Determining Stand Value and Log Product Yields Using Terrestrial Lidar and Optimal Bucking: A Case Study

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1 measurement Determining Stand Value and Log Product Yields Using Terrestrial Lidar and Optimal Bucking: A Case Study ABSTRACT Glen Murphy Nine plots in three Douglas-fir stands of different tree sizes were scanned using terrestrial lidar systems. Tree locations in each plot and their stem profiles were automatically detected using commercially available software. Actual stem profile measurements were made on all trees after felling. Stems were optimally bucked based on log specifications and prices for two log markets: the western United States and New Zealand. Stand values and log product yields were estimated for the terrestrial lidar-derived data and compared with estimates based on the actual stem profiles. Stand and undergrowth density and tree size affected the accuracy of automated stem detection and stem profile measurements. After manual adjustments for stem quality and height, lidar-derived estimates of average stand value and log product yields were within 7% of actual estimates. Differences were noted between stand types and markets. Suggestions for future research are provided. Keywords: terrestrial laser scanning, inventory, manual and automated methods, Douglas-fir Sustainability and globally competitive forest product supply markets are two of the pressures that forest managers face today. Both require that the manager have good metrics of the quantity, quality, and location of timber resources within the forest. These metrics can help the forest manager to ensure that (a) wastage is minimized, (b) harvest and volume growth increments are balanced, (c) log products are optimally matched to markets, and (d) 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. Emerging technologies include harvester data collection and data mining (Murphy et al. 2006), satellite imagery (Tommpo et al. 1999), and airborne and terrestrial laser scanning (Reutebuch et al. 2005, Bienert et al. 2006). In a comparison of different approaches for predicting timber assortment and value recoveries, Malinen et al. (2007) reported that bucking simulation, based on detailed stem descriptions, produces the most accurate estimates for specific stands. Although appropriate localized taper functions can provide detailed descriptions of stem profiles they can not provide descriptions of sweep or other quality features (e.g., branching characteristics) of individual stems. Selection of inappropriate taper functions can lead to large errors in total volume (e.g., Wiant et al. [2002] report errors of up to 30%) and product yield estimates (Murphy et al. 2006). Terrestrial laser scanning uses a groundbased laser to automatically measure the three dimensional (3D) coordinates of an Received January 9, 2008; accepted June 3, Glen Murphy (glen.murphy@oregonstate.edu) is professor, Forest Engineering, Resources, and Management Department, Oregon State University, Corvallis, OR The author thanks the following people and institutions for their support in carrying out this research project: Port Blakely Tree Farms L.P. for providing the three stands and for allowing portions of two of the stands to be felled at earlier ages than normal; FARO Technologies, Inc., and Basis Software, Inc., for supplying laser scanning equipment and operators; TreeMetrics, Ltd., for supplying their forest laser scanning expertise and the Autostem tree profiling software; University of Washington Precision Forestry Cooperative, US Forest Service San Dimas Technology Center, and Oregon State University for providing research funds; and Josh Clark, Scott Hanna, and Dzhamal Amishev for undertaking the fieldwork and laboratory work. Copyright 2008 by the Society of American Foresters. Journal of Forestry September

2 Table 1. Stand characteristics based on three plots per stand type. object s surface in a systematic order in near real time. It is receiving attention in Europe (Watt and Donoghue 2005, Thies and Spiecker 2004, Keane 2007, Weyzk et al. 2007), New Zealand (Anonymous 2007) and the eastern United States (Henning and Radtke 2006) as a technology for gathering detailed descriptions of individual stems and their location. Work to date has focused on measurement of forest parameters such as dbh, height, stand density, and tree identification. In the case study presented here, we evaluated the use of terrestrial laser scanning technology and an optimal bucking algorithm as the bases for determining log product yields and stand value in Douglas-fir. Evaluations were performed for a range of stand conditions and market types. Comparisons were made between laser-scanned standing tree and actual felled tree measurements. Methods Site Description. Three forest stands were selected on flat terrain (slopes