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Drainage ditch extraction from airborne LiDAR point clouds

Author
Organization
Abstract
Ditches are often absent in hydrographic geodatasets and their mapping would benefit from a cost and labor effective alternative to field surveys. We propose and evaluate an alternative that makes use of a high resolution LiDAR point cloud dataset. First the LiDAR points are classified as ditch and non-ditch points by means of a random forest classifier which considers subsets of the topographic and radiometric features provided by or derived from the LiDAR product. The LiDAR product includes for each georeferenced point, the elevation, the returned intensity value, and RGB values from simultaneously acquired aerial images. Next so-called ditch dropout points are reconstructed for the blind zones in the dataset using a new geometric approach. Finally, LiDAR ditch points and dropouts are assembled into ditch objects (2D-polygons and their derived centre lines). The procedure was evaluated for a grassland and a peri-urban agricultural area in Flanders, Belgium. A good point classification was obtained (Kappa = 0.77 for grassland and 0.73 for peri-urban area) by using all the features derived from the LiDAR product, whereby the geometric features had the greatest influence. However, even better results were obtained when the radiometric component of the LiDAR product was also taken into account. For the tested models for the extraction of ditch centre lines, the best resulted in an error of omission of 0.03 and an error of commission of 0.08 for the grassland study area and an error of omission of 0.14 and an error of commission of 0.07 for the peri-urban study area.
Keywords
LiDAR, Point cloud, Signal intensity, Ditch, Classification, Dropout, WATER LEVELS, NETWORKS, CLASSIFICATION, FLOW, FLOODPLAINS, LANDSCAPES, TOPOGRAPHY, MANAGEMENT, CATCHMENT, OPENNESS

Citation

Please use this url to cite or link to this publication:

MLA
Roelens, Jennifer, Bernhard Höfle, Stefaan Dondeyne, et al. “Drainage Ditch Extraction from Airborne LiDAR Point Clouds.” ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 146 (2018): 409–420. Print.
APA
Roelens, Jennifer, Höfle, B., Dondeyne, S., Van Orshoven, J., & Diels, J. (2018). Drainage ditch extraction from airborne LiDAR point clouds. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 146, 409–420.
Chicago author-date
Roelens, Jennifer, Bernhard Höfle, Stefaan Dondeyne, Jos Van Orshoven, and Jan Diels. 2018. “Drainage Ditch Extraction from Airborne LiDAR Point Clouds.” Isprs Journal of Photogrammetry and Remote Sensing 146: 409–420.
Chicago author-date (all authors)
Roelens, Jennifer, Bernhard Höfle, Stefaan Dondeyne, Jos Van Orshoven, and Jan Diels. 2018. “Drainage Ditch Extraction from Airborne LiDAR Point Clouds.” Isprs Journal of Photogrammetry and Remote Sensing 146: 409–420.
Vancouver
1.
Roelens J, Höfle B, Dondeyne S, Van Orshoven J, Diels J. Drainage ditch extraction from airborne LiDAR point clouds. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING. 2018;146:409–20.
IEEE
[1]
J. Roelens, B. Höfle, S. Dondeyne, J. Van Orshoven, and J. Diels, “Drainage ditch extraction from airborne LiDAR point clouds,” ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, vol. 146, pp. 409–420, 2018.
@article{8582322,
  abstract     = {Ditches are often absent in hydrographic geodatasets and their mapping would benefit from a cost and labor effective alternative to field surveys. We propose and evaluate an alternative that makes use of a high resolution LiDAR point cloud dataset. First the LiDAR points are classified as ditch and non-ditch points by means of a random forest classifier which considers subsets of the topographic and radiometric features provided by or derived from the LiDAR product. The LiDAR product includes for each georeferenced point, the elevation, the returned intensity value, and RGB values from simultaneously acquired aerial images. Next so-called ditch dropout points are reconstructed for the blind zones in the dataset using a new geometric approach. Finally, LiDAR ditch points and dropouts are assembled into ditch objects (2D-polygons and their derived centre lines). The procedure was evaluated for a grassland and a peri-urban agricultural area in Flanders, Belgium. A good point classification was obtained (Kappa = 0.77 for grassland and 0.73 for peri-urban area) by using all the features derived from the LiDAR product, whereby the geometric features had the greatest influence. However, even better results were obtained when the radiometric component of the LiDAR product was also taken into account. For the tested models for the extraction of ditch centre lines, the best resulted in an error of omission of 0.03 and an error of commission of 0.08 for the grassland study area and an error of omission of 0.14 and an error of commission of 0.07 for the peri-urban study area.},
  author       = {Roelens, Jennifer and Höfle, Bernhard and Dondeyne, Stefaan and Van Orshoven, Jos and Diels, Jan},
  issn         = {0924-2716},
  journal      = {ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING},
  keywords     = {LiDAR,Point cloud,Signal intensity,Ditch,Classification,Dropout,WATER LEVELS,NETWORKS,CLASSIFICATION,FLOW,FLOODPLAINS,LANDSCAPES,TOPOGRAPHY,MANAGEMENT,CATCHMENT,OPENNESS},
  language     = {eng},
  pages        = {409--420},
  title        = {Drainage ditch extraction from airborne LiDAR point clouds},
  url          = {http://dx.doi.org/10.1016/j.isprsjprs.2018.10.014},
  volume       = {146},
  year         = {2018},
}

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