A new application of ICT for operational monitoring in forestry

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1 Ref. C0283 A new application of ICT for operational monitoring in forestry sector Raimondo Gallo, Toni Untherhofer and Fabrizio Mazzetto, Faculty of Science and Technology, Free University of Bozen-Bolzano, Piazza Universitá 5, Bolzano, Italy. Abstract A new approach for the operational monitoring for implementing the performance control of forest mechanisation chains is here proposed and discussed. The present study aims to develop a new approach of Precision Forestry. This innovative tool is developed around a GNSS device the core of data-logging - and a specific inference engine. Thanks to the inference engine, process time, work distance, forward speed and number of working cycles in forest operations were assessed. In order to perform this assessment, the GNSS device was installed directly on the equipment to be monitored. The study areas were set in the Autonomous Province of Bolzano (N-E of Italy). The logging system applied in the different study areas was the same, despite the different forest characteristics and the different terrain morphology. The tower yarder system one of the most used logging systems in the alpine regions was the machinery assessed during this study. This machinery is composed by a power unit and a carriage. The carriage is the component which carries out the harvesting operations, transporting the biomass from the felling land to the log land. The forest operations through the use of the tower yarder are composed of five working phases: outhaul empty, hook, inhaul and unhook. The study consisted of recording and elaboration of the times detected by the GNSS device at each position detections, their elaboration through the inference engine and finally their validation. Indeed, simultaneously, an integration of the GNSS information with a time study of work elements based on the continuous time methods supported by a time study board were performed. All the recorded GNSS data were integrated with the work elements study. Thanks to this new operational monitoring approach it was possible to detect all the carriage s movements. It was therefore possible to monitor the entire working cycles as well as each elementary working phase. As far as the entire working time is concerned, the comparison between the results obtained through automatic and manual study presented good correlation values. The absolute differences between the elemental times automatically recorded with those manually logged by the clock were below 10%. Keywords: Precision Forestry, forest operation performance, operational-monitoring, time study, cable logging 1. Introduction As in agriculture, also in the forest sector the information technology has been introduced with the aim to improve the efficiency and performance of the sector. Indeed the Precision Forestry is the approach that employs the strategies and methodology of older Precision Agriculture and Precision Farming to the forest sector in order to solve issues of decision support system (DSS) (Lubello & Cavalli, 2006), and improve the efficiency and the performance of the working procedures. The Precision Forestry, starting from the well-established Precision Agriculture, presents some research topics not yet completely explored. One of these is the study of operational monitoring in automatic ways. The aim of this study is to detect the operative times through the use of a GNSS unit, one of the most used devices applied in Precision Forestry. The operational time study is considered as a set of procedures for determining the amount of time required, under certain standard conditions of measurement, for tasks involving some human, machine, or combined activities Proceedings International Conference of Agricultural Engineering, Zurich, /8

