Study of the LiDAR accuracy in mapping forest road alignments and estimating the earthwork volume B., Parsakhoo A., Mohamadi J., Shataee Jouibary S. (2018): Study of the LiDAR accuracy in mapping forest road alignments and estimating the earthwork volume. J. For. Sci., 64: 469-477.
download PDF

Today, differential geographical position system and total station devices are improving the accuracy of positioning information, but in critical locations such as steep slopes and closed canopy cover, the device accuracy is limited. Moreover, field surveying in this technique is time-consuming and expensive. For this reason, remote sensing technique such as light detection and ranging (LiDAR) laser scanner should be used in field measurements. The objective of this study was to evaluate and compare precision and time expenditure of total station and airborne LiDAR in producing horizontal and vertical alignments and estimating earthwork volume of two proposed forest roads in a deciduous forest of Iran. To investigate this task, the geographical position of proposed forest roads were detected by differential geographical position system and then marked on land. Mentioned roads were taken again with Leica Total Station (LTS) on control points with same 5 m intervals from start point. Recent data served as a reference value for comparison with LiDAR measurements. The data were processed in Civil 3D, Fusion and Leica geo office software. Results showed that in comparison to field-surveyed routes by LTS, the LiDAR-derived routes exhibited a horizontal accuracy of 0.23 and 0.47 m and vertical accuracy of 0.31 and 0.66 m for road 1 and road 2, respectively. The LiDAR-derived sections every 1 m exhibited cut and fill accuracy of 2.39 and 3.18 m3 for road 1 and 2.98 and 5.60 m3 road 2, respectively. In this study, it was proved that the road project can be prepared faster by LiDAR than that of LTS. Therefore, high accuracy of road projection by LiDAR is useful for terrain analysis without the need for field reconnaissance.

