Estimating harvester productivity in<i>Pinus radiata</i>plantations using StanForD stem files

Strandgard, Martin; Walsh, Damian; Acuña, Mauricio

doi:10.1080/02827581.2012.706633

Cited by 47 publications

(44 citation statements)

References 11 publications

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“…However, it has recently become possible to collect follow-up data automatically using the forest machines' on-board computers (see, e.g., Gerasimov et al 2012;Nuutinen 2013;Palander et al 2013;Strandgard et al 2013). Automated dataloggers attached to harvesters' computers and controller area network (CAN)-bus enable more precise data collection than human observers (see, e.g., Palander et al 2013).…”

Section: Introductionmentioning

confidence: 99%

Load level forwarding work element analysis based on automatic follow-up data

Manner¹,

Palmroth²,

Nordfjell³

et al. 2016

Silva Fenn.

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Highlights• Recent developments in on-board technology enables automatic collection of follow-up data on forwarder work.• Time consumption per load was more strongly associated with Loading drive distance than with extraction distance, indicating that the relevance of extraction distance as a main indicator of forwarding productivity should be re-considered.• Data, within variables, were positively skewed with a few exceptions with normal distributions. AbstractRecent developments in on-board technology have enabled automatic collection of follow-up data on forwarder work. The objective of this study was to exploit this possibility to obtain highly representative information on time consumption of specific work elements (including overlapping crane work and driving), with one load as unit of observation, for large forwarders in final felling operations. The data used were collected by the John Deere TimberLink system as nine operators forwarded 8868 loads, in total, at sites in mid-Sweden. Load-sizes were not available. For the average and median extraction distances (219 and 174 m, respectively), Loading, Unloading, Driving empty, Driving loaded and Other time effective work (PM) accounted for ca. 45, 19, 8.5, 7.5 and 14% of total forwarding time consumption, respectively. The average and median total time consumptions were 45.8 and 42.1 minutes/load, respectively. The developed models explained large proportions of the variation of time consumption for the work elements Driving empty and Driving loaded, but minor proportions for the work elements Loading and Unloading. Based on the means, the crane was used during 74.8% of Loading PM time, the driving speed was nonzero during 31.9% of the Loading PM time, and Simultaneous crane work and driving occurred during 6.7% of the Loading PM time. Time consumption per load was more strongly associated with Loading drive distance than with extraction distance, indicating that the relevance of extraction distance as a main indicator of forwarding productivity should be re-considered.

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Section: Introductionmentioning

confidence: 99%

Load level forwarding work element analysis based on automatic follow-up data

Manner¹,

Palmroth²,

Nordfjell³

et al. 2016

Silva Fenn.

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“…The on-board data loggers that are standard equipment in many modern forest machines record several types of data that may be useful for real-time analysis of production, such as log piece size and numbers of stems processed [8,9]. The addition of discrete equipment position, navigation, and timing (PNT) information to the standard data stream would make it possible for operators to account for and adapt to unforeseen delays in machine cycles or site conditions.…”

Section: Introductionmentioning

confidence: 99%

“…Experienced operators do this naturally over short time steps on the fly, but computer-aided analysis can help expose patterns in operation that are not always apparent, even to the most experienced personnel. Utilization of real-time position data for individual pieces of equipment and among multiple pieces of equipment in a system could make it possible for higher resolution and higher order complexity operational models that monitor individual equipment cycle elements and suggest efficiency improvements based on variables like terrain, timber quality, and other site characteristics [8,9].…”

Section: Introductionmentioning

confidence: 99%

Use of Real‐Time GNSS‐RF Data to Characterize the Swing Movements of Forestry Equipment

Becker

Keefe

Anderson

2017

Forests

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Abstract:The western United States faces significant forest management challenges after severe bark beetle infestations have led to substantial mortality. Minimizing costs is vital for increasing the feasibility of management operations in affected forests. Multi-transmitter Global Navigation Satellite System (GNSS)-radio frequencies (RF) technology has applications in the quantification and analysis of harvest system production efficiency and provision of real-time operational machine position, navigation, and timing. The aim of this study was to determine the accuracy with which multi-transmitter GNSS-RF captures the swinging and forwarding motions of ground based harvesting machines at varying transmission intervals. Assessing the accuracy of GNSS in capturing intricate machine movements is a first step toward development of a real-time production model to assist timber harvesting of beetle-killed lodgepole pine stands. In a complete randomized block experiment with four replicates, a log loader rotated to 18 predetermined angles with GNSS-RF transponders collecting and sending data at two points along the machine boom (grapple and heel rack) and at three transmission intervals (2.5, 5.0 and 10.0 s). The 2.5 and 5.0 s intervals correctly identified 94% and 92% of cycles at the grapple and 92% and 89% of cycles at the heel, respectively. The 2.5 s interval successfully classified over 90% of individual cycle elements, while the 5.0 s interval returned statistically similar results. Predicted swing angles obtained the highest level of similarity to observed angles at the 2.5 s interval. Our results show that GNSS-RF is useful for real-time, model-based analysis of forest operations, including woody biomass production logistics.

