Use of Individual Tree and Product Level Data to Improve Operational Forestry

Robert F. Keefe; Eloise G. Zimbelman; Gianni Picchi

doi:10.1007/s40725-022-00160-3

Use of Individual Tree and Product Level Data to Improve Operational Forestry

Keefe, Robert F.; Zimbelman, Eloise G.; Picchi, Gianni 2022-06-01 00:00:00 Purpose of Review Individual tree detection (ITD) methods and technologies for tracking individual forest products through a smart operational supply chain from stump to mill are now available. The purpose of this paper is to (1) review the related literature for audiences not familiar with remote sensing and tracking technologies and (2) to identify knowledge gaps in operational forestry and forest operations research now that these new data and systems are becoming more common. Recent Findings Past research has led to successful development of ITD remote sensing methods for detecting individual tree information and radio frequency identification (RFID), branding, and other product tracing methods for individual trees and logs. Blockchain and cryptocurrency that allow independent verification of transactions and work activity recognition based on mobile and wearable sensors can connect the mechanized and motor-manual components of supply chains, bridg- ing gaps in the connectivity of data. However, there is a shortage of research demonstrating use of location-aware tree and product information that spans multiple machines. Summary Commercial products and technologies are now available to digitalize forest operations. Research should shift to evaluation of applications that demonstrate use. Areas for improved efficiencies include (1) use of wearable technology to map individual seedlings during planting; (2) optimizing harvesting, skidding and forwarder trails, landings, and deck- ing based on prior knowledge of tree and product information; (3) incorporation of high-resolution, mapped forest product value and treatment cost into harvest planning; (4) improved machine navigation, automation, and robotics based on prior knowledge of stem locations; (5) use of digitalized silvicultural treatments, including microclimate-smart best management practices; and (6) networking of product tracking across multiple, sensorized machines. Keywords Forest operations · Individual tree detection · Traceability · RFID · Location analytics · Activity recognition · Smart forestry · Supply chain Introduction and tracing individual forest products through the sup- ply chain, introduces new opportunities and challenges The maturing of remote sensing methods to perform for operational forest management. Conventional forest individual tree detection (ITD) [1 , 2], as well as tim- inventory techniques have traditionally focused on use ber harvesting applications of radio frequency identifi - of mean, stand-level estimates of forest attributes in the cation (RFID) [3 ] and other technologies for labeling context of silvicultural and operational planning. Many forest managers, foresters, and researchers are accustomed to working with coarse estimates of stand merchantable This article is part of the Topical Collection on Forest Engineering volume and stems per hectare, stem piece size distribu- tions, and other average stand conditions as the basis for * Robert F. Keefe harvest planning. Although light detection and ranging robk@uidaho.edu (lidar) and other remote sensing methods have been used University of Idaho Experimental Forest, College of Natural to detect and map data in fine resolution for decades, the Resources, Moscow, ID 83844-3322, USA resultant products have generally been summarized in con- Consiglio Nazionale delle Ricerche - Istituto per la ventional ways for the purposes of harvest planning and Bioeconomia (CNR-IBE), Via Madonna del Piano 10, implementation. However, an aspirational goal for the use 50019 Sesto Fiorentino, Italy 1 3 Current Forestry Reports (2022) 8:148–165 149 of lidar by forest managers and operational foresters has for shifting research to increasingly operational uses of long been the development of functional, ITD mapping these data, as opposed to continuing along prior avenues that could help foster a new era of high precision, smart, of methodological research, development, and testing? forest operations. An underlying theme and trend in many In order to address these questions, we reviewed recent current regional and global smart and precision forestry literature using Google Scholar with emphasis on publica- initiatives is the integration of ITD and stem characteris- tions from the last 5 years and highly cited manuscripts tics based on lidar point cloud or other remotely sensed (Fig. 2). Our review was further shaped by experience using data with increasingly sensorized, automated, and data- ForestView® ITD inventory data (Northwest Management driven harvest systems that would allow for supply chain Inc., Moscow ID) in parallel with conventional, stand-based level big data analysis and optimization [4, 5 ]. This can inventory (SBI) data to inform silvicultural treatments and be seen, for example, in the FPInnovations’ Forestry 4.0 timber management operations on the University of Idaho program, which is based on the application of Industry 4.0 Experimental Forest for approximately 9 months prior to digital transformation concepts to forestry, an idea that manuscript submission. Our objectives in the review are (1) has gained traction in many countries [6, 7]. This direc- to provide a current review of ITD methods based on recent tion and momentum in the operational research community literature, especially for forest operations specialists who are generally associated with a broad push to improve and are not necessarily experts in remote sensing; (2) to review advance smart, precision forest management that lever- the use of individual tree-level data in other subject areas in ages the merging of big data and Internet-of-Things (IoT) forestry such as growth and yield modeling, silviculture, and concepts associated with increased forest auomation and forest planning; (3) to provide current, state-of-the-art infor- • • robotics [8 , 9 , 10], as well as increasing use of a wide mation on individual forest product tracing using methods variety of electronic sensors on equipment. Additionally, like RFID tags placed on trees and logs that can be read by improving verifiable chain-of-custody criteria associated operational equipment attachments on harvesters and pro- with sustainable forestry certification programs to curb cessors; (4) to review blockchain and other cryptocurrency illegal logging may be a further driver of research into methodologies for distributed payment systems that may labeling and tracking individual trees and products. be paired with individual forest product tracing; and (5) to As discussed as recently as 2017 by Talbot et al. [9 ], identify and discuss research gaps in the current literature. many current precision remote and proximal sensing In the latter case, our primary emphasis was on identify- methods for operational forestry are still experimental and ing and suggesting research on (1) application, rather than require further research and development. For example, development, of ITD and individual product tracing data in many demonstrated industrial uses of ITD have been lim- operational forestry and (2) systems-level traceability from ited to single species plantations. However, individual tree the individual tree to the mill that spans multiple pieces of stem map products for complex, mixed-species and multi- equipment. aged stands over the extent of large forest ownerships based on lidar-derived ITD are now commercially avail- able. As a result, forestry professionals ranging from on- Remote Sensing Methods for Individual Tree the-ground foresters to academic researchers must increas- Detection (Airborne, Drone, Terrestrial Lidar) ingly make the challenging and quick transition from very minor, occasional uses or somewhat passive monitoring Both remote and proximal sensing offer opportunities of lidar’s gradual development over the last few decades for improving precision in forest operations [9 ]. Talbot to suddenly having massive, rich, high-resolution ITD et al. [9 ] distinguish remote sensing from proximal sens- datasets available for large ownerships. For example, the ing largely based on the platforms used. Remote sens- data shown in Fig. 1 are a small subset of a commercially ing includes airborne laser scanning (ALS) and generally available lidar-derived ITD product with predicted indi- offers higher spatial coverage but lower spatial resolution vidual tree location, diameter at breast height (DBH), tree while proximal sensing includes terrestrial laser scan- height, species, and crown characteristics for all trees on ning (TLS) and usually offers lower spatial coverage but • • the main 3,300-ha University of Idaho Experimental For- higher spatial resolution [9 ]. Wang et al. [1 ] charac- est. In this context, an important question for many profes- terizes three approaches to ITD based on ALS: raster- sionals in silviculture and forest operations becomes the based, point-based, and hybrid approaches combining the following: now that we have these detailed ITD maps and two. In raster-based approaches, the information in raster the underlying information, how do we make use of the products such as crown height models (CHMs) is used to new data in practical ways to improve stand treatments and detect trees based on a variety of segmentation algorithms harvesting operations to fully implement a smarter and [1 ]. In point-based approaches, the ALS point cloud is more advanced supply chain? What are the opportunities used to derive 3D individual tree crown shape directly 1 3 150 Current Forestry Reports (2022) 8:148–165 Fig. 1 Subset of commer- cial 3300-ha ForestView® Individual tree stem map product encompassing over 700 mixed-species stands of vary- ing ages on the University of Idaho Experimental Forest. The underlying ITD product data used to create this figure was developed by Dr. Mark Corrao at Northwest Management, Inc. in Moscow, ID. The horizontal extent of the image shown is approximately 250 m. Tree and product information can be inferred from ITD data (shown below), in which each row cor- responds to one dot on the map. The author has color coded dots by species and set dot size proportional to DBH using conventional GIS software for visualization purposes. Color coding is as follows. Red: Douglas-fir ( Pseudotsuga menziesii); yellow: ponderosa pine (Pinus ponderosa); dark green: Western red cedar (Thuja plicata); orange: grand fir ( Abies grandis); and light green: western larch (Larix occidentalis). Information about general locations of products such as Douglas-fir veneer logs (DFPE1), high-value cedar utility poles (RCU1), Douglas-fir sawlogs (DFS1), and grand fir pulpwood (GFP1) can be inferred from the data graphically, coupled with field observation [1 ]. Some newer point-based approaches normalize the circle and cylinder detection methods and/or voxel and ALS point cloud and transfer it to a local voxel space point clustering algorithms [2]. from which trees are detected [1 ]. In a hybrid approach, trees are delineated in rasterized data, and the 3D canopy of detected crowns is subsequently modeled from the Airborne Lidar and ITD point cloud data using various clustering and segmenta- tion algorithms [1 ]. The authors compared one point- ALS data has been used to detect individual trees with based, two raster-based, and two hybrid approaches and accuracies of 83.84 [11] to 87% [12]. While evaluating found detection accuracy was affected by canopy structure various approaches to detecting individual trees in dif- • • and point density [1 ]. In terms of TLS, Liang et al. [2] ferent crown classes, Wang et al. [1 ] found that most describe and evaluate 18 different algorithms for ITD. methods provided detection rates greater than 85% for These approaches have varying degrees of automation, dominant trees and greater than 70% for codominant and include raster-, voxel-, and point-based techniques trees. Individual tree-based features derived from ALS [2]. Individual trees can be identified through different data have been shown to improve the accuracy of pre- dicted attributes such as mean height, mean DBH, and 1 3 Current Forestry Reports (2022) 8:148–165 151 Fig. 2 Flow of the motivating topic area, review of recent and well-cited literature, and development of conceptual ideas presented in the manu- script volume [11]. Most notably, volume was estimated with more detailed information such as branch attributes in an RMSE of 20.32% using both individual tree-based fea- order to estimate branch angle, length, and volume among tures and point height metrics, compared to an RMSE of individual trees [16]. 25.41% using point height metrics only [11]. ALS data has been used to estimate forest above-ground biomass (AGB) through a process that involves detecting indi- Terrestrial Lidar vidual tree crowns and then calculating tree density, tree height, individual crown length, and mean height of forest Because it provides a different, below-canopy perspective, layers [13]. AGB was calculated with an RMSE of 17.1% terrestrial lidar is also being used to quantify and map at the forest plot level using lidar-derived individual tree individual tree data [17]. Trochta et al. [17] developed an data, compared to an RMSE of 31.1% using a traditional open-source application called 3D Forest which is capable regression model approach incorporating both field meas- of extracting DBH, tree positions and heights, stem curves, urements and lidar point cloud metrics [13]. Individual and tree planar projections from TLS data. The applica- tree data from ALS has also been used to estimate indi- tion can also describe detailed crown parameters such as vidual tree crown width and crown volume [14] as well crown base height, crown height, and crown volume and as to optimize tree removal and inform thinning prescrip- surface area [17]. Liang et al. [2] found that TLS-based tions in order to reduce crown fire potential within stands approaches to ITD can estimate DBH and stem curves with [15]. Luo et al. [12] developed a method of estimating accuracies of 1–2 cm. After extracting trees, wood and individual tree crown base height directly from ALS data leaf points are often separated using classical machine without the need for field measurements with an RMSE learning and computer vision algorithms [18 ]. Quanti- of 1.62 m, which has applications for tree growth and tative structure modeling (QSM) algorithms can then be health monitoring as well as for ecosystem and wildfire used to estimate wood volume and model tree morphology modeling. Recent work has utilized ALS data to model [18 ] and to segment and classify the physiological age of annual shoots from tree skeletons [19]. Recent QSM 1 3 152 Current Forestry Reports (2022) 8:148–165 approaches have investigated the potential for deep learn- and smartphone-based SfM has been used to estimate DBH ing to delineate tree stems and branches [20] as well as to and stem volume [29]. SfM is also used in assessing and detect structural properties of branches [21]. Calders et al. monitoring forest condition by classifying tree health [27 ]. [18 ] summarize a variety of applications of TLS ITD data in the measurement of forest attributes, including calcu- lating DBH, estimating wood volume, and incorporating ITD from Forestry Equipment crown structure information into traditional allometric models. In the context of operational forestry, these novel As mentioned above, there is a strong effort to increase measurement techniques may provide more detailed infor- digitalization of the forestry supply chain broadly in the mation on log geometry and quality in addition to offering context of Forestry 4.0 concepts [30 ]. The increasing use a better understanding of growth and changes in biomass of big data and AI in smart forestry is associated with the [18 ]. The structural and spatial information derived from drive to automate