less then 5 ) near Molalla, Oregon (45 08 N, 122 o 30 E). The stands ranged in age between 20 and 67 years (Table 1). Douglas-fir (Pseudotsuga menziesii (Mirbel) Franco) was the dominant species in the stands. Big leaf maple (Acer macrophyllum Pursh) and western redcedar (Thuja plicata Donn) were also present in the overstory. Vine maple (Acer circinatum Pursh) was the predominant understory species, although the occasional holly (Ilex sp.) was also present. Three circular plots, each 0.04 ha in size, were located in each of the three stands, giving a total of nine plots. All trees over 9 Stand type DFL DFM DFS Age (yr) Merchantable trees (stems/ha) Average Minimum Maximum dbh (mm) Average Minimum Maximum Total height (m) Average Minimum Maximum Understory vegetation (stems/ha) DFL, M, S, Douglas-fir large, medium, small, respectively. cm were numbered, labeled with paint, and had their species recorded. For ease of reference the three stands are hereafter referred to as DFL (Douglas-fir large), DFM (Douglasfir medium), and DFS (Douglas-fir small). Standing and Felled Tree Measurements. The breast height diameters of all standing trees were measured, to the nearest millimeter, using a steel tape. Douglas-fir was the timber species of interest. After felling, overbark diameters (to the nearest 5 mm) were measured with calipers on all felled Douglas-fir trees at 3-m intervals from 0 to 6 m above the stump, and then at intervals of 6mtothetopofthestem. All Douglas-fir trees were cut into merchantable logs. Heights were gathered to the nearest decimeter for stump heights, green crown base heights, height to an 80-mm top (inside bark) and total stem height using a measuring tape. Crown widths (CW), to the nearest meter, were measured at the base of the green crown. Table 1 provides a summary of the key plot data for the three stands. Laser Scan Data Collection and Manual Measurements. Laser scan data were captured in May 2007 when much of the understory maple vegetation was in full leaf. Two laser scanners with similar features were made available for this case study. An LS800 HE80 (FARO Technologies, Inc., Lake Mary, FL) was used in the DFM and DFS plots. A Surphaser 25HS (Basis Software, Inc., Redmond, WA) was used in the DFL plots. [1] Both scanners provided 360 hemispherical coverage to distances of more than 30 m. The FARO and Surphaser scanners used wavelengths of 785 and 690 nm and captured data at the rate of 120,000 and 190,000 points/second, respectively. Two scans were taken within each plot one at the plot center and the other at a different location to facilitate measurement of any trees that were likely to have been occluded by other stems within the first scan. Both scanners took less than 10 minutes to set up and capture data for a 360 hemispherical coverage that provided millions of 3D coordinates per scan. Scan data from the DFM and DFS plots could be viewed in Scene software (FARO Technologies, Inc.) because the scan data was collected using FARO scanner hardware. Scan data from the DFL plots could not be viewed in FARO Scene. Figure 1 provides an example of scan data from a DFM plot as seen using FARO Scene software. A total of 150 measurement points, at heights ranging between 0 and 240 dm (tree heights were not checked before selecting a measurement point), were randomly selected on trees (124 in total) within the DFM and DFS plots. The heights randomly selected were from those that had been measured on the felled trees. At each of these measurement points, FARO Scene software was used to manually measure overbark stem diameter to the nearest millimeter from the scan images. If the measurement point was greater than the total tree height or hidden by other stems or branches this was noted and an alternative measurement point was located on the same stem. Measurements were in a single plane which, for each tree, was perpendicular to the radius from the scanner located at the plot center. Automated and Manual Stem Profile Descriptions. Autostem Forest software (Keane 2007) was used to detect tree locations and extract overbark stem profile descriptions from the laser scan data for all trees within each of the nine plots. Autostem includes primary (automated) and secondary (manual) processing features. The two segmentation algorithms used in Autostem to automatically detect trees, the diameter profiling and polynomial smoothing algorithms used to fit diameters to the observed portion of each stem, and the functions used to predict tree heights and diameters at nonobservable stem heights based on observed measurements are described by Bienert et al. (2007). 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 318 Journal of Forestry September 2008

