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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

Abstract

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.

Keywords

forest road LiDAR SVM IDW method 

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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

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