Aimad El Issaoui

Purpose of Review: Forest roads are vital for the relocation of wood harvesting machinery, secondary transportation, forest management, fire prevention, and recreational activities. Monitoring and maintaining the condition of a large and spatially distributed road network requires resource-efficient methods and new expertise. This review examines methods for measuring and predicting the condition and trafficability of unpaved forest roads, based on 87 publications from 2010 to 2025. The aim was to examine evaluated road features and the methods used to measure them. Recent Findings: Forest road condition is commonly evaluated by structural strength, surface quality, geometry, and drainage. Traditional techniques (e.g., bearing capacity measurements and visual inspections) are the most frequently used. Mobile mapping approaches and remote sensing methods, which include airborne laser scanning, unmanned aerial vehicle surveys, and satellite‑based observations, have been increasingly applied to assess road condition. Machine and deep learning methods have recently been used to identify potholes, rutting, and surface roughness based on image, video, and sensor data. Other approaches employed to predict and evaluate road condition include laboratory testing, traffic monitoring, transportation data, and meteorological records. Summary: Although traditional field methods remain dominant, emerging technologies are increasingly used to assess forest road condition. Reliable prediction of trafficability requires integrating multiple data sources. Despite technological advances, real-time dynamic road condition maps and situation awareness are lacking, and determining the actual condition of a road often requires a field visit. Novel digital solutions and data sources could improve condition forecasting and support more efficient forest road network maintenance. However, the generalizability of these findings may be limited by the scope of the review and language constraints.

Mobile Laser Scanning (MLS) provides high-accuracy 3D point clouds essential for road infrastructure monitoring. However, multi-temporal MLS analysis is often limited by non-constant, spatially varying trajectory drift caused by GNSS outages and IMU inaccuracies. These misalignments can exceed the magnitude of the changes being monitored, such as pavement deformation, making accurate change detection challenging. This paper presents a fully automatic, semantically driven registration pipeline designed to correct spatially varying drift in directly georeferenced MLS data. The method first applies Principal Component Analysis (PCA) and intensity-based filtering to classify points into stable geometric categories, including flat horizontal surfaces, flat vertical structures, and linear vertical features. A correspondence-based filtering step removes dynamic objects and temporal changes to ensure that registration is driven by stable geometry. The core of the method is an adaptive piecewise registration strategy, where the reference point cloud is divided into sequential 1-meter patches. Each patch is assigned a local rigid transformation estimated using an adaptively expanding registration window guided by the availability of stable vertical features. A final smoothing step ensures spatial continuity between adjacent transformations. The method was evaluated on two MLS datasets collected one year apart along a 3 km road corridor using the FGI Roamer-R4DW system. Validation using 30 independent ground signals showed that the 3D RMSE improved from 3.38 cm to 1.54 cm, with vertical RMSE improving from 2.54 cm to 0.67 cm. The results demonstrate that the proposed approach enables centimeter-level alignment suitable for high-precision multi-temporal road monitoring and change detection applications.

This study investigates the efficacy of Mobile Laser Scanning (MLS) for detecting pavement distresses from precise 3D point cloud data. A conventional MLS system was evaluated against a high-accuracy pavement mapping reference system. The study data were collected by a Roamer-R4DW mobile mapping system equipped with a Riegl VUX-1HA scanner, while the reference data were acquired using a Fraunhofer PPS-plus-based mobile mapping system (PPS-plus), which maps road surfaces at sub-millimeter level. A novel automatic distress detection algorithm was developed, improving upon previous methods to significantly enhance accuracy. For algorithm validation, nine 10-meter-long pavement sections were annotated using PPS-plus data to serve as ground truth. The algorithm achieved an F₁ score of 0.87 for detecting distresses wider than 2 cm. The same algorithm was applied to Roamer R4DW data and compared to the manual reference, resulting in an F₁ score of 0.77. To assess the generalizability of the previous findings, the evaluation was expanded to a 3.2-km-long road section. Utilizing algorithm-generated reference from PPS-plus data, the automatic distress detection algorithm resulted in an F₁ score of 0.66 for the Roamer-R4DW data. Additionally, this study addressed a critical research gap: the lack of distress volume and area assessments using MLS data. We achieved coefficients of determination of 0.93 and 0.77 for distress volume and area respectively for the same 3.2-km-long road section. The results of this study underscore the potential of conventional MLS systems, such as the Roamer-R4DW, for surveying and mapping pavement and road environments in 3D. Representative dataset: https://doi.org/10.5281/zenodo.19267189.

Physiological processes cause movements of tree stems and branches that occur in a circadian rhythm and over longer time periods, but there is a lack of quantitative understanding of the cause-and-effect relationships. We investigated the movement of tree branches in a long-term drought experiment and at a circadian time scale using time-series of terrestrial laser scanning measurements coupled with measurements of environmental drivers and tree water status. Our results showed that movement of branches was largely explained by leaf water status measured as leaf water potential in a controlled environment for both measured trees (R² = 0.86 and R² = 0.75). Our hypothesis is that changes in leaf and branch water status would cause branch movements was further supported by strong relationship between vapor pressure deficit and overnight branch movement (R² = [0.57–0.74]). Due to lower atmospheric water demand during the nighttime, tree branches settle down as the amount of water in leaves increases. The results indicate that the quantified movement of tree branches could help us to further monitor and understand the water relations of tree communities.

The deterioration of road conditions and increasing repair deficits pose challenges for the maintenance of reliable road infrastructure, and thus threaten, for example, safety and the fluent flow of traffic. Improved and more efficient procedures for maintenance are required, and these require improved knowledge of road conditions, i.e., improved data. Three-dimensional mapping presents possibilities for large-scale collection of data on road surfaces and automatic evaluation of maintenance needs. However, the development and, specifically, evaluation of large-scale mobile methods requires reliable references. To evaluate possibilities for close-range, static, high-resolution, three-dimensional measurement of road surfaces for reference use, three measurement methods and five instrumentations are investigated: terrestrial laser scanning (TLS, Leica RTC360), photogrammetry using high-resolution professional-grade cameras (Nikon D800 and D810E), photogrammetry using an industrial camera (FLIR Grasshopper GS3-U3-120S6C-C), and structured-light handheld scanners Artec Leo and Faro Freestyle. High-resolution photogrammetry is established as reference based on laboratory measurements and point density. The instrumentations are compared against one another using cross-sections, point–point distances, and ability to obtain key metrics of defects, and a qualitative assessment of the processing procedures for each is carried out. It is found that photogrammetric models provide the highest resolutions (10–50 million points per m²) and photogrammetric and TLS approaches perform robustly in precision with consistent sub-millimeter offsets relative to one another, while handheld scanners perform relatively inconsistently. A discussion on the practical implications of using each of the examined instrumentations is presented.