for cumulated nominal
length, no significant differences were observed between TLS and REF on tree level, while APP
result was inferior throughout. Volume and value recovery in CHA was significantly higher
(2.1%; 2.4%), but mainly due to a small disadvantage of the optimization algorithm.
The investigated approach based on terrestrial laser scanning data proved to provide highly
accurate and precise estimations of the taper curves. Therefore, it can be considered a further
step towards increased accuracy, precision and efficiency of bucking pattern optimization in
chainsaw bucking.
Keywords
Hrčak ID:
290851
URI
Publication date:
30.6.2022.
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