GEO 565, Geographic Information Systems & Science
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| Image courtesy of Chris Kemp, TerraSond, Ltd. Hood Canal Bridge image using Fledermaus software. Bathymetric data collected with Reson 8108, water-borne LiDAR acquired with Riegl LMS-Q120 2D Laser Scanner, airborne LiDAR from the LiDAR Consortium, and CAD drawings of bridge cables were visual interpretations. |
Guenther, G.C., Cunningham, A.G., LaRocque, P.E., and Reid, D.J., 2000. Meeting the Accuracy Challenge in Airborne LiDAR Bathymetry. In: Proceedings of EARSeL-SIG-Workshop LIDAR, Dresden, Germany: June 16 – 17, 2000
This paper describes the challenges of Airborne LiDAR Hydrography or ALH. With the use of the U.S. Army Corps of Engineers’ SHOALS operational airborne lidar bathymetry (ALB) system, it is demonstrated that there are several obstacles to overcome to obtain repeatable results that meet the customer’s operational requirements and international accuracy standards. Limitations are defined by the attributes of a survey area such as depth, water clarity, safety, and weather conditions. Both airborne and water-borne surveys can be used in conjunction as complementary tools. It is suggested that survey planning by a lidar system prior to a sonar survey can outline dangerous areas and/or features that can damage survey vessels and can lead to the loss of life. It is shown that small object detection (< 1-meter square) is not appropriate for lidar acquisition and thus should not be used to determine the navigability of a water way. To meet the customer’s need for efficient and safe surveys, operational procedures are developed for quality control, calibration, and maintenance to improve the land/sea boundary survey.
Allouis, T., Bailly, J.S., and Feurer, D., 2007. Assessing water surface effects on LiDAR bathymetry measurements in very shallow rivers: a theoretical study. In: Programme/Abstracts/ Presentations/Posters of ESA 2nd Space for Hydrology Workshop, Geneva, Switzerland: November 12 –14, 2007
This study uses a specific LiDAR full wave form simulation model (GLFW) and various water surface attributes to determine the minimum measurable water depth. Using a function to describe the reflection of light on the microfacets of wavelets and the geometric relationship the facets have with each other. The GLFW simulation model used the system parameters found in the Hawk Eye II system. Two case studies were compared: one of smooth homogeneous surface and no slope; the other with surface roughness and slope. The geometric parameters were from observations made on the Durance river (South of France). The results determined by this model showed a minimum detectable depth to be 0.41m by a LiDAR system using only the green laser signal.
Sinclair, M. and Penley, M., 2007. Processing Lidar Data for Charting Applications – Understanding the Trade-Offs and Challenges. In: Technical papers presented at the U.S. Hydro 2007 Conference, Norfolk, Virginia: May 15 –17, 2007
This paper outlines how signal strength effects LiDAR acquisition and the detection of small objects on the seafloor. As the sounding energy decreases so does the ability to detect smaller objects. Beam size, defined by laser eye safety requirements, is large to begin with. Further broadening of the beam is caused by sea state refraction and scattering by turbidity. Turbidity also increases beam attenuation through seawater decreasing the amplitude of signal returns from the seabed and seafloor objects. A case study performed in Long Island Sound to determine LiDAR effectiveness was conducted using Laser Airborne Depth Sounder (LADS) and the acoustic system of the NOAA vessel Thomas Jefferson. This case study suggests best practices for data acquisition to maximize the laser systems abilities and also suggest a rules based approach to processing the acquired data.
Moyles, D., Orthmann, A., Lockhart, C., and DaSilva Lage, J., 2005. Hydrographic Mapping by Combined Operations Using Bathymetric LIDAR and Multibeam Echosounder in Alaska. In: Technical papers presented at the U.S. Hydro 2005 Conference, San Diego, California: March 29 – 31, 2005
This paper shows the significance of complementary survey systems of LiDAR and MBES. The LiDAR system used was the SHOALS-1000T which had the additional feature of geo-referenced digital ortho-photos. The area surveyed had a complicated shoreline with islets, rocks, foul areas, and kelp. Multibeam surveys were conducted using Reson 8101 and 8111 and LiDAR and shoreline investigations were conducted using a small 15ft skiff and all soundings on submerged features were acquired by the use of a lead line. The individual digital photos increased data processing efficiency by reducing shoreline ambiguities. The results of using LiDAR, MBES, and geo-referenced digital ortho-photos not only created a seamless product for the customer but reduced the turn time for nautical chart updates.
Skinner, K.D., 2009, Evaluation of LiDAR-acquired bathymetric and topographic data accuracy in various hydrogeomorphic settings in the lower Boise River, southwestern Idaho, 2007: U.S. Geological Survey Scientific Investigations Report 2009-5260, 12 p.
The Experimental Advanced Airborne Research LiDAR (Light Detection and Ranging), or EAARL, was evaluated by comparing data acquired by EAARL to ground-surveyed data collected using survey grade RTK-GPS. The areas to be assessed were in various hydrogeomorphic settings. The EAARL system collected a 2 – 5 km wide swath along the 103-km stretch of river. Three ground survey areas were selected within this 103-km swath to represent different hydrogeomorphic settings. Five cross-sections with the three ground survey areas were chosen to represent differing channel characteristics. Data was collected in a very limited time window to ensure that environmental conditions were consistent for each transect. The EAARL survey compared favorably with the ground survey with three exceptions. In-channel turbidity, areas of dense vegetation, and steep slope angles resulted in decreased accuracies.
