Vegetation plays an important role in stabilizing the soil and decreasing fluvial erosion. In certain cases, vegetation increases the accumulation of fine sediments. Efficient and accurate methods are required for mapping and monitoring changes in the fluvial environment. Here, we develop an area-based approach for mapping and monitoring the vegetation structure along a river channel. First, a 2 × 2 m grid was placed over the study area. Metrics describing vegetation density and height were derived from mobile laser-scanning (MLS) data and used to predict the variables in the nearest-neighbor (NN) estimations. The training data were obtained from aerial images. The vegetation cover type was classified into the following four classes: bare ground, field layer, shrub layer, and canopy layer. Multi-temporal MLS data sets were applied to the change detection of riverine vegetation. This approach successfully classified vegetation cover with an overall classification accuracy of 72.6%; classification accuracies for bare ground, field layer, shrub layer, and canopy layer were 79.5%, 35.0%, 45.2% and 100.0%, respectively. Vegetation changes were detected primarily in outer river bends. These results proved that our approach was suitable for mapping riverine vegetation.
References
[1]
Connor, D.J.; Loomis, R.S.; Cassman, K.G. Crop Ecology: Productivity and Management in Agricultural Systems; Cambridge University Press: Cambridge, UK, 2011; p. 351.
[2]
Styczen, M.E.; Morgan, R.P.C. Engineering Properties of Vegetation. In Slope Stabilization and Erosion Control: A Bioengineering Approach; Morgan, R.P.C., Rickson, R.J., Eds.; Taylor & Francis: London, UK, 1995.
[3]
Beeson, C.E.; Doyle, P.F. Comparison of bank erosion at vegetated and non-vegetated channel bends. J. Am. Water Resour. Assoc 1995, 31, 983–990.
[4]
Hyypp？, J.; Hyypp？, H.; Yu, X.; Kaartinen, H.; Kukko, H.; Holopainen, M. Forest Inventory Using Small-Footprint Airborne Lidar. In Topographic Laser Ranging and Scanning: Principles and Processing; Shan, J., Toth, C.K., Eds.; CRC Press: Boca Raton, FL, USA, 2008; pp. 335–370.
[5]
Naesset, E. Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data. Remote Sens. Environ 2002, 80, 88–99.
[6]
Koch, B. Status and future of laser scanning, synthetic aperture radar and hyperspectral remote sensing data for forest biomass assessment. ISPRS J. Photogramm. Remote Sens 2010, 65, 581–590.
[7]
N？sset, E.; Gobakken, T.; Holmgren, J.; Hyypp？, H.; Hyypp？, J.; Maltamo, M.; Nilsson, M.; Olsson, H.; Persson, ？.; S？derman, U. Laser scanning of forest resources: The Nordic experience. Scand. J. For. Res 2004, 19, 482–499.
[8]
Vastaranta, M.; Holopainen, M.; Karjalainen, M.; Kankare, V.; Hyypp？, J.; Kaasalainen, S. TerraSAR-X stereo SAR and airborne scanning LiDAR height metrics in imputation of forest above-ground biomass and stem volume. IEEE Trans. Geosci. Remote Sens. 2013, doi:10.1109/TGRS.2013.2248370.
[9]
Kankare, V.; Vastaranta, M.; Holopainen, M.; R？ty, M.; Yu, X.; Hyypp？, J.; Hyypp？, H.; Alho, P.; Viitala, R. Retrieval of forest aboveground biomass and stem volume with airborne scanning LiDAR. Remote Sens 2013, 5, 2257–2274.
[10]
Raumonen, P.; Kaasalainen, M.; ？kerblom, M.; Kaasalainen, S.; Kaartinen, H.; Vastaranta, M.; Holopainen, M.; Disney, M.; Lewis, P. Fast automatic precision tree models from terrestrial laser scanner data. Remote Sens 2013, 5, 491–520.
[11]
Kankare, V.; Holopainen, M.; Vastaranta, M.; Puttonen, E.; Yu, X.; Hyypp？, J.; Vaaja, M.; Hyypp？, H.; Alho, P. Individual tree biomass estimation using terrestrial laser scanning. ISPRS J. Photogramm. Remote Sens 2013, 75, 64–75.
De Rose, R.C.; Basher, L.R. Measurement of river bank and cliff erosion from sequential LiDAR and historical aerial photography. Geomorphology 2011, 126, 132–147.
[14]
Nasermoaddeli, M.H.; Pasche, E. Application of Terrestrial 3D Laser Scanner in Quantification of the Riverbank Erosion and Deposition. Proceedings of International Conference on Fluvial Hydraulics (Riverflow 2008), Cesme-Ismir, Turkey, 3–5 September 2008; 3, pp. 2407–2416.
[15]
Resop, J.P.; Hession, W.C. Terrestrial laser scanning for monitoring streambank retreat: Comparison with traditional surveying techniques. J. Hydraul. Eng 2010, 136, 794–798.
[16]
Vaaja, M.; Hyypp？, J.; Kukko, A.; Kaartinen, H.; Hyypp？, H.; Alho, P. Mapping topography changes and elevation accuracies using a mobile laser scanner. Remote Sens 2011, 3, 587–600.
[17]
O’Neal, M.A.; Pizzuto, J.E. The rates and spatial patterns of annual riverbank erosion revealed through terrestrial laser-scanner surveys of the Sourth River, Virginia. Earth Surf. Process. Landf 2011, 36, 695–701.
[18]
Kasvi, E.; Vaaja, M.; Petteri, A.; Hyypp？, H.; Hyypp？, J.; Kaartinen, H.; Kukko, A. Morphological changes on meander point bars associated with flow structure at different discharges. Earth Surf. Process. Landf 2013, 38, 577–590.
