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.

Abstract:The western United States faces significant forest management challenges after severe bark beetle infestations have led to substantial mortality. Minimizing costs is vital for increasing the feasibility of management operations in affected forests. Multi-transmitter Global Navigation Satellite System (GNSS)-radio frequencies (RF) technology has applications in the quantification and analysis of harvest system production efficiency and provision of real-time operational machine position, navigation, and timing. The aim of this study was to determine the accuracy with which multi-transmitter GNSS-RF captures the swinging and forwarding motions of ground based harvesting machines at varying transmission intervals. Assessing the accuracy of GNSS in capturing intricate machine movements is a first step toward development of a real-time production model to assist timber harvesting of beetle-killed lodgepole pine stands. In a complete randomized block experiment with four replicates, a log loader rotated to 18 predetermined angles with GNSS-RF transponders collecting and sending data at two points along the machine boom (grapple and heel rack) and at three transmission intervals (2.5, 5.0 and 10.0 s). The 2.5 and 5.0 s intervals correctly identified 94% and 92% of cycles at the grapple and 92% and 89% of cycles at the heel, respectively. The 2.5 s interval successfully classified over 90% of individual cycle elements, while the 5.0 s interval returned statistically similar results. Predicted swing angles obtained the highest level of similarity to observed angles at the 2.5 s interval. Our results show that GNSS-RF is useful for real-time, model-based analysis of forest operations, including woody biomass production logistics.

Activity recognition modelling using smartphone Inertial Measurement Units (IMUs) is an underutilized resource defining and assessing work efficiency for a wide range of natural resource management tasks. This study focused on the initial development and validation of a smartphone-based activity recognition system for excavator-based mastication equipment working in Ponderosa pine (Pinus ponderosa) plantations in North Idaho, USA. During mastication treatments, sensor data from smartphone gyroscopes, accelerometers, and sound pressure meters (decibel meters) were collected at three sampling frequencies (10, 20, and 50 hertz (Hz)). These data were then separated into 9 time domain features using 4 sliding window widths (1, 5, 7.5 and 10 seconds) and two levels of window overlap (50% and 90%). Random forest machine learning algorithms were trained and evaluated for 40 combinations of model parameters to determine the best combination of parameters. 5 work elements (masticate, clear, move, travel, and delay) were classified with the performance metrics for individual elements of the best model (50 Hz, 10 second window, 90% window overlap) falling within the following ranges: area under the curve (AUC) (95.0% - 99.9%); sensitivity (74.9% - 95.6%); specificity (90.8% - 99.9%); precision (81.1% - 98.3%); F1-score (81.9% - 96.9%); balanced accuracy (87.4% - 97.7%). Smartphone sensors effectively characterized individual work elements of mechanical fuel treatments. This study is the first example of developing a smartphone-based activity recognition model for ground-based forest equipment. The continued development and dissemination of smartphone-based activity recognition models may assist land managers and operators with ubiquitous, manufacturer-independent systems for continuous and automated time study and production analysis for mechanized forest operations.

Fuel reduction in forests is a high management priority in the western United States and mechanical mastication treatments are implemented common to achieve that goal. However, quantifying post-treatment fuel loading for use in fire behavior modeling to forecast treatment effectiveness is difficult due to the high cost and labor requirements of field sampling methods and high variability in resultant fuel loading within stands after treatment. We evaluated whether pre-treatment LiDAR-derived stand forest characteristics at 20 m × 20 m resolution could be used to predict post-treatment surface fuel loading following mastication. Plot-based destructive sampling was performed immediately following mastication at three stands in the Nez Perce Clearwater National Forest, Idaho, USA, to correlate post-treatment surface fuel loads and characteristics with pre-treatment LiDAR-derived forest metrics, specifically trees per hectare (TPH) and stand density index (SDI). Surface fuel loads measured in the stand post-treatment were consistent with those reported in previous studies. A significant relationship was found between the pre-treatment SDI and total resultant fuel loading (p = 0.0477), though not between TPH and fuel loading (p = 0.0527). SDI may more accurately predict post-treatment fuel loads by accounting for both tree number per unit area and stem size, while trees per hectare alone does not account for variations of tree size and subsequent volume within a stand. Conditions within treated stands and fuels produced during mastication are highly variable and may explain the lack of relationship between fuel classes and loading. Root-mean-square errors of 36 and 46 percent of the random forest LiDAR models for SDI and TPH may limit the ability to predict the highly variable fuel loads produced from mastication. Use of LiDAR to predict fuel loading after mastication is a useful approach for managers to understand the efficacy of fuel reduction treatments by providing information that may be helpful for determining areas where treatments can be most beneficial.

Mobile technologies are rapidly advancing the field of forest operations and providing opportunities to quantify management tasks in new ways through increased digitalization. For instance, devices equipped with global navigation satellite system and radio frequency transmission (GNSS-RF) enable real-time data collection and sharing of positional data in remote, off-the-grid environments where cellular and internet availability are otherwise inaccessible. In this study, consumer-grade GNSS-RF data were evaluated to determine their effectiveness in developing activity recognition models for excavator-based mastication operations. The ability to automate the classification of cycle elements for operations is valuable for quickly and efficiently quantifying production rates for research and industry applications. The GNSS-RF-based activity recognition model developed successfully classified productive elements versus delay with over 95 per cent accuracy. Individual cycle elements were classified with an overall model accuracy of 73.6 per cent, with individual element classification accuracy ranging from 51.3 per cent for walk/reposition to 95.6 per cent for mastication elements. Reineke’s stand density index, basal area (m2 ha−1) of treated areas and the duration of cycle elements impacted the classification accuracy of the activity recognition model. Impacts of forest stand characteristics on the production rate of mastication treatments were also assessed. Production rates (ha·hr−1) for mastication treatments were affected by the basal area of treated areas. However, the degree to which this would impact operations in practice is minimal. Determining the proper application and capabilities of mobile technologies and remote sensing for quantifying forest operations is valuable in continuing the innovation and advancement of forest digitalization.

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