There are two current approaches to data collection and road condition monitoring. Manual data collection used in many countries is slow and it provides poor data [1
]. The most common method of measurement is still the geodetic method, with the use of a total station in combination with Global Navigation Satellite System (GNSS). In connection with the use of GNSS, Abdie et al. [2
] published an article on the accuracy and the usability of the technology for forest road mapping in the forest environment. According to the article, the forest road network inventory using GNSS is a method often used due to its relatively low purchase price. In line with other authors [3
], they state that the utilization of GNSS in the forest ecosystem brings problems with signal reception under the forest cover, including the fact that a different number of satellites is visible in the territory at different times.
Remote sensing technologies have also been commonly used in many applications during the last fifteen years. The derived three-dimensional data are regularly used for digital terrain and surface modelling. Detailed information about roads and their surroundings is very important because of the ever-increasing number of applications, such as noise modelling, traffic safety, road maintenance and repair, driver assistance, or car and pedestrian navigation [4
Recently, methods of automated data collection and the already mentioned remote sensing methods have become increasingly widespread with the development of new technologies [5
]. The modelling technology for obtaining 3D information can be divided by the used mapping method [4
]. According to [8
], Light Detection and Ranging (LiDAR) is currently the most sophisticated method for the exploration of the forest road network. LiDAR is a method of distance measurement based on the speed calculation of the reflected laser beam pulse from the scanned object. The method is primarily used for the creation of a digital terrain model (DTM).
The LiDAR method is mostly used as airborne laser scanning (ALS) in which the scanner is mounted on an aircraft. ALS is increasingly used for city modelling, for the generation of digital terrain models, archeological studies [9
] or for forest inventory [10
]. The distance of the airborne scanner from the scanned location on the Earth’s surface is several hundred metres or several kilometres. According to [3
], the density of the obtained points (1–20 b/m2
) is sufficiently high for a rough extraction of the contour and the structure of building roofs. However, it is not sufficient for their detailed modelling or for detailed modelling of the road surfaces; for example, road wearing course damage or road debris cannot be detected and modelled.
In the field of opening-up forests, ALS was used to determine the layout of the forest roads within forest stands with an accuracy of one to two metres [8
]. The method can facilitate the updating of the forest road network maps, increase the efficiency of the accessibility and it can be used to plan harvesting and transport processes, e.g., the layout and the lengths of skidding and hauling roads. Nevertheless, data processing may be more time consuming and not applicable for more accurate and detailed data on road wearing course damage.
Saito et al. [15
] published an article on the possible use of the ALS method for the automatic design of a forest road network that takes into account negative points for its layout, such as sites threatened by landslides. Based on accurate DMT created using LiDAR data, it is also possible to localize the drainage objects on the forest roads and minimize the erosion resulting from the construction of forest roads [16
]. Contreras et al. [17
] attempted to use the created DMT to determine the extent of groundworks during a hauling road design and compared its precision with the data obtained by the conventional ground-based method. Aricak et al. [18
] used the commercial satellite imagery system GeoEye-1 with high spatial resolution of 0.46 m pixels to create the DTM. However, the resulting definitions of about 1 meter cannot be used to evaluate the status of the wearing course [19
In addition to airborne laser scanning, terrestrial laser scanning (TLS) and mobile laser scanning (MLS) can also be used. These methods provide a greater point cloud density, up to thousands of points per m2
. The TLS method is one of the most progressively developing methods of 3D data acquisition. Nowadays, the TLS can be applied in various fields: topographical measurements [20
], exploration of landslides [21
], forest inventory [23
], documentation of the actual state of buildings [26
], mapping of industrial sites and underground areas [20
], monitoring of ground surface erosion [30
], documentation of cultural heritage [31
], and many others.