thinning and harvesting through robot- TLS data can also provide insights on the effects of vari - ics [31]. Use of individual tree metrics like stem volume ous silvicultural practices [18 ]. improves estimates of operational performance, produc- tivity, and costs [32] and may also provide the basis for equipment positioning and navigation using simultaneous localization and mapping (SLAM) [5 , 33]. For example, Drone Lidar Miettinen et al. [34] describe the use of machine vision and 3D scanning to measure trees during harvesting High pulse density drone lidar is able to incorporate wider operations, in order to optimize felling and increase har- scan angle than airborne (by virtue of being at lower eleva- vester automation. A combination of 3D laser scanning tion) and is being used for high-resolution mapping of and SLAM was used to measure standing tree stems prior localized forest structure in order to calibrate space-borne to felling in order to estimate crown base height, taper, lidar [22 ]. Whereas acquisition of terrestrial lidar can be sweep, trunk dimensions, branches, and lean [34]. Cam- relatively slow, drone-based high density lidar can span eras attached to an ATV have been used to measure trees large areas quickly and still retain pulse density up to sev- and estimate structure from a distance through motion eral thousand points per square meter [22 ]. ITD from vision while cameras on the harvester head were used to drone lidar has been used to identify priority areas for measure length and thickness of processed stems [34]. wildfire prevention [ 23] as well as to detect invasive spe- Based on similar methodology, SLAM has also been con- cies when fused with imagery data [24]. Drone lidar is sidered a general forest inventory technique, as well as for also being used to delineate individual trees and predict equipment positioning and navigation [35]. An underlying DBH and species in order to estimate tree volume at the concept in SLAM is that a stem map is constructed on-the- plot, stand, and forest levels without the need for field data fly based on equipment-mounted sensor data. The map for calibration [25]. Recent work has evaluated the poten- becomes more complete as equipment works. This work- tial for under-canopy drone lidar to extract stems and tree ing map of the stand environment is recorded in memory, heights in order to calculate individual tree volume with a and the resulting map then forms the basis for subsequent, level of accuracy similar to TLS [26]. autonomous machine guidance, which could occur inde- pendently of GNSS-based navigation [36]. Structure from Motion ITD and Chain‑of‑Custody The Structure from Motion (SfM) methodology uses low-cost imaging platforms to extract forest biophysi- Many forest legal and sustainability certification programs cal parameters for both aerial and terrestrial applications include product chain-of-custody documentation [37]. In [27 ]. SfM applied to unmanned aerial vehicle (UAV) addition to broad interest in the operational forestry com- image data has been used in individual tree approaches munity to advance smart and precision forest operations, to detect trees, classify species, and measure tree heights the development of verifiable chain-of-custody is a key [27 ]. Both UAV lidar and SfM were recently used to accu- motivator for developing and testing individual forest rately measure individual seedling height down to a 1-m product labeling and tracing systems. A natural outcome threshold [28]. Most terrestrial applications of SfM have of increasing interest in chain-of-custody is the desire to focused on modeling individual trees, primarily to estimate track products from their actual, exact place of origin. tree positions and heights, DBH, and stem curvature [27 ] In the context of forest operations, the place of origin 1 3 Current Forestry Reports (2022) 8:148–165 153 is ultimately the individual tree’s verifiable geographic the dense undergrowth vegetation, which reduced the effec - coordinates in the woods. This could be the location of tive range of tags, and (2) the unpredictable mutual position a residual stump, or a verifiable individual stem that was of the tags and the reader, which further compromised the removed, as evident in multitemporal satellite imagery, capacity to detect a return signal. Given the operating condi- aerial photography, or lidar acquisitions. Chain-of-cus- tions, active RFID tags performed better due to their stronger tody technologies include log branding, bar coding, use signal and higher reading range, but their higher unitary cost of tracer paints, RFID, and chemical or biological finger - and battery duration should be carefully considered when printing [38] as well the recent use of DNA-based track- planning each specific application. ing [39]. Coupled with remote sensing, cloud computing, For the requirements of forestry and timber supply chains, and big data, fiber sourcing and supply chain technologies passive and disposable UHF RIFD tags are regarded as the are helping to improve sustainable forestry as originally best solution [51, 52]. These types of RFID tags may be envisioned and intended [40]. deployed for a wide range of services beyond single-tree retrieval. For instance, they can be used to associate digital information with standing trees for recreational purposes, Use of RFID to Store Information forest management and protection, or timber production with Individual Standing Trees [10]. In these applications, long-range readability is less critical as trees can also be marked with visual systems (e.g., At present, several systems for tree and timber marking are color spray), allowing the operators to approach the tree and available in the market, varying from simple paint or chalk identify the tag with manual readers. In a similar application, to systems capable of storing a certain amount of informa- commercial UHF RFID tags proved capable of withstanding tion retrievable with automatic systems [41]. Among the the harsh climatic conditions of an Alpine forest and retained different alternatives for data transmission, RFID technol - their operative capacity for 2 years [3 ]. In this environment, ogy is regarded as the best solution for a number of rea- resin emission and tree growth proved to be the most chal- sons: painted codes or logos may wear out due to weather lenging factors as the former can attack the plastic layers exposition over time [42], and, compared to manual sys- protecting lighter tag models, while the latter can break the tems, automatically readable systems avoid human errors hard case of “heavy duty” tags fixed with screws on the and drastically reduce recording time [43]. RFID also allows trunk. The study suggests that long-term tagging of high- reading in any light conditions, without requiring a precise value trees (e.g., nesting trees for protected species) should line of sight and even when tags are hidden by mud, dirt, rely on large tags with strong protective cases fixed to the or resin [44]. Thanks to its performance, RFID is currently trunk with plastic screws. Metal screws should be avoided deployed in a wide range of industrial sectors, ranging from due to the possible damages they may cause to the sawmill- manufacturing to logistics. In agriculture, RFID technol- ing equipment. The screw should also be long enough to ogy is already a standard for cattle identification [ 45] and leave a gap between the tag and the bark in order to avoid provides promising applications in nursery management damages due to tree growth. [46] and potted plants distribution [47]. Additionally, the When marking trees selected to be felled in forest opera- identification of living plants in open field, generally per - tions, simpler RFID tag models with large transponder and manent crops, is raising an increasing interest as it would low unitary cost (< 0.40 €/0.48 $) are preferred. These can facilitate several precision agriculture practices, such as the be easily stapled to bark and provide the best reading per- selective and optimized application of chemicals [48]. The formance, which may enable both manual and automatic tag implementation of RFID in forestry shares similar chal- detection (e.g., with a processor head). This last applica- lenges of tag survivability and reading capacity related to tion was tested by Pichler et al. [53 ] in a study comparing open air deployment. However, in agriculture the position the technical feasibility and costs of a digital forest inven- of plants, tags and reading devices can be planned in order tory with traditional manual-based forest inventory. Simple to maximize the efficiency of the whole system, while in UHF RFID tags were used to connect information derived forest conditions, such planning has a much higher degree of from UAV-aerial images and TLS scanning to each tree. uncertainty, which may lead to poor results. For this reason, The whole digital system proved technically mature, yet its among the possible operating frequencies for RFID, ultra overall cost resulted in 1.55 € per marked tree against 0.36 high frequency (UHF) is regarded as the most suitable, as it € for the traditional inventory and marking system. In the allows the longest reading ranges [49 ]. Marczewski et al. former, the main cost factor was the high density of TLS [50] tested the suitability of passive and active UHF RFID plots, which aimed to directly scan every trunk rather than tags to mark and retrieve trees in a natural forest combining improve statistical inventory (as currently used in commer- GPS positioning and RFID identification at long range (>10 cial applications). The high-quality data was used to gener- m). Results with passive tags were unsatisfactory due to (1) ate a digital model of each trunk and relate it to the value of 1 3 154 Current Forestry Reports (2022) 8:148–165 the local timber assortments, returning cutting instructions future as multitemporal lidar increasingly becomes used as the that were linked to each tree and transferred to the processor basis for modeling stand growth in ways that support climate with the RFID tags, maximizing the timber value recovery. smart silvicultural and operational planning. In motor-manual felling operations followed by whole-tree cable yarder extraction, the simple RFID tags used in the previous study were able to withstand hauling operations Individual Tree Data in Silviculture with a survival rate of 97%, proving to be a reliable tool for and Forest Planning data transfer among the different steps of the timber supply chain [44]. RFID tag components (transponder and chip) ITD methods discussed in the preceding sections vary in are currently made with metal protected by a plastic case, their ability to detect trees in the different forest canopy and since tags attached to the logs would enter the stream of classes (dominant, codominant, intermediate, suppressed) sawmill residues, this could reduce acceptability from the that form the basis of stand development and silvicultural industry. A possible solution to this issue is printing RFID treatments [1 ]. Accurate estimation of tree location, tags directly on renewable materials [54]. This technique height, and crown structure have been proposed as a use- was also tested to produce RFID tags with dual frequency ful application of SfM data in order to define treatments (UHF and near field contact, NFC) [ 55], which could fur- for individual tree selection systems used in Ponderosa ther reduce the investment costs of the system: foresters and pine (Pinus ponderosa Lawson & C. Lawson) stands in chainsaw operators could deploy common smartphones with Colorado, USA [62]. Recently, preliminary methods for NFC functions to read the tags and relate them to the data- optimizing stand prescriptions for individual tree and base, while the UHF potential would still allow long distance group selection and clearcut systems in ways that account and machine identification. for the spatial distribution of the stand based on analysis of ITD data have been presented [63]. Similarly, Wing et al. [64] developed a method to generate group selection treatment options utilizing ALS-derived stem map data in Individual Tree Data in Growth and Yield order to meet various management objectives. These meth- ods are promising, and modeling and optimization show Growth functions that predict stem diameter or basal area that accounting for individual tree characteristics during • • increment over time based on individual tree attributes and the thinning is economically beneficial [ 65 , 66 ]. Large data- surrounding, competitive environment have long been used bases are available to quantify fire effects of individual as the basis for stand growth simulators deployed widely for trees to support planning of prescribed fire and to assist forest management [56, 57]. Recent models represent growth with post-fire evaluation [ 67]. in structurally complex stands [58]. Multitemporal SfM and multitemporal lidar are being used to quantify growth [59]. However, relatively few simulators leverage height increment, crown surface area, and other canopy metrics that are more Direct Log Measurements Using Encoders easily inferred from remotely sensed data collected from air- in Harvesters and Processors craft or UAVs, rather than DBH increment. This is largely a function of DBH being the common measurement conven- Harvester and processor heads measure stem diameter with tionally available in manual, ground-based stand inventory. sensors in the delimbing knives or feed rollers that use While non-spatial individual tree competition indices appear either three points of measurement or the average of two to predict growth generally as well as spatial indices in natu- perpendicular measurements to determine log diameter at rally regenerated mixed-species stands [60], multitemporal multiple points along the stem [68]. As rollers feed the lidar provides the data to quickly parameterize new kinds of stem, length is determined using a digital measuring wheel models that fully leverage individual crown metrics, which mounted directly on the head and intermediate points are are closely related to the photosynthetic capacity of trees, in interpolated based on stem taper. A range of algorithms order to forecast individual stem volume growth in biologi- can then be used to estimate timber volume based on log cally meaningful ways. A good example of a physiological, length and diameter measurements [69]. Stem swell asso- process-based model that represents individual tree and stand ciated with branch whorls or stem defect is accounted for growth as a function of tree crown characteristics over time via algorithms in the onboard computer. Harvester-derived •• is the Amorphys model [61 ]. Though developed prior to estimates of stem defects can be used in bucking simu- the maturing of current, lidar-derived ITD analysis, models lations designed to predict product recovery [70]. While conceptually similar to the approach described in Valentine measurements are precise, harvesting and processing head •• and Mäkelä [61 ] may become extremely important in the measurements require regular monitoring and calibration 1 3 Current Forestry Reports (2022) 8:148–165 155 [68]. Harvester measurements of tree length, diameter, data transfer have been proposed and evaluated experimen- volume, and location have been used as reference data for tally or developed into commercial products [41]. Most solu- ALS-based predictions of forest inventory attributes [71]. tions rely on robust and cost-effective visual systems, such as Because harvesters do not measure the top crown section, barcode-like marks printed with spray nozzles installed on they provide total log length but not total tree height [72]. the chainsaw bar of the processor head [76] or with punch- Thus, recent work has attempted to develop improved ing systems. The latter is a device that consists of 12 ham- models for estimating individual tree heights from har- mer brands that punch wood with a combination of marks vester data in order to facilitate harvester-based