3 Figure 1. 3D image based on laser scan point cloud data gathered in a Douglas-fir stand. On the left side of the image is a tree with diameter measurement displayed. The diameter measurement was manually obtained using one of the measurement tools available in the FARO Scene software. 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 overridden if their heights are known. Both of these features were used to produce overbark stem profiles for each plot, referred to as adjusted Autostem profiles (AP) in this article. Actual stem measurements from the felled trees were used to develop overbark stem profiles at decimeter increments up each tree (actual profile). Linear interpolation of diameters between measurement points was used. Overbark and underbark diameters were collected at the top and bottom of each merchantable log for 30 randomly selected trees within each stand type. Three bark thickness functions, one each for the DFL, DFM, and DFS stands, were developed based on the felled tree measurements described previously. Heights along the bole and butt diameter were initially included as predictor variables but did not significantly improve the model for the stems sampled. The bark thickness models used are shown in DIB b i DOB, where DIB is the diameter inside bark (centimeters), DOB is the diameter outside bark, and b i is the coefficient for stand type i. The b i values were 0.895, 0.896, and for the DFL, DFM, and DFS stands, respectively. A single model could have been used for the DFL and DFM stand; however, a second model was required for the DFS stand. The bark thickness functions were used to obtain underbark stem profiles for the AP and actual profiles because some log markets (e.g., United States and New Zealand) quote prices based on underbark measurements. Other Stem Measurements and Functions. FARO Scene was used to categorize sections along each stem based on maximum branch size observed for the DFM and DFS plots. Six branch size categories were used: less than 40 mm, mm, mm, mm, mm, and more than150 mm. These branch size categories are used later in the descriptions for log markets. A branch size model, based on green CW at the base of the crown and depth into crown (DINC), was also used to predict maximum branch size at various heights within each tree (D. Maguire, pers. comm., Oregon State University, Dec. 6, 2007). The model was particularly useful for the trees within the DFL plots, but was also useful for checking branch sizes in nonobserved sections of stem in the DFM and DFS plots. The branch size model used is shown in maximum branch size (cm) CW DINC where CW is crown width (meters) and DINC is depth into crown (tree height branch height; meters). Stem segment quality categories, derived from FARO Scene measurements and the branch size model, were linked to the AP and actual stem profiles to provide detailed quality and dimension measurements for each stem (refer to notes for Table 2); AP profile with quality overlay (APQ) and ActualQ stem profiles, respectively. Prediction of Log Product Yields and Stand Value Log Markets. Douglas-fir is of considerable economic importance, especially for the forest products industries of the western United States, Canada, New Zealand, and some areas in Europe. Two Douglas-fir log markets were included in the analysis: a western US market and a New Zealand market. Seven and eight log types were included in the New Zealand and US markets, respectively (Table 2). Prices are estimates of average stumpage prices for these markets in mid In the USA market, log types ES1, ES3, and ES4 are export logs. The rest are domestic logs. In the NZ market, log types AL, AM, KL, and KM are export logs and the rest are domestic logs. Optimal Bucking Software and Analysis. Bucking is the process of cutting a stem into logs. VALMAX optimal bucking software, developed by the author, was used to determine the optimal log product yields that could be obtained from each plot based on stem profiles, sinuosity, and user-defined stem qualities (e.g., branch size and stem decay) and market conditions. VALMAX uses a dynamic programming algorithm to maximize value recovery from the stand for unconstrained, supply limited markets. The optimal bucking algorithm is similar to those described by Geerts and Twaddle (1985) and Murphy et al. (2004). All Douglas-fir 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 secondary scan location. All falsely detected trees and non Douglas-fir trees were manually deleted from the Autostem tree profile lists and excluded from the volume and value analyses. Results Automated Tree Detection. In the nine plots there were a total of 220 trees that Journal of Forestry September

