Key words: Harvesting system, Remote sensing, LiDAR, Productivity, Harvester, Radiata pine

Transcription

1 Harvesting productivity analysis using LiDAR Alam, M. M. 1, Strandgard, M. 2, Brown, M. 3 & Fox, J. C. 4 Department of Forest and Ecosystem Science The University of Melbourne 1 mmalam@student.unimelb.edu.au 2 mnstra@unimelb.edu.au 3 mwbrown@unimelb.edu.au 4 jcfox@unimelb.edu.au Abstract Mechanised harvesting operations are common in Australia because of their increased productivity and efficiency, improved worker safety and reduced cost of operations. Most research has found that the productivity and efficiency of a mechanised harvesting system is affected by a number of factors including forest stand characteristics (tree size or piece size, stand density, undergrowth), terrain variables (slope, rocks, woody debris), operators skill and machinery limitations. The purpose of the study was to use remote sensing technology to quantify these forest stand and terrain factors (particularly slope) and hence derive relationships to predict harvester productivity from remote sensing data. A case study was conducted in mature radiata pine (Pinus radiata) plantation at Mount Burr Reserve Forest, South Australia (37.61 S, E). LiDAR (Light Detection And Ranging) flown in 2007 was used to identify and quantify stand and terrain factors (particularly tree size). A time and motion study conducted during final harvest was used to estimate the impact of each factor (tree size and slope) on harvester productivity. Tree size estimates derived from the LiDAR data were grown to the point of harvest using empirical growth models. The point of harvest tree size estimates were ground-truthed against harvester measurements of the same trees. Empirical models were then developed to enable the LiDAR-derived estimates of tree size to be used to estimate productivity of harvesting equipment. The robustness of these relationships will be tested by applying the model to areas not used in the development process. Key words: Harvesting system, Remote sensing, LiDAR, Productivity, Harvester, Radiata pine 1. Introduction Productivity (m 3 per Productive Machine Hours (PMH)) and efficiency of a harvesting machine and/or system is affected by the characteristics of the forestry machinery, stand condition (i.e. tree shape, tree size, crown size, tree volume and the type and density of trees), extraction rate, (i.e. the ratio between basal area harvested and basal area before harvest), terrain conditions (i.e. slope, ground roughness, ground strength, water course, roads etc.), and the skill of operator (Brunberg et al. 1989; Lageson 1997; Visser et al. 2009). Many studies have demonstrated tree size to be the most influential factor in harvesting and extraction productivity, with productivity increasing and costs decreasing with increasing tree size (Kellogg and Bettinger 1994; Nakagawa et al. 2007; Visser et al. 2009). However, the rate of increase in harvesting productivity is less for larger trees and, beyond a sweet spot, further increases in tree size reduce productivity as the extra time to cut and process the stem outweighs the volume gain (Visser et al. 2009). This study will evaluate only tree size (tree volume) as an influential factor affecting harvester productivity for the study area. Time study is a common practice to measure work or productivity of a system. For machines and equipment, time consumption is calculated for every work element which in turn is used to 1