inventory that represent an alphanumeric code [77]. In both cases, an and analytics through integration with traditional inven- optical reader installed on the sawmill feeding line acquires tory and lidar data [72]. Advanced characteristics of forest images that are converted to a unique ID code for each log, products, such as wood stiffness and related properties of making it possible to retrieve any information provided by logs determined from acoustic time-of-flight measure - the processor head and stored in a shared database. These ments, can increasingly be integrated into harvesting or solutions are robust and cost-effective since they do not rely processing heads and used to inform processor bucking on disposable tags, yet they require a line of sight and proper decisions in real-time [73]. illumination to be detected. Reading Individual Log Identification RFID and Biometric Log Identification on Harvester or Processor Heads on Equipment Attachments Research and industrial development are increasingly focus- An interesting alternative is the use of RFID, which had ing on the potential of data provided by sensors installed on been successfully deployed on prototype processor heads forest machines, and particularly the harvester heads that in the frame of two European projects. The first system was fell and process trees into commercial timber assortments equipped with a device for marking logs with a special RFID in cut-to-length (CTL) harvesting systems. In addition to tag composed of wood composites. This resulted in a neutral the valuable information provided by the standard encod- material for the pulp industry [78]. The second prototype ers, several sensors have been tested on processor heads to was designed so that the processor head could read RFID measure timber density [74] or a combination of quality fac- tags attached to the incoming stems (e.g., in cable yarding tors (timber density, knottiness, crosscut section defects) to operations), assess timber quality with sensors, and relate •• return a quality index value [75 ] as shown in Fig. 3. The the relevant data to each log with a new RFID tag automati- •• information generated is meant to be used to optimize trans- cally attached to the crosscut section [75 ]. The advantage portation, purchase, and industrial processing of timber in of this solution is allowing successful log ID detection in the supply chain. To maximize the value of the information poor visibility conditions as well as providing a mecha- generated, a number of solutions for log identification and nism for efficient and fast bulk reading of whole log trucks, Fig. 3 A RFID tags on log ends for stump-to-mill traceability tests in sity, quantity of knots, presence of rot), an RFID reader designed to the Italian Alps (Trento), B sensorized processor head described in detect the ID of trees to be processed and an RFID tagger deployed to •• Sandak et al. [75 ] featuring log-grading sensors (i.e., timber den- mark each log and associate it with the quality data generated 1 3 156 Current Forestry Reports (2022) 8:148–165 particularly the guaranteed detection of all tagged logs [79]. or attachment heads provide a mechanism for maintaining Biometric fingerprinting of forest products is another prom - a record of individual log movements from stump to land- ising technique that utilizes scanning and image analysis ing and onto loaded trucks. Location based on GNSS can to identify patterns in the cross-sectional ends of logs for define the productive work of logging equipment, including improved individual product tracing. Two- and three-dimen- skyline carriages on cable operations and boom movements sional log biometric fingerprinting is used operationally in [85–87]. GNSS-based location can also define safe work advanced sawmills, and has shown very promising results zones on the fly in dynamic environments and quantify com - for accuracies up to 100% using methods similar to those plex, location-based functions of multiple moving resources applied in the context of fingerprint and iris recognition in to improve situational awareness and safety [88, 89]. Forest controlled studies [80]. canopy and topography differentially affect GNSS accuracy and radio network communication quality due to multipath error and radio signal interference in real-time data networks Sustainable Sourcing, Cryptocurrency, [90]. The majority of research to date has evaluated a small and the Blockchain number of pieces of equipment [85]. However, Wempe et al. [88] has also demonstrated simultaneous monitoring of mul- To fully address legal and sustainability certification and tiple pieces of equipment and rigging crew workers in near chain-of-custody compliance, digitization of traceability real-time with movements recorded at a rate of one location requires networking methodologies that ultimately connect per second using GNSS-enabled radios. Differentially cor - information flow from the forest to the mill. Additionally, a rected GNSS positioning of the harvester head can now also visionary goal in smart and precision forestry systems is the be used to measure individual tree geographic positions with realization of improved inventory monitoring. For example, sub-meter accuracy [91 ]. Positioning the harvester head, in the preferred case, mill owners should be able to monitor when coupled with StanForD files, permits growth rate and and evaluate the current product volumes remaining to be taper estimation for forest inventory from harvesters. How- harvested, current inventory volumes in each sorted log deck ever, because the resulting inventory data collected in this at the landings in active timber sale areas, and specific prod - way is only applicable for post-harvest reconstruction of ucts in transit. A motivating example is to achieve the level stand attributes [92 ], it is more likely that future ITD map- of information availability and inventory control that occurs ping and inventory control systems will rely on prior ITD with modern, online purchasing through large suppliers like from one of the available remote sensing methods which Amazon, and parcel delivery management. Among a range foster pre-treatment planning and smart equipment guidance. of possible solutions to achieve this level of verifiability in remote, forested areas, the blockchain technology that forms the basis of cryptocurrency transactions has become a front- runner. Blockchain is a shared database ledger system that Activity Recognition Based on Wearable stores all transactions in encrypted packets that provide a and Mobile Device Sensors verifiable record over time [ 81]. Blockchain-based crypto- currencies such as Bitcoin or Ethereum can form the basis The data collected in real-time by accelerometers and other for Evidence, Verifiability, and Enforcement (EVE) systems microelectromechanical sensors in consumer-grade smart- using smart contracts [82]. Blockchain can now be deployed phones, smartwatches, or other mobile and IoT devices can off-grid using mesh radio networks such as goTenna (Brook - be used to develop accurate models predicting activities in lyn, NY, USA), the LoRa network [83], or coupled with motor-manual operations, such as felling trees [93 ]. This the Blockstream or Swarm low-cost data satellite networks concept has further been adapted to smartwatches to model [84] that have been designed to provide local to global IoT choker-setter and chaser operations (setting and releasing networking solutions. chokers) on conventional cable logging operations [94]. The appeal of using common phones and watches to quantify motor-manual logging and forestry support services such Activity Recognition Based on Location as tree planting and thinning is that once predictive models are encoded in phone or watch apps, predicted activities can As harvested logs are skidded or forwarded and handled then be logged for complete work shifts indefinitely, creat - by the different pieces of equipment used in cut-to-length, ing a rich array of data to support subsequent analyses. The whole-tree, shovel-logging, steep slope, or traditional cable concept underlying this approach with integrated activity systems, coupling RFID or other technologies with equip- recognition modeling and remote networking to provide a ment activity recognition based on global navigation satellite consistent, real-time summary of simultaneous supply chain system (GNSS) devices deployed on either equipment cabs components and operational activities at active sites in the 1 3 Current Forestry Reports (2022) 8:148–165 157 Fig. 4 Conceptual mobile application framework for maintaining the equipment movements of each individually identified tree or prod - individual motor-manual worker and equipment activity recognition uct as they are transferred among equipment. Smartphone illustration information coupled with individual tree and product data in a net- credit: Ryer Becker, Ph.D., University of Idaho worked, relational database at the jobsite. Tables are associated with woods is shown in Fig. 4. Methods exist to render data inde- in the design of wearable and IoT devices [98]. This is par- pendent of sensor orientation [93 ] or the device location or ticularly true because wearable sensor data often is not cov- position on handheld equipment using a multi-layer percep- ered by legal and privacy protections associated with con- tron [95 , 96]. Machine learning methods such as random ventional medical data [99]. forests and artificial neural networks, as well as deep learn - ing, are useful methods for automating artificial intelligence based either on videography [97] or accelerometer data [94]. Knowledge Gaps and Emerging In the context of individual product tracing, a key benefit Opportunities of these device-based methods for monitoring manual activi- ties is that most smartphones and watches are also now read- Based on our review of available methods and technolo- ily capable of scanning RFID and NFC tags using a variety gies for carrying individual tree and product data through of available apps that can be automated to store these data. the supply chain from stump to mill, we find there are For example, since 2020, consumer smartphones have been currently few remaining barriers inhibiting their adop- used regularly to read and write information to RFID stick- tion. The component building blocks needed for complete ers associated with all log truck load tickets in harvesting systems that locate trees and monitor forest products with operations on the University of Idaho Experimental For- high precision using RFID, branding or other techniques est. The same approach can be used to scan individual tree have largely been established through continued research, RFID records during manual felling activities, and existing, as have methods to share data through remote networks phone-based models [93 ] can easily log GNSS coordinates that support shared databases, blockchain, or other tech- associated with tree felling events. Another important benefit niques for tracing and verifying products. Spatial ITD of phone- and watch-based activity recognition is that activ- lidar products are now being marketed commercially for ity and product tracing data logged in this way can readily forest management purposes in mixed-species stands. be transferred to shared databases on equipment using either However, our review reveals that because new ITD prod- mesh radios, or the emerging data satellite networks [84, ucts and tracing methods are just now emerging, there is 88, 90]. comparatively little research demonstrating how use of An important consideration for use of wearable and ITD products can improve silvicultural treatments and mobile device sensor data in human activity recognition operational efficiencies in practice. After almost three related to quantifying work productivity and the tracing of decades of research and development in industry, aca- individual trees and forest products is the use of personal demia, and federal agencies, operational forestry seems information collected by these devices [8 ]. The protection unprepared to make use of new ITD products now that and security of personal information is often an afterthought they are available. As methods for ITD and product 1 3 158 Current Forestry Reports (2022) 8:148–165 detection and monitoring continue to mature and be an opportunity to map seedlings during planting opera- refined, a significant shift is needed from development tions based on smartphones, smartwatches, or real-time and evaluation of measurement and remote sensing meth- GNSS in order to generate maps that can subsequently odologies in the forest inventory context to the evaluation be incorporated into lidar-based ITD inventory programs of applied methods for using individual tree and product over time. information in technology-informed silvicultural treat- ments, and planning and implementation of operations. Optimizing Skidding and Forwarding, Processing, To establish an agenda for this exciting area of research, and Decking we propose the following priorities to help advance use of individual tree and product information. Depending on the details of silvicultural prescriptions, it may be preferable to remove more young or old trees, trees of a particular species, or to minimize trail-related impacts Mapping Individual Seedling Locations During to the residual stand entirely. Prior, complete knowledge of Planting Operations stem spatial pattern within the stand makes it possible to optimize routes that function within desired constraints such As operational forestry incorporates use of ITD data as minimizing removal of one or more species, minimizing throughout the life cycle of stand management, a current stems removed solely to support the trail system, or location gap appears to exist in the consistent, accurate mapping of of trails in areas of low soil productivity. young, regenerating stands. For example, adjacent stands In regions with mixed-species stands and many product to the northeast and southwest of those identified in Fig. 5 sorts, ITD maps introduce the possibility of minimizing include young, planted seedlings and saplings that are not overall product handling and distance traveled from stump indicated as stems in the commercial ITD product. This to deck (Fig. 5). By identifying species or product category presents a knowledge gap for managers in a period that locations at their point of origin through spatial analysis, often involves the largest economic investment (costs) it may be possible to tailor log landings to minimize the associated with site preparation and planting, especially timing and skidding or forwarding direction of individual in intensively managed and even-aged stands. Human sorts. This approach can build on recent work using GIS to activity recognition coupled with GNSS may provide optimize precision planning of skid trails and winching [5 , Fig. 5 The boundaries of two stands managed historically on the University of Idaho Experi- mental Forest are not well correlated with species compo- sition. Within each stand, topo- graphic influences heavily affect the distribution of Douglas-fir (red) and grand fir (orange) on more eastern aspects and pon- derosa pine (yellow) on more western aspects. Based on this knowledge, landings (DFL1, PPL1, etc.) are being planned to minimize skidding and handling of sorted products, rather than using a central landing and then subsequently sorting logs into decks. Horizontal extent of clipped image is approximately 345 m 1 3 Current Forestry Reports (2022) 8:148–165 159 100], and to increase the sustainability of forwarding [101]. DBH, height, crown ratio, species, and target spacing in Rather than feeding all wood to one or more general landings ways that have not previously been possible. In this regard, and sorting products to decks from that point, ITD makes it one area that is particularly interesting is the possibility of possible to define smart landings. In regions where feller- accounting for topography and light interception associated bunchers are used, prior knowledge