# Positioning Methods and the Use of Location and Activity Data in Forests
## 6 Emerging Research Needs  
Technology for the use of real-time PNT in natural resources is evolving rapidly. As we develop new, mobile applications and data communications topologies to take advantage of improved real-time location and data sharing in forest operations, wildland fire, and more broadly in natural resources, several research needs emerge. Addressing these topics is necessary for moving from concept to practice and actualizing the potential benefits of PNT technologies, LBS, activity recognition, and IoT devices for analytics and decision support in natural resource management. Development of Activity Recognition Models for Individual Worker and Equipment Tasks in Forestry, Wildland Firefighting, and Natural Resources  
To further integrate activity recognition into natural resources, activity recognition models must be developed to characterize movements and conditions associated with various human, animal, and equipment work tasks. Activity profiles are being rapidly created for fitness and recreational activity monitoring, but applications quantifying work productivity in natural resources are limited. [PERSON] et al. developed methodology and code to model swing movements of forestry machines [86], and [PERSON] et al. developed an activity recognition model for timber fallers [14]. However, further equipment and worker activity recognition detailing a wide range of forestry and wildland fire tasks are needed to characterize work activities in integrated, real-time analyses to improve productivity and efficiency and to reduce costs. Similarly, in fisheries and wildlife, development of individual-level activity data based on inertial sensors may enhance future research characterizing behavioral patterns in a variety of ways. Development of New Sampling, Analytical, and Statistical Methods to Quantify Real-Time Resource Movements in Time and Space for Many Agents  
Through our literature review process, it has been evident that GNSS-related research in natural resources has predominantly focused on accuracy of stationary GNSS devices or relatively simple movements and work processes of individual pieces of equipment or personnel [42; 63]. An exception is [PERSON] and [PERSON], in which the location and movements of equipment, ground workers, and multiple mobile jobsite hazards were quantified and summarized simultaneously at one second intervals using a GNSS-RF network [21]. By quantifying all components of logging, firefighting, or other systems simultaneously, there is tremendous potential to advance these fields and improve efficiency and safety through more sophisticated types of analytics. Informatics associated with real-time positioning and equipment- and personnel-based work activity recognition is both an exciting and intimidating field due to the sheer volume of data being generated and needing to be quantified. As an example, [PERSON] et al. showed that recording smartphone sensor data for just one work day for hand fallers generates approximately 118,000 rows of data when recorded at 10 Hz [14]. Consequently, quantifying both the movements and work activities of, for example, 500-1000 wildland firefighters and other support resources in real-time presents formidable challenges, particularly when data processing occurs on mobile devices without access to cloud computing. In light of the diffusion of big data into IoT applications for natural resources, an important subject area for future research is the development and testing of methods to (a) transfer data efficiently in the hierarchical model, and (b) quantify forestry and firefighting operations in ways that reduce the dimensionality in time and space of data as it is collected and processed vertically (upstream) in the hierarchy. In addition to applied evaluation of functional data processing and communication systems in the field, there is also a need to address more basic analytical problems to support big data and IoT growth in natural resources.