Hybrid DTMs Derived by LiDAR and Colour Bathymetry for Assessing Fluvial Geomorphic Changes After Flood Events in Gravel-Bed Rivers (Tagliamento, Piave and Brenta Rivers, Italy)

Hybrid DTMs Derived by LiDAR and Colour Bathymetry for Assessing Fluvial Geomorphic Changes After Flood Events in Gravel-Bed Rivers (Tagliamento, Piave and Brenta Rivers, Italy)

J. Moretto F. Delai M.A. Lenzi 

Department of Land, Environment, Agriculture and Forestry, University of Padova, Padova, Italy

Page: 
128-140
|
DOI: 
https://doi.org/10.2495/SAFE-V3-N2-128-140
Received: 
N/A
|
Accepted: 
N/A
|
Published: 
30 June 2013
| Citation

OPEN ACCESS

Abstract: 

Risk management and flood protection are frequently assessed through geo-morphometric evaluations resulting by floods events. If we aim at elevation models with high resolutions and covering large areas, airborne laser imaging detection and ranging (LiDAR) surveys can represent a good compromise among costs, time and uncertainty. The major limitation of the non-bathymetric LiDAR surveys con-sists in the detection of wet areas. Indeed, accounting for more than 20 cm of water depth, LiDAR signal increases its error exponentially. In this article we present a comparison of the results concerning the application of a colour bathymetry methodology for the production of hybrid digital terrain models. These elevation models were derived by merging LiDAR data for the dry areas and colour bathymetry for the wet areas. The methodological approach consists in a statistical regression between water depth and RGB band intensity values from contemporary aerial images. This methodology includes the use of filters to reduce possible errors due to the application of the model and to estimate precise ‘in-channel’ points. The study areas are three different human-impacted gravel-bed rivers of the north-eastern Italy. This methodology has been applied in three sub-reaches of the Brenta River, two of the Piave River and two of the Tagliamento River before and after relevant flood events with return intervals of ≥10 years. Potentials and limitations of the applied bathymetric method, the comparison of its use in dif-ferent fluvial contexts and its possibility of employment for geo-morphometric evaluations, were then tested. DGPS control points (1841, 2638 and10 473 for the Brenta, Piave and Tagliamento Rivers, respectively) were finally used to evaluate the accuracy of the wet areas. The results showed that, in each model, the wet areas’ vertical errors were comparable with those featured by LiDAR data for the dry areas.

Keywords: 

Colour bathymetry, DGPS survey, floods, geomorphic changes, gravel-bed river, LiDAR data

  References

[1] Moretto, J., Rigon, E., Mao, L., Picco, L., Delai, F. & Lenzi, M.A., Channel adjust-ments and island dynamics in the Brenta River (Italy) over the last 30 years. River Research and Applications, 2013. doi: http://dx.doi.org/10.1002/rra.2676

[2] Legleiter, C.J. & Roberts, D.A, A forward image model for passive optical remote sensing of river bathymetry. Remote Sensing of Environment, 113, pp. 1025–1045, 2009. doi: http:// dx.doi.org/10.1016/j.rse.2009.01.018

[3] Lane S.N., Widdison P.E., Thomas R.E., Ashworth P.J., Best J.L. & Lunt I.A., Sambrook Smith G.H.& Simpson C.J., Quantification of braided river channel change using archi-val digital image analysis. Earth Surface Processes and Landforms, 35, pp. 971–985. doi: http://dx.doi.org/10.1002/esp.2015

[4] Hicks, D.M., Remotely sensed topographic change in gravel riverbeds with flowing channels. Gravel-bed Rivers: Processes, Tool, Environments, eds. M. Church, P.M. Biron, & A.G. Roy, Wiley-Blackwell: Oxford, UK, pp. 303–314, 2012.

[5] Picco, L., Mao, L., Cavalli, E., Buzzi, E., Rigon, E., Moretto J., Delai F., Ravazzolo D. & Lenzi M.A., Using a terrestrial laser scanner to assess the morphological dynamics of a gravel-bed river, IAHS-AISH Publication, Issue 356, pp. 428–437, 2012.

[6] Rennie, C.D., Mapping water and sediment flux distributions in gravel-bed rivers using ADCPs. Gravel-bed Rivers: Processes, Tool, Environments, Wiley-Blackwell: Oxford, UK, pp. 342–350, 2012.

[7] Winterbottom, S.J. & Gilvear, D.J., Quantification of channel bed morphology in gravel-bed rivers using airborne multispectral imagery and aerial photography. Regulated Rivers: Research and Management, 13, pp. 489–499, 1997. doi: http://dx.doi. org/10.1002/(SICI)1099-1646(199711/12)13:6<489::AID-RRR471>3.0.CO;2-X

[8] Carbonneau, P.E., Lane, S.N. & Bergeron, N.E., Feature based image processing methods applied to bathymetric measurements from airborne remote sensing in fluvial environments. Earth Surface Processes and Landforms, 31, pp. 1413–1423, 2006. doi: http://dx.doi.org/10.1002/esp.1341

[9] Moretto, J., Rigon, E., Mao, L., Picco, L., Delai, F. & Lenzi, M.A., Assessing morphological changes in gravel bed rivers using LiDAR data and colour bathymetry. IAHS-AISH Publication, Issue 356, pp. 419–427, 2012.

[10] Legleiter, C.J., Kinzel, P.J. & Overstreet, B.T., Evaluating the potential for remote bathymetric mapping of a turbid, sand-bed river: 1. Field spectroscopy and radiative transfer modeling. Water Resources Research, 47, W09531, 2011. doi: http://dx.doi. org/10.1029/2011WR010591

[11] Moretto, J., Delai, F., Picco, L., Rigon, E., Ravazzolo D. &Lenzi, M.A., Integration of colour bathymetry, LiDAR and DGPS survey for assessing fluvial changes after floods events in the Tagliamento River (Italy). Accepted at Agricultural Sciences. ISSN Print: pp. 2156–8553, 2013. doi: http://dx.doi.org/10.4236/as.2013.48A004.

[12] Comiti, F., Da Canal, M, Surian, N., Mao, L., Picco, L. & Lenzi, M.A., Channel adjustments and vegetation cover dynamics in a large gravel bed river over the last 200 years. Geomorphology, 125(1), pp. 147–159, 2011. doi: http://dx.doi.org/10.1016/j. geomorph.2010.09.011

[13] Delai, F., Moretto, J., Picco, L., Rigon, E., Ravazzolo, D. & Lenzi, M.A., Analysis of morphological processes in a disturbed gravel-bed river (Piave River): integration of LiDAR data and colour bathymetry. Journal of Civil Engineering and Architecture, USA JCEA-E 20130528-4, 2013. (In press).

[14] Moretto, J., Rigon, E., Mao, L., Picco, L., Delai, F. & Lenzi, M.A., Medium- and short-term channel and island evolution in a disturbed gravel bed river (Brenta River, Italy). Journal of Agricultural Engineering, 43(4), pp. 176–188, 2012. doi: http://dx.doi. org/10.4081/jae.2012.e27

[15] Burnham, K.P. & Anderson, D.R., Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach, 2nd edn., Springer: New York, p. 488, 2002.

[16] Legleiter, C.J., Mapping river depth from publicy available aerial images. River Research and Applications, 2012. doi: http://dx.doi.org/10.1002/rra.2560

[17] Legleiter, C.J., Roberts, D.A. & Lawrence, R.L., Spectrally based remote sensing of river bathymetry. Earth Surface Processes and Landforms, 34, pp. 1039–1059, 2009. doi: http://dx.doi.org/10.1002/esp.1787