The deterioration of the surface of forest roads is an important factor affecting the safe navigation of vehicles and traffic safety. In addition to traditional methods, automated methods are also used to determine the deterioration of the road surface. UAV systems, which are among the automated methods, are widely used to determine surface deformations with high accuracy. This study aimed to evaluate the advantages and disadvantages of two different flight modes of UAV, including autonomous flight and manual flight, in mapping road surface deformations. Within the scope of this study, the 50-meter section of the Type B forest road located in Kardüz Forest Management Chief (Düzce/Türkiye), was selected. For this study, first the pros and cons of the autonomous and manual flight data acquisition process were evaluated. Then, the photogrammetric data processing results were compared in terms of data size, with precision and accuracy. In addition, the deformation status on the surface within the selected road was determined using the average Z value differences obtained with two flight methods. The result of the study showed that, the number of images obtained from manual flights was 5.5 times higher than from autonomous flights and the flight time was taken four times longer. The average ground sampling distance of the orthophotos generated from two different light modes indicated that the manual flight mode provided seven times higher resolution than autonomous flight. Moreover, the results from the statistical tests for the two flight modes showed differences. When manual flights and autonomous flights are evaluated in terms of reducing the shadow effect, manual flights can be considered more advantageous. Furthermore, it was found that the dynamic mobility of erosion and accumulation on the road surface continued in time series in both flight methods.
Scientific and Technological Research Council of Turkey (TUBITAK)
118O309
We are very grateful to the Scientific and Technological Research Council of Turkey (TUBITAK) and we thank them for supporting this study (Project No. 118O309).
118O309
Primary Language | English |
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Subjects | Engineering |
Journal Section | Research Articles |
Authors | |
Project Number | 118O309 |
Publication Date | December 30, 2022 |
Published in Issue | Year 2022 |
The works published in European Journal of Forest Engineering (EJFE) are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.