COMPARISON OF OPERATOR-DESIGNED AND COMPUTER- GENERATED SKID-TRAIL NETWOKS

Transcription

1 COMPARISON OF OPERATOR-DESIGNED AND COMPUTER- GENERATED SKID-TRAIL NETWOKS ABSTRACT David Parrott 1 and Marco A. Contreras 2 For ground-based timber harvests, it is essential that skid-trail networks connect log-piles to extraction points with minimal skidding costs and soil disturbance. While there are computer models that can generate optimal skid-trail network designs, the benefits of using these computer-generated designs over operator-designed skid-trail networks have not been quantified. In this study, we compared a computer-generated skid-trail network with a skid-trail network implemented by a skidder operator for a 132 ha harvest performed in 2008 through Using data collected from GPS equipment mounted on harvesting equipment at the time of harvest, aerial imagery, and a high-resolution LiDAR derived digital elevation model (DEM), we identified the original operator-implemented skid-trail network. To replicate pre-harvest conditions, we recontoured the slope along skid-trails in the DEM to simulate the natural topography and reconstructed the harvestable volume distribution throughout the study area based on pre-harvest inventories and harvest records. An optimized skid-trail network was designed using these pre-harvest conditions and compared to the original skid-trail network used in the harvest. This assessment quantifies the economic and environmental advantages of computer-generated skid-trail networks and justifies their implementation for ground-based skidding operations. KEYWORDS: timber harvesting, forest operations, network optimization INTRODUCTION Timber harvest operations in the eastern United States are typically performed using groundbased harvest systems with skidders or forwarders (Kellogg et al. 1992). In these systems, an efficient skid-trail network is necessary to minimize the economic and environmental costs of logs skidding. Heavy-machine traffic can cause significant soil disturbance along skid trails (Gent et al. 1984; Han et al. 2006; Jamshidi et al. 2008; Williamson and Neilsen 2000) that can lead to erosion, compaction (Croke et al. 2001; Lynch and Corbett 1990; Rab 1996; Williamson and Neilsen 2000), a shift in vegetation composition (Avon et al. 2013; Buckley et al. 2003; Marshall and Buckley 2008; Zenner and Berger 2008), and a decrease in vegetation productivity (Lockaby and Vidrine 1984). Best management practices, which carry costs of $445 to $8,000 ha -1 (Conrad et al. 2012; Kolka and Smidt 2004; Smidt and Kolka 2001), are often required to mitigate these impacts. While skid-trail networks have been traditionally designed manually using maps and field observations, Contreras et al. (2014) presented a computerized model to generate an optimized skid-trail network that minimizes economic and environmental costs 1 Senior Research Assistant, Department of Forestry, University of Kentucky, david.parrott@uky.edu 2 Assistant Professor of Forest Management, Department of Forestry, University of Kentucky, marco.contreras@uky.edu 37th Council on Forest Engineering Annual Meeting Moline, Illinois 1

2 based on terrain, volume distribution, and extraction locations. However, there has been no formal comparison between computer-generated and operator-generated skid-trail networks to quantify potential monetary savings. In this study, we performed a retroactive comparison of the skid-trail network created for a harvest operation in eastern Kentucky with the optimized computer-generated skid-trail network produced using simulated pre-harvest conditions. Using a high resolution LiDAR-derived digital elevation model (DEM), pre-harvest inventories, and timber sale records, pre-harvest volume distribution and terrain were replicated to create a computer-generated skid-trail network. We evaluated the costs of the computer- and operator-generated skid-trail networks to quantify the benefits of using an optimized skid-trail network. METHODOLOGY Study Area Study sites were located in the University of Kentucky s Robinson Forest (lat N, long W), which is located within the Northern Cumberland Plateau region in eastern Kentucky. The landscape is deeply dissected with steep slopes (Smalley 1986), and forest overstories are primarily composed of oak (Quercus spp.), yellow-poplar (Liriodendron tulipifera L.), and hickory (Carya spp.). For the study, we focused on three watersheds, approximately 132 ha, that were harvested in 2008 through A deferment harvest with a target residual basal area of 3.4 m ha -1 was performed resulting in the removal of 16,164 tons of merchantable products. The harvest was administered as part of a study investigating the effects of varying streamside management zone (SMZ) widths and stream crossings (Witt et al. 2013), which resulted in an increase of canopy retention in the SMZs of two of the watersheds. Harvest skid-trails were constructed using John Deere 700 and 850 bulldozers, and logs were skidded using a Caterpillar 525 and 545 grapple skidder. For the harvest, three log landings located on ridgetops surrounding the watersheds were utilized (Figure 1a). Simulating Pre-harvest Conditions LiDAR data collected in 2013 was used to create a digital elevation model for input into the computerized model. LiDAR data was collected during leaf-off conditions with a density of 1 pt m -2 and then collected again with a density of 25 pt m -2 during leaf-on conditions. Ground points from each dataset were combined to produce a DEM with 0.61 m resolution using ESRI ArcMap software. Since the DEM was created from data collected 5 years following harvest operations, the remnant skid-trail network was still present in the model. To ensure a fair comparison with the computerized skid-trail model, we removed these alterations and created a DEM that mimicked the natural landscape prior to harvest for input in to the computerized skid-trail model program. Using the high resolution DEM, aerial photos, and GPS data collected from units mounted on harvesting equipment, the skid-trail network was identified and each skid-trail segment was digitized as a line through the center of each skid trail. A 5.79 m buffer was applied to the digitized skid-trail network in order to encompass the entire area disturbed by the trail construction, and this entire affected area was removed from the DEM. Areas used as a skidtrails that were roads or trails prior to the harvest were exempt from removal. 37th Council on Forest Engineering Annual Meeting Moline, Illinois 2

