Enhancement of Forest Inventory and Management with Tree List Generator Products Frank Liu 1, Dongmei Wang 2, Kim Rymer 3, Dave Cheyne 3, Robert O Keefe 4 1. Timberline Forest Inventory Consultants. Suite 315, 10357-109 th Street. Edmonton, AB, T5J 1N3. Canada. Tel: 780-425-8826 / Fax 780-428-6782. Email: fql@timberline.ca 2. Forest Protection Division, Alberta Sustainable Resource Development. 10th Floor, 9920-108 Street. Edmonton, AB, T5K 2M4. 3. Alberta Pacific Forest Products Inc. Box 8000, Boyle, AB, T0A 0M0. Canada 4. Timber Management Branch, Forestry Section, Nova Scotia Department of Natural Resources. PO Box 68. Truro, NS B2N 5B8. Canada. Acknowledgements: The authors express their appreciation to Dave Downing for the valuable suggestion and comments on the manuscript. 1
Abstract: To determine the quantity and quality of large hardwood logs available over a portion of Alberta- Pacific Forest Management Agreement area, a study was undertaken to produce and compile tree lists, and grade log volumes. The stand-based tree size information, which is not available in current forest inventory, is generated by Tree List Generator and provides possibility of standbased log population study. Individual tree and log volumes were compiled using a set of utilization standards specifying stump height and diameter, log top diameter and length. Next, log volumes were summarised according to stand types, log diameter classes and locations. Then, log volumes were graded using grading rules developed from two trials of truck-load logs that collected diameters, length, total and graded volumes of gradable logs. Results from tree list products were also compared with a cutblock volume sample and the actual harvested volumes were 98% agreeable to the predicted volumes. Keywords: Log quality, tree list generation, validation, log grading 2
INTRODUCTION History: Alberta-Pacific Industries needed detailed information on log size and spatial distribution to support a log-mill planning. This study was to provide an estimate of the availability of aspen (Populus tremuloides) and other deciduous species saw logs from the proposed operating area in North-Central Alberta. The operating area used for the analysis included Forest Management Unit (FMU) S14 and the west side of the Alberta-Pacific Forest Industries Inc. s (Alberta-Pacific) Forest Management Agreement (FMA) area (Fig. 1). The detailed tree information (diameters, height and number of stems in each diameter class) was needed for this log quality study and is not directly available in a typical forest vegetation inventory database, such as Alberta Forest Vegetation Inventory (AVI). The conventional log and volume estimations through yield curves and stock tables was considered inadequate because: Volume estimates based on yield curves are generalizations of broad strata Differences in stand conditions within a stratum are not represented Detailed tree information is not available Tree lists were generated for all merchantable stands through the newly developed Tree List Generator (TLG) as an enhancement to current forest inventory database. The stand-based tree size information from TLG provides possibility of stand-based forest operational and management planning, such as the log population in the current study. Tree List Generator was previously developed for Alberta-Pacific in 1999 and recently upgraded in 2002. The TLG is a set of statistical models that were developed from temporary sample plot (TSP) data and validated with permanent sample plot (PSP) data collected across the study area. The TLG allowed the expansion of the forest inventory information into the detailed tree and log information needed for the analysis. The study also conducted a validation process that cross checked the findings to actual delivered logs scaled at the Alberta-Pacific wood yard. The outline of this study is presented below in Fig. 2. PROJECT OBJECTIVES To update and merchandize for all forested stands in the western portion of Alberta-Pacific forest management agreement area and Forest Management Unit S14. To estimate the quantity and quality of large logs ( 20 cm top diameter inside bark, DIB) of the selected forest stands and access the log grading system To evaluate the log quality estimates by a comparison between tree list-based log volumes to actual harvested volumes 3
Liu, Wang, Rymer, Cheyne, and O Keefe Figure 1. Study Area 4