of preferred bunch char- with forest microclimates to inform both “smart” silvicul- acteristics and landing direction to minimize overall product tural treatments and operational best management practices handling seems feasible. Increasingly, the use of ITD data is simultaneously. For example, during marginal seasons when associated with a shift in thinking to address the question: temperatures fluctuate above and below freezing in temperate how can we optimize systems of equipment, or systems of regions, maintaining partial canopy south or west of road and motor-manual and mechanized equipment together in ways trail systems may provide shade that extends available operat- that are based on location-aware analytics, rather than opti- ing periods by maintaining frozen road or trail conditions. In mizing individual equipment independently for mean han- many ways, the presence of detailed, tree-level spatial data dling of average stem volume and piece size in ways that provides the opportunity to automate or intelligently augment have been largely non-spatial? many of the decision processes that field foresters and har - vester operators undertake when selecting trees for removal. Incorporating High‑Resolution Forest Product Value Individual tree information forms the basis for most underly- and Treatment Cost into Harvest Planning ing silvicultural theory, yet detailed, quantitative silvicultural prescriptions are often complex and challenging to translate Prior knowledge of individual forest product locations cor- and implement in the field, particularly in uneven-aged and responds directly to both resource value and operational treat- mixed-species stands or to meet multiple resource objectives ment cost within the treated stand. ITD data facilitates within- when implementing forest management plans. Rather than stand planning that may affect not only spatial arrangement estimating the spacing of leave trees in the field when mark - of skidding or forwarding trails, landings, and product sort ing trees individually, spatial point pattern analysis can be locations, but also the timing of harvest treatments. This is used to digitally select and refine stand density management particularly true in mixed-species forests where the value of treatments over thousands or millions of trees in ways that different products may vary considerably and the preferred incorporate the effects of slope, aspect, topography, stand timing of harvesting, processing, and delivering products to adjacency, and soil productivity within harvest units to best one or another mill destination may vary based on weather, optimize growth, the regeneration environment, and other market demand, or value. For example, in the Inland North- objectives. Furthermore, in regions where “operator choice” west United States, products in the same stands range from or “operator select” prescriptions are given to operators to very low-value pulpwood to cedar utility poles that are par- implement thinning treatments, spatial queries of ITD data ticularly high value. Using ITD data, knowledge of within- provide an opportunity to provide advanced, preliminary stand value may directly improve operational budget planning selections of candidate, individual trees to inform these pro- over time scales from weeks to years based on the sequence cedures in more advanced ways. and timing of resource extraction in ways that augment and enhance conventional, stand-level harvest planning. Machine Navigation for Improved Automation and Robotics Digitalizing Advanced Silvicultural Treatments Research on SLAM methods to help automate equip- Along with forest operations, silviculture stands to grow in ment guidance and robotics assume that stem locations new and exciting ways due to the availability of ITD data. For are unknown at the start of operations (hence localization example, advanced treatments based on actual, high-resolu- and mapping). However, adoption of ITD remote sensing tion individual tree growth environments can be designed to and the subsequent existence of digital forest maps prior reflect the alternative spacing requirements of different target to operations simplifies navigation algorithm processing species within stands. In uneven-aged, continuous cover, or for equipment guidance, automation, and robotics. The any non-clearcut silvicultural systems, ITD data affords the localization step in equipment guidance becomes a much opportunity to inform the selection of leave trees by apply- simpler matter of matching patterns of detected trees (via ing spatial queries to a digital inventory list rather than an equipment-mounted lidar, machine vision, or other sensors) assumed distribution of stem sizes and locations. As evident to an existing map that improves on the position accuracy in Fig. 1, mean, stand-level estimates of species volume can or becomes an improved substitute for GNSS-based location represent substantial variability in product type and location of the machine and attachment head. Instead of relying on within harvest units. Using digitized ITD inventory data, can- remote, mobile satellites, the forest itself becomes a power- didate leave trees can be pre-determined based on location, ful constellation of fixed referenced points that remain in 1 3 160 Current Forestry Reports (2022) 8:148–165 place for the length of stand rotation. Rather than a source utilizing forestry equipment computing and data strategies of canopy interference and multipath error, we need to shift that function independently of proprietary machine software, our paradigm to recognizing that these trees are valuable and associated work has evaluated M2M internet options [6]. geolocation points for precision guidance. Systems that reduce the dimensionality of data at low local levels in IoT processing can help alleviate networking band- Sharing Product Locations from Stump to Mill width needs in remote environments [8 ]. However, further research is needed to empirically test the reliability of various Methods exist for integrating RFID into the harvester head networking options in the field in order to identify system in cut-to-length systems. After the felling step, an RFID capabilities and limitations in real-world environments. nail inserted into a tree at breast height could remain in the lower log and be monitored through the supply chain using RFID readers mounted on equipment. However, any Conclusion secondary logs or products bucked from the stem become separated and of unknown origin. In order to trace these As new ITD data products for forest managers become avail- other products, the harvester head must either fingerprint able, researchers and managers will need to shift the weight the cross-section of upper stem logs in order to assign their of research and development effort from prediction algo - geographic coordinates and tree number of origin or have the rithms, evaluation, and validation of remote sensing prod- capability to insert additional RFID tags or other tracing ele- ucts to the operational application and use of ITD data in ments. Thus, automated application of bar codes, QR codes, silviculture and operations. This is particularly true in the RFID, or other low-cost identification via the harvester head design of high-resolution silvicultural prescriptions, optimal is an important area of development. In whole-tree systems, harvest unit layout, harvesting, skidding, and processing to the head of a processor working at the landing must have fully utilize prior knowledge of individually stem-mapped the capability to both scan incoming parent information on stands, and even the locations of individual products within whole trees and also apply that information to child logs as trees. the stem is bucked, ultimately using the system described Based on our review, airborne, drone, and terrestrial lidar in this paper two or more times during processing and sub- and SfM remote sensing techniques, as well as other sensor and sequent handling of the logs. For complete automation and photogrammetry methods not addressed in this review, provide digitization, both the skidder or forwarder and log loader useful data for informing ITD. In practice, these techniques should also have the ability to scan and record product iden- are also used concurrently to inform ITD products. For exam- tification as materials are handled in the supply chain, such ple, terrestrial lidar, drone lidar, or both may be used concur- that ultimately the current locations of all products on the rently in the collection of field plot data that is then utilized to jobsite are known and accounted for at all times. improve segmentation models for ALS predictions over large spatial extents. RFID scanning has been demonstrated success- Managing Big Data in Remote Environments fully as a method to maintain readable data on single trees over time in ways that could be accessed by scanners mounted on As ITD and individual forest product data are transferred equipment or UAVs. RFID has also been used to record infor- among machines using RFID or other product tracing meth- mation about individual log products using a scanner mounted ods and distributed, verifiable payment methods such as on the processor head. Branding and biometric fingerprinting blockchain technology extend to the edge of networking in are feasible technologies for product tracing. Cryptocurrency the forest, associated supply chain analytics must increasingly payment and ledger systems that utilize the blockchain provide manage big data. Research on developing functional machine- a distributed, verifiable mechanism that may supplement and to-machine (M2M) internet capability in remote forests, as strengthen chain-of-custody verification for the purposes of well as research connecting data from the forest jobsite back forest certification. Human and machine activity recognition to the mill or operational headquarters in real-time, is needed modeling, coupled with RFID or other scanning and identifica - [8 ]. There has been little research truly leveraging data across tion methods, provide a framework for tracing individual forest the full spectrum of the individual tree and product supply products from a source tree location through logging systems chain [6]. Use of artificial intelligence (AI) to improve opera - and to the mill. In short, all the components of a smart, ITD, tions or supply chain efficiencies in real-time in response to and product tracing system exist. mill needs, market factors, and other considerations linked Given that ITD products and methodologies for equip- between harvest operations and product marketing will require ment-based scanning and tracing of individual products massive data storage, processing power, and network band- exist, we have identified several opportunities for research width in areas that currently often lack any internet or cel- utilizing these technologies to help optimize the digital lular data coverage. Canadian systems are the most advanced supply chain. These include studies demonstrating and 1 3 Current Forestry Reports (2022) 8:148–165 161 2016.25432 25 This paper summarizes the accuracy of differ - evaluating the following: (1) mapping of individual seed- ent ITD methods across a wide range of conditions. ling locations during planting operations; (2) optimizing 2. Liang X, Hyyppä J, Kaartinen H, Lehtomäki M, Pyörälä J, skidding and forwarding, processing, and decking in ways Pfeifer N, et al. International benchmarking of terrestrial laser that incorporate ITD product information; (3) incorporat- scanning approaches for forest inventories. ISPRS J Photogramm Remote Sens. 2018;144:137–79. https:// doi. org/ 10. 1016/j. isprs ing high-resolution forest product value and treatment costs jprs. 2018. 06. 021. into harvest planning; (4) digitalizing advanced silvicultural 3.• Picchi G. Marking standing trees with RFID tags. Forests. treatments based on current and future ITD information; 2020;11:150. https:// doi. org/ 10. 3390/ f1102 0150 This paper (5) improving machine location accuracy and navigation provides an evaluation of the performance of RFID tags after being attached to trees and exposed to weather over time. to increase automation and robotics; (6) sharing of product 4. Feng Y, Audy J-F. Forestry 4.0: A framework for the forest sup- information from M2M and stump to mill; and (7) manag- ply chain toward Industry 4.0. Gest Prod. 2020;27:e5677. https:// ing the big data associated with ITD and individual product doi. org/ 10. 1590/ 0104- 530X5 677- 20. information in remote environments. Each of these research 5.• Picchio R, Proto AR, Civitarese V, Di Marzio N, Latterini F. Recent contributions of some fields of the electronics in areas contributes to an agenda that can help managers and development of forest operations technologies. Electronics. researchers utilize newly available ITD and individual prod- 2019;8:1465. https:// doi. or g/ 10. 3390/ elect r onic s8121 465 uct data to continue advancing the digitalization of forest This paper provides a nice, summary review of a variety operations. of current electronic sensors that are being used in forest operations. 6. Gingras J-F, Charette F. FPInnovations’ Forestry 4.0 initiative. Proceedings of the 2017 Council on Forest Engineering Annual Funding Keefe and Zimbelman have previously received funding Meeting [Internet]. Bangor, ME, USA; 2017 [cited 2021 Dec for real-time GNSS research on logging safety through United States 5]. Available: http:// cofe. or g/ f iles/ 2017_ Pr oce edings/ FPInn Centers for Disease Control (CDC)/National Institute of Occupational ovati ons% 20Gin gras% 20Cha rette% 20For estry% 204.0% 20for% Safety and Health (NIOSH) grant number 5 U01 OH010841. Keefe and 20COFE% 202017. pdf. Zimbelman1 have previously received funding for smartwatch activ- 7. Brown M, Ghaffariyan MR, Berry M, Acuna M, Strandgard ity recognition modeling to improve logging safety on University of M, Mitchell R. The progression of forest operations technology Washington Pacific Northwest Ag Safety and Health (PNASH) Center and innovation. Aust For. 2020;83:1–3. https://doi. or g/10. 1080/ pilot project grant UWSC10722. 00049 158. 2020. 17230 44. 8.• Keefe RF, Wempe AM, Becker RM, Zimbelman EG, Nagler ES, Declarations Gilbert SL, et al. Positioning methods and the use of location and activity data in forests. Forests. 2019;10:458. https://doi. or g/10. 3390/ f1005 0458 This paper provides a general summary of Conflict of Interest The authors declare no competing interests. real-time positioning and wearable and mobile technologies to support individual tree and product big data applications Open Access This article is licensed under a Creative Commons Attri- in smart and precision forestry. bution 4.0 International License, which permits use, sharing, adapta- 9.• Talbot B, Pierzchała M, Astrup R. Applications of remote and tion, distribution and reproduction in any medium or format, as long proximal sensing for improved precision in forest operations. as you give appropriate credit to the original author(s) and the source, Croat J For Eng. 2017;38:327–36 This paper provides a sum- provide a link to the Creative Commons licence, and indicate if changes mary of remote and proximal sensing technologies and were made. The images or other third party material in this article are reviews current applications for improving precision in for- included in the article's Creative Commons licence, unless indicated est operations. otherwise in a credit line to the material. If material is not included in 10. Torresan C, Benito Garzón M, O’Grady M, Robson TM, Picchi the article's Creative Commons licence and your intended use is not G, Panzacchi P, et al. A new generation of sensors and monitor- permitted by statutory regulation or exceeds the permitted use, you will ing tools to support climate-smart forestry practices. Can J For need to obtain permission directly from the copyright holder. To view a Res [Internet]. 2021 [cited 2021 May 3]; https://doi. or g/10. 1139/ copy of this licence, visit http://cr eativ ecommons. or g/licen ses/ b y/4.0/ . cjfr- 2020- 0295. 11. Hyyppä J, Yu X, Hyyppä H, Vastaranta M, Holopainen M, Kukko A, et al. Advances in forest inventory using airborne laser scanning. 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Publisher: Springer Journals
