Journal of Forestry Research

, Volume 25, Issue 4, pp 975–980 | Cite as

Forest Road Detection Using LiDAR Data

  • Zahra Azizi
  • Akbar NajafiEmail author
  • Saeed Sadeghian
Original Paper


We developed a three-step classification approach for forest road extraction utilizing LiDAR data. The first step employed the IDW method to interpolate LiDAR point data (first and last pulses) to achieve DSM, DTM and DNTM layers (at 1 m resolution). For this interpolation RMSE was 0.19 m. In the second step, the Support Vector Machine (SVM) was employed to classify the LiDAR data into two classes, road and non-road. For this classification, SVM indicated the merged distance layer with intensity data and yielded better identification of the road position. Assessments of the obtained results showed 63% correctness, 75% completeness and 52% quality of classification. In the next step, road edges were defined in the LiDAR-extracted layers, enabling accurate digitizing of the centerline location. More than 95% of the LiDAR-derived road was digitized within 1.3 m to the field surveyed normal. The proposed approach can provide thorough and accurate road inventory data to support forest management.


forest road LiDAR SVM IDW method 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Abdi E, Sisakht SR, Goushbor L, Soufi H. 2012. Accuracy assessment of GPS and surveying technique in forest road mapping. Annals of Forest Research, 55: 309–317.Google Scholar
  2. Ali TA. 2004. On the selection of an interpolation method for creating a terrain model (TM) from LIDAR data. In: Proceedings of the American Congress on Surveying and Mapping (ACSM) Conference, Nashville TN, USA.Google Scholar
  3. Axelsson P. 1999. Processing of laser scanner data-algorithms and applications. ISPRS Journal of Photogrammetry and Remote Sensing, 54: 138–147.CrossRefGoogle Scholar
  4. Baltsavias EP. 1999. Airborne laser scanning: existing systems and firms and other resources. ISPRS Journal of Photogrammetry and Remote Sensing, 54: 164–198.CrossRefGoogle Scholar
  5. Bandara KRMU, Samarakoon L, Shrestha RP, Kamiya Y. 2011. Automated generation of digital terrain model using point clouds of digital surface model in forest area. Remote Sensing, 3: 845–858.CrossRefGoogle Scholar
  6. Bazi Y, Melgani F. 2006. Toward an optimal SVM classification system for hyperspectral remote sensing images. IEEE Transactions Geoscience and Remote Sensing, 44(11): 3374–3385.CrossRefGoogle Scholar
  7. Blaschke T, Tiede D, Heurich M. 2004. 3D landscape metrics to modelling forest structure and diversity based on laser scanning data. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 36: 129–132.Google Scholar
  8. Boggess JE. 1993. Identification of roads in satellite imagery using artificial neural networks: A contextual approach. Starkville, USA: Mississippi State University Press, p. 46.Google Scholar
  9. Camps-Valls G, Gomez-Chova L, Calpe-Maravilla J, Martin-Guerrero JD, Soria-Olivas E, Alonso-Chorda L, Moreno J. 2004. Robust support vector method for hyperspectral data classification and knowledge discovery. IEEE Transactions Geoscience and Remote Sensing, 42(7): 1530–1542.CrossRefGoogle Scholar
  10. Cortes C, Vapnik V. 1995. Support-Vector networks. Machine Learning, 20: 273–297.Google Scholar
  11. Doucette P, Grodecki J, Clelland R, Hsu A, Nolting J, Malitz S, Kavanagh C, Barton S, Tang M. 2009. Evaluating automated road extraction in different operational modes. In: Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV. Proceedings of the SPIE, Orlando, Florida, USA, 7334: p. 12.Google Scholar
  12. Feret JB, Asner GP. 2012. Semi-supervised methods to identify individual crowns of lowland tropical canopy species using imaging spectroscopy and LiDAR. Remote Sensing, 4: 2457–2476.CrossRefGoogle Scholar
  13. Gallay M, Lloyd C, Mckinley J. 2012. Optimal interpolation of airborne laser scanning data for fine-scale Dem validation purposes. In: Symposium GIS Ostrava 2012 — Proceedings Surface models for geosciences. Ostrava, Czech Republic.Google Scholar
  14. Gomez AG, Alvarez DF, Velasco JAM. 2010. Comparative analysis of Support Vector Machines and Mahalanobis algorithms for road extraction from high resolution satellite imagery. In: Symposium GIS Ostrava 2010 — GIS Meets Remote Sensing and Photogrammetry towards Digital World Proceeding. Ostrava, Czech Republic.Google Scholar
  15. Goodchild MF, Hunter GJ. 1997. A simple positional accuracy measure for linear features. International Journal of Geographical Information Science-GIS, 11(3): 299–306.CrossRefGoogle Scholar
  16. Hinz S, Baumgartner A. 2003. Automatic extraction of urban road networks from multi-view aerial imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 58(1): 83–98.CrossRefGoogle Scholar
  17. Huising EJ, Gomes Pereira LM. 1998. Errors and accuracy estimates of laser data acquired by various laser scanning systems for topographic applications. ISPRS Journal of Photogrammetry and Remote Sensing, 53(5): 245–261.CrossRefGoogle Scholar
