Publication details for Dr Patrice CarbonneauCarbonneau, P.E., Lane, S.N. & Bergeron, N.E. (2006). Feature based image processing methods applied to bathymetric measurements from airborne remote sensing in fluvial environments. Earth Surface Processes and Landforms 31(11): 1413-1423.
- Publication type: Journal Article
- ISSN/ISBN: 0197-9337, 1096-9837
- DOI: 10.1002/esp.1341
- Keywords: Bathymetry, Image processing, Remote sensing, Rivers.
- Further publication details on publisher web site
- Durham Research Online (DRO) - may include full text
Author(s) from Durham
Bathymetric maps produced from remotely sensed imagery are increasingly common. However, when this method is applied to fluvial environments, changing scenes and illumination variations severely hinder the application of well established empirical calibration methods used to obtain predictive depth-colour relationships. In this paper, illumination variations are corrected with feature based image processing, which is used to identify areas in an image with a near-zero water depth. This information can then be included in the depth-colour calibration process, which results in an improved prediction quality. The end product is an automated bathymetric mapping method capable of a 4 m2 spatial resolution with a precision of ±15 cm, which allows for a more widespread application of bathymetric mapping.
Carbonneau PE., Bergeron NE, Lane SN. 2005. Automated grain size measurements from airborne remote sensing for long profile measurements
of fluvial grain sizes. Water Resources Research 41: W11426 doi: 10.1029/2005WR003994.
Carbonneau PE, Lane SN, Bergeron NE. 2004. Catchment-scale mapping of surface grain size in gravel-bed rivers using airborne digital
imagery. Water Resources Research 40: 7.
Castleman KR. 1996. Digital Image Processing. Prentice-Hall: Erplewood Cliffs, NJ.
Fonstad MA, Marcus WA. 2005. Remote sensing of stream depths with hydraulically assisted bathymetry (HAB) models. Geomorphology
Legleiter CJ, Roberts DA, Marcus WA, Fonstad MA. 2004. Passive remote sensing of river channel morphology and in-stream habitat:
physical basis and feasibility. Remote Sensing of Environment 93: 493 –510.
Lyon JG, Hutchinson WS. 1995. Application of a radiometric model for evaluation of water depths and verification of results with airborne
scanner data. Photogrammetric Engineering and Remote Sensing 61: 161–166.
Lyon JG, Lunetta RS, Williams DC. 1992. Airborne multispectral scanner data for evaluating bottom sediment types and water depths of the
St. Marys river, Michigan. Photogrammetric Engineering and Remote Sensing 58: 951–956.
Marcus WA. 2002. Mapping of stream microhabitats with high spatial resolution hyperspectral imagery. Journal of Geographical Systems 4:
Marcus WA, Legleiter CJ, Aspinall RJ, Boardman JW, Crabtree RL. 2003. High spatial resolution hyperspectral mapping of in-stream
habitats, depths, and woody debris in mountain streams. Geomorphology 55: 363–380.
Richards JA, Xiuping J. 1999. Remote Sensing Digital Image Analysis, an Introduction, 3rd edn. Springer: Berlin, Heidelberg: New York.
Roberts ACB, Anderson JM. 1999. Shallow water bathymetry using integrated airborne multi-spectral remote sensing. International Journal
of Remote Sensing 20(3): 497–510.
Serway RA. 1983. Physics for Scientists and Engineers. CBS; 132–133.
Westaway RM, Lane SN, Hicks DM. 2003. Remote survey of large-scale braided rivers using digital photogrammetry and image analysis.
International Journal of Remote Sensing 24: 795–816.
Whited D, Stanford JA, Kimball JS. 2002. Application of airborne multispectral digital imagery to quantify riverine habitats at different base
flows. River Research and Applications 18: 583 –594.
Winterbottom SJ, Gilvear DJ. 1997. Quantification of channel bed morphology in gravel-bed rivers using airborne multispectral imagery and
aerial photography. Regulated Rivers: Research and Management 13: 489–499.