2009 Council on Forest Engineering (COFE) Conference Proceedings: Environmentally Sound Forest Operations. Lake Tahoe, June 15-18, 2009

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1 2009 Council on Forest Engineering (COFE) Conference Proceedings: Environmentally Sound Forest Operations. Lake Tahoe, June 15-18, 2009 Determining Radiata pine tree value and log product yields using terrestrial LiDAR and optimal bucking in South Australia. Mauricio Acuna *1, Glen Murphy 2, Jan Rombouts 3 * corresponding author 1 CRC Forestry University of Tasmania, Hobart, Australia, Contact information: Private Bag 12, Hobart, TAS, 7001, Australia, Mauricio.Acuna@utas.edu.au 2 Forest Engineering, Resources and Management Department, Oregon State University, Corvallis, Oregon 3 ForestrySA, Mt. Gambier, SA, Australia Abstract Eighteen plots in three radiata pine stands of different tree sizes were scanned using terrestrial LiDAR systems. Tree locations were automatically detected using commercially available software. Stem profiles were measured using three methods: (1) from LiDAR scans, (2) by the harvester and (3) manually after felling. Stems were optimally bucked based on log specifications and prices for Australian log markets. Tree values and log product yields were estimated for the terrestrial LiDAR derived data and compared with estimates based on the harvester and manual stem profiles. Plot preparation and tree characteristics affected the accuracy of automated stem detection and stem profile measurements. Suggestions for future research are provided. Keywords: terrestrial laser scanning, inventory, optimal bucking, manual and automated methods, radiata pine. Introduction Good metrics of the quantity, quality and location of timber resources within each forest are essential for ensuring that wastage is minimized, harvest and volume growth increments are balanced, log products are optimally matched to markets, and the value of the forest is maximized at the time of harvest. New approaches to obtaining these metrics are being examined with the goals of increasing their accuracy and reducing their data gathering costs. Terrestrial laser scanning (also known as terrestrial LiDAR) is receiving attention in Europe (Keane 2007), New Zealand (Anonymous 2007) and USA (Henning and Radtke 2006, Murphy 2008) as a new approach for gathering detailed descriptions of individual stems and their location. Interest in the technology is also expanding in other parts of the world (e.g. South Africa and Uruguay). The research presented here evaluated the use of terrestrial laser scanning technology and an optimal bucking algorithm as the bases for determining log product yields and tree value in South Australian radiata pine plantations. Specific objectives of this research were: (1) to compare terrestrial LiDAR with manual measurements, (2) to compare harvester with manual measurements, and (3) to compare terrestrial LiDAR with harvester measurements. Methods Site description

2 Three radiata pine stands were selected on flat terrain (slopes < 5 degrees) near Mount Gambier, South Australia. A total of 18 rectangular plots, each 0.1 ha in size, were located in the three stands (Table 1). Understory vegetation was most plentiful in the clearfelling stand and least plentiful in the thinning stands. Table 1. Average characteristics of the stands Stand type Clearfelling (CF) Thinning (T3) Thinning (T2) Age Stocking (spha) Tree height (m) Volume (m 3 per ha) Number of plots Laser scan data collection Laser scan data was captured in September Two FARO laser scanners were used: a LS880 HE80 and a Photon 80. Both scanners provided 360 o hemispherical coverage to 30 m+ distances. Four to five scans were taken within each plot one at the plot center and the others on the plot corners - to facilitate measurement of any trees that were likely to have been occluded by other stems within the other scans. Standing and felled tree measurements The breast height diameters of all standing trees were measured to the nearest millimeter using a steel tape. After felling, overbark diameters and bark thickness were measured on all felled trees at 0.5 m and 1.5 m, and then at intervals of 3 m to the top of the stem. Heights were also gathered using a measuring tape to the base of the green crown, to an 80 mm top, and total stem height. Actual stem measurements from the felled trees were used to develop overbark stem profiles at decimeter increments up each tree (actual profile). The bark thickness at each point on the stem was determined using a set of equations provided by Strandgard (2009). Strangard s equations were developed for South Australia from a sample of about 410 Radiata pine logs. Two sets of coefficients are provided representing the bottom 10% and top 90% of the stem in terms of relative height. Linear interpolation of diameters between measurement points was used. Harvester measurements Stem profiles of the 343 trees harvested were downloaded from the harvester *.stm and *.prd files. Overbark diameters were obtained at 10 cm intervals up each stem. The harvester bark thickness equation was used to calculate underbark diameters and volumes. Unfortunately the equation used was inappropriate for Radiata pine; effectively estimating a maximum double bark thickness of only 7 mm. Information for each log cut (type, length, and small end diameter) was also obtained from the *.stm file. The harvester was calibrated once for diameter 2

