Prediction of stem attributes by combining airborne laser scanning and measurements from harvesters

Holmgren, Johan; Barth, Andreas; Larsson, Henrik; Olsson, Håkan

doi:10.14214/sf.56

Cited by 53 publications

(59 citation statements)

References 17 publications

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“…However, entirely different estimation and operation models are required for tasks that would extend our knowledge available from a forest, compared to the traditional, subjective forest mapping inventories. For example, in ABA, stem-quality attributes required by the forest industry, such as species-specific timber assortments, cannot be obtained accurately [5,[8][9][10]. Single tree-level information would be required to solve the above-mentioned limitations [11,12].…”

Section: Towards Precision Forestrymentioning

confidence: 99%

Outlook for the Next Generation’s Precision Forestry in Finland

Holopainen

Vastaranta

Hyyppä

2014

Forests

155

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During the past decade in forest mapping and monitoring applications, the ability to acquire spatially accurate, 3D remote-sensing information by means of laser scanning, digital stereo imagery and radar imagery has been a major turning point. These 3D data sets that use single-or multi-temporal point clouds enable a wide range of applications when combined with other geoinformation and logging machine-measured data. New technologies enable precision forestry, which can be defined as a method to accurately determine characteristics of forests and treatments at stand, sub-stand or individual tree level. In precision forestry, even individual tree-level assessments can be used for simulation and optimization models of the forest management decision support system. At the moment, the forest industry in Finland is looking forward to next generation's forest inventory techniques to improve the current wood procurement practices. Our vision is that in the future, the data solution for detailed forest management and wood procurement will be to use multi-source and -sensor information. In this communication, we review our recent findings and describe our future vision in precision forestry research in Finland. OPEN ACCESSForests 2014, 5 1683

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Section: Towards Precision Forestrymentioning

confidence: 99%

Outlook for the Next Generation’s Precision Forestry in Finland

Holopainen

Vastaranta

Hyyppä

2014

Forests

155

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“…In general, the ITC approach is at its best in single-layered mature stands. Holmgren et al [16] showed that if the harvester measurements are used as reference data for ALS-based tree-level inventories, highly accurate estimates can be obtained. Their imputation accuracies (root mean squared error percentage RMSE-%) for stem volume, mean tree height, mean stem diameter, and stem density were 11%, 8%, 12%, and 19%, respectively.…”

Section: Introductionmentioning

confidence: 99%

Accuracy of Kinematic Positioning Using Global Satellite Navigation Systems under Forest Canopies

Kaartinen

Hyyppä

Vastaranta

et al. 2015

Forests

112

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Abstract:A harvester enables detailed roundwood data to be collected during harvesting operations by means of the measurement apparatus integrated into its felling head. These data can be used to improve the efficiency of wood procurement and also replace some of the field measurements, and thus provide both less costly and more detailed ground truth for remote sensing based forest inventories. However, the positional accuracy of harvester-collected tree data is not sufficient currently to match the accuracy per individual trees achieved with remote sensing data. The aim in the present study was to test the accuracy of various instruments utilizing global satellite navigation systems (GNSS) in motion under forest canopies of varying densities to enable us to get an understanding of the current state-of-the-art in GNSS-based positioning under forest canopies. Tests were conducted using several different combinations of GNSS and inertial measurement unit (IMU) mounted OPEN ACCESSForests 2015, 6 3219 on an all-terrain vehicle (ATV) "simulating" a moving harvester. The positions of 224 trees along the driving route were measured using a total-station and real-time kinematic GPS. These trees were used as reference items. The position of the ATV was obtained using GNSS and IMU with an accuracy of 0.7 m (root mean squared error (RMSE) for 2D positions). For the single-frequency GNSS receivers, the RMSE of real-time 2D GNSS positions was 4.2-9.3 m. Based on these results, it seems that the accuracy of novel single-frequency GNSS devices is not so dependent on forest conditions, whereas the performance of the tested geodetic dual-frequency receiver is very sensitive to the visibility of the satellites. When postprocessing can be applied, especially when combined with IMU data, the improvement in the accuracy of the dual-frequency receiver was significant.

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“…Harvester data have been used in combination with conventional inventory data to develop harvester-based forest inventory systems (Stendahl and Dahlin 2002;Murphy et al 2006;Holopainen et al 2010) and with remote sensing imagery and LiDAR data to predict attributes of individual trees, stands and forests (Rasinmäki and Melkas 2005;Holmgren et al 2012;Söderberg 2015), product recovery (Peuhkurinen et al 2008;Barth and Holmgren 2013;Barth et al 2015), and wood properties (Möller et al 2003). Such combined use of harvester data creates a representation of the entire forest area in great detail, allowing forest managers to see what trees are standing where and to predict what volume or value of a certain product or a product mix the forest can yield.…”

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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Prediction of stem attributes by combining airborne laser scanning and measurements from harvesters

Cited by 53 publications

References 17 publications

Outlook for the Next Generation’s Precision Forestry in Finland

Outlook for the Next Generation’s Precision Forestry in Finland

Accuracy of Kinematic Positioning Using Global Satellite Navigation Systems under Forest Canopies

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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