4 Table 2. Log markets used to determine stand values. Market Log-type name Log lengths (dm) Minimum small-end diameters (mm) Price a ($/m 3 ) Maximum sweep b Allowable qualities c USA ES A ES AB ES A DS AB DS ABC DS ABC CNS ABCD Fiber ABCDEFX NZ AL AB AM AB KL AB KM AB S AB L ABCD Pulp ABCDEX a Prices are US dollars per cubic meter underbark. b Sweep is expressed as the maximum deviation from a straight line between the centers of the log ends. For the USA market the measure is in millimeters of deviation. For the NZ market it is a function of the small end diameter (SED) of the log; e.g., a value of 4 means that the maximum deviation can be no greater than the SED divided by 4. c Qualities A to F relate to branch size categories (A 40 mm, B mm, C mm, D mm, E mm, F 150 mm). Quality X means dead wood. Quality R means a large ramicorn. USA, United States of America; NZ, New Zealand. had dbh of 9 cm or greater (Table 3). Autostem detected 231 objects (with dbh greater than 9 cm and at least 10 good diameter readings up the stem); 5% greater than the number of trees. Seven percent of the trees within the plots were hidden behind other trees. This meant that 12% of the objects were falsely identified as trees. Table 3 shows that real trees were less likely to be hidden in the DFL plots (0%), where the number of stems per hectare was relatively low and more likely to be hidden in the DFS plots (11%) where the number was relatively high. Table 3 also shows that falsely identified trees were greater (20%) in the DFL plots, where understory vegetation densities were high, than in the DFM and DFS plots (approximately 10%) where understory vegetation densities were low. The results of the automated process are shown in Table 3 for completeness. However, occluded trees were manually identified and added to tree profile descriptions, for later analysis, as part of the Autostem secondary processing procedures. Similarly, falsely identified trees were manually deleted from the tree profile lists. Manual Measurement of Overbark Diameters from Laser Scans. An indication of the utility of laser scans for obtaining measurements of stem diameters can be obtained by comparing the actual measurements with those made by a researcher carefully taking measurements from laser scan images. Of the 150 randomly selected measurement points, 21 had to be excluded from Table 3. Automated tree detection from laser scan point clouds. further analysis because the randomly selected measurement points were greater than the total tree height for the stem. Of the remaining 129 measurement points, 2% were on stems hidden by other trees and 42% were either too high in the crown to be seen (usually above 12 m) or were hidden by leafy undergrowth. This left 56% of the measurement points with relatively unobstructed views. The mean difference between the laser scan derived measurements and the actual measurements was 0 mm; the mean absolute difference was 6 mm. The range in differences was from 31 mm to 26 mm. Height up the stem did not affect the accuracy of the measurements with unobstructed views, all of which came from 12 m in height or below. Automated Stem Diameter Measurements. On average, automated stem diameter measurements, based on laser scans, were 6 mm lower (standard deviation [SD], 24 mm) than actual stem measurements. These Douglas-fir plots Large Medium Small Combined Total objects detected after filtering a 55 [120] 67 [105] 109 [99] 231 [105] False positive trees 9 [20] 6 [9] 11 [10] 26 [12] Hidden trees 0 [0] 3 [5] 12 [11] 15 [7] Total actual trees 46 [100] 64 [100] 110 [100] 220 [100] Figures in brackets are totals for the three plots in each stand type presented as percentages of total actual trees. a Trees with less than 10 diameter measurements up the stem were automatically deleted. differences relate to AP stem profiles. Regression analysis indicated that stem measurements were likely to be underestimated very low in the tree and high in the tree. A polynomial function fitted to the differences was statistically significant at the P 0.05 level, but only accounted for a small percentage of the variation. stem diameter difference (mm) H H 2 R where H is the height (decimeters) at which the measurement was taken. Total Volume Comparisons. Total actual overbark volume ranged from 265 m 3 /ha on the DFS plots to 586 m 3 /ha on the DFL plots (Table 4). Total volume estimates based on AP profiles were within 3% of actual volumes for the DFM and DFS plots and within 6% for the DFL plots. Underestimating diameters may have accounted for the remaining difference. 320 Journal of Forestry September 2008