3 measurements (two days prior to the trial beginning) and three times for length measurements (prior to starting a new stand type). Automated laser-scan based stem profile descriptions Autostem Forest software (Keane 2007) was used to detect tree locations and extract stem profile descriptions from the laser scan data for all trees within each of the eighteen plots. Overbark diameters and sweep (sinuosity) were measured or estimated at decimeter increments up the tree. The Autostem software allows the user to check results and manually intervene in the detection and profiling procedures in a number of ways; objects incorrectly identified as commercial timber production trees can be excluded, and predicted tree heights for individual stems can be over-ridden if their heights are known. Both these features were used to produce overbark stem profiles for each tree. Strangard s bark thickness equations were used to estimate underbark diameters. Prediction of log product yields and tree value Radiata pine is of considerable economic importance for the Australian forest products industry. Nine log types destined for domestic Australian markets were included in the analysis (Table 2). The relative prices ($/m3) in the price matrix on the harvester s computer were used to determine tree values. Prices and specifications are based on underbark diameters and volumes. Table 2. Log types included in the study Log Type Length (m) Small end diameter (mm) Clearfelling (CF) Thinning (T3) Thinning (T2) CH Saw CH Saw SM Saw SM Saw DCL RCL Pulp Pulp Pulp Waste Optimal bucking software and analysis VALMAX optimal bucking software (Murphy 2008) was used to determine the optimal log product yields that could be obtained from each plot based on user defined stem profiles, stem qualities and sinuosity, and market conditions. VALMAX employs a dynamic programming algorithm to maximize value recovery from the stand for unconstrained, supplylimited markets. It is similar to optimal bucking procedures described by Pnevmaticos and Mann (1972) and Murphy et al. (2004). 3

4 All harvested radiata pine trees within each plot were included in the value analyses. Because some trees were occluded by others in the laser scans taken from the plot center, Autostem generated center-plot tree profiles were sometimes supplemented with profiles taken from the other scan locations. Results In the eighteen plots there were a total of 726 stems that had DBH s of 10 cm or greater (Table 3). Discounting occluded trees, approximately 83 stems were not detected in single scans. Automated detection was considerably more successful in clearfelling plots as compared to thinning plots. Only 2 trees were not detected in clearfelling plots, whereas 60 and 20 trees were not detected in T2 and T3 plots, respectively. Table 3. Automated tree detection from laser scan point clouds. Clearfelling Thinning (T3) Thinning (T2) Total plot stems Stems not detected 2% 10% 15% in single scans % of stems detected using multiple scans 100% 99% 98% The fewer number of trees automatically detected in thinning stands are explained by the higher proportion of lower limbs and needles pockets in these smaller trees, as well as by the presence of bifurcated sections near the bottom of the stems. However, multiple scans eliminated problems with occlusion and reduced to 10 the number of trees not detected automatically. Figure 1 shows the average diameter difference between actual (manual) and scan measurements. Trends were very similar for underbark and overbark measurements. In both cases, the scan underestimated diameters along the stem, reaching a maximum difference (30 mm) near the top of the stem. This is the consequence of using a taper equation for a species different from radiata pine, which is embedded in the automated software to estimate diameters from scanner measurements. Small underbark diameter differences along the stem (with the exception of the top portion of the stem) are explained by the use of a more appropriate bark thickness equation. Likewise, scan measurements tended to underestimate the overbark diameter near the base of the stem. Figure 2 shows the average diameter difference between actual and harvester measurements. The average overbark diameter difference is very high near the base of the tree, remains positive up to 15 meters and turns negative from this point up to the top of the tree. This means that the harvester underestimated overbark diameter up to 15 meters and overestimated overbark diameter near the top of the tree. In comparison, the harvester underestimated underbark diameter at all points up the stem. Average diameter differences were, however, more pronounced in the first 5 meters, remained constant from this point up to a height of about 20 meters and increased from this point up to the top of the tree. These trends are basically explained by the lack of suitable calibration for diameter and the use of an inappropriate single bark thickness equation used by the harvester. In general, radiata pine bark is proportionally thicker near the base of the tree and more consistent over the 4