2 estimate its productivity. Work elements of time study in a harvesting system include moving of the machine and/or its boom, felling, delimbing, crosscutting, and bunching (Nakagawa et al. 2007). Early time study methods involved using stopwatches and paper to measure and record time for each machine activity (Howard 1989). This method is tedious, expensive and error prone (Olsen and Kellogg 1983). Another method uses a video camera to capture the harvesting operation activities and record time simultaneously (Wang et al. 2003). This method requires field measurements, so extra time and resources are also required. However, such traditional methods are being replaced by computer-based time study methods. In this method, the computer program records time with the built-in clock and collects volume information at the same time. Other studies developed an automated time study of felling and skidding (McDonald 1999; McDonald and Rummer 2000) which recorded machine movement with a GPS and provided gross time study data but not detailed elemental times. This study used computer-based time study to collect tree volume information including tree Diameter at Breast Height (DBH) and processed length with their GPS (Global Positioning System) locations. Time elements were recorded using Timer Pro Professional software 2010 instead of using computer generated tree-wise time elements in order to achieve time elements for each work element including moving / positioning of the machine, tree felling, processing of each log (cut-to-length), stacking / bunching, travel time, delay time etc. Time study-based regression equations are generated to express equipment productivity (Gardner 1982). In this study, regression models will be developed which in turn will be used to develop models that will predict the productivity of a harvesting system based on LiDAR (Light Detection And Ranging)-derived estimates of tree size. LiDAR has been used to detect individual trees, predict tree heights and volume (e.g. Brandtberg 1999; Hyyppa and Inkinen 1999; Lim et al. 2003a; Naesset 2004; Woods et al. 2008) and diameter distributions (e.g. Gobakkenn and Næsset 2005; Thomas et al. 2008). It can also accurately and cost-effectively derive DTM (Digital Terrain Model) or DEM (Digital Elevation Model) compared with conventional photogrammetry especially where the ground surface is not visible in dense forest or in low relief areas such as wetlands or flat plains (Baltsavias 1999; Lefsky et al. 2002; Younan et al. 2002; Patenaude et al. 2004). Construction of LiDAR-derived Canopy Height Models (CHMs) with distinguishable tree crowns is the prerequisite in order to apply any method for individual tree detection (Holmgren and Persson 2004). The canopy height models (CHMs) are obtained by subtracting the DTMs from corresponding DSMs (Digital Surface Models). Estimation of tree-level productivity requires the measurements of a large number of trees which is time consuming, laborious, tedious and expensive. However, LiDAR is widely used to derive tree characteristics at the individual or stand level. The aim of this paper is to (i) Examine the effect of tree size on productivity of a harvester (Valmet 475), for a plantation forest in clear felling operation using a Cut-to-Length (CTL) harvesting method, (ii) Undertake statistical analysis of time elements and productivity of the harvester, (iii) Develop predictive models for the harvester (iv) Evaluate the ability of LiDAR to estimate tree volumes and (v) Develop a productivity model based on LiDAR-derived tree volume. 2

3 2. Material and methods 2.1 Study area A 35-year old radiata pine plantation with a density of 230 trees ha -1 was selected at Mount Burr Reserve Forest (37.61 S, E), Brennans locality, South Australia. Trees were planted at 4m X 2m spacing. However, it had been thinned three times prior to the final harvest. Heights and DBHs of the trees ranged from 20 to 35m and 26.4 to 53.5cm respectively. Forest soil comprised of aeolian sands. The site was almost flat with slopes less than 11 (approximately). 2.2 LiDAR Data LiDAR data was collected for the study area using an ALTM (Airborne Laser Terrain Mapping) 3100 LiDAR system and an inertia measurement unit (IMU) on 20 th July, The scanner system transmitted laser pulses at 1064 nm (near-infrared) and received multiple returns of each pulse. First return density was 2.6m -2 and returns per pulse were 1 to 4. First and last return pulses were acquired to characterize forest tree structure and terrain surface respectively. The flying altitude was 1100 meter Above Sea Level (masl), pulse repetition rate was s -1, maximum scanning angle was 12.5, beam divergence was 0.20 (mradians) and footprint diameter was 22cm. The horizontal and vertical accuracy of data were 0.55m and 0.20m, respectively. Processed LiDAR data was supplied by Forestry South Australia in LAS file format with average point spacing of 0.52 points m -2. Ground and Non-ground returns were classified by the data provider. Ground and non-ground LiDAR points were used to construct a digital elevation model (DEM) and a digital surface model (DSM) with a 2m cell size for the study area. A slope class map was derived from the DEM. Slope classes were within a range of approximately 0-11 degree where the trees were felled and processed. There were some small areas with more than 11 slope that would not have affected the operation of the harvester. Since LiDAR points provide GPS (Global Positioning System) locations, DSM was used to match field measured tree locations (GPS). The DEM was subtracted from DSM in order to acquire tree heights. 2.3 Time study data Harvester selection A harvester (Valmet 475 with a Rosin 997 harvesting head) fitted with DASA Control Systems computer was used to carry out the harvesting operation. The harvester head was properly calibrated at the beginning of the operation in order to accurately record tree measurements. The harvester is designed to perform harvesting operation up to 20 slope and to efficiently handle trees with DBH up to 80cm Harvesting operation recording The harvesting operation carried out by an experienced operator was recorded by a video camera from approximately 20m distance under normal and sunny weather condition on 03 February, 2011 between 11:05:40 am to 12:55:30pm. One hundred and one trees were felled and processed in the operation. Data on time elements and tree characteristics were collected for all trees Time elements extraction The video was played with MS Windows media player and time in centi-second for each work element was recorded with PDA (Personal Digital Assistant, also known as hand-held computer)-based Timer Pro Professional software. Moving / Positioning of the machine, Tree felling, Processing of each log, Stacking / Bunching, Travel Time, Brushing and Clearing were considered as work elements. 3

