Forest Harvesting and Forest Product Supply Chain

A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Forest Operations and Engineering".

Deadline for manuscript submissions: closed (25 November 2023) | Viewed by 8509

Special Issue Editors


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Guest Editor
Division of Forest Sciences, College of Forest & Environmental Sciences, Kangwon National University, Chuncheon-si, Gangwon-do, Republic of Korea
Interests: forest operations; harvesting logisitics; biomass utilization; evironmental impacts of forest operations; forest road

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Guest Editor
College of Forest and Environmental Sciences, Kangwon National University, Chuncheon-si, Gangwon-do, Republic of Korea
Interests: precision forestry; robotics; remote sensing; forest operation; supply chain management

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Guest Editor
Forest Technology and Management Research Center, National Insitute of Forest Science, Pocheon-si, Gyeonggi-do, Republic of Korea
Interests: forestry machines; forest operations; biomass utilization

Special Issue Information

Dear Colleagues,

Forest biomass and carbon are key elements in the development of climate change mitigation strategies. Internationally forest biomass is considered to be a valuable renewable energy feedstock. However, utilization of forest biomass is challenging because they are highly varied, generally of low quality and usually widely distributed across timber harvesting sites. In recent years, sensing technologies for monitoring forest resources including forest residue, and assessing environmental impacts associated with forest biomass utilization have been rapidly developing in the fields of forest management and harvesting operation. The use of sensors and ICT techniques has changed the management paradigm in forest industry, it can be used in all phases of forest activity such as planning, harvesting, and monitoring process. Taking advantage of state-of-the-art sensing technologies, forest information regarding the individual trees, forest biomass estimation, stand-level attributes and structures, and landscape-level can be measured or monitored through UAV, sensors, and laser scanning systems with high-resolution optical images and Lidar data. The precise level of data collection and analysis enables the forest managers/owners to make decisions for site-specific management. Additionally, automation, unmanned devices, remote control, artificial vision, and machine-to-machine communication are examples of these new approaches to forest operation management. The purpose of this Special Issue is to highlight the significance and contribution of sensing technologies in forest management and harvesting operation. It will focus on theoretical and experimental studies including individual or integrated devices and machinery, sensors, and technologies that enable the more efficient management of forest biomass resources.

Dr. Sang-Kyun Han
Dr. Heesung Woo
Dr. Jae-Heun Oh
Guest Editors

Manuscript Submission Information

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Keywords

  • advanced forest operation techniques
  • forest biomass estimation and utilization
  • environmental impacts of forest operations
  • precision forestry
  • mobile Lidar application in 3D mapping forest structure
  • Lidar slam application in forest machine
  • AI/ML application in forestry
  • UAV (unmanned aerial vehicle) application in forestry
  • ALS (aerial laser system) technology for forest inventory assessment
  • TLS (terrestrial laser system) technology for forest road application

Published Papers (5 papers)