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“…Besides northern and other European countries, the utilisation of CTL technology has greatly improved the productivity of harvesting in many other countries around the world (e.g., Huyler and LeDoux 1999;Murphy 2003;Gerasimov et al 2012Gerasimov et al , 2013Strandgard et al 2013;Olivera et al 2016;Williams and Ackerman 2016), particularly in plantation forestry where trees are usually harvested before they become too large to be handled by machines and tree size is more uniform. The past 40 years has seen great technological advances in the mechanical design of harvesters and in the harvester head measurement and optimization systems as reviewed by Heinimann (2007), Nordfjell et al (2010), Uusitalo (2010) and Malinen et al (2016).…”

Section: Introductionmentioning

confidence: 99%

“…Monitoring and summarizing harvesting production and log yields have remained the main use of harvester data in many forestry companies. However, researchers have used harvester data to estimate harvester productivity and, in combination with terrestrial laser scanning, to determine tree value and log yields in P. radiata plantations (Strandgard et al 2013;Murphy et al 2010). A research project is currently being carried out to combine harvester data with airborne LIDAR to estimate stand volume and log yield of a plantation, for which trees and stands needs to be reconstructed from harvester data.…”

Section: Introductionmentioning

confidence: 99%

Reconstructing the size of individual trees using log data from cut-to-length harvesters in Pinus radiata plantations: a case study in NSW, Australia

Watt

et al. 2017

J. For. Res.

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With their widespread utilization, cut-to-length harvesters have become a major source of ''big data'' for forest management as they constantly capture, and provide a daily flow of, information on log production and assortment over large operational areas. Harvester data afford the calculation of the total log length between the stump and the last cut but not the total height of trees. They also contain the length and end diameters of individual logs but not always the diameter at breast height overbark (DBHOB) of harvested stems largely because of time lapse, operating and processing issues and other system deficiencies. Even when DBHOB is extracted from harvester data, errors and/or bias of the machine measurements due to the variation in the stump height of harvested stems from that specified for the harvester head prior to harvesting and diameter measurement errors may need to be corrected. This study developed (1) a system of equations for estimating DBHOB of trees from diameter overbark (DOB) measured by a harvester head at any height up to 3 m above ground level and (2) an equation to predict the total height of harvested stems in P. radiata plantations from harvester data. To generate the data required for this purpose, cut-to-length simulations of more than 3000 trees with detailed taper measurements were carried out in the computer using the cutting patterns extracted from the harvester data and stump height survey data from clearfall operations. The equation predicted total tree height from DBHOB, total log length and the small end diameter of the top log. Prediction accuracy for total tree height was evaluated both globally over the entire data space and locally within partitioned subspaces through benchmarking statistics. These statistics were better than that of the conventional height-diameter equations for P. radiata found in the literature, even when they incorporated stand age and the average height and diameter of dominant trees in the stand as predictors. So this equation when used with harvester data would outperform the conventional equations in tree height prediction. Tree and stand reconstructions of the harvested forest is the necessary first step to provide the essential link of harvester data to conventional inventory, remote sensing imagery and LiDAR data. The equations developed in this study will provide such a linkage for the most effective combined use of harvester data in predicting the attributes of individual trees, stands and forests, and product recovery for the management and planning of P. radiata plantations in New South Wales, Australia.

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Estimating harvester productivity inPinus radiataplantations using StanForD stem files

Cited by 47 publications

References 11 publications

Load level forwarding work element analysis based on automatic follow-up data

Load level forwarding work element analysis based on automatic follow-up data

Use of Real‐Time GNSS‐RF Data to Characterize the Swing Movements of Forestry Equipment

Reconstructing the size of individual trees using log data from cut-to-length harvesters in Pinus radiata plantations: a case study in NSW, Australia

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