4 Description of time elements: Moving: Begins when the harvester starts to move and when it stops moving to perform some activity. Positioning: This is the time between the boom starting to swing toward a tree and machine head is clamped on the tree. Tree Felling: This is the time between when the felling starts and the tree touches the ground. Tree Processing: This is the time between the harvester head starts to run and the last processed log is dropped onto the ground. Stacking / Bunching: This is the time between when the harvester grabs a log and drops it onto the pile. Brushing/ Clearing: This is the time taken to process unmerchantable trees and clear undergrowth. Travel time: This is the time to travel to and from where the harvesting took. Delay time (Mechanical): This is the time that occurs due to mechanical failure. Delay time (Personal): This is the time that occurs due to operator s personal activity Tree characteristics extraction The DASA onboard computer system, fitted to the harvester was used to measure tree characteristics including tree DBH, log length and volume of processed logs and GPS tree location. Merchantable tree length (m) and volume (m 3 ) for each tree were estimated by summing lengths and volumes of all logs of the relevant trees respectively. Unmerchantable top end of each tree was ocularly measured (average length, 1.75m) and added to the merchantable tree length of the same tree in order to estimate whole tree height (m) of each tree Productivity model development Productivity was calculated individually for each tree using the following formula- Productivity (m 3 )/ PMH0 = tree volume (m 3 ) / cycle time (PHM0) * 60 Where tree volume was extracted from the DASA computer system and cycle time is the time spent by the harvester to completely process an individual tree. Linear regression [Y = a + b(x)] analysis was performed to predict productivity of the harvester where X is the independent variable, tree volumes (m 3 ); Y is the dependent variable, productivity (m 3 /PMH0) and a & b are the coefficients. Statistical software Minitab16 was used to derive the productivity model. In the analysis natural log (LN) of volume was used to fit the data Volume model development The same software package and linear regression equation [Y = a + b(x)] was used to predict individual tree volume with the exception that X is the independent variable, tree heights (m); Y is the dependent variable, tree volume (m 3 ) and a & b are the coefficients. 3. Results and Discussion 3.1. Productivity prediction No relationship was found between tree volume and harvesting time elements other than felling and processing. Therefore these time elements (moving / positioning of the machine, stacking / bunching of logs, travel and brushing and clearing) were averaged separately and added to measured felling, and processing time for all trees to estimate pro rata harvesting cycle times (Nurminen et al. 2006). Productive time was defined as machine operating hours excluding delay time (PMH0). 4

5 Summary statistics of tree characteristics and cycle time are presented in Table 1 and Table 2 respectively: Table 1: Tree characteristics of the study area Attribute Mean Std. Dev. Min. Max. Tree height(m) DBH(cm) Tree vol.(m3) Table 2: Pro rata cycle time statistics Pro Rata Cycle Time (min.) Mean Standard Deviation Minimum Maximum Figure 1 represents the productivity model for tree volume (m 3 ) vs productivity (m 3 /PMH0): Productivity (m3/pmh0) = LN (Tree Vol (m3)) 175 S R-Sq 61.0% R-Sq(adj) 60.6% Productivity (m3/pmh0) LN (Tree Volume (m3)) Figure 1: Productivity (m 3 /PMH0) (pro rata time) against tree volume (m 3 ) Linear regression based productivity model describes a good fit to the data (R 2 = 61.0%) (Figure 1) and RMSE (Root Mean Squared Error) of The general trend of the model shows that productivity increases with the increase of tree volume. This result is consistent with other studies (e.g. Jirousek et al. 2007; Nakagawa et al. 2007). The study was confined to tree volumes from 0.78m 3 to 3.13m 3. Therefore, the model may not be suitable to predict productivity beyond this volume range (e.g. greater than 3.5m 3 ). In addition, this model is based on a single data set. To achieve predictability for larger trees (volume) further study is required which would be based on a wider range of tree volumes and more study sites. 5

