Correlation Between Tree Size and Disc Saw Speed During Felling Using a Wheel-Mounted Feller-Buncher

Transcription

1 Correlation Between Tree Size and Disc Saw Speed During Felling Using a Wheel-Mounted Feller-Buncher Timothy McDonald 1, John Fulton 2, Tom Gallagher 3, Mathew Smidt 4 Abstract The sounds emitted during felling of 37 pines in a 15-year-old plantation were recorded. Trees ranged in size from 8 to 24 cm DBH and were harvested using a Tigercat 720E wheeled fellerbuncher. Sounds were recorded using a custom Labview VI at a rate of 44.1 khz and were analyzed to identify methods to accomplish two purposes: 1) distinguish cutting from not cutting, and 2) correlate cutting sound features with tree size. It was found both objectives could be accomplished using a frequency domain approach involving principal component analysis of spectral data. Identification of cut/not-cut sound sequences was accomplished with error rates of 92% and 88%, respectively, using a single principal component. Prediction of DBH from principal component scores of cutting sounds was possible, but R 2 values were low, on the order of 0.4 for the range of diameters tested. Keywords: precision forestry, yield mapping, principal component analysis Introduction The ability to measure tree size during cutting would be beneficial in at least two applications in tree-length harvesting. It would be useful, first of all, in tracking productivity of a harvest system over time. Data on tree size at the time of cut would be useful in directly measuring the productivity of a feller, but, combined with data on bunch sizes, could also be used to quantify skidder productivity. Secondly, size data would also be useful in maintaining a running inventory of wood in a stand, showing how much was on the ground, in transit, and waiting to be felled. These data would feed directly into planning operations plus provide a tool for reconciling the amounts hauled to mills with cruise information. Finally, on a more speculative note, knowledge of spatial distribution of tree size may prove important in optimizing timber management in the future much as spatial yield information has revolutionized farming. Actually developing a system to measure stem size in tree-length harvesting has proven difficult, especially for sawhead feller-bunchers. McDonald and others (2003) reported on a method using lasers to time the motion of trees into a felling head to measure size, but the measurement required a great deal of instrumentation be mounted on the felling head itself, which proved to be less than reliable. More recently, McDonald and others (2012) reported on using hydraulic pressure in a shear head feller to measure tree size. The approach was reasonably accurate (R 2 = 1 Associate Professor, Biosystems Engineering, Auburn University, mcdontp@auburn.edu 2 Professor, Biosystems Engineering, Auburn University, fultojp@auburn.edu. 3 Associate Professor, School of Forestry and Wildlife Sciences, Auburn University, gallatv@auburn.edu 4 Associate Professor, School of Forestry and Wildlife Sciences, Auburn University, smidtmf@auburn.edu. 37th Council on Forest Engineering Annual Meeting Moline, Illinois 1

2 0.63) over a fairly broad range of diameters, but there are relatively few shear machines operating in the South and the method would not likely be widely applicable. Measuring tree size on a sawhead feller is complicated because of the harsh working environment. It is difficult to envision something not built into the machine itself that could survive any length of time and measure tree size directly, but there are also indirect methods by which size could be determined and also be built to survive operating in the woods. The simplest of these could be the use of sound that can be measured easily and cheaply at a location some distance from the saw itself. Most people having heard a sawhead feller working would probably guess that sound would be somehow correlated with tree size, but so far no one has reported on its use for measuring diameter. Our goal in this study was to investigate methods for detecting tree diameter using sound recorded while operating a sawhead feller-buncher in a pine plantation. The objectives were twofold: 1. Identify a relationship between frequency elements of feller sounds to predict tree size, and 2. Develop a rudimentary algorithm for extracting size data from felling sounds that could be implemented in a production environment. Methods A simple sound recording program was built using Labview and its custom sound recording VI. The user interface consisted of a button to start recording, an indicator to show sound was being captured, and a waveform plot to verify the validity of the sound data. The application was run on a Windows 7 tablet computer having an external sound input port into which was plugged a small electret microphone. The computer was put into the cab of a Tigercat 720E feller-buncher equipped with a 5000 felling head and the microphone was taped to the top of the cab. The pictures in Figure 1 show the microphone placement and the condition of the teeth in the sawhead. Figure 1. Mounting of the microphone on the roof of the feller cab (left, center) and the condition of the saw s teeth (right). The data collection software was programmed to record sound for 30 seconds at a time. The operator of the machine was instructed to drive up to the first tree in the row where he was operating, hit the Start button on the software interface, then collect a full head of trees. The 37th Council on Forest Engineering Annual Meeting Moline, Illinois 2

