Automatic GNSS-enabled harvester data collection as a tool to evaluate factors affecting harvester productivity in a Eucalyptus spp. harvesting operation in Uruguay

Olivera, Alejandro; Visser, Rien; Acuña, Mauricio; Morgenroth, Justin

doi:10.1080/14942119.2015.1099775

Cited by 38 publications

(26 citation statements)

References 18 publications

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“…In-situ data can be automatically gathered during various phases of the forest management process, in particular, during operations where heavy machinery offers a platform for the data collection equipment. One example of a forestry machine collecting such data is the forest harvester used in the wood harvesting stage (Hauglin et al 2017;Olivera et al 2016). However, other stages of forest management, such as forest regeneration, utilize other types of heavy machinery; excavators being a case in point.…”

Section: Introductionmentioning

confidence: 99%

Classifying soil stoniness based on the excavator boom vibration data in mounding operations

Melander¹,

Ritala²,

Strandström³

2019

Silva Fenn.

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Highlights• An excavator was equipped with an inertial measurement unit for taking automatic measurements of soil stoniness during mounding work. • Supervised machine-learning classifiers were trained utilizing both the automatically measured data and manual stoniness measurements. • The class prediction for the soil stoniness achieved an accuracy of 70% when assigned to constant grid cells. AbstractThe stoniness index of forest soil describes the stone content in the upper soil layer at depths of 20-30 centimeters. This index is not available in any existing map databases, and traditional measurements for the stoniness of the soil have always necessitated laborious soil-penetration methods. Knowledge of the stone content of a forest site could be of use in a variety of forestry operations. This paper presents a novel approach to obtaining automatic measurements of soil stoniness during an excavator-based mounding operation. The excavator was equipped with only a low-cost inertial measurement unit and a satellite navigation receiver. Using the data from these sensors and manually conducted soil stoniness measurements, supervised machine learning methods were utilized to build a model that is capable of predicting the stoniness class of a given mounding location. This study compares different classifiers and feature selection methods to find the most promising solution for this learning problem. The discussion includes a proposition for a meaningful measurement resolution of the soil's stoniness, and a practical method for evaluating the variability of the stone content of the soil. The results indicate that it is possible to predict the soil stoniness class with 70% accuracy using only the inertial and location measurements.

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Section: Introductionmentioning

confidence: 99%

Classifying soil stoniness based on the excavator boom vibration data in mounding operations

Melander¹,

Ritala²,

Strandström³

2019

Silva Fenn.

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“…Software, for example SilviA, is used by both John Deere and Waratah to make StanForD files easier to be read, created, and edited. Advanced software, such as Timber Office from John Deere and Ponsse Opti from Ponsse, can be used to manage operations and for fleet control [46]. Moreover, several harvester control systems have a navigation system capable of displaying a range of base layer maps, which can include raster and vector data, such as digital elevation models (raster feature), stand maps (polygon feature), and power lines (line feature).…”

Section: Gnss Instrumentsmentioning

confidence: 99%

“…Integrating GNSS and StanForD data could lead to various important and interesting forestry applications such as: Developing forest yield maps, useful for harvesting and management planning or evaluating work productivity to other parameters, for example stem diameter at breast eight (DBH), species, shift (day/ night), slope, and operator [46].…”

Section: Gnss Instrumentsmentioning

confidence: 99%

Recent Contributions of Some Fields of the Electronics in Development of Forest Operations Technologies

et al. 2019

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In the last years, there has been a growing need to improve forest-wood chain concerning all three pillars of sustainability (economic, environmental, and social). Using electronic systems, in particular GIS, GNSS, and various kinds of sensors related to forest harvesting, is clearly one of the most powerful instruments to reach this aim. The contribution of these tools to forest operation is wide and various. One of the most important application was integrating ICT and GPS/GNSS on-board systems on modern forest machines. This allowed one to ensure multiple benefits to forest operation field. On the one hand, electronic systems, and particularly GIS, could be used to improve forest harvesting with a previous planning of the skid trails network, in order to minimize utilization impacts and risks for operators, ensuring at the same time high work productivity. Moreover, GIS developed files could also be implemented in modern forest machine GPS/GNSS systems, helping forest machines operators to move only along a designed skid trails network or making it possible to avoid restricted access areas. On the other hand, modern forest machines could be equipped with complex and accurate sensors that are able to determine, register, and share information about wood biomass quantity and quality and even undertake economic evaluation of stumpage value. Finally, the input and output of these systems and sensors could be implemented in a decision support system (DSS) ensuring the best silvicultural and operative alternative from a sustainable forest management point of view. A detailed review of the contribution of electronics in the development of forest operations is provided here.

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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%

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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Automatic GNSS-enabled harvester data collection as a tool to evaluate factors affecting harvester productivity in a Eucalyptus spp. harvesting operation in Uruguay

Cited by 38 publications

References 18 publications

Classifying soil stoniness based on the excavator boom vibration data in mounding operations

Classifying soil stoniness based on the excavator boom vibration data in mounding operations

Recent Contributions of Some Fields of the Electronics in Development of Forest Operations Technologies

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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