Give Me the Dirt: Detection of Gully Extent and Volume Using High-Resolution Lidar

  • Alisa EustaceEmail author
  • Matthew Pringle
  • Christian Witte
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)


The Fitzroy catchment drains into the Great Barrier Reef lagoon. It is the largest catchment on the east coast of Australia, with an area of over 140,000 km2. To ascertain point-sources of erosion, and to quantify the volume of sediment lost from gullies, within the Fitzroy catchment is a major challenge for land-management; despite this, gully locations and volumes have never been thoroughly investigated. This study aims to develop a semi-automated method to detect and map gully extent and volume, using aircraft-mounted Light Detection and Ranging (LiDAR) technology within the Fitzroy catchment. Twenty LiDAR transects were acquired in 2007 (5000 × 275 m). The average distance between points of the LiDAR data was 0.3 m on the ground, with a height accuracy of within 0.1 m. Digital Elevation Models were derived for the transects, with a 0.5-m spatial resolution. We delineated gullies using terrain attributes and the backscatter intensity of the LiDAR returns. Transects were classified as ‘gully’ or ‘non-gully’ using objected-oriented classification. Gully volume was estimated for each pixel of the twenty transects. For four transects, we used a random forest algorithm to model the relation between gully presence and a set of readily available ancillary variables. We also modelled the relation between gully volume and the ancillary variables. These models were used to predict gully presence and volume at unsampled locations. We considered the extrapolation a success. The products generated from this study will be used to inform water-quality models, to asses land condition, and to improve our understanding of the dynamics of gully erosion under different climate and land-management regimes.


Random Forest LiDAR Data Backscatter Intensity Unsampled Location Random Forest Classification 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We would like to thank Dr Robert Denham for his statistical and programming advice.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Alisa Eustace
    • 1
    Email author
  • Matthew Pringle
    • 1
  • Christian Witte
    • 1
  1. 1.Queensland Department of Natural Resources and WaterRemote Sensing CentreIndooroopillyAustralia

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