5 Stand Value Comparisons. Predicted Douglas-fir stand values, based on actual measurements, ranged from $2,600 to $41,100/ha and were dependent on stand types and markets (Table 5). The difference between actual and laser scan-based (AP) values was about 5%, but stem quality was not accounted for in these stand value estimates. When both height and quality adjustments were included (APQ stem profiles); the value gap was, as expected, slightly larger ( 7 to 9%). Individual plots and stands had differences that were sometimes larger and sometimes smaller than those noted here. It should be noted that actual stand values do not include the effects of sweep because sweep could not be accurately measured on the felled trees. An indication of the impacts of sweep on actual stand value could be evaluated, however; by assuming that APQ stem profiles were correct and then comparing the stand values from stems that had been optimally bucked with and without limits on sweep. It was found that taking sweep into consideration could be expected to reduce actual stand values by about 1.5% for the US and New Zealand markets. The impact of adjusting actual stand values for sweep would be that the gap between the actual and APQ stand values would be consistent for both markets ( 6 to 7%). Product Yield Comparisons. When stand types were combined, the difference between total merchantable volume (TMV), for the US and New Zealand markets, from the laser scan derived estimates and actual measurements was 5% for the APQ stem profiles. This is similar to the difference in total volume reported previously ( 4%). Comparisons of the product yield estimates derived from laser scans and actual stem measurements for both markets are provided in Tables 6 and 7. Products have been collapsed into fewer categories to make comparisons easier. For actual measurements, estimated product yields, expressed on a volume per hectare basis and as a percentage of total merchantable volume, are shown. For laser scan derived estimates, yields are shown on a volume per hectare basis and on an absolute percentage difference basis. For example, if actual TMV was 400 m 3 /ha and the actual yield for product A was 40 m 3 /ha the percentage of TMV would be 10%. If the APQ TMV was 350 m 3 /ha and the APQ yield for product A was 28 m 3 /ha the percentage of TMV would be 8% Table 4. Total overbark volume (m 3 /ha) comparisons based on actual tree measurements and measurements generated automatically by Autostem software from laser scan point clouds. Stand type and the absolute percentage difference would be 2%. The log product yields from the combined stand data for the laser scan APQ stem profiles were within 6% of actual percentage yields for both markets. Yields were within 3% for the US market. For the US market, the greatest differences occurred in the DFS stand (10%), where CNS1 and fiber yields were overestimated. For the New Zealand market, the greatest differences occurred in the DFL stand (11%) where export sawlog yields were overestimated. Discussion We found that when automatic detection algorithms were used to identify tree locations from laser scan point clouds, trees were more likely to be hidden behind other trees in stands with high densities (11%) than with low densities (0%). These numbers are similar to other recent studies that report values from 0 to 13% (Simonse et al. 2003; Bienert et al. 2006, 2007; Kiraly and Brolly 2007). Weyzk et al. (2007) show, however, that this can be affected by the location selected for the scanner; they found that hidden trees ranged between 10 and 37% in five scans of the same plot area. We found that 12% of objects were incorrectly identified as trees and that this was related to the amount of understory vegetation and tree branching. Other studies also comment on the effect of heavy undergrowth and have shown falsely identified trees can range from 0 to 37% (Bienert et al. 2006, 2007; Kiraly and Brolly 2007). Falsely identified trees were easily detected in the office on FARO Scene scan images and removed as part of the manual checking procedures. The laser scan based manual measurements of diameter from this case study (mean of 0 mm; range 31 to 26 mm) were similar in precision to standard caliper Douglas-fir Large Medium Small Combined Total volume: Actual 586 0% 485 0% 265 0% 445 0% Total volume: AP 551 6% 481 1% 256 3% 429 4% Volumes are for the Douglas-fir trees only. Figures in brackets are the difference between actual and laser scanned volumes expressed as a percentage of actual volume. AP, after noncommercial trees were removed from the plot and adjustments were made for known tree heights. or diameter tape measurements (Behre 1926, Myers 1961,Elzinga et al. 2005) and to other recent terrestrial laser scanning studies (Henning and Radtke 2006, Thies and Spiecker 2004, Wezyk et al. 2007). We compared automated measurements at multiple points above and below breast height and found average diameter measurement and prediction errors on AP stem profiles of 6 mm (SD, 24 mm) for single scans. Other studies, using single scans, have reported average errors in measuring diameter (usually dbh) of 17 mm (range, 58 to 56 mm; Simonse et al. 2003), 3 mm (SD for four plots ranged from 12 to 25 mm; Bienert et al. 2007), and 7 mm (range, 73 to 58 mm; Thies and Spiecker 2004). Multiple scans can reduce these errors (Pfeifer and Winterhalder 2004, Thies and