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Figure 1: Basic set up of a Lidar measurement (Mallet 2010).
Figure 2: Comparison red vs green laser (Mallet 2010).
Figure 3: Discrete Echo and waveform signal comparison for Airborne Laser Scanning (

Developed by:

Date: 1998

Type: Device, Tool


Lidar is a measurement technique that measures the distance from a georeferenced laser sensor to a ground target by illuminating the target with pulsed light (Figure 1). The sensor detects the reflected pulses. The time shifts in laser return and wavelengths can then be used to create digital 3-D representations of the target areas, e.g. a river section. The laser pulses can have different wavelengths, most commonly red and green.

The red laser is more common and therefore cheaper. However, other than the green NeoDyn Yag laser it cannot penetrate the water surface. Therefor the green laser (wavelength 532nm) is most preferred in scientific and especially river related measurements (Figure 2). However, a certain distance from the source of the light to the eye of any person, passing accidentally the measurement, must be guaranteed due to safety reasons. This is usually not a problem, as this application is used for large scale approaches, mainly with so called Airborne Laser Scanning (ALS) where the laser is mounted to a small plane, a helicopter or even a large (more than 15 kg load) drone.


As the laser and also the plane or helicopter are a quite expensive set of devices, the measurements are usually taken by private contractors. Most of the time the client is a municipality, the government or a hydropower operator interested in the geometry of floodplains and the bathymetry of rivers.

In Norway, most of the rivers are currently measured with a red laser (hence no underwater registrations). These are relatively easily available for research institutions through the public website Also available on this website are green laser derived bathymetry data of a selection of river reaches.

The penetration depth of the green laser through water depends highly on turbidity, flow velocity, reflections on the surface / waves and water depth. It also depends most probably on the type of suspended load. Thus, green laser measurements are in many cases supplemented with echosounding data.

Problems can also be caused by any other overlap of objects such as trees above ground or submerged vegetation above river bottoms (Figure 3). This can lead to blurred areas and various z-data (height marker) for the same x/y coordinates. The measurements generate a point cloud. Post-processing is usually done in a specific software (commercial software, but also freeware) as is the case many other applications (such as SfM). However, as it is a usual output format, it is not a specific part of the Lidar system.

Relevant mitigation measures and test cases

Relevant measures
Bypass combined with other solutions
Cleaning of substrate - ripping, ploughing and flushing
Construction of a 'river-in-the-river'
Construction of off-channel habitats
Drawdown reservoir flushing
Fish guidance structures with narrow bar spacing
Fish refuge under hydropeaking conditions
Mechanical removal of fine sediments (dredging)
Placement of dead wood and debris
Placement of spawning gravel in the river
Placement of stones in the river
Removal of weirs
Restoration of the riparian zone vegetation
Relevant test cases Applied in test case?
Altheim test case -
Altusried test case -
Anundsjö test case -
Freudenau test case -
Gotein test case -
Günz test case -
Ham test case -
Las Rives test case -
Trois Villes test case -

Other information

Relevant literature

  • Bizzi, S., Demarchi, L., Grabowski, R.C., Weissteiner, C.J., Van de Bund, W., 2016. The use of remote sensing to characterise hydromorphological properties of European rivers. Aquatic Sciences 78, 57–70.
  • Brock, J.C., Purkis, S.J., 2009. The Emerging Role of Lidar Remote Sensing in Coastal Research and Resource Management. Journal of Coastal Research 10053, 1–5.
  • Brock und Purkis - 2009 - The Emerging Role of Lidar Remote Sensing in Coast.pdf, n.d.
  • Costa, B.M., Battista, T.A., Pittman, S.J., 2009. Comparative evaluation of airborne LiDAR and ship-based multibeam SoNAR bathymetry and intensity for mapping coral reef ecosystems. Remote Sensing of Environment 113, 1082–1100.
  • Costa et al. - 2009 - Comparative evaluation of airborne LiDAR and ship-.pdf, n.d.
  • Gao, J., 2009. Bathymetric mapping by means of remote sensing: methods, accuracy and limitations. Progress in Physical Geography 33, 103–116.
  • Hilldale, R.C., Raff, D., 2008. Assessing the ability of airborne LiDAR to map river bathymetry. Earth Surface Processes and Landforms 33, 773–783.
  • Hilldale und Raff - 2008 - Assessing the ability of airborne LiDAR to map riv.pdf, n.d.
  • Irish, J.L., White, T.E., 1998. Coastal engineering applications of high-resolution lidar bathymetry. Coastal Engineering 35, 47–71.
  • Kinzel, P.J., Legleiter, C.J., Nelson, J.M., 2013. Mapping River Bathymetry With a Small Footprint Green LiDAR: Applications and Challenges : Mapping River Bathymetry with a Small Footprint Green LiDAR: Applications and Challenges. JAWRA Journal of the American Water Resources Association 49, 183–204.
  • Langhammer, J., Janský, B., Kocum, J., Minařík, R., 2018a. 3-D reconstruction of an abandoned montane reservoir using UAV photogrammetry, aerial LiDAR and field survey. Applied Geography 98, 9–21.
  • Langhammer, J., Janský, B., Kocum, J., Minařík, R., 2018b. 3-D reconstruction of an abandoned montane reservoir using UAV photogrammetry, aerial LiDAR and field survey. Applied Geography 98, 9–21.
  • Lejot, J., Delacourt, C., Piégay, H., Fournier, T., Trémélo, M.-L., Allemand, P., 2007. Very high spatial resolution imagery for channel bathymetry and topography from an unmanned mapping controlled platform. Earth Surface Processes and Landforms 32, 1705–1725.
  • Lükő, G., Rüther, D.N., n.d. UAV Based Hydromorphological Mapping of a River Reach to Improve Hydrodynamic Numerical Models.
  • Marcus, W.A., Fonstad, M.A., 2008. Optical remote mapping of rivers at sub-meter resolutions and watershed extents. Earth Surface Processes and Landforms 33, 4–24.
  • Mallet C., 2010; LIDAR aéroportéstopographiques & bathymétriques ;
  • Saylam, K., Brown, R.A., Hupp, J.R., 2017. Assessment of depth and turbidity with airborne Lidar bathymetry and multiband satellite imagery in shallow water bodies of the Alaskan North Slope. International Journal of Applied Earth Observation and Geoinformation 58, 191–200.
  • Wang, C.-K., Philpot, W.D., 2007. Using airborne bathymetric lidar to detect bottom type variation in shallow waters. Remote Sensing of Environment 106, 123–135.
  • Wang und Philpot - 2007 - Using airborne bathymetric lidar to detect bottom .pdf, n.d.
  • Zhang, K., Yang, F., Zhang, H., Su, D., Li, Q., 2017. Morphological characterization of coral reefs by combining lidar and MBES data: A case study from Yuanzhi Island, South China Sea: MORPHOLOGICAL STUDY OF CORAL REEF. Journal of Geophysical Research: Oceans 122, 4779–4790.
  • Zhang, K., Frey, H.C., 2006. Road Grade Estimation for On-Road Vehicle Emissions Modeling Using Light Detection and Ranging Data. Journal of the Air & Waste Management Association 56, 777–788.

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