# Use of Real-Time GNSS-RF Data to Characterize the Swing Movements of Forestry Equipment
## 5 Conclusions  
Both 2.5 and 5.0 s transmission intervals correctly characterized cycles and cycle elements in the rotation movements of log loaders. However, the 2.5 s interval was the most successful in allowing for the prediction of accurate swing angle measures. Additional studies should be conducted to further refine the methodology and analysis techniques to foster use of this approach in real-time analysis of equipment movements on active beetle-killed harvesting operations. Accurate characterization of machine movements from spatially explicit data through the use of simple, non-intrusive multi-transmitter GNSS-RF allows for an increased level of situational awareness for improved streamlined operational production in the woods where obstructed views from topography or other site features may limit the ability to visually identify the actions of forest machinery at all times. The consumer-grade transponders incorporated into this study returned promising results in this preliminary study as to their accuracy and use in real-time production analysis and support models. The designed and controlled experiment performed in this study provided valuable information which will be used to further develop the real-time analysis model in subsequent studies. Executing these focused studies will allow various machine processes to be analyzed individually, resulting in strong model components representing the various cycle elements of associated harvest machinery. As previously mentioned, once activity profiles for harvest systems are completed, the model will be applied at a landscape scale, providing valuable information to operations foresters and contractors regarding harvest site selection and real-time decision support during harvest.  
Further, connecting machine movements to the onboard computers of multiple machines interacting with one another opens new possibilities for real-time decision-support and logistics analysis in beetle-killed harvests and forest operations in general. For example, the movements of a shovel can be paired with subsequent processing activities of additional machinery and personnel working within the harvest boundaries. Incorporation of real-time machine production analysis and model-assisted decision-making will prove a valuable development in the continued advancement of precision forestry. These technologies will allow for streamlined real-time production analysis and feedstock logistics in not only beetle-killed harvests, but more broadly in a wide range of forest operations.  
This project was funded by the Agriculture and Food Research Initiative Competitive Grant No. 2013-68005-21298 from the USDA National Institute of Food and Agriculture under the Bioenergy Alliance Network of the Rockies (BANR) project. Special thanks to [PERSON] and [PERSON] for their assistance in the field work portion of this study and graciously making time and machinery available, and [PERSON] for her help with equipment setup and sampling.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON], [PERSON], and [PERSON] analyzed the data; [PERSON] and [PERSON] wrote the paper with edits and additional text from [PERSON].  
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.