The use of such technology can provide a large amount of up-to-date high-resolution data over a short period and it enables very accurate modelling of the environment. Laser scanners use various methods for data acquisition, such as triangulation, Time of Flight (TOF), Phase-Shift [35
]. A 3D laser scanner with the triangulation method based on a line-laser is used for very short range scans and collects data to micron level accuracy. This system is mainly used in engineering applications, in archeology, and in precise scanning of the texture on road pavements [36
]. A TOF 3D laser scanner measures the time duration in which a laser travels between the scanner and the object so that the space resolution depends on the accuracy of measuring time [23
]. According to [23
], these characteristics allow very long measurement distances, but relatively low acquisition speeds. This method is well suited to 3D reconstruction of scenes at larger distances [23
] and for large areas or objects [35
]. A phase-shift type determines the position of points based on the continual measurement of the phase shift between the laser beams transmitted and received. Due to their range, phase-shift based scanners are well suited for high precision and detailed measurements of relatively near scenes [23
]. This method enables very fast surface data obtaining (up to million points per second). The phase-shift scanners are generally faster, more accurate and versatile than the TOF scanners.
The TLS technology can be effectively used for a road surface survey before reconstruction, together with documentation of the current condition to detect damage (e.g., rutting, potholes) or to determine the amount of material needed for the road repair. Moreover, the TLS can be used to generate a model for the milling quality control and subsequent laying of a new surface layer. There are studies [37
] that dealt with the assessment of road surface inequalities and road shape analysis. The main problem of the surface of roads, including forest roads, is rut development [40
]. Water flowing in these ruts concentrates in some places, the road surface is flooded and subsequent freezing causes surface damage [41
]. These deformations on the road surface affect the gradual disintegration of the forest road surfaces, which require more frequent repairs [42
The main advantage of TLS is rapid and easy non-contact collection of high-precision data with very high resolution obtained in an optimal quantity/time ratio (thousands of points per second) [43
]. Due to the advantages, especially high-precision data collection, TLS technology can be used in the field of road construction to monitor the wear of the road surface [40
]. Forest roads should always have a stable and quality surface for safe driving. Therefore, expensive periodic maintenance and repairs of forest roads are carried out [40
Mobile laser scanning (MLS) systems are widely used in urban areas, especially for scanning and evaluation of paved roads. The scanning process is fast and easy and it allows obtaining very dense point clouds precisely representing reality. That is why it is widely used in transportation management, extraction of the road objects, and even for monitoring of natural objects. For example, Jaakkola et al. [44
] presented automatic methods for road marking and kerbstone classification; Bitenc et al. [45
] used MLS for monitoring of a sandy coast; Wang et al. [46
] used roadside environment from MLS data to model water flow. It has also been used to evaluate road traffic safety by analysing pavement cracks [47
] or road roughness detection [48
]. A mobile scanner can also be placed on a vessel and used, for example, for mapping rivers or shore erosion [49
]. According to [52
], MLS can collect 3-dimensional road and road-related geospatial information accurately and efficiently. Their paper provides a complete workflow for the detection and classification of pole-like road objects from MLS in a motorway environment. In article [53
], the authors present a method for estimating the condition of a road using the MLS measurement technique. They state that the application of MLS could provide valuable proof of the road technical condition. Vallet et al. [54
] propose a pipeline to produce road orthophotos and DTM from MLS, as for DTM, MLS offers a much higher accuracy and density than aerial products. However, most of these studies deal with civil engineering in urban areas for traffic and city planning and modeling. Kukko et al. [55
] present multiplatform MLS solutions for mapping applications that require mobility in various terrains and river environments but produce high density point clouds with good reliability and accuracy. They used MLS under difficult measurement conditions such as the sea or river with accurate information over a large open area, which is in contrast with our study.
There are different approaches and algorithms for DTM generation from MLS data in urban or rural areas [56
]. In our work, the robust filtering algorithm in combination with hierarchical interpolation [59
] was used to generate the DTM from the MLS data. The algorithm has proved to be one of the efficient algorithms for generating DTM in forest areas. The algorithms were applied in software OPALS [62
]. Unfortunately, mobile laser scanning of forest roads remains challenging due to the low GNSS signal and the airborne laser scanner cannot always get through the dense canopy.
