Using GPS to evaluate productivity and performance of forest machine systems

This paper reviews recent research and operational applications of using GPS as a tool to help monitor the locations, travel patterns, performance, and productivity of forest machines. The accuracy of dynamic GPS data collected on forest machines under different levels of forest canopy is reviewed first. Then, the paper focuses on the use of GPS for monitoring forest harvesting and site preparation equipment. Finally, the paper discusses future trends in precision forestry for intensive forest operations. INTRODUCTION The Global Positioning System (GPS) is being used in an ever-increasing array of applications for managing forests and our natural resources. When used on mobile forest harvesting machines, data collected from GPS and additional external sensors can improve forest engineering design and management decisions based on machine performance data as a function of terrain and timber stand variables. Applications employing GPS capabilities are being developed for use in site preparation, planting, and managing intensive culture plantations. Many of these applications stem from the successful integration of GPS into “precision agriculture”, which can be defined as managing crop inputs, such as fertilizer, herbicide, etc. on a site-specific basis to reduce waste, increase profits, and maintain the quality of the environment. Developments in GPS technology and precision agriculture are readily adapted to problems in forest operations, particularly in intensive forest production systems. This paper will review recent work at Auburn using GPS as a tool to help researchers and practitioners measure performance and productivity of forest machines. Also, future trends for precision forestry in intensive forest management will be discussed. ACCURACY CONSIDERATIONS IN GPS MACHINE TRACKING Before discussing applications for GPS machine tracking, it is important to understand the accuracy of dynamic GPS data collected in forest conditions. Most previous studies on GPS accuracy under a forest canopy were concerned with static positions. Spruce et al. (1993) used a typical mapping-grade GPS receiver and measured relative accuracy of travel patterns and velocities of a tractor operating in open sky and forest canopy conditions. They reported that GPS successfully tracked machines under open sky conditions; however, under forest canopies there was a major decrease in accuracy. Veal et al. (2001) further quantified accuracy of GPS position data collected on wheeled skidders. Two different commercially available GPS receivers (12 channel Trimble ProXR and six-channel GeoExplorer II) were used to track wheeled skidders under different canopy conditions at two different vehicle speeds (5.4 kph and 9.1 kph). Three different courses were established in a loblolly pine plantation under different forest canopy density conditions: open canopy (0% crown cover in a recent clearcut), light canopy (57% crown cover in a heavily thinned area), and heavy canopy (85% crown cover in a lightly thinned area). While the skidders traversed each course, locations of the tires were marked. Traditional optical surveying techniques were used subsequently to determine the actual tire track locations. These actual vehicle locations were compared to the GPS machine paths to determine errors. Maps from data collected by both receivers showed general travel patterns of the skidders; however, as canopy density increased, more discontinuities and irregularities were observed in GPS maps, especially for the GeoExplorer II. These discontinuities were attributed to multipath effects and to receivers switching satellite constellations. Figure 1 shows mean position errors in GPS data collected by both receivers under different canopy conditions. Position accuracy showed a decreasing trend as the canopy changed from open to heavy. For example, mean 3D position errors for the ProXR were 1.26 m, 1.77 m, and 3.76 m, for the open, light, and heavy canopy conditions, respectively. It is important to note that some of these errors are similar to the width of the machine. Tests conducted before and after deactivation of Selective Availability showed little differences in differentially-corrected GPS data. Finally, the machine speeds tested did not significantly affect accuracy of GPS positions for either receiver type. These results indicate that researchers or practitioners need to be cautious when relying on GPS to track forest machines in heavy forest canopies. For general knowledge on where machines travel, many mapping-grade GPS receivers, when using differential correction, will probably be sufficient. However, for more detailed studies of travel patterns or environmental impacts at specific points (e.g. soil compaction studies), typical mapping grade receivers may not have the level of accuracy needed. For detailed studies that require sub-meter or sub-centimeter accuracy, more sophisticated hardware and firmware will be needed. USING GPS TO MONITOR FOREST MACHINE SYSTEMS Several recent research efforts have been aimed at learning more about performance, productivity, and site impacts from forest harvesting machines and from site preparation equipment by using GPS to monitor machine movements. Traditional methods of studying machine productivity and site impacts required researchers to work in close proximity to machines to observe and videotape activities and travel paths of the machines and people. These labor-intensive methods pose safety problems for personnel involved in the study. To alleviate these problems, GPS receivers can be mounted on each machine of interest to determine travel paths and velocities of machines during operations. Additional sensors and data acquisition equipment also can be installed on machines to record information on machine functions or performance. Using these types of data allows researchers to quantify and model productivity or potential site impacts of individual machines or the entire machine system. Background McMahon (1997) used a GPS-based system to evaluate site disturbance of treelength harvesting systems by mapping paths of harvesting machines, then transforming the data to calculate area disturbed by machines. Thompson et al. (1998) used GPS to map the movements of tracked skidders in southeastern Australia. They produced maps of skid trail networks and measured traffic intensities (in terms of number of passes) over the network. Also, they estimated the time that the machine was performing each of six different machine cycle elements. Reutebuch et al. (1999) tried to determine time study data using GPS receivers on a feller buncher, a hydraulic shovel, and a tracked skidder. Machines were monitored so cycle distances and times could be calculated. Due to apparent large position errors (occasionally over 100 m), travel distances could not be calculated accurately. Stjernberg (1997) used the commercial Silvitracs system to map movements of site preparation equipment and develop coverage efficiency data for the equipment. Estimating Harvesting Impacts from GPS Machine Tracking In work at Auburn, McDonald et al. (1998a) developed a method to use GPS tracking data to determine the area impacted by a machine as it traveled over a site. The method, which was similar to one presented by McMahon (1997), used pairs of x,y position data to represent sampled locations of a machine, then assumed that machinery moved linearly between adjacent location samples. These x,y pairs were transformed into a map showing how many times the machine passed a given location. Final output of the transformation was a raster map, with cells in the raster having a value equal to the number of times the object, or machine, passed over a particular location in a rectangular region. The model was tested initially by using data collected from a rubber-tired skidder working in part of a clearcut harvest. Several features of the harvesting operation were discernable from the mapped paths: the deck or landing, the delimbing area, the main skid trail, and the return skid trails. They noted that the receiver type made a significant difference in the apparent accuracy of the maps. Although they did not conduct any detailed position error determination, they concluded that overall the calculated travel patterns matched the true machine movements closely enough for stand-level assessments. In a later study, McDonald et al. (1998b) and Carter et al. (2000a) used the same methods to map the travel paths of feller bunchers and skidders over an entire harvest tract. The output from this study was a traffic map of cumulative totals of traffic intensities and their distribution in the tract. Figure 2 shows the traffic intensity map resulting from this study. They found that 25 percent of the stand received no traffic, 25 percent received more than five tire passes, and 50 percent received one to five tire passes. When visual disturbance assessment methods were compared with GPS estimated traffic intensities, the visual methods overestimated the presence of heavily trafficked areas. They noted that the GPS-based method was superior to the traditional methods because it was less time consuming and presumably more accurate. Carter et al. (1999, 2000a, 2000b) presented detailed results of the soil physical responses measured during the study introduced by McDonald et al. (1998b). They assessed the impact of traffic intensity on spatial variability of soil physical properties by measuring changes in the properties at select points that corresponded to estimated traffic intensities within the harvest tract. They found that bulk density and cone index responded to increased traffic intensities and achieved peak values after a limited number of passes. This ability to compare detailed data on traffic intensities with soil strength properties would not have been possible without integration of GPS into the impact assessment process. Harvesting Machine Tracking – Automa