Akay Abdullah E. (2006): Minimizing total costs of forest roads with computer-aided design model. Sadhana, 31, 621-633
Alharthy A., Bethel J. (2004): Automated road extraction from LiDAR data. In: Bethel J. (ed.): Proceedings of the American Society of Photogrammetry and Remote Sensing Annual Conference, Anchorage, June 23–24, 2004: 51–58.
Amo M., Martinez F., Torre M. (2006): Road extraction from aerial images using a region competition algorithm. IEEE Transactions on Image Processing, 15, 1192-1201
Azizi Zahra, Najafi Akbar, Sadeghian Saeed (2014): Forest Road Detection Using LiDAR Data. Journal of Forestry Research, 25, 975-980
Chekole S.D. (2014): Surveying with GPS, total station and terrestrial laser scanner: A comparative study. [MSc Thesis.] Stockholm, School of Architecture and the Built Environment, KTH Royal Institute of Technology: 55.
Clode Simon, Rottensteiner Franz, Kootsookos Peter, Zelniker Emanuel (2007): Detection and Vectorization of Roads from Lidar Data. Photogrammetric Engineering & Remote Sensing, 73, 517-535
Coffin Alisa W. (2007): From roadkill to road ecology: A review of the ecological effects of roads. Journal of Transport Geography, 15, 396-406
Contreras M.A., Aracena P., Chung W. (2012): Improving accuracy in earthwork volume estimation for proposed forest roads using a high-resolution digital elevation model. Croation Journal of Forest Engineering, 33: 125–134.
Coulter E., Chung W., Akay A., Sessions J. (2001): Forest road earthwork calculations for linear road segments using a height resolution digital terrain model generated from LiDAR data. In: Proceedings of the First International Precision Forestry Symposium, Seattle, June 17–20, 2001: 125–129.
Craven Michael, Wing Michael G. (2013): Applying airborne LiDAR for forested road geomatics. Scandinavian Journal of Forest Research, 29, 174-182
David N., Mallet C., Pons T., Chauve A., Bretar F. (2009): Pathway detection and geometrical description from ALS data in forested mountaneous area. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVIII (Part 3/W8): 242–247.
Espinoza F., Owens R.E. (2007): Identifying roads and trails hidden under canopy using LiDAR. [MSc Thesis.] Monterey, Naval Postgraduate School: 25.
Evans Jeffrey, Hudak Andrew, Faux Russ, Smith Alistair M. (2009): Discrete Return Lidar in Natural Resources: Recommendations for Project Planning, Data Processing, and Deliverables. Remote Sensing, 1, 776-794
Ferraz António, Mallet Clément, Chehata Nesrine (2016): Large-scale road detection in forested mountainous areas using airborne topographic lidar data. ISPRS Journal of Photogrammetry and Remote Sensing, 112, 23-36
Gomes Pereira L.M, Janssen L.L.F (1999): Suitability of laser data for DTM generation: a case study in the context of road planning and design. ISPRS Journal of Photogrammetry and Remote Sensing, 54, 244-253
Grote Anne, Heipke Christian, Rottensteiner Franz (2012): Road Network Extraction in Suburban Areas. The Photogrammetric Record, 27, 8-28
He Yi, Song Ziqi, Liu Zhaocai (2017): Updating highway asset inventory using airborne LiDAR. Measurement, 104, 132-141
Hickerson T.F. (1964): Route Location and Design. New York, McGraw-Hill: 634.
Hinz Stefan, Baumgartner Albert (2003): Automatic extraction of urban road networks from multi-view aerial imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 58, 83-98
Hui Zhenyang, Hu Youjian, Jin Shuanggen, Yevenyo Yao Ziggah (2016): Road centerline extraction from airborne LiDAR point cloud based on hierarchical fusion and optimization. ISPRS Journal of Photogrammetry and Remote Sensing, 118, 22-36
Ichihara Kouichi, Tanaka Tosimi, Sawaguchi Isao, Umeda Shuji, Toyokawa Katsumi (2017): The Method for Designing the Profile of Forest Roads Supported by Genetic Algorithm. Journal of Forest Research, 1, 45-49
Krogstad F., Schiess P. (2004): The allure and pitfalls of using LiDAR topography in harvest and road design. In: Nelson J., Clark M. (eds): Proceedings of the Joint Conference of IUFRO 3.06 Forest Operations in Mountainous Conditions and the 12th International Mountain Logging Conference, Vancouver, June 13–16, 2004: 1–10.
Lacoste C., Descombes X., Zerubia J. (2005): Point processes for unsupervised line network extraction in remote sensing. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27, 1568-1579
Liu Kevin, Sessions John (2013): Preliminary Planning of Road Systems Using Digital Terrain Models. Journal of Forest Engineering, 4, 27-32
Mayer H., Hinz S., Bacher U., Baltsavias E. (2006): A test of automatic road extraction approaches. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 36: 209–214.
Mena J.B., Malpica J.A. (2005): An automatic method for road extraction in rural and semi-urban areas starting from high resolution satellite imagery. Pattern Recognition Letters, 26, 1201-1220
Mnih V., Hinton G. (2012): Learning to label aerial images from noisy data. In: Langford J., Pineau J. (eds): Proceedings of the 29th International Conference on Machine Learning, Edinburgh, June 26–July 1, 2012: 203–210.
Reutebuch Stephen E, McGaughey Robert J, Andersen Hans-Erik, Carson Ward W (2014): Accuracy of a high-resolution lidar terrain model under a conifer forest canopy. Canadian Journal of Remote Sensing, 29, 527-535
Robinson C., Duinker P.N., Beazley K.F. (2010): A conceptual framework for understanding, assessing, and mitigating ecological effects of forest roads. Environmental Reviews, 18, 61-86
Saito M., Aruga K., Matsue K., Tasaka T. (2008): Development of the filtering technique of the intersection angle method using LiDAR data of the Funyu Experimental Forest. Journal of the Japan Forest Engineering, 22: 265–270.
Satio M., Goshima M., Aruga K., Matsue K., Shuin Y., Taska T. (2013): Study of automatic forest road design model considering shallow landslides with LiDAR data of Funyu Experimental Forest. Croatian Journal of Forest Engineering, 34: 1–15.
Sessions J., Wimer J., Costales F., Wing M. (2010): Engineering considerations in road assessment for biomass operations in steep terrain. Western Journal of Applied Forestry, 25: 144–153.
Sidle Roy C., Ziegler Alan D. (2012): The dilemma of mountain roads. Nature Geoscience, 5, 437-438
Souleyrette R., Hallmark S., Pattnaik S., O’Brien M., Veneziano D. (2003): Grade and Cross Slope Estimation from LiDAR-based Surface Models. Ames, Midwest Transportation Consortium, Iowa State University: 73.
Türetken E., Benmansour F., Andres B., Pfister H., Fua P. (2013): Reconstructing loopy curvilinear structures using integer programming. In: Le Borgne H., Moo Yi K., Karaman S., Tron R., Khan S. (eds): Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Portland, June 23–28, 2013: 1822–1829.
Umeda S., Suzuki H., Yamaguchi S. (2007): Considerations in the construction of a spur road network. Journal of the Japan Forest Engineering Society, 22: 143–152.
Veneziano D., Souleyrette R., Hallmark S. (2002): Elevation of LiDAR for highway planning, location and design. In: Zhou G., Kafatos M. (eds): Proceedings of the 15th William T. Pecora Memorial Remote Sensing Symposium/Landsatellite Information IV/ISPRS Commission I/FIEOS 2002 Conference, Denver, Nov 10–15, 2002: 10–22.
White Russell A., Dietterick Brian C., Mastin Thomas, Strohman Rollin (2010): Forest Roads Mapped Using LiDAR in Steep Forested Terrain. Remote Sensing, 2, 1120-1141
Xiao Liang, Wang Ruili, Dai Bin, Fang Yuqiang, Liu Daxue, Wu Tao (2018): Hybrid conditional random field based camera-LIDAR fusion for road detection. Information Sciences, 432, 543-558
Ziems M., Breitkopf U., Heipke C., Rottensteiner F. (2012): Multiple-model based verification of road data. ISPRS Annals of the Photogrammetry, Remote Sensing & Spatial Information Sciences, I-3: 329–334.
download PDF

© 2022 Czech Academy of Agricultural Sciences | Prohlášení o přístupnosti