Nowadays, photogrammetric image processing is commonly used for creating 3D models of objects such as buildings and trees even in large areas, so it is possible to map the structure of forest stands, for example. The Structure from Motion (SfM) algorithm is the most frequently used for the processing. This photogrammetric method is designed for creating three-dimensional models of a feature or topography from overlapping two-dimensional photographs taken from many locations and orientations to reconstruct the photographed scene. This technology has existed in various forms since 1979 [65
], but applications were uncommon until the early 2000′s. The utilization of SfM is wide-ranging, from many subfields of geoscience (geomorphology, tectonics, structural geology, geodesy, and mining) to archaeology, architecture, and agriculture. In addition to ortho-rectified imagery, SfM produces a dense point cloud dataset that is similar in many ways to that produced by airborne or terrestrial LiDAR.
Hrůza et al. [66
] tested the use of SfM technology to detect road damage to a forest path using UAV with high quality results. The results of the tested road section showed that unmanned aircraft systems can be used to detect the forest road surface damage with a difference in accuracy of up to 2 cm compared with the accuracy of the current tachymetric methods. However, Unmanned aerial vehicle (UAV) flight over the forest road carries the risk of collision with the tree crowns and puts high demands on drone control. Therefore, the same author [67
] later tested the imaging method using cameras carried on a rod with a steady height of about 3 m. The method achieved similar results; the height differences reached 0.026 m, the X and Y horizontal differences were 0.019 m and 0.029 m, respectively.
The aim of this study is to verify whether it is possible, and with what precision, to detect the damage of the wearing course by means of different 3D imagining methods, which would facilitate and accelerate this process. Four different methods were used for the purpose of the study: mobile laser scanning, terrestrial laser scanning, close-range photogrammetry and airborne laser scanning.
The results show that the method of mobile photogrammetry is very accurate but also allows imaging by commercially available cameras. The resulting point cloud reaches very high density even when a 12MP camera is used, due to the scanning height of about 1.5 m. Higher vertical accuracy of the model was achieved in comparison with other studies [66
], where a 16-MP camera was used and the respective RMSE reached 0.0198 m and 0.0260 m. Large model errors at the road shoulders are caused by the technology itself because the point cloud is formed from a visible surface, i.e., including vegetation. However, this problem is reflected in all methods used, and therefore the data capture after the maintenance of the road is a prerequisite for the successful use of the methods. A high amount of ground control points (GCPs) surveyed either using GNSS or total station is required to create an accurate model. A lower amount of GCPs caused higher deviations from profiles surveyed by geodetic methods. The close range photogrammetric method also achieves the highest density of points per square meter, which is essential in practical use for the detailed calculation of road wearing course damage. This method is also independent of the car movement and possible vibrations while driving do not affect the final results. The SfM algorithm handles the images based on the current camera position and the basic condition is therefore only to obtain a good-quality image. The only condition for successful model creation is lower car speed due to optimum overlay of individual pictures. High-quality results achieved by the TLS method come from the scanner parameters and the used procedure where the individual scans were carried out, each about 40 m, to gain a sufficient overlap. The spacing between points within 10 m from the scanner was less than the 6 mm as mentioned in Section 2.3
. Similar settings were used in Choi et al. [35
], who noted that spacing between points within 10 m from the scanner was less than 5 mm. However, even with this setting, the resulting accuracy and point density are lower than in the case of the photogrammetry but much higher than in the case of the MLS. The registration of individual scans and the georeferencing are the important factors for the accuracy of the resulting surface model [39
]. The georeferencing of the model in the coordinate system is done based on control points (located throughout the road) which also serve as points for alignment of the scans. The precision of these activities plays a very important role in accuracy evaluation of the used method. In this study, alignment including georeferencing was performed with a mean error of 0.0012 m and the maximum deviation was 0.0095 m. These values guarantee that TLS can provide very accurate information about the location and extent of wearing course damage. This is also confirmed by Valença [68
], who pointed out that cracks in the wall of bridge pillars can be detected with 2 mm spatial resolution and scanner distance of 12.5 m from the object. There are also disadvantages in spite of all the advantages of this method. The main disadvantage is the lowest time efficiency: the method requires frequent transfer and placement (positioning) of the scanner [3