3 A routine was then developed to fill the vacant elevation. The elevation of a given DEM cell was calculated as a weighted average of the elevation of the closest eight DEM cell along transect starting from north and generated every 45 degrees. Once the DEM was recountoured and representative of the assumed pre-harvest condition, volume distributions were created to characterize the placement of harvested timber volume. In 2006, a pre-harvest inventory was conducted using 186 points systematically distributed throughout the harvested watersheds. Inventory methods consisted of point sampling for trees larger than 33 cm and the point sampling with diameter obviation method described in Beers (1964) for trees less than 33 cm. The inventory only recorded trees that were marked for harvest, and points within streamside management zones where no volume would be removed were omitted. Total inventoried volume data was used to extrapolate per hectare volume for each point. With the assumption that the distribution of volume throughout each harvested watershed followed the distribution represented by the inventory points, volumes between inventory points were interpolated based on the inventoried volume of each point. The interpolation procedure used the inverse distance weighted interpolation method in ArcMap 10.1 to create a 1 m resolution distribution raster with per cell volume values for the entire harvested area. Using the sum of these values, a new raster was created with each cell value represented the percentage of the total interpolated volume. To ensure that the recreated pre-harvest volume was equivalent to the actual harvested volume, sale tickets from the harvest were used to calculate the exact volume sold and transported from each watershed. This total volume was then distributed according to the distribution raster. Computerized Skid-Trail Network Model To develop the computer generated optimal skid-trail network, we used the model presented in Contreras et al. (2014). This model creates skid-trail networks based on volume and elevation data, skidder maximum loading capacity (MLC), obstacles within the harvesting area, and the costs of skid-trail construction and skidding. Log-pile locations and volumes are created using a log-bunching routine based on the skidder MLC. In the volume raster, the routine establishes a log-pile at the first accessible cell with volume and adds the volume to the log pile. If the volume is less than the MLC, the algorithm searches the neighboring cells for volume. If present, the volume is added to the log-pile and the cell is assigned to the log pile. The search window continues to expand, and available volume is added to the log-pile until additional volume would exceed the MLC. Once this volume has been achieved, a log-pile location is established in the center of the search window. The model then establishes the next log-pile at the next unassigned accessible cell with volume and repeats the process until all cells with volume are assigned to a log-pile. To create the skid-trail network, vertices were distributed with a 6.4 m (20 ft.) horizontal distance spacing placed at the center of accessible cells in addition to log-piles and extraction points. Vertices are linked to neighboring vertices, unless the link covers an area where the gradient and side slopes area outside a user-defined limits. To find the optimal network, the model considers variable (skidding) and fixed (skid-trail construction and recovery) costs. Skidding costs are based on skidder rental rate and cycle time where the cycle time for uphill and downhill links are determined using the following equations from Contreras and Chung (2007): CT ds = ( D) CT us = ( D) 37th Council on Forest Engineering Annual Meeting Moline, Illinois 3