Copyright: Copyright © The Author(s) 2022
eISSN: 2198-6436
DOI: 10.1007/s40725-022-00160-3
Publisher site: See Article on Publisher Site

Abstract

Purpose of Review Individual tree detection (ITD) methods and technologies for tracking individual forest products through a smart operational supply chain from stump to mill are now available. The purpose of this paper is to (1) review the related literature for audiences not familiar with remote sensing and tracking technologies and (2) to identify knowledge gaps in operational forestry and forest operations research now that these new data and systems are becoming more common. Recent Findings Past research has led to successful development of ITD remote sensing methods for detecting individual tree information and radio frequency identification (RFID), branding, and other product tracing methods for individual trees and logs. Blockchain and cryptocurrency that allow independent verification of transactions and work activity recognition based on mobile and wearable sensors can connect the mechanized and motor-manual components of supply chains, bridg- ing gaps in the connectivity of data. However, there is a shortage of research demonstrating use of location-aware tree and product information that spans multiple machines. Summary Commercial products and technologies are now available to digitalize forest operations. Research should shift to evaluation of applications that demonstrate use. Areas for improved efficiencies include (1) use of wearable technology to map individual seedlings during planting; (2) optimizing harvesting, skidding and forwarder trails, landings, and deck- ing based on prior knowledge of tree and product information; (3) incorporation of high-resolution, mapped forest product value and treatment cost into harvest planning; (4) improved machine navigation, automation, and robotics based on prior knowledge of stem locations; (5) use of digitalized silvicultural treatments, including microclimate-smart best management practices; and (6) networking of product tracking across multiple, sensorized machines. Keywords Forest operations · Individual tree detection · Traceability · RFID · Location analytics · Activity recognition · Smart forestry · Supply chain Introduction and tracing individual forest products through the sup- ply chain, introduces new opportunities and challenges The maturing of remote sensing methods to perform for operational forest management. Conventional forest individual tree detection (ITD) [1 , 2], as well as tim- inventory techniques have traditionally focused on use ber harvesting applications of radio frequency identifi - of mean, stand-level estimates of forest attributes in the cation (RFID) [3 ] and other technologies for labeling context of silvicultural and operational planning. Many forest managers, foresters, and researchers are accustomed to working with coarse estimates of stand merchantable This article is part of the Topical Collection on Forest Engineering volume and stems per hectare, stem piece size distribu- tions, and other average stand conditions as the basis for * Robert F. Keefe harvest planning. Although light detection and ranging robk@uidaho.edu (lidar) and other remote sensing methods have been used University of Idaho Experimental Forest, College of Natural to detect and map data in fine resolution for decades, the Resources, Moscow, ID 83844-3322, USA resultant products have generally been summarized in con- Consiglio Nazionale delle Ricerche - Istituto per la ventional ways for the purposes of harvest planning and Bioeconomia (CNR-IBE), Via Madonna del Piano 10, implementation. However, an aspirational goal for the use 50019 Sesto Fiorentino, Italy 1 3 Current Forestry Reports (2022) 8:148–165 149 of lidar by forest managers and operational foresters has for shifting research to increasingly operational uses of long been the development of functional, ITD mapping these data, as opposed to continuing along prior avenues that could help foster a new era of high precision, smart, of methodological research, development, and testing? forest operations. An underlying theme and trend in many In order to address these questions, we reviewed recent current regional and global smart and precision forestry literature using Google Scholar with emphasis on publica- initiatives is the integration of ITD and stem characteris- tions from the last 5 years and highly cited manuscripts tics based on lidar point cloud or other remotely sensed (Fig. 2). Our review was further shaped by experience using data with increasingly sensorized, automated, and data- ForestView® ITD inventory data (Northwest Management driven harvest systems that would allow for supply chain Inc., Moscow ID) in parallel with conventional, stand-based level big data analysis and optimization [4, 5 ]. This can inventory (SBI) data to inform silvicultural treatments and be seen, for example, in the FPInnovations’ Forestry 4.0 timber management operations on the University of Idaho program, which is based on the application of Industry 4.0 Experimental Forest for approximately 9 months prior to digital transformation concepts to forestry, an idea that manuscript submission. Our objectives in the review are (1) has gained traction in many countries [6, 7]. This direc- to provide a current review of ITD methods based on recent tion and momentum in the operational research community literature, especially for forest operations specialists who are generally associated with a broad push to improve and are not necessarily experts in remote sensing; (2) to review advance smart, precision forest management that lever- the use of individual tree-level data in other subject areas in ages the merging of big data and Internet-of-Things (IoT) forestry such as growth and yield modeling, silviculture, and concepts associated with increased forest auomation and forest planning; (3) to provide current, state-of-the-art infor- • • robotics [8 , 9 , 10], as well as increasing use of a wide mation on individual forest product tracing using methods variety of electronic sensors on equipment. Additionally, like RFID tags placed on trees and logs that can be read by improving verifiable chain-of-custody criteria associated operational equipment attachments on harvesters and pro- with sustainable forestry certification programs to curb cessors; (4) to review blockchain and other cryptocurrency illegal logging may be a further driver of research into methodologies for distributed payment systems that may labeling and tracking individual trees and products. be paired with individual forest product tracing; and (5) to As discussed as recently as 2017 by Talbot et al. [9 ], identify and discuss research gaps in the current literature. many current precision remote and proximal sensing In the latter case, our primary emphasis was on identify- methods for operational forestry are still experimental and ing and suggesting research on (1) application, rather than require further research and development. For example, development, of ITD and individual product tracing data in many demonstrated industrial uses of ITD have been lim- operational forestry and (2) systems-level traceability from ited to single species plantations. However, individual tree the individual tree to the mill that spans multiple pieces of stem map products for complex, mixed-species and multi- equipment. aged stands over the extent of large forest ownerships based on lidar-derived ITD are now commercially avail- able. As a result, forestry professionals ranging from on- Remote Sensing Methods for Individual Tree the-ground foresters to academic researchers must increas- Detection (Airborne, Drone, Terrestrial Lidar) ingly make the challenging and quick transition from very minor, occasional uses or somewhat passive monitoring Both remote and proximal sensing offer opportunities of lidar’s gradual development over the last few decades for improving precision in forest operations [9 ]. Talbot to suddenly having massive, rich, high-resolution ITD et al. [9 ] distinguish remote sensing from proximal sens- datasets available for large ownerships. For example, the ing largely based on the platforms used. Remote sens- data shown in Fig. 1 are a small subset of a commercially ing includes airborne laser scanning (ALS) and generally available lidar-derived ITD product with predicted indi- offers higher spatial coverage but lower spatial resolution vidual tree location, diameter at breast height (DBH), tree while proximal sensing includes terrestrial laser scan- height, species, and crown characteristics for all trees on ning (TLS) and usually offers lower spatial coverage but • • the main 3,300-ha University of Idaho Experimental For- higher spatial resolution [9 ]. Wang et al. [1 ] charac- est. In this context, an important question for many profes- terizes three approaches to ITD based on ALS: raster- sionals in silviculture and forest operations becomes the based, point-based, and hybrid approaches combining the following: now that we have these detailed ITD maps and two. In raster-based approaches, the information in raster the underlying information, how do we make use of the products such as crown height models (CHMs) is used to new data in practical ways to improve stand treatments and detect trees based on a variety of segmentation algorithms harvesting operations to fully implement a smarter and [1 ]. In point-based approaches, the ALS point cloud is more advanced supply chain? What are the opportunities used to derive 3D individual tree crown shape directly 1 3 150 Current Forestry Reports (2022) 8:148–165 Fig. 1 Subset of commer- cial 3300-ha ForestView® Individual tree stem map product encompassing over 700 mixed-species stands of vary- ing ages on the University of Idaho Experimental Forest. The underlying ITD product data used to create this figure was developed by Dr. Mark Corrao at Northwest Management, Inc. in Moscow, ID. The horizontal extent of the image shown is approximately 250 m. Tree and product information can be inferred from ITD data (shown below), in which each row cor- responds to one dot on the map. The author has color coded dots by species and set dot size proportional to DBH using conventional GIS software for visualization purposes. Color coding is as follows. Red: Douglas-fir ( Pseudotsuga menziesii); yellow: ponderosa pine (Pinus ponderosa); dark green: Western red cedar (Thuja plicata); orange: grand fir ( Abies grandis); and light green: western larch (Larix occidentalis). Information about general locations of products such as Douglas-fir veneer logs (DFPE1), high-value cedar utility poles (RCU1), Douglas-fir sawlogs (DFS1), and grand fir pulpwood (GFP1) can be inferred from the data graphically, coupled with field observation [1 ]. Some newer point-based approaches normalize the circle and cylinder detection methods and/or voxel and ALS point cloud and transfer it to a local voxel space point clustering algorithms [2]. from which trees are detected [1 ]. In a hybrid approach, trees are delineated in rasterized data, and the 3D canopy of detected crowns is subsequently modeled from the Airborne Lidar and ITD point cloud data using various clustering and segmenta- tion algorithms [1 ]. The authors compared one point- ALS data has been used to detect individual trees with based, two raster-based, and two hybrid approaches and accuracies of 83.84 [11] to 87% [12]. While evaluating found detection accuracy was affected by canopy structure various approaches to detecting individual trees in dif- • • and point density [1 ]. In terms of TLS, Liang et al. [2] ferent crown classes, Wang et al. [1 ] found that most describe and evaluate 18 different algorithms for ITD. methods provided detection rates greater than 85% for These approaches have varying degrees of automation, dominant trees and greater than 70% for codominant and include raster-, voxel-, and point-based techniques trees. Individual tree-based features derived from ALS [2]. Individual trees can be identified through different data have been shown to improve the accuracy of pre- dicted attributes such as mean height, mean DBH, and 1 3 Current Forestry Reports (2022) 8:148–165 151 Fig. 2 Flow of the motivating topic area, review of recent and well-cited literature, and development of conceptual ideas presented in the manu- script volume [11]. Most notably, volume was estimated with more detailed information such as branch attributes in an RMSE of 20.32% using both individual tree-based fea- order to estimate branch angle, length, and volume among tures and point height metrics, compared to an RMSE of individual trees [16]. 25.41% using point height metrics only [11]. ALS data has been used to estimate forest above-ground biomass (AGB) through a process that involves detecting indi- Terrestrial Lidar vidual tree crowns and then calculating tree density, tree height, individual crown length, and mean height of forest Because it provides a different, below-canopy perspective, layers [13]. AGB was calculated with an RMSE of 17.1% terrestrial lidar is also being used to quantify and map at the forest plot level using lidar-derived individual tree individual tree data [17]. Trochta et al. [17] developed an data, compared to an RMSE of 31.1% using a traditional open-source application called 3D Forest which is capable regression model approach incorporating both field meas- of extracting DBH, tree positions and heights, stem curves, urements and lidar point cloud metrics [13]. Individual and tree planar projections from TLS data. The applica- tree data from ALS has also been used to estimate indi- tion can also describe detailed crown parameters such as vidual tree crown width and crown volume [14] as well crown base height, crown height, and crown volume and as to optimize tree removal and inform thinning prescrip- surface area [17]. Liang et al. [2] found that TLS-based tions in order to reduce crown fire potential within stands approaches to ITD can estimate DBH and stem curves with [15]. Luo et al. [12] developed a method of estimating accuracies of 1–2 cm. After extracting trees, wood and individual tree crown base height directly from ALS data leaf points are often separated using classical machine without the need for field measurements with an RMSE learning and computer vision algorithms [18 ]. Quanti- of 1.62 m, which has applications for tree growth and tative structure modeling (QSM) algorithms can then be health monitoring as well as for ecosystem and wildfire used to estimate wood volume and model tree morphology modeling. Recent work has utilized ALS data to model [18 ] and to segment and classify the physiological age of annual shoots from tree skeletons [19]. Recent QSM 1 3 152 Current Forestry Reports (2022) 8:148–165 approaches have investigated the potential for deep learn- and smartphone-based SfM has been used to estimate DBH ing to delineate tree stems and branches [20] as well as to and stem volume [29]. SfM is also used in assessing and detect structural properties of branches [21]. Calders et al. monitoring forest condition by classifying tree health [27 ]. [18 ] summarize a variety of applications of TLS ITD data in the measurement of forest attributes, including calcu- lating DBH, estimating wood volume, and incorporating ITD from Forestry Equipment crown structure information into traditional allometric models. In the context of operational forestry, these novel As mentioned above, there is a strong effort to increase measurement techniques may provide more detailed infor- digitalization of the forestry supply chain broadly in the mation on log geometry and quality in addition to offering context of Forestry 4.0 concepts [30 ]. The increasing use a better understanding of growth and changes in biomass of big data and AI in smart forestry is associated with the [18 ]. The structural and spatial information derived from drive to automate thinning and harvesting through robot- TLS data can also provide insights on the effects of vari - ics [31]. Use of individual tree metrics like stem volume ous silvicultural practices [18 ]. improves estimates of operational performance, produc- tivity, and costs [32] and may also provide the basis for equipment positioning and navigation using simultaneous localization and mapping (SLAM) [5 , 33]. For example, Drone Lidar Miettinen et al. [34] describe the use of machine vision and 3D scanning to measure trees during harvesting High pulse density drone lidar is able to incorporate wider operations, in order to optimize felling and increase har- scan angle than airborne (by virtue of being at lower eleva- vester automation. A combination of 3D laser scanning tion) and is being used for high-resolution mapping of and SLAM was used to measure standing tree stems prior localized forest structure in order to calibrate space-borne to felling in order to estimate crown base height, taper, lidar [22 ]. Whereas acquisition of terrestrial lidar can be sweep, trunk dimensions, branches, and lean [34]. Cam- relatively slow, drone-based high density lidar can span eras attached to an ATV have been used to measure trees large areas quickly and still retain pulse density up to sev- and estimate structure from a distance through motion eral thousand points per square meter [22 ]. ITD from vision while cameras on the harvester head were used to drone lidar has been used to identify priority areas for measure length and thickness of processed stems [34]. wildfire prevention [ 23] as well as to detect invasive spe- Based on similar methodology, SLAM has also been con- cies when fused with imagery data [24]. Drone lidar is sidered a general forest inventory technique, as well as for also being used to delineate individual trees and predict equipment positioning and navigation [35]. An underlying DBH and species in order to estimate tree volume at the concept in SLAM is that a stem map is constructed on-the- plot, stand, and forest levels without the need for field data fly based on equipment-mounted sensor data. The map for calibration [25]. Recent work has evaluated the poten- becomes more complete as equipment works. This work- tial for under-canopy drone lidar to extract stems and tree ing map of the stand environment is recorded in memory, heights in order to calculate individual tree volume with a and the resulting map then forms the basis for subsequent, level of accuracy similar to TLS [26]. autonomous machine guidance, which could occur inde- pendently of GNSS-based navigation [36]. Structure from Motion ITD and Chain‑of‑Custody The Structure from Motion (SfM) methodology uses low-cost imaging platforms to extract forest biophysi- Many forest legal and sustainability certification programs cal parameters for both aerial and terrestrial applications include product chain-of-custody documentation [37]. In [27 ]. SfM applied to unmanned aerial vehicle (UAV) addition to broad interest in the operational forestry com- image data has been used in individual tree approaches munity to advance smart and precision forest operations, to detect trees, classify species, and measure tree heights the development of verifiable chain-of-custody is a key [27 ]. Both UAV lidar and SfM were recently used to accu- motivator for developing and testing individual forest rately measure individual seedling height down to a 1-m product labeling and tracing systems. A natural outcome threshold [28]. Most terrestrial applications of SfM have of increasing interest in chain-of-custody is the desire to focused on modeling individual trees, primarily to estimate track products from their actual, exact place of origin. tree positions and heights, DBH, and stem curvature [27 ] In the context of forest operations, the place of origin 1 3 Current Forestry Reports (2022) 8:148–165 153 is ultimately the individual tree’s verifiable geographic the dense undergrowth vegetation, which reduced the effec - coordinates in the woods. This could be the location of tive range of tags, and (2) the unpredictable mutual position a residual stump, or a verifiable individual stem that was of the tags and the reader, which further compromised the removed, as evident in multitemporal satellite imagery, capacity to detect a return signal. Given the operating condi- aerial photography, or lidar acquisitions. Chain-of-cus- tions, active RFID tags performed better due to their stronger tody technologies include log branding, bar coding, use signal and higher reading range, but their higher unitary cost of tracer paints, RFID, and chemical or biological finger - and battery duration should be carefully considered when printing [38] as well the recent use of DNA-based track- planning each specific application. ing [39]. Coupled with remote sensing, cloud computing, For the requirements of forestry and timber supply chains, and big data, fiber sourcing and supply chain technologies passive and disposable UHF RIFD tags are regarded as the are helping to improve sustainable forestry as originally best solution [51, 52]. These types of RFID tags may be envisioned and intended [40]. deployed for a wide range of services beyond single-tree retrieval. For instance, they can be used to associate digital information with standing trees for recreational purposes, Use of RFID to Store Information forest management and protection, or timber production with Individual Standing Trees [10]. In these applications, long-range readability is less critical as trees can also be marked with visual systems (e.g., At present, several systems for tree and timber marking are color spray), allowing the operators to approach the tree and available in the market, varying from simple paint or chalk identify the tag with manual readers. In a similar application, to systems capable of storing a certain amount of informa- commercial UHF RFID tags proved capable of withstanding tion retrievable with automatic systems [41]. Among the the harsh climatic conditions of an Alpine forest and retained different alternatives for data transmission, RFID technol - their operative capacity for 2 years [3 ]. In this environment, ogy is regarded as the best solution for a number of rea- resin emission and tree growth proved to be the most chal- sons: painted codes or logos may wear out due to weather lenging factors as the former can attack the plastic layers exposition over time [42], and, compared to manual sys- protecting lighter tag models, while the latter can break the tems, automatically readable systems avoid human errors hard case of “heavy duty” tags fixed with screws on the and drastically reduce recording time [43]. RFID also allows trunk. The study suggests that long-term tagging of high- reading in any light conditions, without requiring a precise value trees (e.g., nesting trees for protected species) should line of sight and even when tags are hidden by mud, dirt, rely on large tags with strong protective cases fixed to the or resin [44]. Thanks to its performance, RFID is currently trunk with plastic screws. Metal screws should be avoided deployed in a wide range of industrial sectors, ranging from due to the possible damages they may cause to the sawmill- manufacturing to logistics. In agriculture, RFID technol- ing equipment. The screw should also be long enough to ogy is already a standard for cattle identification [ 45] and leave a gap between the tag and the bark in order to avoid provides promising applications in nursery management damages due to tree growth. [46] and potted plants distribution [47]. Additionally, the When marking trees selected to be felled in forest opera- identification of living plants in open field, generally per - tions, simpler RFID tag models with large transponder and manent crops, is raising an increasing interest as it would low unitary cost (< 0.40 €/0.48 $) are preferred. These can facilitate several precision agriculture practices, such as the be easily stapled to bark and provide the best reading per- selective and optimized application of chemicals [48]. The formance, which may enable both manual and automatic tag implementation of RFID in forestry shares similar chal- detection (e.g., with a processor head). This last applica- lenges of tag survivability and reading capacity related to tion was tested by Pichler et al. [53 ] in a study comparing open air deployment. However, in agriculture the position the technical feasibility and costs of a digital forest inven- of plants, tags and reading devices can be planned in order tory with traditional manual-based forest inventory. Simple to maximize the efficiency of the whole system, while in UHF RFID tags were used to connect information derived forest conditions, such planning has a much higher degree of from UAV-aerial images and TLS scanning to each tree. uncertainty, which may lead to poor results. For this reason, The whole digital system proved technically mature, yet its among the possible operating frequencies for RFID, ultra overall cost resulted in 1.55 € per marked tree against 0.36 high frequency (UHF) is regarded as the most suitable, as it € for the traditional inventory and marking system. In the allows the longest reading ranges [49 ]. Marczewski et al. former, the main cost factor was the high density of TLS [50] tested the suitability of passive and active UHF RFID plots, which aimed to directly scan every trunk rather than tags to mark and retrieve trees in a natural forest combining improve statistical inventory (as currently used in commer- GPS positioning and RFID identification at long range (>10 cial applications). The high-quality data was used to gener- m). Results with passive tags were unsatisfactory due to (1) ate a digital model of each trunk and relate it to the value of 1 3 154 Current Forestry Reports (2022) 8:148–165 the local timber assortments, returning cutting instructions future as multitemporal lidar increasingly becomes used as the that were linked to each tree and transferred to the processor basis for modeling stand growth in ways that support climate with the RFID tags, maximizing the timber value recovery. smart silvicultural and operational planning. In motor-manual felling operations followed by whole-tree cable yarder extraction, the simple RFID tags used in the previous study were able to withstand hauling operations Individual Tree Data in Silviculture with a survival rate of 97%, proving to be a reliable tool for and Forest Planning data transfer among the different steps of the timber supply chain [44]. RFID tag components (transponder and chip) ITD methods discussed in the preceding sections vary in are currently made with metal protected by a plastic case, their ability to detect trees in the different forest canopy and since tags attached to the logs would enter the stream of classes (dominant, codominant, intermediate, suppressed) sawmill residues, this could reduce acceptability from the that form the basis of stand development and silvicultural industry. A possible solution to this issue is printing RFID treatments [1 ]. Accurate estimation of tree location, tags directly on renewable materials [54]. This technique height, and crown structure have been proposed as a use- was also tested to produce RFID tags with dual frequency ful application of SfM data in order to define treatments (UHF and near field contact, NFC) [ 55], which could fur- for individual tree selection systems used in Ponderosa ther reduce the investment costs of the system: foresters and pine (Pinus ponderosa Lawson & C. Lawson) stands in chainsaw operators could deploy common smartphones with Colorado, USA [62]. Recently, preliminary methods for NFC functions to read the tags and relate them to the data- optimizing stand prescriptions for individual tree and base, while the UHF potential would still allow long distance group selection and clearcut systems in ways that account and machine identification. for the spatial distribution of the stand based on analysis of ITD data have been presented [63]. Similarly, Wing et al. [64] developed a method to generate group selection treatment options utilizing ALS-derived stem map data in Individual Tree Data in Growth and Yield order to meet various management objectives. These meth- ods are promising, and modeling and optimization show Growth functions that predict stem diameter or basal area that accounting for individual tree characteristics during • • increment over time based on individual tree attributes and the thinning is economically beneficial [ 65 , 66 ]. Large data- surrounding, competitive environment have long been used bases are available to quantify fire effects of individual as the basis for stand growth simulators deployed widely for trees to support planning of prescribed fire and to assist forest management [56, 57]. Recent models represent growth with post-fire evaluation [ 67]. in structurally complex stands [58]. Multitemporal SfM and multitemporal lidar are being used to quantify growth [59]. However, relatively few simulators leverage height increment, crown surface area, and other canopy metrics that are more Direct Log Measurements Using Encoders easily inferred from remotely sensed data collected from air- in Harvesters and Processors craft or UAVs, rather than DBH increment. This is largely a function of DBH being the common measurement conven- Harvester and processor heads measure stem diameter with tionally available in manual, ground-based stand inventory. sensors in the delimbing knives or feed rollers that use While non-spatial individual tree competition indices appear either three points of measurement or the average of two to predict growth generally as well as spatial indices in natu- perpendicular measurements to determine log diameter at rally regenerated mixed-species stands [60], multitemporal multiple points along the stem [68]. As rollers feed the lidar provides the data to quickly parameterize new kinds of stem, length is determined using a digital measuring wheel models that fully leverage individual crown metrics, which mounted directly on the head and intermediate points are are closely related to the photosynthetic capacity of trees, in interpolated based on stem taper. A range of algorithms order to forecast individual stem volume growth in biologi- can then be used to estimate timber volume based on log cally meaningful ways. A good example of a physiological, length and diameter measurements [69]. Stem swell asso- process-based model that represents individual tree and stand ciated with branch whorls or stem defect is accounted for growth as a function of tree crown characteristics over time via algorithms in the onboard computer. Harvester-derived •• is the Amorphys model [61 ]. Though developed prior to estimates of stem defects can be used in bucking simu- the maturing of current, lidar-derived ITD analysis, models lations designed to predict product recovery [70]. While conceptually similar to the approach described in Valentine measurements are precise, harvesting and processing head •• and Mäkelä [61 ] may become extremely important in the measurements require regular monitoring and calibration 1 3 Current Forestry Reports (2022) 8:148–165 155 [68]. Harvester measurements of tree length, diameter, data transfer have been proposed and evaluated experimen- volume, and location have been used as reference data for tally or developed into commercial products [41]. Most solu- ALS-based predictions of forest inventory attributes [71]. tions rely on robust and cost-effective visual systems, such as Because harvesters do not measure the top crown section, barcode-like marks printed with spray nozzles installed on they provide total log length but not total tree height [72]. the chainsaw bar of the processor head [76] or with punch- Thus, recent work has attempted to develop improved ing systems. The latter is a device that consists of 12 ham- models for estimating individual tree heights from har- mer brands that punch wood with