2 (Mundel & Danner, 1994). At this regard, the operational time study is an important parameter for the assessment of the productivity. Indeed, knowing the time spent for each operation it is possible to evaluate which is the efficiency of the entire work chain (machineries and humans activities), the singular equipment or the crew. The analysis of the operational times, in the forestry sector, is usually done thanks to the use of manual chronometers time board with three analogical chronometers (Berti, Piegai, & Verani, 1989), or digital chronometers (Manzone, 2012), both require the use of stopwatches and paper (Wang, McNeed, & Baumgras, 2003). Otherwise, a data collector developed for industrial management, based on handheld computer with specifics software (Spinelli & Kofman, 1995; Visser & Stampfer, 1998) was used. Also an automatic operational time study based on the installation of GNSS on machineries that operate in forest was developed. This methodology is mainly applied for the tracking of the movements of the machineries for felling and skidding operations in forest (Taylor et al., 2006; Cordero R., et al., 2005; McDonald and Fulton 2005, McDonald et al., 2001, Taylor, et al., 2001; Veal, et al., 2001; McDonald 1999) and for tracking the trucks for timber haulage (Simwanda et al., 2011; Devlin and McDonnell, 2009; Sikanen et al., 2005). Only a document on the use of GPS for the analysis of tower yarder was found (Nitami et al., 2011). The target of the research is to develop a methodology, as well as algorithms, for acquisition, elaboration and interpretation of the data autonomously. The methodology must recognize and relief the number of cycles of work, each elementary phase and also the respective operational time study. To do that dedicated algorithms were developed. 2. Material & Methods The four study-cases were set in a typical alpine forest of spruce (Picea abies L.), where operations of low thinning were organized. The canopy coverage was always present. The logging operations were organized in the Autonomous Province of Bolzano (N-E of Italy) in a forest managed by the State-Owned Forest Company (Fig. 1). A mobile tower yarder with crane and processor head configured with a standing skyline rigged with a radio controlled motorized slack pulling carriage was the monitored equipment. The forest machine was the Koller K507, a mobile tower yarder with crane and processor-head. Whereas the carriage was a Koller MSK3, with 3 tons of load capacity. The data-logger used for the automatic time study was a topography GNSS mobile device installed on the cable yarder s carriage. The unit is an ASCTECH MobileMapper 6, a 12- channel singular frequency device; which runs Windows Mobile 6, and the specific software MobileMapper Field on a 400 MHz cpu. The GNSS detects only GPS satellite signals. To protect the GPS unit from accidental shocks or hits, a plastic box filled with foam was built. The box was fixed at carriage s frame thanks to a couple of plastic ties (fig. 2a). An external antenna was also connected to the GPS unit in order to assure a better satellite s signal reception. The external antenna was placed on top of the frame by means of a magnet. Meanwhile, the connection cable was fixed to the lateral frame of the carriage with scotch tape in order to avoid any entanglement with branches (Fig. 2b). The device was fixed in the safest place, while the antenna in the place where the sky visibility was best. For the automatic time study the GNSS device was set for an automatic acquisition of the carriage s position every 2 seconds. At every long recovery period and at lunch break the carriage was dropped down in order to save the recorded data and restart with a new acquisition session. The validation of these data was done comparing the automatic time study with the manual time study. In parallel with the automatic survey, a manual time study has been performed in order to validate the data collected by the GPS unit following the continuous time methods. Due to the characteristics of the harvesting site, as well as the distance between the felling and landing areas, the survey required the presence of two surveyors. One of those placed close to the felling site and the other one placed on the landing site. The communication between the two surveyors was done through walkie-talkie. For the manual time study a digital stopwatch was used. The operational monitoring measured in minutes is based on the identification of the following elementary operations as well as the calculation of the gross operative time: Outhaul: when the carriage starts to move from the download area to when it stops along the line for starting the hooking phase; Proceedings International Conference of Agricultural Engineering, Zurich, /8

3 Hooking: from when the carriage stops the outhaul to the unlock of the carriage; Inhaul: from the unlock of the carriage to the next lock in proximity of the tower yarder for the downloading operations; Unhooking: from the lock of the carriage and start of the download operation to the unlock for the start of the next cycle. The architecture of the approach here proposed is composed by a set of procedures that perform, in an automatic way, tasks of interpretation, monitoring, diagnosis, controlling, planning and reporting the performance of the logging activities monitored. Aim of the approach is to understand if the working operations have been performed in the best way. The overall structure of the system consists in the data detection, data analysis and user interface (Fig. 3). The procedure starts with the raw data collection through the GPS, which, besides recording geographical coordinates, it also records the time of each detection. A dedicated inference engine elaborates all the data collected. The inference engine is a tool able to transform, through a set of logical, statistic and physical algorithms, raw data into intelligible management information, such as messages that can be used to make decisions or to perform controlling tasks (Mazzetto, et al., 2012). Since the automatic system operates without any external data input, it must be able to perform all the operations by itself. First of all, the inference engine recognizes the logging direction analysing the direction of the set of points collected after the first one detected by the GPS unit. Then, knowing the logging direction, the inference engine proceeds with the determination of a starting point of reference P r. The coordinates of P r has been attributed considering the maximum or minimum coordinates of the points of the cloud in the proximity of the initial point. When the referencing point of the system has been determined, it is possible to define several parameters. The inference engine calculates the distance from the starting point, which almost corresponds with the yarder of the carriage at each detection, the instantaneous speed (Fig. 4a), the distribution of the speed frequencies (Fig. 4b) and finally the working cycle analysis. The analysis of the working cycle has been carried out considering the relationship between progressive time and the distance from the starting point P r. For this elaboration the inference engine recognizes those points at which one elementary phase concludes and the next one starts. The time necessary to carry out every singular phase has been calculated by subtracting two subsequent progressive time values (Fig. 5). 3. Results & discussion During this study a total of about 41 hours (2450 min) of effective manual time study (M_TS) were performed and an amount of 228 working cycles have been detected. Out of the total working cycles detected by the automatic procedure (A_TS), 220 cycles were successfully recognized. Hence, for gross time study, the automatic data acquisition system performed well, recognizing the 96.5% of the working cycles (Tab. 1). In the study cases carried out in Smudres and Carezza the missed cycles happened in the first part of the afternoon, mainly between 1pm to 3pm. This lack of information has been due to a not sufficient number of available satellites during the monitoring. This aspect has been confirmed by GPS data almanac. The presence of these missed data is the cause of a time discrepancy between the two systems respectively of and 4.5 minutes. The first one consists in an overestimation of 6%, while the second one in an underestimation of 0.6%. This time difference is in line with those recorded in the first two data-set. If the missed cycle were not considered, the differences recorded between manual and automatic time study would be below the 1% (Fig. 6). Very good correlation values have been obtained. For the analysis of the elemental time study the missed cycles have not been considered in the result s discussion (Tab. 2). In this analysis, the differences between the automatic and manual time study are slightly higher than in the gross time study. The correlation values obtained from the comparison between the automatic and manual time study has shown very good results. In general the R- square has a value greater than 0,70, just in two cases the value is lower. This happens in one of the data-set where missed cycles are present. Nevertheless, considering the absolute values, the differences have never been higher than 10%. The maximum difference values have been recorded in Aica: inhaul operation, equal to- Proceedings International Conference of Agricultural Engineering, Zurich, /8

