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Selective Filtering of Light Detection and Ranging (LiDAR) Data for Enhanced Surface Representation of River Geomorphology

Project ID: 657
Principal Investigator: Lanie Paquin
Research Topic: Water Resource Data Analysis
Funded Fiscal Years: 2012
Keywords: lidar, light detecting and ranging, river geomorphology, surface model, riparian, hydrologic and hydraulic modeling

Research Question

What studies, methods, and best practices are available for geospatial data processing and selective filtering of LiDAR to improve surface representation of river geomorphologic features, particularly streambanks, levees, and stream water surfaces, to support hydrologic and hydraulic modeling?

Need and Benefit

Reclamation is rapidly increasing its use of terrestrial LiDAR elevation data to support a wide variety of programs and applications. In particular, geospatial information derived from LiDAR data is proving valuable for not only understanding riparian habitat structures that support threatened and endangered aquatic species, such as salmon and steelhead, but also planning and designing river restoration projects to benefit those species.

LiDAR data are acquired as a dense collection of surface points (point cloud) that identify the location and elevation of multiple features on the earth. While the massive quantity of points allow for high-resolution representation of the earth's surface, the raw point cloud is generally too dense for input into hydrologic and hydraulic models. Approaches for intelligent filtering of LiDAR point returns are needed to ensure that surface representations are accurate and do not introduce error that could adversely impact hydrologic and hydraulic modeling, which are central to Reclamation design and construction efforts.

An additional challenge, which is particularly relevant to many Reclamation applications, is that LiDAR returns on water are unreliable. Much of the light wavelengths used by terrestrial LiDAR systems are absorbed or attenuated by water. Pulse returns on water introduce anomalies in the dataset that are problematic for delineating the edges of rivers and reservoirs and the elevations of streambanks and other constructed features such as levees. Current techniques to remove unreliable water returns while retaining valid streambank elevations often involve manual delineation of wetted areas observable from aerial imagery. This approach is time consuming, subjective (not replicable), and can introduce unintended errors in vertical accuracy. The results can be poorly defined stream gradients, streambanks, and constructed levees, which are key features in hydrologic and hydraulic models.

Contributing Partners

None

Research Products

Not Reviewed

The following documents were not reviewed. Statements made in these documents are those of the authors. The findings have not been verified.

Annotated Bibliography of Literature Review of Selective Filtering of LiDAR Data (interim, PDF, 150KB)
By Mr. Dale Lindeman
Report completed on November 06, 2012

Companion annotated bibliography to the literature review of current studies, methods, and best practices for geospatial data processing and selective filtering of LiDAR to improve surface representation of river geomorphologic features, particularly streambanks, levees, and stream water surfaces, to support hydrologic and hydraulic modeling.
Keywords: lidar, river geomorphology, hydrologic and hydraulic modeling

Literature Review Of Selective Filtering of LiDAR Data Processing Techniques (final, PDF, 155KB)
By Mr. Dale Lindeman
Report completed on November 06, 2012

This literature review examines current studies, methods and best practices for geospatial data processing and selective filtering of LiDAR to improve surface representation of river geomorphologic features, particularly streambanks, levees, and stream water surfaces, to support hydrologic and hydraulic modeling. It identifies the opportunity for a more focused comparison of filtering algorithms in areas along active stream channels, heavily vegetated streambanks, low terraces and levees.
Keywords: lidar, river geomorphology, hydrologic and hydraulic modeling

This information was last updated on July 30, 2014
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