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Forests
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Modeling of Vehicle Mobility in Forests and Rugged Terrain
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Modeling of Vehicle Mobility in Forests and Rugged Terrain

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

Deadline for manuscript submissions: 30 June 2025 | Viewed by 4264

Special Issue Editors

Prof. Dr. Marian Rybanský

E-Mail Website
Guest Editor
Department of Military Geography and Meteorology, Faculty of Military Technology, University of Defence, Kounicova 65, 66210 Brno, Czech Republic
Interests: vehicle mobility in forest and rugged terrain; terrain analysis
Dr. Tomáš Mikita

E-Mail Website
Guest Editor
Department of Forest Management and Applied Geoinformatics, Faculty of Forestry and Wood Technology, Mendel University in Brno, Zemědělská 1, 61300 Brno, Czech Republic
Interests: vegetaion mapping; forestry; applied geoinformatics

Special Issue Information

Dear Colleagues,

The analysis of the mobility of off-road vehicles in forest stands has always been of interest for logging and the movement of military units and rescue systems. The movement of forest tractors, harvesters, and heavy and light military vehicles in terrain requires knowledge of the location of forest paths, intersections, the structure of vegetation, i.e., the distance between trees and trunk diameters (DBH), and knowledge of the roughness of the terrain surface.

Potential topics related to vehicle mobility in forests and rugged terrain include, but are not limited to:

- Modeling the possibilities of vehicle movement through forest units;

- Possibilities of using remote sensing data (RGB images, multispectral images, LIDAR, RADAR) for modeling the mobility of off-road vehicles in forest stands;

- Modeling of maneuring vehicles around trees or overcoming trees by heavy vehicles;

- The influence of terrain microrelief objects (forest paths, erosional forms of watercourses, fallen tree trunks, boulders, etc.) on mobility;

- The effect of the movement of heavy off-road vehicles on root systems and damage to forest stands;

- Analysis of GNSS signal quality for off-road vehicle navigation in the forest;

- Use of autonomous systems, inertial systems, and others for navigation in the forest.

We seek research in the field of mobility connected to robotic systems moving independently in the forest (unmanned vehicles) and equipped with their own sensors (RGB, LIDAR, RADAR, etc.). These vehicles can be supported by unmanned air vehicles (UAVs), drones, and satellite systems that will scan the forest from above and transmit information about location and obstacles and thus support the navigation of ground vehicles.

We are interested in papers aimed at supporting the decision-making process in the deployment and navigation of all-terrain vehicles capable of moving in forest units, as well as papers analyzing the effects of vehicle movement on forest ecosystems.

Prof. Dr. Marian Rybanský
Dr. Tomáš Mikita
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Forests is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • forestry vehicle
  • military vehicle
  • rescue vehicle
  • forest robotic systems
  • forest structure
  • tree resilience
  • DBH
  • navigation in the forest
  • forest damage

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Published Papers (4 papers)

