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Forest Road Detection Using LiDAR Data

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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.

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Authors and Affiliations

  1. Faculty of Natural Resources, Tarbiat Modares University, P.O. Box: 46414-356, Noor, Iran

    Zahra Azizi & Akbar Najafi

  2. Geomatics College of National Cartographic Center, P.O.BOX: 13185-1684, Tehran, Iran

    Saeed Sadeghian

Authors
  1. Zahra Azizi
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  2. Akbar Najafi
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  3. Saeed Sadeghian
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Corresponding author

Correspondence to Akbar Najafi.

Additional information

Project funding: This work was supported by Tarbiat Modares University (TMU) of Iran.

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Azizi, Z., Najafi, A. & Sadeghian, S. Forest Road Detection Using LiDAR Data. Journal of Forestry Research 25, 975–980 (2014). https://doi.org/10.1007/s11676-014-0544-0

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  • DOI: https://doi.org/10.1007/s11676-014-0544-0

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