Merchandised TLG Forest Inventory (AVI) Volume Log profile Landbase Netdown Log Grading Stand Selection Summary & Report Comparison to Actual Towed Figure 2. Study Flowchart 5
WHAT ARE TREE LISTS? TREE LISTS AND TREE LIST GENERATION Tree lists are stand tables produced by tree list generator (TLG) an exclusive product of Timberline provide diameter distributions and height by species be unique forested polygon-based. Here is an example: WHAT IS TREE LIST GENERATOR? The TLG is a set of statistical models and procedures that project stand-specific tree lists for individual diameter class of each species based on current inventory at very low cost. These models were developed from ground sample plot data and validated with an independent set of sample plot data. The validation of TLG models and a comparison of TLG based to yield curves-based volumes are also conducted to ensure statistical significance of the models and representation of the current forest conditions by TLG products. In short, tree list generator is a set of statistical model system that are: developed from forest inventory and sample plot data enhanced by the nearest neighborhood approaches applied to the inventory database, producing a tree list database 6
WHAT DOES TLG DO? The TLG models the relationship between forest inventory data and cruise plot data to develop a diameter-class distribution by species for any given forest inventory label. The process is illustrated in Fig. 3. mc18aw 10 92-M TLG Figure 3. Tree list generator processing TREE LIST MODEL DEVELOPMENT Diameter distribution prediction models are the major model forms for tree list prediction, i.e., the frequency distribution of diameter classes will be estimated for stands using mensurational AVI attributes and the ecological information. The Weibull distribution is a widely used model in diameter distribution prediction (Bailey and Burgan, 1989, Borders and Patterson, 1990, Knowe and Stein, 1995). To define the Weibull distribution model, stand attributes available from AVI labels are applied in estimating the parameters of the Weibull distribution. As a result, the Weibull distribution is specified by stand characteristic and can be used to estimate the diameter distribution of a given stand; every tree list created for a specific AVI label is unique. Following steps were taken to develop TLG models and tree lists. STEP ONE: ASSEMBLE THE DATA a. photo-interpreted inventory data stand height (±1m) stand age (10-year class) crown closure (10% class) species composition (10% classes by species clown closure) density (total stems/ha) 7
b. Ground-based sample plots species, diameter at breast height, height, age, tree quality, etc. from cruise data 3400 temporary sample plots were used for TLG development STEP TWO: DEVELOP TLG MODELS a. Partition plot data into broad cover type groups by similar range of inventory labels (e.g., pure deciduous) b. Develop statistical relationships between plot data and inventory attributes within each broad group enhanced by the nearest neighborhood approaches c. Predict diameter distribution use Weibull cumulative distribution, by species estimate Weibull parameters by species group, total, major and minor species Results were validated against a set of independent sample data The models that had the minimum mean squared error (MSE) and the least parameters in the model were selected. Model selection also considered the asymptotic 95% confidence intervals for any coefficient estimate (Hostin and Titus, 1996). In other words, each variable included in the model has to be significant at 95% confidence level. STEP THREE: NEAREST NEIGHBORHOOD APPROACHES In addition the statistical modeling process, nearest neighborhood approaches were also employed to enhance the simulation process. Stands of same or similar forest types were grouped and assessed for their neighborhood relationship, as indicated by species composition, height, density, TPR, and age. The abundance of ground plots and the reprehensibility of the landscape by these stands were also considered. Tree lists were generated by stands meet the criteria and used to populate the tree lists from TLG statistical models. STEP FOUR: MODEL VALIDATION The diameter distribution predictions were evaluated by using a two-sample Kolmogorov- Smirnov test (Sokal and Rohlf 1969). This procedure is well established for testing whether or not the two cumulative distributions come from the same population. It has been commonly used to evaluate the Weibull diameter distribution predictions (Bailey and Burgan, 1989, Borders and Patterson, 1990, Knowe and Stein, 1995). The test is conducted based on the largest difference between the predicted and the observed cumulative distributions. Tree list generator models were validated with Alberta-Pacific PSP data and results suggested that the TLG simulated current forest conditions very well. Figure 4 shows a sample of diameter distribution comparison. 8