Gesch, D. and Wilson, R., 2001. Development of a Seamless Multisource Topographic/ Bathymetric Elevation Model. Proceedings of the Twenty-First Annual ESRI User Conference
Intelmann, S.S. 2006. Comments on hydrographic and topographic LIDAR acquisition and merging with multibeam sounding data acquired in the Olympic Coast National Marine Sanctuary. Marine Sanctuaries Conservation Series ONMS-06-05. U.S. Department of Commerce, National Oceanic and Atmospheric Administration, National Marine Sanctuary Program, Silver Spring, MD. 18 pp.
The hazardous coastline within the Olympic Coast National Marine Sanctuary was surveyed using LiDAR to acquire the nearshore bathymetry and intertidal zone. This area consists of islets and rocks that would pose a hazardous survey environment under normal conditions for water-borne systems. Turbidity, surf break, and kelp beds presented a challenge to survey acquisition. VDatum model was used to merge existing multibeam data with the newly acquired LiDAR data by transforming the LiDAR vertical datum to MLLW. Large differences found between the data sets were attributed to false bottom detections in two areas that were the result of errors in cleaning of water column interference. The overall difference between the two surveys showed a 1 to 1.5m shoal bias in the multibeam survey over the LiDAR survey which can be attributed to the denser multibeam data delineating more shoal features as well as the data, with respect to danger to navigation, being gridded conservatively shoal biased. As found in all forms of survey acquisition modes whether it be bathymetric, LiDAR, or ground survey, environmental conditions control the speed and quality of the acquired data.
Moegling, C.H., 2007. VDatum and SBET to Improve Accuracy of NOAA’s High-Resolution Bathymetry. In: Index of /academic/undergrad/HonorsPapers07, Department of Geography, University of Maryland.
This paper explores testing two methods for improving horizontal and vertical accuracy of high-resolution bathymetry. Those two methods are VDatum transformation, developed by National Oceanic and Atmospheric Administration’s (NOAA) National Ocean Service (NOS) and National Geodetic Survey (NGS) scientists, and application of Smoothed, Best
Estimated Trajectory (SBET). Instruments used for this study were an airborne LiDAR system, a ship-borne LiDAR or laser scanner, POS/MV Inertial Navigation System, Reson 7125 multibeam, and a Interferometric Sonar. Method one: Bathymetry was processed and soundings reduced to MLLW. Method two: utilized SBET and VDatum to reduce soundings to NAD83 which would theoretically match the topographic data set that was also reduced to NAD83. Horizontal agreement was noted between the topographic data sets and bathymetry. However, differences of approximately 1m were noted in the vertical. Catastrophic data loss during processing limited exploring the sources of the vertical offset.
Quadros, N.D., Collier, P.A., Fraser, C.S., 2008. Integration of Bathymetric and Topographic LiDAR: A Preliminary Investigation. In: Proceedings of ISPRS Congress, Beijing, China: July 3–11, 2008
This paper explores a pilot project to look at the issues and technical challenges of a LiDAR based terrain model that can cross over the land/water interface in to the littoral zone. The pilot project was to analyze the bathymetric LiDAR performance to determine whether or not this data could be integrated with topographic LiDAR creating a seamless DEM for the coastal land/water interface. Both bathymetric and topographic LiDAR systems have differences that prevent integration. These differences are tabulated in a side-by-side comparison. Both the vertical and horizontal accuracy favors the topographic LiDAR as well as a higher resolution. The technique used to establish the datum for topographic LiDAR is more accurate and more precise than that used for bathymetric LiDAR and should be taken into account when trying to integrate the two data sets. Vertical offsets were also noted with the bathymetric LiDAR consistently above the topographic data set which can be attributed to shoal bias especially in steep or irregular bathymetric surfaces. Conclusion of this pilot project find that integration of bathymetric LiDAR is not yet possible due to three limiting conditions: the first are the differences that need to somehow be accounted for when height/depth are related to a common datum; secondly, overlap is limited in extent; and finally, both systems have physical limitations imposed by the dynamic littoral zone.
Smart, G.M., Bind, J., and Duncan, M.J., 2009. River bathymetry from conventional LiDAR using water surface returns. In: 18th World IMACS / MODSIM Congress, Cairns, Australia: July 13 - 17 2009
Use of a hydraulic model and LiDAR water surface returns to investigate calculations to predict the height of the underlying river bed is explored. Known water flow values, river bed type, and sufficient LiDAR returns must be acquired for this study. Initially, river bed topography is assumed and the hydraulic model uses this to predict the water surface elevation. The difference between the predicted and LiDAR measured water surface elevations is used iteratively to adjust the assumed river bed topography until the two water surface elevations match – iteration convergence technique. The technique should not be applied still ponds or lakes or very slow flowing water.
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