6 3.2. Volume prediction The model predicting volume from field measured height was developed to predict volume from LiDAR-derived height, because LiDAR can directly derive only height. Figure 2 represents the model predicting volume (m 3 ) from field measured height: Volume (m3) = Height (m) 3.0 S R-Sq 29.4% R-Sq(adj) 28.6% 2.5 Volume (m3) Height (m) Figure 2: Volume (m3) prediction from height (m) The model shows that volume increases with increasing height. However, the model describes only 29.4 % of the variability in the data (R 2 = 29.4%) of the data. Thus the model indicates that tree height alone may not be a good predictor of volume. However, Holmgren et al.(2003) estimated stem volume with somewhat lower accuracy from LiDAR-derived tree height and stem number as predicting variables. This model is limited to a tree height range of 20 to 36m (approximately) and volume range 0.78 to 3.15m 3. This model may not be suitable for tree sizes out of this range. Because, the rate of increase in machine productivity decreases with increasing tree size with Visser et al. (2009) finding that beyond a sweet spot further increases in tree size reduce productivity as the extra time to cut and process the stem outweighs the volume gain. 4. Data validation This model [Volume (m 3 ) = Height (m)] would be used to predict volume (m 3 ) from LiDAR-derived height. In order to check the accuracy of LiDAR-derived height, LiDAR-height class was compared with field height class. LiDAR-derived tree heights were measured from the LiDAR points surrounding field measured trees. Figure 3 represents the distribution of height from field measurements and LiDAR: 6

7 Figure 3: Field and LiDAR-tree distribution (raster format) Lidar was taken in 2007 and this height has been adjusted at the time of harvesting operation (2011) using the yield table produced for the study area (Lewis et al. 1976). It was found that the predominant height increased by 1-1.5m. The result shows that the mean LiDAR height range (approximately 20-34m, including adjustment) is similar to field measured height (20-35m). Underestimation of LiDAR-derived mean heights is consistent with other studies as LiDAR pulses rarely hit the tip of the tree (e.g. Magnussen and Boudewyn 1998; Heurich 2008). LiDAR-derived tree height or crown height may not be a reliable predictor for volume estimation (Figure 2). LiDAR-derived tree height and crown diameter can used to predict individual tree stem diameter and then tree height and stem diameter can be used to calculate stem volume (e.g. Persson et al. 2002). LiDAR-derived crown width (Hyyppa and Inkinen 1999; Hyyppa et al. 2001) may enhance its ability to estimate tree volume, because crown width or crown height is highly correlated to DBH (Jakobsons 1970; Sprinz and Burkhart 1987; Gill et al. 2000; Peper et al. 2001). DBH is a function of tree height and tree height can be derived from LiDAR, thus volume of gross-merchantable timber can indirectly be modelled from LiDAR tree heights (Lim et al. 2003b). 5. Conclusion The methods for estimating productivity based on field measured volume data showed greater predictability. Therefore, LiDAR-derived tree volume may be used to estimate productivity. Volume prediction from height alone shows poor predictability. Therefore it is suggested that DBH and / or crown width functions combined with height should be established for more accurate tree volume prediction and hence better estimation of harvester productivity from LiDAR data. Acknowledgements The authors would like to thank Cooperative Research Centre for Forestry (CRC for Forestry) for providing fund for the project and other supports. We also would like to thank Forestry South Australia for organizing harvest operation and providing LiDAR data. References 1. Baltsavias, E.P., A comparison between photogrammetry and laser scanning. Photogrammetry and Remote Sensing, 54,

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