Measurement of sawlog stacks on an individual log basis using LiDAR

• A novel algorithm for measuring logs and wood stacks with LiDAR is introduced. • Logs are automatically reconstructed from LiDAR data with three different sensors. • Net volume of stacks and log parameters were estimated. • Good quality point clouds are essential for accurate log detection and measurement. • Stem bases are potential sources of error for accurate detection and measurement. Sawlog measurement is essential in forestry for accurate timber invoicing. However, possibilities to acquire precise knowledge about wood quantity before sawmill measurements are limited. This paper presents a novel approach for measuring wood stacks and individual logs using LiDAR technology. The approach was tested on 10 wood stacks (in total 427.9 m 3 ) using three different LiDAR devices: a terrestrial laser scanner (TLS), a personal laser scanner (PLS), and a tablet with a LiDAR sensor. The algorithm effectively detected log end faces, enabling virtual reconstruction of logs with all devices. Mid-diameter (MD) and log volume (LV) in three variants and taper of the reconstructed logs were calculated. Summing up the LVs of all reconstructed logs the net volume of all wood stacks was calculated. All measurement results were compared to electronic reference measurements of a sawmill. The TLS outperformed other devices, achieving a 98.94 % detection rate and reconstructing 93.3 % of logs. The RMSE of the MD and LV estimation of correctly reconstructed logs was 5.11 % and 11.24 % respectively with the TLS. The taper estimations did not fit the reference. The mean difference of the total net volume ranged from 105.22 % to 92.7 % depending on the LV variant. Despite promising results that highlight the capability of LiDAR for these measurement tasks, the method should be improved before its application in forestry practice. Future research should focus especially on improving the algorithm’s accuracy when handling PLS and tablet data as these devices seem most promising for the use in practice.

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Publication Details

Journal
Computers and Electronics in Agriculture
Published
2025-05-20
DOI
https://doi.org/10.1016/j.compag.2025.110493
Citations
2
Primary Topic
Remote Sensing and LiDAR Applications
Type
article
Field-Weighted Citation Impact
1.60
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article

Measurement of sawlog stacks on an individual log basis using LiDAR

Tim Ritter, Karl Stampfer, Arne Nothdurft, Ralf Kraßnitzer et al.
2 citations
Computers and Electronics in Agriculture
Remote Sensing and LiDAR Applications
1.60
article

Measurement of sawlog stacks on an individual log basis using LiDAR

Tim Ritter, Karl Stampfer, Arne Nothdurft, Ralf Kraßnitzer, Sarah Witzmann, Christoph Gollob, Andreas Tockner, Tobias Ofner-Graff, Valentin Sarkleti, Lukas Moik
article en
2 citations

Abstract

• A novel algorithm for measuring logs and wood stacks with LiDAR is introduced. • Logs are automatically reconstructed from LiDAR data with three different sensors. • Net volume of stacks and log parameters were estimated. • Good quality point clouds are essential for accurate log detection and measurement. • Stem bases are potential sources of error for accurate detection and measurement. Sawlog measurement is essential in forestry for accurate timber invoicing. However, possibilities to acquire precise knowledge about wood quantity before sawmill measurements are limited. This paper presents a novel approach for measuring wood stacks and individual logs using LiDAR technology. The approach was tested on 10 wood stacks (in total 427.9 m 3 ) using three different LiDAR devices: a terrestrial laser scanner (TLS), a personal laser scanner (PLS), and a tablet with a LiDAR sensor. The algorithm effectively detected log end faces, enabling virtual reconstruction of logs with all devices. Mid-diameter (MD) and log volume (LV) in three variants and taper of the reconstructed logs were calculated. Summing up the LVs of all reconstructed logs the net volume of all wood stacks was calculated. All measurement results were compared to electronic reference measurements of a sawmill. The TLS outperformed other devices, achieving a 98.94 % detection rate and reconstructing 93.3 % of logs. The RMSE of the MD and LV estimation of correctly reconstructed logs was 5.11 % and 11.24 % respectively with the TLS. The taper estimations did not fit the reference. The mean difference of the total net volume ranged from 105.22 % to 92.7 % depending on the LV variant. Despite promising results that highlight the capability of LiDAR for these measurement tasks, the method should be improved before its application in forestry practice. Future research should focus especially on improving the algorithm’s accuracy when handling PLS and tablet data as these devices seem most promising for the use in practice.

Computers and Electronics in AgricultureVol. 237
BOKU University (AT)
Openalex Percentile: Top 18%
Remote Sensing and LiDAR Applications
1.60
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