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Research

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20 pages, 11043 KiB  
Article
An Innovative Approach to Surface Deformation Estimation in Forest Road and Trail Networks Using Unmanned Aerial Vehicle Real-Time Kinematic-Derived Data for Monitoring and Maintenance
by Evangelia Siafali and Petros A. Tsioras
Forests 2024, 15(1), 212; https://doi.org/10.3390/f15010212 - 21 Jan 2024
Viewed by 1331
Abstract
The significant increase in hiking, wood extraction, and transportation activities exerts a notable impact on the environmental balance along trails and forest roads in the form of soil degradation. The aim of this study was to develop a Deformation Classification Model for the [...] Read more.
The significant increase in hiking, wood extraction, and transportation activities exerts a notable impact on the environmental balance along trails and forest roads in the form of soil degradation. The aim of this study was to develop a Deformation Classification Model for the surface of a multi-use trail, as well as to calculate sediment deposition and generate a flood hazard map in a partially forested region. The eBee X mapping Unmanned Aerial Vehicle (UAV) equipped with the senseFly S.O.D.A. 3D camera and Real-Time Kinematic (RTK) technology flew over the study area of 149 ha in Northern Greece at an altitude of 120 m and achieved a high spatial resolution of 2.6 cm. The specific constellation of fixed-wing equipment makes the use of ground control points obsolete, compared to previous, in most cases polycopter-based, terrain deformation research. Employing the same methodology, two distinct classifications were applied, utilizing the Digital Surface Model (DSM) and Digital Elevation Model (DEM) for analysis. The Geolocation Errors and Statistics for Bundle Block Adjustment exhibited a high level of accuracy in the model, with the mean values for each of the three directions (X, Y, Z) being 0.000023 m, −0.000044 m, and 0.000177 m, respectively. The standard deviation of the error in each direction was 0.022535 m, 0.019567 m, and 0.020261 m, respectively. In addition, the Root Mean Square (RMS) error was estimated to be 0.022535 m, 0.019567 m, and 0.020262 m, respectively. A total of 20 and 30 altitude categories were defined at a 4 cm spatial resolution, each assigned specific ranges of values, respectively. The area of each altitude category was quantified in square meters (m2), while the volume of each category was measured in cubic meters (m3). The development of a Deformation Classification Model for the deck of a trail or forest road, coupled with the computation of earthworks and the generation of a flood hazards map, represents an efficient approach that can provide valuable support to forest managers during the planning phase or maintenance activities of hiking trails and forest roads. Full article
(This article belongs to the Special Issue Forest Harvesting and Forest Product Supply Chain)
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16 pages, 13315 KiB  
Article
UAV Photogrammetry for Soil Surface Deformation Detection in a Timber Harvesting Area, South Korea
by Jeongjae Kim, Ikhyun Kim, Eugene Ha and Byoungkoo Choi
Forests 2023, 14(5), 980; https://doi.org/10.3390/f14050980 - 10 May 2023
Cited by 4 | Viewed by 1576
Abstract
During forest operations, canopy removal results in the soil surface being vulnerable to deformation, negatively impacting soil fertility and water quality. This study utilized unmanned aerial vehicle (UAV) photogrammetry to accurately detect soil surface deformation (SSD). Two-dimensional images were safely collected on a [...] Read more.
During forest operations, canopy removal results in the soil surface being vulnerable to deformation, negatively impacting soil fertility and water quality. This study utilized unmanned aerial vehicle (UAV) photogrammetry to accurately detect soil surface deformation (SSD). Two-dimensional images were safely collected on a steep slope without real-time kinematics by conducting vertically parallel flights (VPFs). A high-resolution digital surface model (DSM) with a <3 cm resolution was acquired for precise SSD detection. Using DSM of difference (DoD), SSDs were calculated from DSMs acquired in June, July, September, and October 2022. By checking spatial distances at ground control points, errors of DSM alignments were confirmed as only 3 cm, 11.1 cm, and 4 cm from July to June, September to June, and October to June, respectively. From the first month of monitoring, erosion and deposition of approximately 7 cm and 9 cm, respectively, were detected at validation points (VPs). However, from total monitoring, cumulative SSD was assessed as having deposition tendencies at all VPs, even compared to ground truths. Although UAV photogrammetry can detect SSDs, spatial distortion may occur during UAV surveys. For vegetation growth issues, UAV photogrammetry may be unable to capture data on the soil surface itself. Full article
(This article belongs to the Special Issue Forest Harvesting and Forest Product Supply Chain)
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12 pages, 1868 KiB  
Article
The Impact of Integrated Harvesting Systems on Productivity, Costs, and Amount of Logging Residue in the Clear-Cutting of a Larix kaempferi (Lamb.) Carr. Stand
by Heesung Woo, Eunjai Lee, Mauricio Acuna, Hyunmin Cho and Sang-Kyun Han
Forests 2022, 13(11), 1941; https://doi.org/10.3390/f13111941 - 17 Nov 2022
Viewed by 1449
Abstract
Two integrated harvesting methods have been primarily applied to increase the opportunity for forest biomass utilization. In Korea, small shovels with a carrier for cut-to-length harvesting (CTL system) and tower yarders for whole-tree harvesting (WT system) are commonly used for the transportation of [...] Read more.
Two integrated harvesting methods have been primarily applied to increase the opportunity for forest biomass utilization. In Korea, small shovels with a carrier for cut-to-length harvesting (CTL system) and tower yarders for whole-tree harvesting (WT system) are commonly used for the transportation of tree assortments (i.e., sawlogs and logging residue). No previous studies are available in South Korea that have compared and highlighted the operational performance and yield of logging residues between the CTL and WT harvesting systems. Thus, our study’s main objectives were to (1) evaluate the productivity and costs of the two harvesting systems through a standard time study method and (2) estimate the amount of harvesting logging residue at the landing. The productivities of the CTL and WT systems were 1.45 and 2.99 oven-dried tons (odt)/productive machine hour (PMH), at a cost of 86.81 and 45.41 USD/odt, respectively. In the WT system, the amount of logging residue (2.1 odt/ha) collected at the landing was approximately four-times larger than that of the CTL system (0.5 odt/ha). Our results suggested that the WT system is a less expensive and more suitable system when there are markets demanding logs and biomass, whereas the CTL system remains a less expensive option for stem-only harvesting. Furthermore, these results are important for estimating the economic and environmental amount of residue that could be potentially recovered and utilized from the forest types included in the study. Full article
(This article belongs to the Special Issue Forest Harvesting and Forest Product Supply Chain)
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14 pages, 5662 KiB  
Article
Evaluation of Hyperparameter Combinations of the U-Net Model for Land Cover Classification
by Yongkyu Lee, Woodam Sim, Jeongmook Park and Jungsoo Lee
Forests 2022, 13(11), 1813; https://doi.org/10.3390/f13111813 - 31 Oct 2022
Viewed by 1880
Abstract
The aim of this study was to select the optimal deep learning model for land cover classification through hyperparameter adjustment. A U-Net model with encoder and decoder structures was used as the deep learning model, and RapidEye satellite images and a sub-divided land [...] Read more.
The aim of this study was to select the optimal deep learning model for land cover classification through hyperparameter adjustment. A U-Net model with encoder and decoder structures was used as the deep learning model, and RapidEye satellite images and a sub-divided land cover map provided by the Ministry of Environment were used as the training dataset and label images, respectively. According to different combinations of hyperparameters, including the size of the input image, the configuration of convolutional layers, the kernel size, and the number of pooling and up-convolutional layers, 90 deep learning models were built, and the model performance was evaluated through the training accuracy and loss, as well as the validation accuracy and loss values. The evaluation results showed that the accuracy was higher with a smaller image size and a smaller kernel size, and was more dependent on the convolutional layer configuration and number of layers than the kernel size. The loss tended to be lower as the convolutional layer composition and number of layers increased, regardless of the image size or kernel size. The deep learning model with the best performance recorded a validation loss of 0.11 with an image size of 64 × 64, a convolutional layer configuration of C→C→C→P, a kernel size of 5 × 5, and five layers. Regarding the classification accuracy of the land cover map constructed using this model, the overall accuracy and kappa coefficient for three study cities showed high agreement at approximately 82.9% and 66.3%, respectively. Full article
(This article belongs to the Special Issue Forest Harvesting and Forest Product Supply Chain)
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Review