Spiecker 2004). It is expected that out-of-roundness (OOR) would have accounted for some of the variation between actual and laser scan measurements for single scans, particularly on the lower portion of the bole. Williamson (1975) found that OOR on Douglas-fir individual trees ranged from 0 to 64% and averaged 12%. Total and merchantable overbark volumes were initially underestimated by an average of 22% when fully automated stem profiling procedures were used. Large tree plots (DFL) had the greatest underestimates in both absolute and relative terms. Underestimating tree heights, based on an inappropriate taper function, was the main source of error. Total and merchantable overbark volume estimates were improved considerably by using a semiautomated stem profiling procedure; when tree heights were known and entered into the Autostem software, total volume predictions from laserscanned stem profiles were underestimated by 5% in comparison with actual volumes. Kiraly and Brolly (2007) and Fleck et al. (2007) report limited success to date with Journal of Forestry September

6 Table 5. Total value ($K/ha) comparisons based on actual tree measurements and measurements generated automatically by Autostem software from laser scan point clouds. Market Stem profile type Douglas-fir Large Medium Small Combined $K/ha % Difference $K/ha % Difference $K/ha % Difference $K/ha % Difference USA ActualQ AP APQ NZ ActualQ AP APQ Values are for two sets of markets United States (USA) and New Zealand (NZ) for the Douglas-fir trees only. Figures in the percent columns are the difference between ActualQ and laser scanned values. ActualQ, Actual stem measurements linked with stem qualities; AP, after height adjustments were made and noncommercial trees were deleted; APQ, after height adjustments were made and stem qualities were linked. three automated tree height measurement procedures using terrestrial laser scanning; root mean square errors ranging from 1.4 to 4.4 m were found. However, regional and stand-specific height and taper equations (Kozak 1998) for Douglas-fir are available. Linking these equations to laser scan measurements of dbh and other lower stem locations may reduce the errors in upper stem diameters and in total volume estimation to a level similar to that found by us, i.e., 5% or less. Only Douglas-fir trees were present in the DFM and DFS plots. In the DFL plots, other tree species (western redcedar and big leaf maple) were present. These were automatically detected by the Autostem software but were manually deleted by us because our main focus was on Douglas-fir. To determine the stand value and potential product yields, it will be necessary to identify the species of each tree so that it can be included in the analysis if it is a commercial species and excluded if it is noncommercial. Different log markets and prices are likely to exist for different species. Haala et al. (2004) comment that texture analysis algorithms and digital photo images of tree bark could be used to automatically identify tree species. Nilsson and Edlund (2005) show that pine and spruce roundwood can be classified with close to 100% accuracy based on multivariate image analysis of tree bark. However, to obtain this level of accuracy, their images were acquired at a distance of 500 mm from the log, using a camera flash and a reference gray scale target. Such a system, while useful on a sawmill infeed chain, is not practical as part of a forest inventory system. An alternative approach to fully automating the identification of tree species would be to have the scan operator participate in the process; a Table 6. Product yield comparisons for a US market based on actual tree measurements and measurements generated by Autostem software from laser scan point clouds. Log types Actual Volume (m3/ha) u.b. default species could be entered for each plot (in our case Douglas-fir) and the scan operator could use a laser rangefinder with digital compass and blue tooth communications to record the distance, bearing, and species of nondefault trees. A second alternative is to visually identify the species in each plot by viewing the trees in FARO Scene laser scan images. Species identification in mixed stands will not only be important for determining what is and is not commercial, but also for ensuring that the right bark thickness or %of Actual Laser scan based measurements a Volume Change from (m3/ha) u.b. actual % Douglas-fir small stand Export sawlogs Domestic sawlogs CNS Fiber Total merchantable Douglas-fir medium stand Export sawlogs Domestic sawlogs CNS Fiber Total merchantable Douglas-fir large stand Export sawlogs Domestic sawlogs CNS Fiber Total merchantable Douglas-fir combined Export sawlogs Domestic sawlogs CNS Fiber Total merchantable a Based on stem profiles adjusted for height and quality (APQ profiles). u.b., underbark. taper functions are used for markets that purchase wood on an underbark-volume basis. Marshall et al. (2006) have shown that errors in estimating both volume (5 15%) and value (2 8%) can occur if bark thickness functions for the wrong species are selected. Stand value was estimated to within 9% of actual value using a combination of laser scan stem profiles, an optimal bucking algorithm, and user-provided height and stem quality adjustments. Comment has already been made on improving height and taper 322 Journal of Forestry September 2008