3 software recorded for the designated time, wrote the sound data to a file, and then displayed the recorded sound for verification. The 30-second interval was chosen because it was long enough to record felling sounds for most stems cut in a cycle but be complete and ready to record again by the time the operator had dropped the bunch and was ready to continue. Trees in a 15-year-old pine plantation were numbered and measured for DBH as part of another study. The stand was being 3 rd -row thinned and trees were removed from rows as part of that test, but those trees remaining in the row after a final, complete bunch were cut were left for recording sounds. The feller operator drove to the end of each cut row, started the recording software, felled the remaining trees, dropped them, and then moved on to the next row. A total of eight row ends were cut in this manner, resulting in sound data for 37 trees ranging in size from 8 to 24 cm DBH. Mean diameter was 18.7 cm, with standard deviation of 3.3 cm. Frequency information from recorded sounds was evaluated using custom programs written in Python and the `Numpy package. Principal components analysis was done using the `prcomp command included in the `R statistical software system. All regressions were calculated using the `lm R command. Results Cut Duration The graphs in Figure 2 show sounds captured while felling two different stems, one of 16cm DBH, the other 19cm. The onset of cutting was visible as a change in sound intensity in each graph, and the vertical blue lines drawn in each were the times judged to be the start and end of the period during which the saw was in contact with the stem. These start and end times were visually (and audibly) assessed for the sounds recorded for each stem and their difference (the cut duration) was plotted with DBH (also Figure 2). There was a significant linear relationship observed between cut duration and DBH, but the variability was relatively high (R 2 = 0.30) and the result was judged not likely to be a good predictor of tree size. It was felt the cut duration was probably affected by factors independent of tree diameter, such as forward speed of the machine. Speed would likely be affected by terrain or ground conditions and might therefore not be replicable between stands. Other measures related to diameter were therefore sought. Frequency Shift An audible downward shift in the dominant frequency was often heard when listening to the sounds made during severing of the stems. This effect was illustrated in Figure 3a, where the spectrogram over time of the tree in Figure 2a was plotted. Vertical lines indicated the beginning and end of contact between the saw and the tree (same as shown in Figure 1), and the horizontal line showed a constant 340 Hz frequency level. This frequency was the nominal rate at which the saw teeth made contact with an object and was calculated based on the number of teeth on the blade (16) and the rotational speed of the saw (1250 rpm). 37th Council on Forest Engineering Annual Meeting Moline, Illinois 3

4 (a) (b) (c) Figure 2. Plot of sound intensity over time for two trees. The tree on the left was 16 cm (6.3 ) in diameter, and on the right 19 cm (7.5 ). The blue vertical lines show a visually determined estimate of the start and end of the period of time during which the saw was actually severing the stem. On the right is a plot of cut duration as a function of stem diameter for all trees in the study. It was clear there was an observable decrease in sound energy at the frequencies near 340 Hz during saw contact with the tree. It was felt the magnitude of this decrease in frequency might be correlated with tree size. To measure this correlation, cutting sounds for each stem were isolated and the first and last 2048 samples of the recordings were extracted. Each of these sounds was low-pass filtered (500 Hz cutoff frequency) and their spectrograms plotted. The frequency at which the sound energy peaked nearest to 340 Hz was recorded for each sample. The difference between these two was taken to be the drop in speed of the saw during the cutting process, and those values for each stem with DBH were plotted in Figure 4. The result was statistically significant (P < 0.01) and the R2 = 0.53 was higher than for the duration data. This result indicated a relatively clear relationship existed between saw speed droop during cutting and tree size. The problem remained, however, of how to actually calculate this value in practice without intervention from a human observer. (a) (b) Figure 3. Spectrograms for the sound data plotted in Figure 2. The vertical lines show the visually assessed start and end of cutting, and the horizontal line shows the anticipated sound frequency of saw/stem interaction (340 Hz). Distinguishing Cutting from Not-Cutting Sounds To measure frequency shift during cutting, it was first necessary to identify the beginning and end of a cutting cycle in a continuous sound recording of feller activity. There were two obvious approaches to solving this problem: a) sound intensity, and b) sound frequency information. From the plots in Figure 2, it was clear cutting sounds were louder than non-cutting. A simple 37th Council on Forest Engineering Annual Meeting Moline, Illinois 4