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35 pages, 1582 KiB  
Review
Systematics of Forestry Technology for Tracing the Timber Supply Chain
by Alexander Kaulen, Lukas Stopfer, Kai Lippert and Thomas Purfürst
Forests 2023, 14(9), 1718; https://doi.org/10.3390/f14091718 - 25 Aug 2023
Cited by 2 | Viewed by 1676
Abstract
Traceability is the ability to follow the processes that a raw material or product goes through. For forestry, this means identifying the wood from the standing tree to the mill entrance and recording all information about the technical (production) and spatial (transportation) manipulation [...] Read more.
Traceability is the ability to follow the processes that a raw material or product goes through. For forestry, this means identifying the wood from the standing tree to the mill entrance and recording all information about the technical (production) and spatial (transportation) manipulation of the timber by linking it to the ID. We reviewed the literature for developments in timber flow traceability. Findings range from disillusionment with the non-application of available forestry technology to enthusiasm for the advancement of technology that—given appropriate incentives of an economic, environmental, consumer-oriented and legislative nature—can rapidly lead to widespread end-to-end media-interruption-free implementation. Based on our research, the solution lies in optical biometric systems that identify the individual piece of wood—without attaching anything—at three crucial points: during assortment at the skid road, at the forest road and at the mill entrance. At all of these points, the data accruing during the timber supply process must be linked to the ID of the piece of wood via data management. Full article
(This article belongs to the Special Issue Forest Harvesting and Forest Product Supply Chain)
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