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Research

19 pages, 11582 KiB  
Article
Assessment of Forest Route Planning Capabilities Using Various Spatial Data Sources: A Case Study of the Mazovia Region, Poland
by Wojciech Dawid and Krzysztof Pokonieczny
Forests 2025, 16(1), 179; https://doi.org/10.3390/f16010179 - 18 Jan 2025
Viewed by 924
Abstract
This study examines the effectiveness of various spatial data sources and pathfinding algorithms for route determination in forested environments, focusing on the Mazovia region of Poland. Accurate and efficient forest route planning is critical for both military operations and crisis management, highlighting the [...] Read more.
This study examines the effectiveness of various spatial data sources and pathfinding algorithms for route determination in forested environments, focusing on the Mazovia region of Poland. Accurate and efficient forest route planning is critical for both military operations and crisis management, highlighting the need for reliable data and robust algorithms. The analysis centers on three primary spatial data sources that can support forest routing: the civilian Topographic Objects Database (TOD) and OpenStreetMap (OSM), along with the military-specific Vector Map Level 2 (VML2). Two commonly used pathfinding algorithms, Dijkstra and A* (the latter with six heuristic variations), were tested to assess their suitability and performance in these contexts. This study was conducted across ten of the largest forested areas in Mazovia, with route determinations performed between selected pairs of start and end points within each forest area. The findings indicate that the TOD database yielded the most stable and consistent routes, while the A* algorithm with Euclidean distance heuristics proved to be the fastest among the tested variants. In contrast, OSM data presented challenges due to inconsistencies, resulting in some routes being undeterminable, where connections between start and end points were lacking. These results underscore the importance of data quality and algorithm selection in effective forest route planning. Full article
(This article belongs to the Special Issue Modeling of Vehicle Mobility in Forests and Rugged Terrain)
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20 pages, 14677 KiB  
Article
Comparison and Evaluation the Forest Spatial Data in the Context of Modeling Terrain Passability for Operational Purposes
by Krzysztof Pokonieczny and Wojciech Dawid
Forests 2025, 16(1), 112; https://doi.org/10.3390/f16010112 - 9 Jan 2025
Viewed by 654
Abstract
This article addresses a significant aspect of evaluating and comparing spatial forest data from various databases developed and maintained in Poland and globally. The study focused on the application of these data to create terrain passability maps, which are useful in planning military [...] Read more.
This article addresses a significant aspect of evaluating and comparing spatial forest data from various databases developed and maintained in Poland and globally. The study focused on the application of these data to create terrain passability maps, which are useful in planning military and crisis operations. The research was conducted in a test area near Warsaw, encompassing the Kampinos Forest. In the study, the “forest” layers from the tested databases were compared. Their spatial extents were analyzed, and terrain passability maps were generated in different configurations, which were comprehensively compared with one another. The results indicated that the quality and detail of forest data are not critical for generating passability maps. Only in the case of creating highly detailed maps does the use of precise data prove justified. As the level of detail in the maps decreases, they become increasingly similar, reducing the influence of the forest data on their accuracy and operational applicability. The study enabled the selection of the most accurate data sources on forested areas—those that most faithfully represent the structure of forested regions in Poland. Full article
(This article belongs to the Special Issue Modeling of Vehicle Mobility in Forests and Rugged Terrain)
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13 pages, 6373 KiB  
Article
Mapping Forest Parameters to Model the Mobility of Terrain Vehicles
by Tomáš Mikita, Marian Rybansky, Dominika Krausková, Filip Dohnal, Ondřej Vystavěl and Sabina Hollmannová
Forests 2024, 15(11), 1882; https://doi.org/10.3390/f15111882 - 25 Oct 2024
Cited by 3 | Viewed by 827
Abstract
This study aims to evaluate the feasibility of using non-contact data collection methods—specifically, UAV (unmanned aerial vehicle)-based and terrestrial laser scanning technologies—to assess forest stand passability, which is crucial for military operations. The research was conducted in a mixed forest stand in the [...] Read more.
This study aims to evaluate the feasibility of using non-contact data collection methods—specifically, UAV (unmanned aerial vehicle)-based and terrestrial laser scanning technologies—to assess forest stand passability, which is crucial for military operations. The research was conducted in a mixed forest stand in the Březina military training area, where the position of trees and their DBHs (Diameter Breast Heights) were recorded. The study compared the effectiveness of different methods, including UAV RGB imaging, UAV-LiDAR, and handheld mobile laser scanning (HMLS), in detecting tree positions and estimating DBH. The results indicate that HMLS data provided the highest number of detected trees and the most accurate positioning relative to the reference measurements. UAV-LiDAR showed better tree detection compared to UAV RGB imaging, though both aerial methods struggled with canopy penetration in densely structured forests. The study also found significant variability in DBH estimation, especially in complex forest stands, highlighting the challenges of accurate tree detection in diverse environments. The findings suggest that while current non-contact methods show promise, further refinement and integration of data sources are necessary to improve their applicability for assessing forest passability in military or rescue contexts. Full article
(This article belongs to the Special Issue Modeling of Vehicle Mobility in Forests and Rugged Terrain)
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17 pages, 6523 KiB  
Article
Lightweight Model Development for Forest Region Unstructured Road Recognition Based on Tightly Coupled Multisource Information
by Guannan Lei, Peng Guan, Yili Zheng, Jinjie Zhou and Xingquan Shen
Forests 2024, 15(9), 1559; https://doi.org/10.3390/f15091559 - 4 Sep 2024
Cited by 1 | Viewed by 997
Abstract
Promoting the deployment and application of embedded systems in complex forest scenarios is an inevitable developmental trend in advanced intelligent forestry equipment. Unstructured roads, which lack effective artificial traffic signs and reference objects, pose significant challenges for driverless technology in forest scenarios, owing [...] Read more.
Promoting the deployment and application of embedded systems in complex forest scenarios is an inevitable developmental trend in advanced intelligent forestry equipment. Unstructured roads, which lack effective artificial traffic signs and reference objects, pose significant challenges for driverless technology in forest scenarios, owing to their high nonlinearity and uncertainty. In this research, an unstructured road parameterization construction method, “DeepLab-Road”, based on tight coupling of multisource information is proposed, which aims to provide a new segmented architecture scheme for the embedded deployment of a forestry engineering vehicle driving assistance system. DeepLab-Road utilizes MobileNetV2 as the backbone network that improves the completeness of feature extraction through the inverse residual strategy. Then, it integrates pluggable modules including DenseASPP and strip-pooling mechanisms. They can connect the dilated convolutions in a denser manner to improve feature resolution without significantly increasing the model size. The boundary pixel tensor expansion is then completed through a cascade of two-dimensional Lidar point cloud information. Combined with the coordinate transformation, a quasi-structured road parameterization model in the vehicle coordinate system is established. The strategy is trained on a self-built Unstructured Road Scene Dataset and transplanted into our intelligent experimental platform to verify its effectiveness. Experimental results show that the system can meet real-time data processing requirements (≥12 frames/s) under low-speed conditions (≤1.5 m/s). For the trackable road centerline, the average matching error between the image and the Lidar was 0.11 m. This study offers valuable technical support for the rejection of satellite signals and autonomous navigation in unstructured environments devoid of high-precision maps, such as forest product transportation, agricultural and forestry management, autonomous inspection and spraying, nursery stock harvesting, skidding, and transportation. Full article
(This article belongs to the Special Issue Modeling of Vehicle Mobility in Forests and Rugged Terrain)
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