4 9.8% (8.7 sec of overestimation) and Smudres: unhook operation, equal to 9.1% (6 sec of underestimation) (Fig. 7). Anyhow, for this purpose, these values of differences are acceptable. These happened in a logging site (Aica) where only one surveyor was present. During the manual time study some errors of the working phase identification were done: the distance between surveyor and felling area, as well as the presence of the canopy, sometimes, did not permit a clear recognition of the inhaul starting point. Meanwhile the differences recorded during the unhook phases in Carezza data-set were due to the high number of cycle slips phenomena happened during the monitoring. This phenomenon happens every time when the GPS receiver performs a recalculation of the position because it catches or loses satellite signals. 4. Conclusion This experiences obtained very interesting and important results for further improvement of the assessment methodology. This initial part of the research has shown that the use of GNSS devices presents a very interesting feasibility for performing operational time monitoring in forest logging operations. All operative cycles and all elementary operations have been exhaustively recognized. The recorded difference could be considered acceptable. In order to increase the accuracy of the automatic time relief it could possible to employ GNSS devices that reach signals from both satellite constellation: GPS and GLONASS. 5. Acknowledgements The Authors wish to thank the State-Owned Forest Company of the Autonomous Province of Bozen for their support and collaboration in this research. 6. References Berti, S., Piegai, F., & Verani, S. (1989). Manuale di istruzione per il rilievo dei tempi di lavoro e delle produttività nei lavori forestali. Firenze: Quaderno dell Istituto di Assestamento e Tecnologia Forestale dell Università degli Studi di Firenze. Cordero, R., Mardones, O., & Marticorena, M. (2006). Evaluation of forestry machinery performance in harvesting operation using GPS technology. Precicion Forestry in Plantations, Semi-Natural and Natural Forest. Stellenbosh, South Africa: Stellenbosh University. Devlin, G., & McDonnell, K. (2009). Assessing Real Time GPS Asset Tracking for Timber Haulage. Open Transportation Journal(3), Lubello, D., & Cavalli, R. (2006). Ambiti applicativi della Precision Forestry. Sherwood Foreste, Manzone, M. (2012). esbosco del legname per via aerea, valutazione delle prestazioni di un carrello forestale motorizzato autotraslante. Sherwood Foreste(185), Mazzetto, F., Secco, P., & Calcante, A. (2012). Algorithms for the interpretation of continuous measurement of the slurry level in storage tanks. Journal od Agricultural Engineering, 1(43), McDonald, T., Rummer, B., & Taylor, S. (2001). Automating time study of feller-bunchers. Technologies for New Millennium Forestry. Kelowna (British Columbia), Canada. McDonald, T. (1999). Time study of harvesting equipment using GPS-derived positional data. Forestry engineering for tomorrow, GIS technical papers. Edinburgh, Scotland: Edinburgh University. McDonald, T., & Fulton, J. (2005). Automated time study of skidders using global positioning system data. Computer and electronics in agriculture, 1(48), Mundel, M., & Danner, D. (1994). Motion and time study - improving productivity (7th ed.). Upper Saddle River: Prentice Hall. Nitami, T., Soil, S., Kataoka, A., & Mitsuyanna, T. (2010). Tower yarder operation in Japan and the performance analysis by GPS-based system. Pushing the boundaries with research and innovation in forest engineering. FORMEC 2011, proceedings of the 44th international Symposium of Forest Mechanisation. Graz, Austia. Proceedings International Conference of Agricultural Engineering, Zurich, /8