7 estimates. Approaches for automatically describing changes in stem quality based on laser scan point clouds are also receiving research attention. Fleck et al. (2007) report that terrestrial laser scan measurements of height of the canopy base for individual trees were well correlated (R ) with Vertex hypsometer (Haglof, Langsele, Sweden) derived height measurements. Watt and Donoghue (2005) reported that, as branching frequency increases in spruce, the amount of useful information in the scan decreases; they note that they were not able to measure branch size with the scanning spatial resolutions they had selected. Pfeifer et al. (2004) note that the crowns of coniferous trees are opaque for the laser scanner for the whole year but automated measuring of branches, while difficult, is possible for lower, thicker branches where point coverage is denser particularly for hardwoods, at leaf-off time. If height to the base of the green crown, and CW can be measured, then branch models, similar to those described previously can be used to segment stem sections on the basis of quality. Using a combination of laser scan stem profiles, an optimal bucking algorithm, and user-provided height and stem quality adjustments, Douglas-fir log product yields were estimated within 6% of actual yields for two sets of markets for the combined stand data. Estimates were better for some stands and some markets. Without good estimates of height, quality, and bark thickness, substantial differences between actual and estimated log product yields may occur. There are at least two limitations to this study. First, the results are based on single scans for each plot. Secondary scans were used to provide stem profiles only for trees that were occluded in the primary scan. Bienert et al. (2006) comment that multiple scans of the same plot area are timeconsuming but provide a much higher level of detail. It is possible that this could lead to improved estimates of stand value and log product yields from better descriptions of stem profiles and quality and a reduction in the need to manually remove false positive trees from the scan data sets. Second, our study was performed on relatively flat terrain. Large areas of Douglas-fir forest in the western United States and New Zealand are on steep terrain. We do not know how a laser scanning system would perform on such terrain. Table 7. Product yield comparisons for a New Zealand market based on actual tree measurements and measurements generated by Autostem software from laser scan point clouds. Log types Actual Volume (m3/ha) u.b. Conclusions In this case study we have shown, for two sets of markets, that Douglas-fir stand values and log product yields can be estimated using an optimal bucking algorithm together with stem profiles automatically generated from hemispherical terrestrial lidar scans. The level of accuracy of stand value estimates was consistent for both markets. Estimates of log product yields were better for some stands than others, but this was not consistent for both markets. As Thies and Specker (2004) have already noted, at this stage it is too early for a final evaluation of the use of terrestrial laser scanning for standardized forest inventories. Further refinements to the terrestrial lidar data collection, stem profile description, and optimal bucking system described in this article are needed for Douglas-fir stands. These include improved description of work procedures to better prepare plots for laser scanning, improved filtering of laser data to automatically eliminate false positive tree detection, improved stem diameter measurements (particularly in the upper stem), and new approaches for fully automated species identification and stem quality assessment. %of Actual Laser scan based measurements a Volume Change from (m3/ha) u.b. actual % Douglas-fir small stand Export sawlogs Domestic saw S Domestic saw L Pulp Total merchantable Douglas-fir medium stand Export sawlogs Domestic saw S Domestic saw L Pulp Total merchantable Douglas-fir large stand Export sawlogs Domestic saw S Domestic saw L Pulp Total merchantable Douglas-fir combined Export sawlogs Domestic saw S Domestic saw L Pulp Total merchantable a Based on stem profiles adjusted for height and quality (APQ profiles). u.b., underbark. 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