Positioning Methods and the Use of Location and Activity Data in Forests

Abstract

In this paper, we provide an overview of positioning systems for moving resources in forest and fire management and review the related literature. Emphasis is placed on the accuracy and range of different localization and location-sharing methods, particularly in forested environments and in the absence of conventional cellular or internet connectivity. We then conduct a second review of literature and concepts related to several emerging, broad themes in data science, including the terms location-based services (LBS), geofences, wearable technology, activity recognition, mesh networking, the Internet of Things (IoT), and big data. Our objective in this second review is to inform how these broader concepts, with implications for networking and analytics, may help to advance natural resource management and science in the future. Based on methods, themes, and concepts that arose in our systematic reviews, we then augmented the paper with additional literature from wildlife and fisheries management, as well as concepts from video object detection, relative positioning, and inventory-tracking that are also used as forms of localization. Based on our reviews of positioning technologies and emerging data science themes, we present a hierarchical model for collecting and sharing data in forest and fire management, and more broadly in the field of natural resources. The model reflects tradeoffs in range and bandwidth when recording, processing, and communicating large quantities of data in time and space to support resource management, science, and public safety in remote areas. In the hierarchical approach, wearable devices and other sensors typically transmit data at short distances using Bluetooth, Bluetooth Low Energy (BLE), or ANT wireless, and smartphones and tablets serve as intermediate data collection and processing hubs for information that can be subsequently transmitted using radio networking systems or satellite communication. Data with greater spatial and temporal complexity is typically processed incrementally at lower tiers, then fused and summarized at higher levels of incident command or resource management. Lastly, we outline several priority areas for future research to advance big data analytics in natural resources.

Effects of Canopy and Multi-Epoch Observations on Single-Point Positioning Errors of a GNSS in Coniferous and Broadleaved Forests

“…If a precise position can be obtained by a technique that does not rely on differential correction, the data processing of the receiver can be simplified, and the cost can be reduced. GNSSs have increasingly been playing roles in positioning forest fires [8], mapping forest resources [9], inventorying forest resources [10], positioning fixed sample sites [11], mapping roads [12], and measuring other natural resources [13].…”

Forestry – Integrating Safety in a Time of Rapid Change

“…10 Using global navigation satellite system (GNSS) enabled radios and smartphones, the research team demonstrated that the movements of hand fallers on cable logging operations could be quantified using activity recognition models based on smartphone sensors. 11,12 Currently, field studies are underway with rigging crews to assess if fitness watches can provide equivalent movement data compared to smartphone sensors. Long-term goals include the production of tools to quantify hazardous work activities, improve incident detection, and decrease response time.…”

A Decade of Forest Engineering: Achievements and Future Directions

“…Remote machine monitoring was explored already many years ago-more than a decade in Northern Europe, for instance-but it is now quite common with modern forest machines used worldwide. In fact, the use of smart phones now allows real-time communication and the automatic collection of production data from manual operations, as well [65].…”