a combination of marks vester data in order to facilitate harvester-based inventory that represent an alphanumeric code [77]. In both cases, an and analytics through integration with traditional inven- optical reader installed on the sawmill feeding line acquires tory and lidar data [72]. Advanced characteristics of forest images that are converted to a unique ID code for each log, products, such as wood stiffness and related properties of making it possible to retrieve any information provided by logs determined from acoustic time-of-flight measure - the processor head and stored in a shared database. These ments, can increasingly be integrated into harvesting or solutions are robust and cost-effective since they do not rely processing heads and used to inform processor bucking on disposable tags, yet they require a line of sight and proper decisions in real-time [73]. illumination to be detected. Reading Individual Log Identification RFID and Biometric Log Identification on Harvester or Processor Heads on Equipment Attachments Research and industrial development are increasingly focus- An interesting alternative is the use of RFID, which had ing on the potential of data provided by sensors installed on been successfully deployed on prototype processor heads forest machines, and particularly the harvester heads that in the frame of two European projects. The first system was fell and process trees into commercial timber assortments equipped with a device for marking logs with a special RFID in cut-to-length (CTL) harvesting systems. In addition to tag composed of wood composites. This resulted in a neutral the valuable information provided by the standard encod- material for the pulp industry [78]. The second prototype ers, several sensors have been tested on processor heads to was designed so that the processor head could read RFID measure timber density [74] or a combination of quality fac- tags attached to the incoming stems (e.g., in cable yarding tors (timber density, knottiness, crosscut section defects) to operations), assess timber quality with sensors, and relate •• return a quality index value [75 ] as shown in Fig. 3. The the relevant data to each log with a new RFID tag automati- •• information generated is meant to be used to optimize trans- cally attached to the crosscut section [75 ]. The advantage portation, purchase, and industrial processing of timber in of this solution is allowing successful log ID detection in the supply chain. To maximize the value of the information poor visibility conditions as well as providing a mecha- generated, a number of solutions for log identification and nism for efficient and fast bulk reading of whole log trucks, Fig. 3 A RFID tags on log ends for stump-to-mill traceability tests in sity, quantity of knots, presence of rot), an RFID reader designed to the Italian Alps (Trento), B sensorized processor head described in detect the ID of trees to be processed and an RFID tagger deployed to •• Sandak et al. [75 ] featuring log-grading sensors (i.e., timber den- mark each log and associate it with the quality data generated 1 3 156 Current Forestry Reports (2022) 8:148–165 particularly the guaranteed detection of all tagged logs [79]. or attachment heads provide a mechanism for maintaining Biometric fingerprinting of forest products is another prom - a record of individual log movements from stump to land- ising technique that utilizes scanning and image analysis ing and onto loaded trucks. Location based on GNSS can to identify patterns in the cross-sectional ends of logs for define the productive work of logging equipment, including improved individual product tracing. Two- and three-dimen- skyline carriages on cable operations and boom movements sional log biometric fingerprinting is used operationally in [85–87]. GNSS-based location can also define safe work advanced sawmills, and has shown very promising results zones on the fly in dynamic environments and quantify com - for accuracies up to 100% using methods similar to those plex, location-based functions of multiple moving resources applied in the context of fingerprint and iris recognition in to improve situational awareness and safety [88, 89]. Forest controlled studies [80]. canopy and topography differentially affect GNSS accuracy and radio network communication quality due to multipath error and radio signal interference in real-time data networks Sustainable Sourcing, Cryptocurrency, [90]. The majority of research to date has evaluated a small and the Blockchain number of pieces of equipment [85]. However, Wempe et al. [88] has also demonstrated simultaneous monitoring of mul- To fully address legal and sustainability certification and tiple pieces of equipment and rigging crew workers in near chain-of-custody compliance, digitization of traceability real-time with movements recorded at a rate of one location requires networking methodologies that ultimately connect per second using GNSS-enabled radios. Differentially cor - information flow from the forest to the mill. Additionally, a rected GNSS positioning of the harvester head can now also visionary goal in smart and precision forestry systems is the be used to measure individual tree geographic positions with realization of improved inventory monitoring. For example, sub-meter accuracy [91 ]. Positioning the harvester head, in the preferred case, mill owners should be able to monitor when coupled with StanForD files, permits growth rate and and evaluate the current product volumes remaining to be taper estimation for forest inventory from harvesters. How- harvested, current inventory volumes in each sorted log deck ever, because the resulting inventory data collected in this at the landings in active timber sale areas, and specific prod - way is only applicable for post-harvest reconstruction of ucts in transit. A motivating example is to achieve the level stand attributes [92 ], it is more likely that future ITD map- of information availability and inventory control that occurs ping and inventory control systems will rely on prior ITD with modern, online purchasing through large suppliers like from one of the available remote sensing methods which Amazon, and parcel delivery management. Among a range foster pre-treatment planning and smart equipment guidance. of possible solutions to achieve this level of verifiability in remote, forested areas, the blockchain technology that forms the basis of cryptocurrency transactions has become a front- runner. Blockchain is a shared database ledger system that Activity Recognition Based on Wearable stores all transactions in encrypted packets that provide a and Mobile Device Sensors verifiable record over time [ 81]. Blockchain-based crypto- currencies such as Bitcoin or Ethereum can form the basis The data collected in real-time by accelerometers and other for Evidence, Verifiability, and Enforcement (EVE) systems microelectromechanical sensors in consumer-grade smart- using smart contracts [82]. Blockchain can now be deployed phones, smartwatches, or other mobile and IoT devices can off-grid using mesh radio networks such as goTenna (Brook - be used to develop accurate models predicting activities in lyn, NY, USA), the LoRa network [83], or coupled with motor-manual operations, such as felling trees [93 ]. This the Blockstream or Swarm low-cost data satellite networks concept has further been adapted to smartwatches to model [84] that have been designed to provide local to global IoT choker-setter and chaser operations (setting and releasing networking solutions. chokers) on conventional cable logging operations [94]. The appeal of using common phones and watches to quantify motor-manual logging and forestry support services such Activity Recognition Based on Location as tree planting and thinning is that once predictive models are encoded in phone or watch apps, predicted activities can As harvested logs are skidded or forwarded and handled then be logged for complete work shifts indefinitely, creat - by the different pieces of equipment used in cut-to-length, ing a rich array of data to support subsequent analyses. The whole-tree, shovel-logging, steep slope, or traditional cable concept underlying this approach with integrated activity systems, coupling RFID or other technologies with equip- recognition modeling and remote networking to provide a ment activity recognition based on global navigation satellite consistent, real-time summary of simultaneous supply chain system (GNSS) devices deployed on either equipment cabs components and operational activities at active sites in the 1 3 Current Forestry Reports (2022) 8:148–165 157 Fig. 4 Conceptual mobile application framework for maintaining the equipment movements of each individually identified tree or prod - individual motor-manual worker and equipment activity recognition uct as they are transferred among equipment. Smartphone illustration information coupled with individual tree and product data in a net- credit: Ryer Becker, Ph.D., University of Idaho worked, relational database at the jobsite. Tables are associated with woods is shown in Fig. 4. Methods exist to render data inde- in the design of wearable and IoT devices [98]. This is par- pendent of sensor orientation [93 ] or the device location or ticularly true because wearable sensor data often is not cov- position on handheld equipment using a multi-layer percep- ered by legal and privacy protections associated with con- tron [95 , 96]. Machine learning methods such as random ventional medical data [99]. forests and artificial neural networks, as well as deep learn - ing, are useful methods for automating artificial intelligence based either on videography [97] or accelerometer data [94]. Knowledge Gaps and Emerging In the context of individual product tracing, a key benefit Opportunities of these device-based methods for monitoring manual activi- ties is that most smartphones and watches are also now read- Based on our review of available methods and technolo- ily capable of scanning RFID and NFC tags using a variety gies for carrying individual tree and product data through of available apps that can be automated to store these data. the supply chain from stump to mill, we find there are For example, since 2020, consumer smartphones have been currently few remaining barriers inhibiting their adop- used regularly to read and write information to RFID stick- tion. The component building blocks needed for complete ers associated with all log truck load tickets in harvesting systems that locate trees and monitor forest products with operations on the University of Idaho Experimental For- high precision using RFID, branding or other techniques est. The same approach can be used to scan individual tree have largely been established through continued research, RFID records during manual felling activities, and existing, as have methods to share data through remote networks phone-based models [93 ] can easily log GNSS coordinates that support shared databases, blockchain, or other tech- associated with tree felling events. Another important benefit niques for tracing and verifying products. Spatial ITD of phone- and watch-based activity recognition is that activ- lidar products are now being marketed commercially for ity and product tracing data logged in this way can readily forest management purposes in mixed-species stands. be transferred to shared databases on equipment using either However, our review reveals that because new ITD prod- mesh radios, or the emerging data satellite networks [84, ucts and tracing methods are just now emerging, there is 88, 90]. comparatively little research demonstrating how use of An important consideration for use of wearable and ITD products can improve silvicultural treatments and mobile device sensor data in human activity recognition operational efficiencies in practice. After almost three related to quantifying work productivity and the tracing of decades of research and development in industry, aca- individual trees and forest products is the use of personal demia, and federal agencies, operational forestry seems information collected by these devices [8 ]. The protection unprepared to make use of new ITD products now that and security of personal information is often an afterthought they are available. As methods for ITD and product 1 3 158 Current Forestry Reports (2022) 8:148–165 detection and monitoring continue to mature and be an opportunity to map seedlings during planting opera- refined, a significant shift is needed from development tions based on smartphones, smartwatches, or real-time and evaluation of measurement and remote sensing meth- GNSS in order to generate maps that can subsequently odologies in the forest inventory context to the evaluation be incorporated into lidar-based ITD inventory programs of applied methods for using individual tree and product over time. information in technology-informed silvicultural treat- ments, and planning and implementation of operations. Optimizing Skidding and Forwarding, Processing, To establish an agenda for this exciting area of research, and Decking we propose the following priorities to help advance use of individual tree and product information. Depending on the details of silvicultural prescriptions, it may be preferable to remove more young or old trees, trees of a particular species, or to minimize trail-related impacts Mapping Individual Seedling Locations During to the residual stand entirely. Prior, complete knowledge of Planting Operations stem spatial pattern within the stand makes it possible to optimize routes that function within desired constraints such As operational forestry incorporates use of ITD data as minimizing removal of one or more species, minimizing throughout the life cycle of stand management, a current stems removed solely to support the trail system, or location gap appears to exist in the consistent, accurate mapping of of trails in areas of low soil productivity. young, regenerating stands. For example, adjacent stands In regions with mixed-species stands and many product to the northeast and southwest of those identified in Fig. 5 sorts, ITD maps introduce the possibility of minimizing include young, planted seedlings and saplings that are not overall product handling and distance traveled from stump indicated as stems in the commercial ITD product. This to deck (Fig. 5). By identifying species or product category presents a knowledge gap for managers in a period that locations at their point of origin through spatial analysis, often involves the largest economic investment (costs) it may be possible to tailor log landings to minimize the associated with site preparation and planting, especially timing and skidding or forwarding direction of individual in intensively managed and even-aged stands. Human sorts. This approach can build on recent work using GIS to activity recognition coupled with GNSS may provide optimize precision planning of skid trails and winching [5 , Fig. 5 The boundaries of two stands managed historically on the University of Idaho Experi- mental Forest are not well correlated with species compo- sition. Within each stand, topo- graphic influences heavily affect the distribution of Douglas-fir (red) and grand fir (orange) on more eastern aspects and pon- derosa pine (yellow) on more western aspects. Based on this knowledge, landings (DFL1, PPL1, etc.) are being planned to minimize skidding and handling of sorted products, rather than using a central landing and then subsequently sorting logs into decks. Horizontal extent of clipped image is approximately 345 m 1 3 Current Forestry Reports (2022) 8:148–165 159 100], and to increase the sustainability of forwarding [101]. DBH, height, crown ratio, species, and target spacing in Rather than feeding all wood to one or more general landings ways that have not previously been possible. In this regard, and sorting products to decks from that point, ITD makes it one area that is particularly interesting is the possibility of possible to define smart landings. In regions where feller- accounting for