  18. Hyyppa J, Yu X, Rönnholm P, Kaartinen H, Hyyppa H. 2003. Factors affecting laser-derived object-oriented forest height growth estimation. Photogramm J Fin, 18: 16–31.Google Scholar
  19. Kraus K, Rieger W. 1999. Processing of laser scanning data for wooded areas. In: Photogrammetric Week 99. Stuttgart, Germany, pp. 221–231.Google Scholar
  20. Liu X, Zhang Z, Peterson J, Chandra S. 2007. LiDAR-derived high quality ground control information and DEM for image orthorectification. GeoInformatica, 11(1): 37–53.CrossRefGoogle Scholar
  21. Liu X. 2008. Airborne LiDAR for DEM generation: some critical issues. Progress in Physical Geography, 32(1): 31–49.CrossRefGoogle Scholar
  22. Matkan AA, Mohamadzadeh A, Sadeghian S, Hajeb M. 2009. Road detection from LiDAR data by used support vector machine and mathematical morphology. Iranian Remote Sensing & GIS, 1(3): 81–97.Google Scholar
  23. Melgani F, Bruzzone L. 2004. Classification of hyperspectral remote sensing images with support vector machines. IEEE Transactions Geoscience and Remote Sensing, 42(8): 1778–1790.CrossRefGoogle Scholar
  24. Meng XL, Wang L, Currit N. 2009. Morphology-based building detection from airborne LIDAR data. Photogrammetric Engineering & Remote Sensing, 75(4): 427–442.CrossRefGoogle Scholar
  25. Meng XL, Wang L, Silván-Cárdenas JL, Currit N. 2009. A multi-directional ground filtering algorithm for airborne LIDAR. ISPRS Journal of Photogrammetry and Remote Sensing, 64(1): 117–124.CrossRefGoogle Scholar
  26. Okagawa M. 2001. Algorithm of multiple filters to extract DSM from LiDAR data. In: Proceedings of ESRI International User Conference, San Diego, CA, USA, p. 200.Google Scholar
  27. Pfeifer N, Briese C. 2007. Geometrical aspects of airborne laser scanning and terrestrial laser scanning. In: Procceedings of the ISPRS Workshop on Laser Scanning and SilviLaser, Espoo, Finland, 36(52): 311–319.Google Scholar
  28. Podobnikar T. 2005. Suitable DEM for required application. In: Proceedings of the 4th International Symposium on Digital Earth. Tokyo, Japan.Google Scholar
  29. Premebida C, Ludwig O, Nunes U. 2009. LIDAR and vision-based pedestrian detection system. Journal of Field Robotics, 26(9): 696–711.CrossRefGoogle Scholar
  30. Reutebuch SE, McGaughey RJ, Andersen HE, Carson WW. 2003. Accuracy of a high-resolution Lidar terrain model under a conifer forest canopy. Canadian Journal of Remote Sensing, 29(5): 527–535.CrossRefGoogle Scholar
  31. Roberts D, Gardner M, Funk C, Noronha V. 2001. Road extraction using mixture and Q-tree filter techniques, Technical Report. Santa Barbara: National Center for Geographical Information & Analysis, University of California, p. 31.Google Scholar
  32. Rodriguez-Perez JR, Alvarez MF, Sanz-Ablanedo E. 2007. Assessment of low-cost receiver accuracy and precision in forest environments. Journal of Surveying Engineering, 133(4): 159–167.CrossRefGoogle Scholar
  33. Shan J, Sampath A. 2005. Urban DEM generation from raw LiDAR data: a labeling algorithm and its performance. Photogrammetric Engineering & Remote Sensing, 71: 217–226.CrossRefGoogle Scholar
  34. Silvan-Cardenas JL, Wang L. 2006. A multi-resolution approach for filtering LiDAR altimetry data. ISPRS Journal of Photogrammetry and Remote Sensing, 61: 11–22.CrossRefGoogle Scholar
  35. Song M, Civco D. 2004. Road extraction using SVM and image segmentation. Photogrammetric Engineering & Remote Sensing, 70: 1365–1371.CrossRefGoogle Scholar
  36. Watanachaturaporn P. 2005. Classification of remote sensing images using support vector machines. In: Information Fusion (2005), 8th International Conference. New York, US.Google Scholar
  37. Wechsler SP. 2007. Uncertainties associated with digital elevation models for hydrologic applications: a review. Hydrology and Earth System Sciences, 11: 1481–1500.CrossRefGoogle Scholar
  38. White RA, Dietterick BC, Mastin T, Strohman R. 2010. Forest roads mapped using LiDAR in steep forested terrain. Remote Sensing, 2: 1120–1141.CrossRefGoogle Scholar
  39. Wiedemann C, Heipke C, Mayer H. 1998. Empirical Evaluation of Automatically Extracted Road Axes, In: KJ Bowyer, PJ Philips (eds), Empirical Evaluation Methods in Computer Vision. Los Alamitos, California: IEEE Computer Society Press.Google Scholar
  40. Wiedemann C. 2003. External evaluation of road networks. In: Proceedings of the ISPRS Workshop on Photogrammetric Image Analysis, Munich, Germany, 34(3): 93–98.Google Scholar
  41. Zhang K, Whitman D. 2005. Comparison of three algorithms for filtering airborne LiDAR data. Photogrammetric Engineering & Remote Sensing, 71: 313–324.CrossRefGoogle Scholar

Copyright information

© Northeast Forestry University and Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Faculty of Natural ResourcesTarbiat Modares UniversityNoorIran
  2. 2.Geomatics College of National Cartographic CenterTehranIran

Personalised recommendations