5 Sikanen, L., Asikainen, A., & Lehikoinen, M. (2005). Transport control of forest fuels by fleet manager, mobile terminals and GPS. Biomass and Bioenergy, 2(28), Simwanda, M., Wing, M., & Sessions, J. (2011). Evaluating global position system accuracy for forest biomass transportation tracking within varying forest canopy. Western Journal of Applied Forestry, 4(26), Spinelli, R., & Kofman, P. (1995). Cantieri agricoli e forestali, informatizzazione dei rilievi. Macchine e motori agricoli(11), Taylor, R., Schrock, M., & Staggenborg, S. (2002). Extracting machinery management information from GPS data. An ASAE Meeting Presentation. Paper (N ). Taylor, S., McDonald, T., Fulton, J., Corley, F., & Brodbeck, C. (2006). Precision Forestry in the southeast US. 1st International Precision Forestry Symposium. (p ). Stellenbosch, South Africa: Stellenbosch University. Veal, M., Taylor, S., McDonald, T., McLemore, D., & Dunn, M. (2001). Accuracy of tracking forest machines with GPS. American Society of Agricultural Engineers, 6(44), Visser, R., & Stampfer, K. (1998). Cable extraction of harvester-felled thinning: an Austrian case study. Journal of Forest Engineering(1), Wang, J., McNeed, J., & Baumgras, J. (2003). A computer-based time study system for timber harvesting operations. Forest Products Journal, 3(53), Fig. 1: Map showing the study-cases locations. a b Fig. 2: Installation of the plastic box on the carriage s frame (a), GNSS device and external antenna (b). Proceedings International Conference of Agricultural Engineering, Zurich, /8

6 Figure 3: Scheme of the proposed approach. a Instantaneous travel speed (m/s) Distance from Pr (m) b Frequency Frequency Cumulative % 6 100% 75% 50% 25% 0% Figure 4: Example of the distribution of the advancement speeds (a) and distribution of the speed frequency during logging operation (b). a b Distance from Pr (m) Progressive time (s) Figure 5:.Theoretical (a) and actual (b) working cycle analysis performed by the Inference Engine. Proceedings International Conference of Agricultural Engineering, Zurich, /8

7 Ita4 Ita3 Ita2 Ita1 0% 1% 2% 3% 4% 5% 6% 7% 8% 9% 10% Figure 6: Differences recorded from the comparison between automatic and manual gross time study. Unhook Inhaul Hook Carezza Smudres Funes Aica Outhaul 0% 1% 2% 3% 4% 5% 6% 7% 8% 9% 10% Figure 7: Differences recorded from the comparison between automatic and manual elemental time study. Proceedings International Conference of Agricultural Engineering, Zurich, /8

8 Data-set Working cycle Gross time (min) Difference Average (min) Difference St. Dev. A_TS M_TS A_TS M_TS (min) (%) A_TS M_TS (min) (%) A_TS M_TS R 2 Aica Funes Smudres Carezza TOTAL Table 1: Summary of gross time of working cycle for each dataset. Data consider dataset with missed cycles. Data-set Aica Funes Smudres Carezza Operation Average (min) Difference St. Dev. (min) R 2 A_TS M_TS min (%) A_TS M_TS Outhaul % Hook % Inhaul % Unhook % Outhaul % Hook % Inhaul % Unhook % Outhaul % Hook % Inhaul % Unhook % Outhaul % Hook % Inhaul % Unhook % Table 2: Statistics of the elemental time study recorded during the cable logging working cycles. Missed cycle do not considered. Proceedings International Conference of Agricultural Engineering, Zurich, /8