Figure 4. Observed vs. predicted diameter distributions STEP FIVE: TREE LIST GENERATION After model validation and necessary modifications, tree lists were generated for all eligible forested polygons. First, forested stands were screened for saw log potential. The selection was based on AVI attributes: species composition (pure hardwood and mixedwood stands), stand height, density, timber productivity rating, stand modifier, and age. Second, stands are stratified for major forest cover groups that are same as those used in model development and then grouped for tree list generation by the nearest neighborhood procedure or by models. Then, tree lists are generated for individual stands. Finally, necessary checks were performed to correct impropriate predictions, such as extreme values and unsuitable predictions for a stand type or age class, etc. 9
LOG QUALITY ASSESSMENT MERCHANDIZE TREE LISTS Because diameter is a key variable in the recovery of lumber grade saw logs we stratified the analysis by top diameter inside bark (dib) class. The assumption we made in doing this was that the different top dib classes would have significantly different log recovery. The analysis used three top dib classes: 1. 20.0cm 25.9cm top dib 2. 26.0cm 31.9cm top dib 3. 32.0cm and greater top dib Log length is also an important factor in quantifying available deciduous saw log volumes. Within the analysis of the forest inventory various log lengths were explored to quantify the effect of deciding on a specific log length. The log lengths explored were: 1. 2.4m log length 2. 3.0m log length 3. 3.6m log length Predict average height by diameter, using local height-diameter functions Estimate merchantable volumes with local taper equation (Kozek, 1988) parameters (Huang 1994) Newton s volume equation (Hussch et al. 1982) specified utilization standards three log lengths for log quality analysis and one for performance LOG SIZE DEFINITION Utilization Top DIB Classes (cm) Standards Very Small Small Medium Large Log-2.4, Log-3.0 and Log-3.6 NA 20.0-25.9 25.0-31.9 32.0 Log-4.0** 10.0-19.0 19.1-25.0 25.1-31.0 31.1 ** Reflect diameter classes used for scaling at the Alberta-Pacific wood yard. 10
LOG ESTIMATION AND GRADING Tasks performed in this section included: a. Predict log profile by 2-cm DIB class for each merchantable stand; and b. Log grading based on three independent log studies with 17 to 23 loads in each study. Following development and validation of tree list models, stands in the study area were selected based on stand types, density, height and productivity rating. Target stands included pure deciduous, mixedwood, and pure white spruce stands. Moreover, a minimum of large log proportion to the total volumes was also applied. Individual tree and stand log population and volumes were compiled. The grading system utilized in the trial was adopted from the National Hardwood Lumber Association. This system placed logs into one of three grades referred to as F1, F2 or F3. For the scaling trial, a forth grade was added (F4) to capture logs that met the dimensional specifications (i.e. minimum 20cm top dib at the specified log length) yet did not make grade F1, F2, or F3. From each of the 34 sample loads used in development of the grade proportions the following log information was collected: Top diameter (cm) Log grade (F1, F2, F3, or F4) Total log volume (m³) Following table summarizes the resulting grade proportions. The proportions were then integrated with the forest inventory analysis to estimate deciduous saw log grade lumber available from the analysis area. Top DIB Log Grade Correlation Percentage Class (cm) F1 F2 F3 F4 S (20-25.9 cm) 0.00% 0.03% 63.50% 36.47% M (26.0-31.9 cm) 0.00% 38.24% 36.18% 25.58% L (>=32 cm) 12.21% 35.68% 22.59% 29.53% All Classes 1.19% 11.25% 53.98% 33.59% LOG POPULATION RESULTS Based on utilization standards and