Research Papers

Assessing stream bank condition using airborne LiDAR and high spatial resolution image data in temperate semirural areas in Victoria, Australia

[+] Author Affiliations
Kasper Johansen

University of Queensland, Joint Remote Sensing Research Program, Biophysical Remote Sensing Group, Brisbane, Queensland 4072, Australia

University of Queensland, School of Geography, Planning and Environmental Management, Centre for Spatial Environmental Research, Brisbane, Queensland 4072, Australia

Ecosciences Precinct, Queensland Government, Remote Sensing Centre, Department of Science, Information Technology, Innovation and the Arts, 41 Boggo Road, Brisbane, Queensland 4102, Australia

James Grove

University of Melbourne, School of Resource Management and Geography, Parkville, Victoria 3010, Australia

Robert Denham

Ecosciences Precinct, Queensland Government, Remote Sensing Centre, Department of Science, Information Technology, Innovation and the Arts, 41 Boggo Road, Brisbane, Queensland 4102, Australia

Stuart Phinn

University of Queensland, Joint Remote Sensing Research Program, Biophysical Remote Sensing Group, Brisbane, Queensland 4072, Australia

University of Queensland, School of Geography, Planning and Environmental Management, Centre for Spatial Environmental Research, Brisbane, Queensland 4072, Australia

J. Appl. Remote Sens. 7(1), 073492 (Oct 28, 2013). doi:10.1117/1.JRS.7.073492
History: Received March 18, 2013; Revised October 2, 2013; Accepted October 3, 2013
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Abstract.  Stream bank condition is an important physical form indicator for streams related to the environmental condition of riparian corridors. This research developed and applied an approach for mapping bank condition from airborne light detection and ranging (LiDAR) and high-spatial resolution optical image data in a temperate forest/woodland/urban environment. Field observations of bank condition were related to LiDAR and optical image-derived variables, including bank slope, plant projective cover, bank-full width, valley confinement, bank height, bank top crenulation, and ground vegetation cover. Image-based variables, showing correlation with the field measurements of stream bank condition, were used as input to a cumulative logistic regression model to estimate and map bank condition. The highest correlation was achieved between field-assessed bank condition and image-derived average bank slope (R2=0.60, n=41), ground vegetation cover (R2=0.43, n=41), bank width/height ratio (R2=0.41, n=41), and valley confinement (producer’s accuracy=100%, n=9). Cross-validation showed an average misclassification error of 0.95 from an ordinal scale from 0 to 4 using the developed model. This approach was developed to support the remotely sensed mapping of stream bank condition for 26,000 km of streams in Victoria, Australia, from 2010 to 2012.

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© 2013 Society of Photo-Optical Instrumentation Engineers

Citation

Kasper Johansen ; James Grove ; Robert Denham and Stuart Phinn
"Assessing stream bank condition using airborne LiDAR and high spatial resolution image data in temperate semirural areas in Victoria, Australia", J. Appl. Remote Sens. 7(1), 073492 (Oct 28, 2013). ; http://dx.doi.org/10.1117/1.JRS.7.073492


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