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. Relatively large root-mean-square errors associated with the random forest models for SDI (36%) and TPH (46%) suggest that increased sampling intensity and modified methods that better account for fine spatial variability in fuels resulting from within-stand conditions, treatment prescriptions and machine operators may be needed. 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.
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
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. 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.
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 realtime, model-based analysis of forest operations, including woody biomass production logistics.
As innovative harvest systems are developed, the extent to which they can be utilized on the landscape based on machine capabilities is often unclear to forest managers. Spatial decision support ...models may aid contractors and forest planners in choosing appropriate logging systems based on topography and stand characteristics. Lidar and inventory data from 91 sample plots were used to model site characteristics for 2627 stands in the Slate Creek drainage on the Nez Perce Clearwater National Forest in north-central Idaho, USA, and were integrated into a decision support model to compare harvest system selection using five harvest systems and three scenarios. In two of the scenarios, shovel harvester-based logging systems, which are not common in the area, were included to determine potential sites where integration of these systems is possible based on landscape and stand conditions. Lidar-derived predictions for volume and trees per hectare were determined with model accuracies of 76.4% and 70.3%, and together with topographic characteristics it was determined that shovel harvester-based options were feasible across a significant portion of the study area (31% and 34% in the two scenarios). Additionally, increasing operable slope for ground-based systems by 10% increased the area in harvestable classification by 21%. Harvest system classification using lidar-derived products and known system capabilities allows contractors and managers to better evaluate alternative harvest system options on landscape scales and may encourage the utilization of innovative machinery not currently integrated into most logging operations.
Abstract
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.
Position, Navigation and Timing (PNT) technologies have rapidly become an integral part of our everyday lives, through mobile applications, activity sharing, global navigation satellite system (GNSS) ...supported devices, real-time monitoring of consumer goods and the Internet of Things (IoT). Further, remote sensing data provide unique solutions for aiding PNT methods and techniques in the planning and assessment of active land management through the derivation of site and stand metrics used to develop models. Increased ease of access to a wide range of data sources has supported the expansion of network infrastructures and availability for data sharing and transfer at local and global scales. High-resolution, remotely sensed data and mobile, off-the-grid data collection and interpretation methods provide valuable solutions for production analysis, treatment analysis and data sharing in natural resource management scenarios. Precision forestry and production analytics for industrial forest and fire operations are rapidly transforming through the integration of global navigation satellite system and radio frequency (GNSS-RF) enabled devices, smartphone-based inertial sensors, and high resolution remotely sensed data. Consumer available technologies and data provide the means to collect, share and analyze spatially explicit data using multi-transponder mesh networks and activity recognition models. However, the application of these technologies in operational forest management scenarios for activity recognition and production analysis has received very little attention in formal research. By evaluating operational applications of PNT technologies in forest management, mangers may be able to maximize their effective mobilization effort to manage land and improve on optimization of the operations themselves. Additionally, coupled use of high resolution remotely sensed data with real-time location and activity analytics may provide insight into management effectiveness and post-treatment stand conditions at landscape scales to assist managers in the strategic planning and implementation of future operations.
Forest management has encountered a face paced evolution integrating technologies and data sources which have in turn helped mold a new paradigm of forestry focused on site specific management ...strategies as opposed to one size fits all management. Precision forestry is rapidly evolving as access to high resolution spatial data and real-time data collection and transfer in remote areas is becoming more possible. This is changing the way decisions are made, especially in forest operations, and the way in which production is analyzed in forest harvesting. Through our work, we established foundational analyses of GNSS-RF data for use in production analysis of forest machinery and the use of lidar-derived products to develop a stand level harvest system selection model. These initial studies will set the groundwork for refining and expanding our analyses as we continue to explore integration of real-time data and high resolution spatial data in forest operations.
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