log grading rules, the individual log volumes were compiled and stand-level volume summaries were then produced. The summaries were then used to summarize total log volumes for the different FMUs, stand types, and top dib classes by each of the three log lengths. Sample outputs present the summary of logs by stand type and log size class for the analysis area. Fig. 5 shows the spatial distribution of the deciduous stands that corresponds to the deciduous stand type volumes reported. Moreover, the figure categorized the deciduous stands by percentage gradable volume highlighting stands that are most attractive for producing aspen saw logs which would assist decision making by managers in the forest management planning. 11
STUDY SAMPLE OUTPUTS BY DIB CLASS AND GRADE DIB Log Volume (m3) (cm) Grad1 Grad2 LGrad1 LGrad2 Total logs x 1000 Gradable (%) Graded (%) 20 0 212,731 0 1,516,874 12,641 6.41 11.5 22 0 335,216 0 2,142,068 17,851 10.1 16.25 24 0 329,125 0 1,763,231 14,694 9.91 13.37 26 0 252,540 0 1,161,417 9,678 7.61 8.81 28 39,366 492,077 154,999 1,937,492 7,750 16.01 15.87 30 27,902 348,770 96,538 1,206,720 4,827 11.34 9.88 32 24,363 304,532 73,551 919,386 3,678 9.91 7.53 34 67,505 260,376 183,457 707,622 2,621 9.87 6.76 : : : : : : : : 58 579 2,233 521 2,011 7 0.08 0.02 60 345 1,331 291 1,121 4 0.05 0.01 Total 287,995 3,032,400 780,750 12,404,742 77,627 BY STAND TYPES Stand Top DIB Log Volume (m3) for Different Lengths (m) Type Class (cm) 3.6 3.0 2.4 Deciduous 20.0-25.9 6,992,134 7,225,648 7,362,815 26.0-31.9 4,149,506 4,503,795 4,306,647 32.0 and up 3,021,997 3,078,878 3,376,032 Sub-Total 14,163,638 14,808,322 15,045,494 Mixedwood 20.0-25.9 2,666,628 2,688,211 2,722,317 26.0-31.9 1,877,674 1,998,926 1,947,840 32.0 and up 1,270,178 1,311,012 1,422,897 Sub-Total 5,814,481 5,998,150 6,093,055 White Spruce 20.0-25.9 987,435 1,017,099 1,030,442 26.0-31.9 708,917 746,948 730,793 32.0 and up 588,477 611,876 659,262 Sub-Total 2,284,830 2,375,924 2,420,498 Grand Total 22,262,950 23,182,396 23,559,049 12
SPATIAL LOG DISTRIBUTION Sample output of spatial log distribution by the proportions of gradable log volumes to the total volume of a given stand - is presented in Fig. 5. Figure 5. Spatial distribution of forest stands with gradable log volumes PERFORMANCE This study also verified TLG products with a 2001 cutblock sample which sampled 1% of the total hardwood volumes harvested by Alberta-Pacific and quote-holders. In addition, the sample also recorded the locations of harvesting and log sizes. To make the comparison between the harvested log volumes and TLG products, TLG-produced tree lists databases were compiled using 15 cm stump / 10 cm top diameter with 4.0 meter log length that were used for the harvested log sample. After the volumes were summarised for different parts of the FMA and log size classes, comparisons between TLG and the harvest sample were made for the total log volumes and their distribution in different log classes. Following steps were taken in the performance assessment of the log population predictions from tree lists: Merchantable volume and log profile were predicted from tree lists for individual stands of cut blocks harvested in the year of 2001 Sample scaling data for these blocks was collected and compiled Results showed that TLG accurately predicted the actual scaling record for both total and log grades. 13
Top Dib Class FMA Deciduous Volumes from 2001-02 Timber Year (m³) (cm) Scale Data TLG Predicted % Difference Under 19 5,258 6,312 9% 19.1-25.0 3,948 2,742-10% 25.1-31.0 1,846 1,647-2% Over 31.1 1,241 1,355 1% Total 12,293 12,057-2% CONCLUSIONS AND APPLICATIONS TLG can generate tree lists for all forested polygons in the area: The model generated polygon based tree information The model fully used expensive volume-sampling data and AVI attributes to extend the utility of both information sources The model produced accurate estimates of log volumes and quality by species that would have not been possible with the conventional approaches, such as yield curves or stock tables. TLG has been successful developed across Canada (British Columbia, Alberta, Saskatchewan, and Ontario). TLG products include: Stand volumes and stock tables Log populations Yield curve enhancement As inputs for growth models To project stand development and growth over time, i.e., trending forest inventory 14
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