4 where CT ds is the cycle time (min) for downhill skidding, CT us is the cycle time (min) for uphill skidding, and D is the slope distance (m) along the network between a log-pile and an extraction point. This cycle time is used to calculate skidding cost as follows: PSC i = CT i RR 60 where PSC i is the skidding cost ($) for the ith log-pile, CT i is round trip skidder cycle time (hr) for the cost of ith log-pile, and RR is the rental rate for the skidder ($). With the fixed and variable cost established, we used NETWORK 2000 (Chung and Sessions 2003) to solve this transportation problem by creating the skid-trail network with the minimum cost. For the model inputs, elevation and volume raster layers were converted to 1 m resolution to maintain consistency between datasets. Slope limitations were set so that the skid-trail network links could not surpass 45% gradient slope and 100% side slope. Skidder capacity and rental rates were based a Caterpillar 525 and a Caterpillar 545 grapple skidders, which were used in the original harvest. The MCL for a Caterpillar 525 and 545 were estimated to be 10 tons based on turn volumes measured at a harvest in a forest type similar to the study area. Rental rates for bulldozers and skidders were assumed to be $120 hr -1 (US Forest Service 2011). The original skid-trails were constructed using John Deere 700 and 800 bulldozers and construction costs were estimated using position data with timestamps collected from GPS receivers mounted on JD650, JD700, and JD850 bulldozers during the original harvest (Bowker 2013). Using construction time and average terrain side slope along each skid-trail section, we found a 30% decrease in construction speed within each 10% increase in slope. Applying this to the average slope and average speed of the original harvest, we estimated construction time for each skidtrail segment based on slope distance and the terrain side slope. All areas designated as protected streamside management zones in the original harvest were identified and as inaccessible in the model. Comparison of Skid-Trail Networks For the comparison between the skid-trail networks, the original operator-designed skid-trail networks were identified using a high resolution DEM and GPS data from receivers mounted on skidding equipment. While the main skid-trail network could be easily identified, the paths used to pick up individual log-piles were unknown. To be consistent with the computerized model inputs, log-piles generated for the computerized models were assumed to represent the locations of the log-piles in the original harvest. These piles were then linked to the identified skid-trail network with straight, shortest distance lines with no restrictions. The original skid- trail was divided into 3.05 m segments, and costs for each segment were calculated following the same procedures as the computerized model. Using network analysis in ArcMap 10.1, the routes and times for each log-pile were calculated assuming shortest distance along the skid-trail network to the nearest extraction point. The results from this analysis and the associated skid-trail network were used for comparison with the computer-generated skid-trail network. Since the actual location of log-piles are unknown and the critical component of efficient skid-trail networks are the appropriate establishment of the high traffic skid-trails, comparisons were only focused on the major skid-trails with more than 20 passes. 37th Council on Forest Engineering Annual Meeting Moline, Illinois 4

5 RESULTS AND DISCUSSION From the volume distribution data, 1,667 log-piles were identified throughout the harvest area (Figure 1). All log-piles were directly connected to the identified operator-generated skid-trail network with no restrictions for terrain conditions (Figure 2). The computer model was successful in creating an optimized skid-trail network connecting all but six log-piles to the extraction points (Figure 3). The exclusion of the six log-piles was due to 45% slope constraints that isolated these piles from the feasible skid-trail network. Table 1 shows the associated lengths and log-pile information for the operator- and computer-generated skid-trail network. Comparisons between the operator- and computer-generated skid-trail networks were focused on the major skid-trail paths that had more than just a few passes. Table 2 compares the lengths of the operator- and computer-generated skid-trails at different traffic intensities. Relative to the operator-designed network, the computer generated skid-trail network was more concentrated on fewer major skid-trails with higher traffic (Figure 4). When comparing the length of the major skid-trails receiving >10 and >20 passes, the computer-generated skid-trail was 4.8 and 3.7 km shorter, respectively. Lengths for skid-trails with >50 passes become longer in the computergenerated skid-trail networks where skidding was concentrated on major high-traffic trails. Results from this comparison, indicate that the computerized model was able to generate a skidtrail network with a lower density of major skid-trails throughout the harvested area. Since the majority of skidding costs will be accrued from the along the major skidding routes, the location of these paths would have the largest impact on total costs and are the primary factor for skidtrail network efficiency. While the model provided skid-trail network information for the entire network connecting each estimated log-pile locations with extraction points, the practical application of this model is to locate the major skid-trails designated for heavy traffic and then add the subsidiary trails branching out to known log-pile locations. The implementation of this skid-trail design would reduce heavy skidder traffic to a smaller area, which is recommended to reduce soil disturbance (Garland 1983; Han et al. 2006; Zenner and Berger 2008). It would also ensure that skidding, particularly with heavy skidder traffic, does occur on areas with slopes above defined slope threshold. In addition to the reduced soil disturbance, costs would be expected to be smaller than the operator design. By increasing the number of passes along major skid-trails, costs should decrease due to the optimally designed routes and costs reduced construction costs. While the total skid-trail network length was slightly higher in the computer generated skid-trail network than the operator-designed skid-trail network, this is likely due to the unrestricted, direct skid-trail branching used to connect log-piles with the identified operator-designed main skidtrails. These straight-line connections, which potentially traversed unrealistic obstacles, shortened the overall skid-trail network in relation to the computer-generated network bounded by slope restrictions. Additionally, the computer-generated skid-trails present many sharp resulting from only assuming vertices can be connected to their 8 adjacent nodes. When implemented on the ground, computer-generated skid-trail will likely have to be re-digitized manually to eliminate these sharp turn, which will likely reduce skid-trail length. 37th Council on Forest Engineering Annual Meeting Moline, Illinois 5