]. Therefore, the method is inappropriate to map linear structures, but more suitable for targeting of local damage. Compared to the TLS, it is possible to survey even larger areas in a shorter time horizon [69
]. However, there is a problem with the GNSS signal reception affected by forest closed canopy in the case of forest road mapping. Therefore, the method is well-suited for road survey outside forest stands, or control points should be measured for better georeferencing in forest areas. Results from places with forest canopy are significantly affected by the GNSS error and require further processing. The resulting MLS accuracy is also affected by the vehicle speed or the type of scanner used. Road scanning was performed at a speed of about 20 km/h, resulting in lower point cloud density (after filtration of 26 p/m2
). However, the point density range is from 1 to 120 (pts/m2
) after filtration. The point cloud covers not only the road but also its surroundings at nearly 80 m distance in both directions. On the road, the point density will be larger than 26 (pts/m2
) because it is closer to the scanner. As we are interested only in the road, which is almost flat, a larger point density will not have a significant influence on the quality of the created DTM. Lim et al. [70
] achieved a vertical accuracy of 0.053 m determined from the point cloud of mean density (128 p/m2
) obtained at a vehicle speed of 8 km/h; the scanning angle also affects the density of the points in addition to the vehicle speed. Zhou and Vosselman [71
] achieved similar accuracy (RMSE = 0.06 m) at a vehicle speed of between 30 and 40 km/h and with density of approximately 1000 p/m2
. Previous studies show that the speed of the car does not affect the resulting accuracy of the model. In this case, the greater density despite the higher speed of the vehicle was given by the flat surface in the urban area. Therefore, it was not necessary to filter points of vegetation. Guan et al. [72
] used 30 control points surveyed by GNSS to assess the accuracy of the MLS. The mean standard deviations of vertical accuracy for two laser scanners were 0.042 m and 0.033 m. In all of these cases, it is evident that the mean quadratic error in the vertical direction is very similar, ranging from 0.03 m to 0.06 m. This precision is sufficient for creation of a very accurate model of a road leading through an open space. To test the quality of the created model from the MLS, we also calculated the differences between 100 measured points and the part of our road (about 150 m) outside the forest. As we can see from the results presented in Table 4
and Figure 6
, the RMSE is in full agreement with other published studies.
It could be stated that MLS is very precise technique for road inspection. Less accuracy in the MLS results would be caused by the lack of an absolute orientation under forest cover. As we can see from the results in Table 2
and Table 3
, in the forest areas with a dense canopy, control points should be measured for better georeferencing of the trajectory, or, for example, to overcome the loss of a GNSS signal in the forest, Kukko et al. [57
] used graph SLAM correction method (tree stem feature location) for correcting the post-processed GNSS-IMU trajectory for positional drift.
The ALS method showed an RMSE accuracy of 0.1392 m, which coincides with already published results [12
]. Surprising is the increase of RMSE in the case of ALS after elimination of sideways. This phenomenon is due to a higher average error, which is probably due to a lower number of values and generally to an ALS error. The effect of vegetation on sideways could also be lower because the ALS was carried out during September when the vegetation on sideways had been cut. The ALS has very low point density and thus the created DTM has lower precision that is also influenced by interpolation of distant points. All these factors affect the accuracy of the final results. However, the method is hardly usable to determine the extent of damage and maintenance needed in individual forest roads. This all gives an impression of dual work for forest managers or planners and it can be one of the reasons why these methods have not been successfully implemented in practice.
In general, it can be stated that a higher point cloud density may affect the resulting detail of road damage, but it does not significantly affect the accuracy of the used methods. In addition to ALS, all methods achieve higher point densities than geodetic measurements where the points were measured at distances of 20 cm. Therefore, all cross profiles from geodetical survey are smoother than the others and eliminate local road roughness because they are affected by linear interpolation of distant points.
The wearing course of the running surface is usually constructed with a 4-cm thick bituminous layer. In the case of a breakdown, the underlying bearing base course layer of the road is further damaged, which has a significant effect on its lifetime. This also results in the necessary vertical accuracy of the presented methods up to 4 cm in height. This is only satisfied by the TLS and CRP used methods with respective RSME values of 0.0110 m and 0.0243 m taken at a distance of 1.5 m of the target. Practically, due to the mobility of the CRP method and the fact that the forest road is a linear construction, the CRP method is more convenient for this mapping.