Extended Abstract
Introduction
One of the road quality control methods is the management and planning system based on the use of UAV, which, in addition to being comprehensive, highly accurate, very low-cost, and of course fast, compared to the old methods where the road surface is measured by workers and experts technical and is done in the form of on-site and field visit, it is much more economical (Zhang et al., 2015:38). (Jafari et al., 1401:2) evaluated the accuracy of the longitudinal profile of the road using the UAV system and mapping cameras. The results of the research showed that the digital model of the surface obtained by overlapping the UAV images is capable of evaluating the accuracy of the longitudinal profile of the road and the quality of the images taken by the UAV is very high and provides better and more details and saves time. Forlani et al., 2022:13 evaluated the influence of ground and aerial control points and their quality on the accuracy of the 3D model obtained from the UAV in the southern part of the University of Parma campus. The results showed that by increasing the quality of the coordinates of the lens centers, the accuracy of the 3D point increases, and the height accuracy of the 3D model of the surface increases twice if a ground control point is added. Due to the great importance of forest roads for optimal management and exploitation of forests, new methods should be sought using remote sensing capabilities. Due to being expensive and time-consuming, traditional methods increase the measurement error and decrease the accuracy in the preparation of information and data obtained at the network and research levels. The use of remote sensing data and drone images with high spatial resolution reduces the cost and facilitates the monitoring of the condition of forest roads. The purpose of this research is to use the images received by Phantom 3 UAV and ground control points with different numbers and distribution as well as without ground control points to check and measure the technical and geometric characteristics of roads and also compare the effect of DSM production quality processing method on The improvement of the quality of the digital model of the earth is the result of UAV images.
Materials and Methods
UAV images in suitable weather conditions with a flight height of 100 meters from the ground, overlap length of 85% and width of 75%, and a speed of 5 meters per second by the Quadcopter Phantom 3 Pro UAV in the winter season and the condition of forest trees without leaves in January 2023 in a part of The area of Darabkola forests, about 2 km long and 200 meters wide around the road, was received in three separate flight projects during different days. In this research, first, the detailed field measurements of the depth and width of some technical specifications and geometry of forest roads (side channels, gabions, and ditches), surface defects of roads (potholes), and waterways leading to the roads were measured with precise meters and the location of the measurement points was done with DGPS. In addition, several ground control points were made for accurate georeferencing of images and preparation of accurate and integrated orthophoto mosaics of the area using differential GPS and processed using the PPK method. Digital surface models of the region in several modes in the form of processed images in two processing methods with high and medium quality, without accurate GCPs and using accurate GCPs and separate and integrated flight projects were prepared, and technical specifications were extracted from the obtained models and compared with ground observations.
Results and Discussion
The results of the whole area with quality control points (high and medium) compared to the whole area without quality control points (high and medium) showed that the square root of the average error is lower and has the best height accuracy. Ground control points should be evenly distributed throughout the study area (ideally in the form of a triangulated grid). In this case, the maximum distance of each control point to other control points will be as small as possible. For a certain number of control points, the accuracy obtained by using an optimal distribution was twice better than when the control points had an inappropriate distribution. The best results for accuracy were obtained by considering the control points in the present study. These results are with the studies of Heydari Mozafar et al. (1401:3), Ghafari Thabit et al. (2018: 6), Abbaspour et al. (2017: 4), Zhang et al. (2015: 38) and Rock et al. (2022: 34) are consistent. Also, flight plan areas 1, 2, and 3 with quality control points (high and medium) have a lower mean square root of error than flight plan areas 1, 2, and 3 without quality control points (high and medium). The dispersion of ground control points is more effective than the number of ground control points on the accuracy of the 3D surface model. These findings are in agreement with the results of some researchers, including Ruzgienė et al. (2015: 33), and Gindraux et al. (2017:16) who concluded in their studies that proper distribution of control points has a significant effect on the accuracy of the 3D model up to a certain range Is Consistent. The addition of control points of the flight plan area 1, 2, and 3 in the present study increased the accuracy of the 3D model In this regard Forlaniet al (2022:13) Adding a ground control point roughly doubles the elevation accuracy of the 3D surface model, noted.
Conclusion
The results showed that the DSM produced from UAV images could extract some technical and geometrical characteristics of forest roads. Overall, the results of the surveys showed that the DSM generated from drone imagery is capable of extracting some technical and geometrical specifications of forest roads and forest land micro topography, but requires the use of precise ground control points and high-quality image processing.