6 CONCLUSIONS In this study, we compared an operator-designed and a computer-generated optimized skid-trail network following a harvest using by recreating pre-harvest conditions. Comparisons between the major skid-trails with high traffic, more than 20 passes, show that the optimized computer generated skid-trail network restricted traffic to fewer high traffic skid-trails throughout the harvest area, thereby reducing the length of major skid-trails by 3.7 km. While these results suggest that a computer-generated optimized skid-trail network can reduced the area disturbed by skidder traffic and create more efficient skid-trail designs, further research should investigate more precise cost estimates for model inputs as well as the inclusion of potential stream crossings. While skid-trails are still currently designed manually in the field with the use of topographic maps, the increased availability of high resolution DEMs and GPS equipment to forest managers is creating more opportunities to develop more efficient harvest practices that can reduce costs and environmental disturbance. 37th Council on Forest Engineering Annual Meeting Moline, Illinois 6

7 Figure 1. Study area showing the location of constructed skid-trails (a) areas with no traffic allowed on stream management zones and near existing intermittent streams (b) volume distribution derived from 186 pre-harvest inventory plots (c), and the resulted simulated location of 10-ton log-piles (d). 37th Council on Forest Engineering Annual Meeting Moline, Illinois 7

8 Figure 2. Operator-designed skid-trail trail network showing constructed and connecting skidtrails from pile-locations (a) and traffic level in terms of machine passes (b). Figure 3. Computer-generated skid-trail trail network showing individual paths from log-pile locations to the three log landings (a) and traffic level in terms of machine passes (b). 37th Council on Forest Engineering Annual Meeting Moline, Illinois 8

9 Figure 4. Operator-designed (a) and computer-generated (b) major skid-trails defined as those with 20 or more machine passes Table 1. Summary of the results of the skid-trail model when applied to the six scenarios in the hypothetical harvest unit. Operator-designed Computer-generated Harvest unit information Number of piles Connected piles Isolated piles 0 6 Volume (ton) Harvestable volume (ton) Un-harvestable volume (ton) Harvesting cost ($) Skidding cost ($) Soil recovery cost ($) Harvesting cost ($ ton) Skid-trail network length (km) Per pile information Minimum volume (ton) Average volume (ton) th Council on Forest Engineering Annual Meeting Moline, Illinois 9

10 Maximum volume (ton) Minimum skidding cost ($) Average skidding cost ($) Maximum skidding cost ($) Minimum skidding distance (m) Average skidding distance (m) Maximum skidding distance (m) Table 2. Total skid-trail network length comparison of between operator-designed and computer-generated for different levels Total skid-trail network length (km) Number of machine passes Operator-designed Computer-generated > > > > > > REFERENCES Avon, C., Dumas, Y., and Bergès, L Management practices increase the impact of roads on plant communities in forests. Biol. Conserv. 159(0): Bowker, D.W Forest harvest equipment movement and sediment delivery to streams. University of Kentucky, Lexington, KY p Buckley, D.S., Crow, T.R., Nauertz, E.A., and Schulz, K.E Influence of skid trails and haul roads on understory plant richness and composition in managed forest landscapes in Upper Michigan, USA. For. Ecol. Manage. 175: Chung, W., and Sessions, J NETWORK 2000: A program for optimizing large fixed and variable cost transportation problems. In Systems Analysis in Forest Resources. Edited by Greg J Arthaud, and Tara M Barrett. Springer Netherlands. pp Conrad, J.L., Ford, W.S., Groover, M.C., Bolding, M.C., and Aust, W.M Virginia Tech Forest Road and Bladed Skid Trail Cost Estimation Method. Southern J. Appl. For. 36(1): Contreras, M., Parrott, D.L., and Chung, W Automatic design of optimal skid-trail networks to reduce skidding cost and soil disturbances during ground-based timber harvest operations. In review International Journal of Forest Engineering. Contreras, M., and Chung, W A computer approach to finding an optimal log landing location and analyzing influencing factors for ground-based timber harvesting. Can. J. For. Res. 37(2): Croke, J., Hairsine, P., and Fogarty, P Soil recovery from track construction and harvesting changes in surface infiltration, erosion and delivery rates with time. For. Ecol. Manage. 143(1 3): th Council on Forest Engineering Annual Meeting Moline, Illinois 10

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