topography and light interception associated bunchers are used, prior knowledge of preferred bunch char- with forest microclimates to inform both “smart” silvicul- acteristics and landing direction to minimize overall product tural treatments and operational best management practices handling seems feasible. Increasingly, the use of ITD data is simultaneously. For example, during marginal seasons when associated with a shift in thinking to address the question: temperatures fluctuate above and below freezing in temperate how can we optimize systems of equipment, or systems of regions, maintaining partial canopy south or west of road and motor-manual and mechanized equipment together in ways trail systems may provide shade that extends available operat- that are based on location-aware analytics, rather than opti- ing periods by maintaining frozen road or trail conditions. In mizing individual equipment independently for mean han- many ways, the presence of detailed, tree-level spatial data dling of average stem volume and piece size in ways that provides the opportunity to automate or intelligently augment have been largely non-spatial? many of the decision processes that field foresters and har - vester operators undertake when selecting trees for removal. Incorporating High‑Resolution Forest Product Value Individual tree information forms the basis for most underly- and Treatment Cost into Harvest Planning ing silvicultural theory, yet detailed, quantitative silvicultural prescriptions are often complex and challenging to translate Prior knowledge of individual forest product locations cor- and implement in the field, particularly in uneven-aged and responds directly to both resource value and operational treat- mixed-species stands or to meet multiple resource objectives ment cost within the treated stand. ITD data facilitates within- when implementing forest management plans. Rather than stand planning that may affect not only spatial arrangement estimating the spacing of leave trees in the field when mark - of skidding or forwarding trails, landings, and product sort ing trees individually, spatial point pattern analysis can be locations, but also the timing of harvest treatments. This is used to digitally select and refine stand density management particularly true in mixed-species forests where the value of treatments over thousands or millions of trees in ways that different products may vary considerably and the preferred incorporate the effects of slope, aspect, topography, stand timing of harvesting, processing, and delivering products to adjacency, and soil productivity within harvest units to best one or another mill destination may vary based on weather, optimize growth, the regeneration environment, and other market demand, or value. For example, in the Inland North- objectives. Furthermore, in regions where “operator choice” west United States, products in the same stands range from or “operator select” prescriptions are given to operators to very low-value pulpwood to cedar utility poles that are par- implement thinning treatments, spatial queries of ITD data ticularly high value. Using ITD data, knowledge of within- provide an opportunity to provide advanced, preliminary stand value may directly improve operational budget planning selections of candidate, individual trees to inform these pro- over time scales from weeks to years based on the sequence cedures in more advanced ways. and timing of resource extraction in ways that augment and enhance conventional, stand-level harvest planning. Machine Navigation for Improved Automation and Robotics Digitalizing Advanced Silvicultural Treatments Research on SLAM methods to help automate equip- Along with forest operations, silviculture stands to grow in ment guidance and robotics assume that stem locations new and exciting ways due to the availability of ITD data. For are unknown at the start of operations (hence localization example, advanced treatments based on actual, high-resolu- and mapping). However, adoption of ITD remote sensing tion individual tree growth environments can be designed to and the subsequent existence of digital forest maps prior reflect the alternative spacing requirements of different target to operations simplifies navigation algorithm processing species within stands. In uneven-aged, continuous cover, or for equipment guidance, automation, and robotics. The any non-clearcut silvicultural systems, ITD data affords the localization step in equipment guidance becomes a much opportunity to inform the selection of leave trees by apply- simpler matter of matching patterns of detected trees (via ing spatial queries to a digital inventory list rather than an equipment-mounted lidar, machine vision, or other sensors) assumed distribution of stem sizes and locations. As evident to an existing map that improves on the position accuracy in Fig. 1, mean, stand-level estimates of species volume can or becomes an improved substitute for GNSS-based location represent substantial variability in product type and location of the machine and attachment head. Instead of relying on within harvest units. Using digitized ITD inventory data, can- remote, mobile satellites, the forest itself becomes a power- didate leave trees can be pre-determined based on location, ful constellation of fixed referenced points that remain in 1 3 160 Current Forestry Reports (2022) 8:148–165 place for the length of stand rotation. Rather than a source utilizing forestry equipment computing and data strategies of canopy interference and multipath error, we need to shift that function independently of proprietary machine software, our paradigm to recognizing that these trees are valuable and associated work has evaluated M2M internet options [6]. geolocation points for precision guidance. Systems that reduce the dimensionality of data at low local levels in IoT processing can help alleviate networking band- Sharing Product Locations from Stump to Mill width needs in remote environments [8 ]. However, further research is needed to empirically test the reliability of various Methods exist for integrating RFID into the harvester head networking options in the field in order to identify system in cut-to-length systems. After the felling step, an RFID capabilities and limitations in real-world environments. nail inserted into a tree at breast height could remain in the lower log and be monitored through the supply chain using RFID readers mounted on equipment. However, any Conclusion secondary logs or products bucked from the stem become separated and of unknown origin. In order to trace these As new ITD data products for forest managers become avail- other products, the harvester head must either fingerprint able, researchers and managers will need to shift the weight the cross-section of upper stem logs in order to assign their of research and development effort from prediction algo - geographic coordinates and tree number of origin or have the rithms, evaluation, and validation of remote sensing prod- capability to insert additional RFID tags or other tracing ele- ucts to the operational application and use of ITD data in ments. Thus, automated application of bar codes, QR codes, silviculture and operations. This is particularly true in the RFID, or other low-cost identification via the harvester head design of high-resolution silvicultural prescriptions, optimal is an important area of development. In whole-tree systems, harvest unit layout, harvesting, skidding, and processing to the head of a processor working at the landing must have fully utilize prior knowledge of individually stem-mapped the capability to both scan incoming parent information on stands, and even the locations of individual products within whole trees and also apply that information to child logs as trees. the stem is bucked, ultimately using the system described Based on our review, airborne, drone, and terrestrial lidar in this paper two or more times during processing and sub- and SfM remote sensing techniques, as well as other sensor and sequent handling of the logs. For complete automation and photogrammetry methods not addressed in this review, provide digitization, both the skidder or forwarder and log loader useful data for informing ITD. In practice, these techniques should also have the ability to scan and record product iden- are also used concurrently to inform ITD products. For exam- tification as materials are handled in the supply chain, such ple, terrestrial lidar, drone lidar, or both may be used concur- that ultimately the current locations of all products on the rently in the collection of field plot data that is then utilized to jobsite are known and accounted for at all times. improve segmentation models for ALS predictions over large spatial extents. RFID scanning has been demonstrated success- Managing Big Data in Remote Environments fully as a method to maintain readable data on single trees over time in ways that could be accessed by scanners mounted on As ITD and individual forest product data are transferred equipment or UAVs. RFID has also been used to record infor- among machines using RFID or other product tracing meth- mation about individual log products using a scanner mounted ods and distributed, verifiable payment methods such as on the processor head. Branding and biometric fingerprinting blockchain technology extend to the edge of networking in are feasible technologies for product tracing. Cryptocurrency the forest, associated supply chain analytics must increasingly payment and ledger systems that utilize the blockchain provide manage big data. Research on developing functional machine- a distributed, verifiable mechanism that may supplement and to-machine (M2M) internet capability in remote forests, as strengthen chain-of-custody verification for the purposes of well as research connecting data from the forest jobsite back forest certification. Human and machine activity recognition to the mill or operational headquarters in real-time, is needed modeling, coupled with RFID or other scanning and identifica - [8 ]. There has been little research truly leveraging data across tion methods, provide a framework for tracing individual forest the full spectrum of the individual tree and product supply products from a source tree location through logging systems chain [6]. Use of artificial intelligence (AI) to improve opera - and to the mill. In short, all the components of a smart, ITD, tions or supply chain efficiencies in real-time in response to and product tracing system exist. mill needs, market factors, and other considerations linked Given that ITD products and methodologies for equip- between harvest operations and product marketing will require ment-based scanning and tracing of individual products massive data storage, processing power, and network band- exist, we have identified several opportunities for research width in areas that currently often lack any internet or cel- utilizing these technologies to help optimize the digital lular data coverage. Canadian systems are the most advanced supply chain. These include studies demonstrating and 1 3 Current Forestry Reports (2022) 8:148–165 161 2016.25432 25 This paper summarizes the accuracy of differ - evaluating the following: (1) mapping of individual seed- ent ITD methods across a wide range of conditions. ling locations during planting operations; (2) optimizing 2. Liang X, Hyyppä J, Kaartinen H, Lehtomäki M, Pyörälä J, skidding and forwarding, processing, and decking in ways Pfeifer N, et al. International benchmarking of terrestrial laser that incorporate ITD product information; (3) incorporat- scanning approaches for forest inventories. ISPRS J Photogramm Remote Sens. 2018;144:137–79. https:// doi. org/ 10. 1016/j. isprs ing high-resolution forest product value and treatment costs jprs. 2018. 06. 021. into harvest planning; (4) digitalizing advanced silvicultural 3.• Picchi G. Marking standing trees with RFID tags. Forests. treatments based on current and future ITD information; 2020;11:150. https:// doi. org/ 10. 3390/ f1102 0150 This paper (5) improving machine location accuracy and navigation provides an evaluation of the performance of RFID tags after being attached to trees and exposed to weather over time. to increase automation and robotics; (6) sharing of product 4. Feng Y, Audy J-F. Forestry 4.0: A framework for the forest sup- information from M2M and stump to mill; and (7) manag- ply chain toward Industry 4.0. Gest Prod. 2020;27:e5677. https:// ing the big data associated with ITD and individual product doi. org/ 10. 1590/ 0104- 530X5 677- 20. information in remote environments. Each of these research 5.• Picchio R, Proto AR, Civitarese V, Di Marzio N, Latterini F. Recent contributions of some fields of the electronics in areas contributes to an agenda that can help managers and development of forest operations technologies. Electronics. researchers utilize newly available ITD and individual prod- 2019;8:1465. https:// doi. or g/ 10. 3390/ elect r onic s8121 465 uct data to continue advancing the digitalization of forest This paper provides a nice, summary review of a variety operations. of current electronic sensors that are being used in forest operations. 6. Gingras J-F, Charette F. FPInnovations’ Forestry 4.0 initiative. Proceedings of the 2017 Council on Forest Engineering Annual Funding Keefe and Zimbelman have previously received funding Meeting [Internet]. Bangor, ME, USA; 2017 [cited 2021 Dec for real-time GNSS research on logging safety through United States 5]. Available: http:// cofe. or g/ f iles/ 2017_ Pr oce edings/ FPInn Centers for Disease Control (CDC)/National Institute of Occupational ovati ons% 20Gin gras% 20Cha rette% 20For estry% 204.0% 20for% Safety and Health (NIOSH) grant number 5 U01 OH010841. Keefe and 20COFE% 202017. pdf. Zimbelman1 have previously received funding for smartwatch activ- 7. Brown M, Ghaffariyan MR, Berry M, Acuna M, Strandgard ity recognition modeling to improve logging safety on University of M, Mitchell R. The progression of forest operations technology Washington Pacific Northwest Ag Safety and Health (PNASH) Center and innovation. Aust For. 2020;83:1–3. https://doi. or g/10. 1080/ pilot project grant UWSC10722. 00049 158. 2020. 17230 44. 8.• Keefe RF, Wempe AM, Becker RM, Zimbelman EG, Nagler ES, Declarations Gilbert SL, et al. Positioning methods and the use of location and activity data in forests. Forests. 2019;10:458. https://doi. or g/10. 3390/ f1005 0458 This paper provides a general summary of Conflict of Interest The authors declare no competing interests. real-time positioning and wearable and mobile technologies to support individual tree and product big data applications Open Access This article is licensed under a Creative Commons Attri- in smart and precision forestry. bution 4.0 International License, which permits use, sharing, adapta- 9.• Talbot B, Pierzchała M, Astrup R. Applications of remote and tion, distribution and reproduction in any medium or format, as long proximal sensing for improved precision in forest operations. as you give appropriate credit to the original author(s) and the source, Croat J For Eng. 2017;38:327–36 This paper provides a sum- provide a link to the Creative Commons licence, and indicate if changes mary of remote and proximal sensing technologies and were made. 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Current Forestry Reports – Springer Journals

Published: Jun 1, 2022

Keywords: Forest operations; Individual tree detection; Traceability; RFID; Location analytics; Activity recognition; Smart forestry; Supply chain

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Use of Individual Tree and Product Level Data to Improve Operational Forestry

Use of Individual Tree and Product Level Data to Improve Operational Forestry

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Use of Individual Tree and Product Level Data to Improve Operational Forestry

Use of Individual Tree and Product Level Data to Improve Operational Forestry

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