5 Diameter Difference (mm) Diameter Difference (mm) middle section of the stem, with a rise in thickness near the top of the tree (Gordon, 1983). If Strandgard s bark thickness equation had been used it the differences between underbark actual and harvester measurements would have been smaller and positive up to a height of about 15 m; that is, the stem would have been bigger than estimated by the harvester. Average Diameter Difference (Actual minus Scan) (Positive means stem was bigger than measured by scan. Negative means stem was smaller) Height up stem (m) Overbark Underbark Figure 1. Average diameter difference between actual (manual) and scan measurements. Average Diameter Difference (Actual minus Harvester) (Positive means stem was bigger than measured by harvester. Negative means stem was smaller) Height up stem (m) Overbark Underbark Figure 2. Average diameter difference between actual (manual) and harvester measurements. A subset of the trees scanned was harvested. A subset of the trees harvested was manually measured. Table 4 shows the volume (underbark) comparison for those trees measured by all three systems; scanner, harvester and manual. In all stand types, the harvester overestimated volume in relation to the actual and scanner volume which is primarily explained by the use of an inappropriate bark thickness equation. Absolute differences between actual and harvester volume as well as actual and scanner volume are consistent for all the plots (CF, T3, 5

6 T2), whereas the differences varied considerably when comparing scanner and harvester volumes. In this latter case, a bigger difference is observed in clearfelling plots because the scan measurements tend to underestimate underbark volume whereas the harvester measurements tend to overestimate underbark volume. Table 4. Volume comparisons between actual, harvester and scanner measurements Stand Type No. of trees Measured (m3 u.b. per tree) Differences Actual Harvester Scanner A-H A-S S-H CF % of -9% 3% -12% Actual T % of -14% -3% -12% Actual T % of Actual -18% -8% -9% Both measurements could be improved by better and prompt calibration procedures and use of appropriate bark thickness equations (harvester measurement), as well as by the use of better taper equations (scanner measurements). Use of Strandgard s bark thickness equation on the harvester would have reduced the volume differences between both the actual and scanner measurements. There was also a range in the accuracy of scanner measurements which affects the comparisons. Bad scans were obtained when trees (1) had needle pockets or swelling near the base of the tree, (2) were partially hidden, (3) were out of round (leaning), and (4) had double leaders below breast height. Limby sections at breast height and regeneration around the base also had an impact on the automated detection and on the quality of the images obtained with the laser scanner. Stand Type Table 5. Value comparisons (%) between actual, harvester and scanner measurements. No. of Differences (% of Actual) trees Actual -Harvester Actual - Scanner Scanner - Harvester CF 42-12% 7% -19% T % -4% -19% T % -2% -19% Table 5 shows the value (underbark) comparison between actual, harvester and scanner measurements. For all stand types, tree value calculated from harvester measurements was greater than tree value calculated from actual and scanner measurements. The greatest absolute difference in value was obtained when comparing scanner with harvester measurements in clearfelling and T3 plots, whereas the greatest difference in T2 plots was obtained when comparing actual with harvester measurements. These results are a consequence of the volume estimates obtained with the three measuring methods and with the mix of product and prices used to calculate tree value. 6

7 Conclusions In this paper we have demonstrated, for one set of Australian markets, that radiata pine tree values and log product yields can be estimated using an optimal bucking algorithm together with stem profiles automatically generated from hemispherical terrestrial LiDAR scans. Currently, the system is only semi-automated and needs human intervention during the data collection and processing phases. Fully automated tree detection and more accurate estimates should be possible as better plot preparation procedures and processing algorithms are developed in the years to come. Harvester data should only be used for comparison with scanner (or manual) measurements if the harvesters are properly configured and calibrated, and appropriate bark thickness equations are used. Future studies should embrace a wider range of stand types, investigate the synergies between terrestrial laser scanning and other inventory procedures, and carry out a cost-benefit analysis. Acknowledgements We thank ForestrySA staff, in particular Tim Murphy, David Kenseley, and Leon Osborne, for their assistance with setting up the trial, field work and data analysis. We also thank Hi-Tech Metrology for providing technical advice and use of their terrestrial laser scanner. References Anonymous Tree attribute profiling. ( view/53/57/ accessed 24 December 2007). Gordon, A. (1983). Estimating bark thickness of Pinus radiata. NZ. J. of For. Sci. 13(3): Henning, J.G. and P.J. Radtke Detailed stem measurements of standing trees from ground-based scanning LiDAR. For. Sc. 52: Keane, E The potential of terrestrial laser scanning technology in pre-harvest timber measurement operations. Harvesting/Transportation No. 7. COFORD Connects, COFORD, Dublin, Ireland. 4 pp. Murphy, G.E., H. Marshall, and M.C. Bolding Adaptive control of bucking on harvesters to meet order book constraints. For. Prod. J. 54(12): Murphy, G Determining stand value and log product yields using terrestrial LiDAR and optimal bucking: a case study. Journal of Forestry 106(6): Pnevmaticos, S.M., and S.H. Mann Dynamic programming in tree bucking. For. Prod. J. 22(2): Strandgard, M Improving harvester estimates of radiata pine (Pinus radiata D.Don.) bark thickness. CRC Forestry unpublished report. Melbourne, Australia. 7