5 threshold should, therefore, suffice to distinguish the two classes of activity. Without a lot of data to evaluate this phenomenon, however, there was no way of knowing the consistency of the threshold value, whether it would be stable over time, or change with surroundings, or tooth wear, for example. And, because frequency information would be needed to measure tree size anyway, it was decided to determine if such frequency data might also be used in distinguishing cut/non-cut sounds. It was observed that, besides being in general louder, there was also a difference in sound quality between the two that might be exploited for that purpose, and that sound quality change might be detected in the spectral response of the sounds. Figure 4. Plot of the shift (start of cut to end) in peak frequency for the sound component closest to 340 Hz as a function of stem diameter. To test this approach, felling sounds for each stem were divided into two parts: those during cutting, and those not during cutting. For each class of sound data, 2048-sample power spectra were calculated sequentially over the duration of the data and averaged. The first 50 samples (representing a range in frequency of from 0 to 1055 Hz) of these averaged power spectra were then recorded. The dimensionality of the frequency spectra was reduced using a principal components analysis. The largest principal component was found to discriminate between cut and non-cut sounds with an accuracy of 92%, sensitivity of 88%, and specificity of 97%. Combined with intensity information, we felt it was very possible to build a robust tool for discriminating the two classes of sounds using this approach. Predicting Diameter Given they could be distinguished, the next question was how well could these frequency data be used to predict diameter? The spectra from the cut sounds were reduced using principal components analysis to the eight largest and a linear model fit between scores for each tree and diameter. The result was significant (P < 0.02) with two terms (principal components 2 and 7) being significant at the alpha = 0.1 level, and the third principal component significant at alpha = Figure 5 is a graph showing the model predicted and measured diameters for two models, the first using three principal components, and the second using only one. Results indicated the 37th Council on Forest Engineering Annual Meeting Moline, Illinois 5

6 3-component model had higher adjusted R 2 (0.4) than the single component model (0.33), both being less than found using the manual method outlined above. Figure 5. Graph of predicted and measured DBH. Predictions were made from regression models built using either one or three principal components. Both models were significant, with R 2 values of 0.33 and 0.4 for one and three PCs, respectively. Discussion Listening to the sounds made by the felling saw on a feller-buncher it seemed obvious there was some relationship between sound and tree size one could simply hear the difference between a large and small tree. When confronted with the task of calculating some measure indicative of tree size from a sound recording, however, the answer was not as simple as it first appeared. The examples shown in Figure 2 illustrated the issues involved. The time-recordings of sound for both trees appeared visually to be very similar, but the second sound was from a larger tree, by about 3cm (11%). To detect a 3 cm dbh class difference between trees, this should have been about the minimum difference we should be able to discriminate but, at least from appearances, there was no obvious characteristic of one sound to distinguish it from the other. This fact lead directly to frequency domain analysis as changes in sound during the cutting process should have shown up more clearly in that realm, as opposed to the time domain. Indeed, as seen in Figure 3a, there was often a clear shift in sound energy to a lower frequency at a speed we could clearly associate with the physical process of cutting. In Figure 3b, however, the same shift in frequency was not as clearly evident, perhaps even nonexistent. This illustrated the difficulty in using sound as a yardstick for measuring tree size many independent factors influence sounds generated and no single yardstick, or measurement, was identified to provide a suitably accurate means of separating trees into meaningful size classes. 37th Council on Forest Engineering Annual Meeting Moline, Illinois 6

7 A better approach might have been to combine all the yardsticks into a single model. To test this we assembled the five highest individual correlates with DBH observed in the study and used them all to collectively predict tree size. The five variables were cut duration, frequency droop, and the three principal components of the sound spectrum explaining the highest amount of variability. A regression analysis showed that only two of the variables were significant in predicting DBH: the frequency droop (P < 0.001), and one principal component (P < 0.015). The model was significant (P < ) and the adjusted R 2 was 0.58, but this model was only marginally better at explaining variations in sound as a function of DBH than the model built from frequency droop alone. Conclusions There was a significant linear relationship found between various measures of tree cutting sounds and stem DBH. The measures having the highest correlation were those from a frequency domain analysis of the sounds occurring during the actual severing of the stem. The highest correlations explained about 58% of the variability in DBH. Overall, the approach of using sound to predict tree size showed promise, but the study presented in this paper suffered from a dearth of data and needs to be expanded greatly to prove the concept would work in general. In particular, the range of diameters measured was small, with all but one stem in the cm DBH range. The results also did not indicate a high degree of accuracy in predicting diameter using the sound information as in this report. Further work needs to be conducted on refining the methods to narrow confidence intervals for diameter predictions to an acceptable level. This will likely require using other data, in addition to sound, in a multi-criteria model. Other information may be available from the machine, including hydraulic pressures in cylinders, or saw rotational speed or pressure, that might be used to improve estimates. Literature Cited McDonald, T. P., R. B. Rummer, and T. E. Grift Diameter sensors for tree-length harvesting systems. In 2nd Forest Engineering Conference. M. I. Wide, and B. Baryd, eds. Växjo, Sweden: Skogforsk, Uppsala Science Park, SE UPPSALA, Sweden. McDonald, Tim; Fulton, John; Pan, Pengmin Mapping machine productivity and tree size of a feller-buncher harvesting biomass in pine plantations. Presented at the 35 th COFE Annual Meeting September, New Bern, NC. 37th Council on Forest Engineering Annual Meeting Moline, Illinois 7