[19]
Alho, P.; Kukko, A.; Hyyppa, H.; Kaartinen, H.; Hyypp？, J.; Jaakkola, A. Application of boat-based laser river survey. Earth Surf. Process. Landf 2009, 34, 1831–1838.
[20]
Barber, D.M.; Mills, J.P. Vehicle Based Waveform Laser Scanning in a Coastal Environment. Proceedings of the 5th International Symposium on Mobile Mapping Technology, Pradua, Italy, 29–31 May 2007.
[21]
Bitenc, M.; Lindenbergh, R.; Khoshelham, K.; van Waarden, A.P. Evaluation of a LiDAR land-based mobile mapping system for monitoring sandy coasts. Remote Sens 2011, 3, 1472–1491.
[22]
Kukko, A.; Kaartinen, H.; Hyypp？, J.; Chen, Y. Multiplatform mobile laser scanning: Usability and performance. Sensors 2012, 12, 11712–11733.
[23]
Glennie, C.; Brooks, B.; Ericksen, T.; Hauser, D.; Hudnut, K.; Foster, J.; Avery, J. Compact multipurpose mobile laser scanning system—Initial tests and results. Remote Sens 2013, 5, 521–538.
[24]
Graham, L. Mobile mapping systems overview. Photogramm. Eng. Remote Sens 2010, 76, 222–228.
[25]
Petrie, G. Mobile mapping systems: An introduction to the technology. GeoInformatics 2010, 13, 32–43.
[26]
Simon, A.; Curini, A.; Darby, S.E.; Langedoen, E.J. Bank and near-bank processes in an incised channel. Geomorphology 2000, 35, 193–217.
[27]
Rinaldi, M.; Darby, S.E. Modelling River-Bank-Erosion Processes and Mass Failure Mechanisms: Progress towards Fully Coupled Simulations. In Gravel-Bed Rivers VI: From Process Understanding to River Restoration; Habersack, H., Piégay, H., Rinaldi, M., Elsevier, B.V., Eds.; Elsevier Science Publishing Company: Oxford, UK, 2008; pp. 213–239.
[28]
Parker, C.; Simon, A.; Thorne, C.R. The effects of variability in bank material properties on riverbank stability: Goodwin Creek, Mississippi. Geomorphology 2011, 101, 533–543.
[29]
Motta, D.; Abad, J.D.; Langedoen, E.J.; Garcia, M.H. A simplified 2D model for meander migration with physically-based bank evolution. Geomorphology 2012, 163–164, 10–25.
[30]
Millar, R.G. Influence of bank vegetation on alluvial channel patterns. Water Resour. Res 2000, 36, 1109–1118.
[31]
Smith, D.G. Effect of vegetation on lateral migration of anastomosed channels of a glacier meltwater river. Geol. Soc. Am. Bull 1976, 87, 857–860.
[32]
Wynn, T.M. The Effects of Vegetation on Stream Bank ErosionPh.D. Dissertation, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA, 14 May 2004.
[33]
Wynn, T.M.; Mostaghimi, S. The effects of vegetation and soil type on stream bank erosion, Southwestern Virginia, USA. J. Am. Water Resour. Assoc 2006, 42, 69–82.
[34]
Gurnell, A. Plants as river system engineers. Earth Surf. Process. Landf. 2013, doi:10.1002/esp.3397.
[35]
Camporeale, C.; Ridolfi, L. Riparian vegetation distribution induced by river flow variability: A stochastic approach. Water Resour. Res. 2006, doi:10.1029/2006WR004933.
[36]
Pollen-Bankhead, N.; Simon, A.; Jaeger, K.; Wohl, E. Destabilization of streambanks by removal of invasive species in Canyon de Chelly National Monument, Arizona. Geomorphology 2009, 103, 363–374.
[37]
Chanson, H. The Hydraulics of Open Channel Flow—An Introduction; Butterworth Heinemann: Oxford, UK, 1999; p. 495.
Midgley, T.L.; Fox, G.A.; Heeren, D.M. Evaluation of the bank stability and toe erosion model (BSTEM) for predicting lateral retreat on composite streambanks. Geomorphology 2012, 145–146, 107–114.
[40]
Horrit, M.; Bates, P. Evaluation of 1D and 2D numerical models for predicting river flood inundation. J. Hydrol 2002, 268, 87–99.
[41]
Pasternack, G B.; Gilbert, A.T.; Wheaton, J.M.; Buckland, E.M. Error propagation for velocity and shear stress prediction using 2D models for environmental management. J. Hydrol 2006, 328, 227–241.
[42]
McMillan, H.K.; Brasington, J. Reduced complexity strategies for modelling urban flood-plain inundation. Geomorphology 2007, 90, 226–243.
[43]
Pender, G.; Néelz, S. Use of computer models of flood inundation to facilitate communication in flood risk management. Environ. Hazard 2007, 7, 106–114.
[44]
Koivum？ki, L.; Alho, P.; Lotsari, E.; K？yhk？, J.; Saari, A.; Hyypp？, H. Uncertainties in flood risk mapping: A case study on estimating building damages for a river flood in Finland. J. Flood Risk Manag 2010, 3, 166–183.
[45]
Kasvi, E.; Alho, P.; Vaaja, M.; Hyypp？, H.; Hyypp？, J. Spatial and temporal distribution of fluvio-morphological processes on a meander point bar during a flood event. Hydrol. Res. 2013, doi:10.2166/nh.2013.091.
[46]
Lotsari, E.; Wainwright, D.; Corner, G.D.; Alho, P.; K？yhk？, J. Surveyed and modelled one-year morphodynamics in the braided lower Tana River. Hydrol. Process. 2013, doi:10.1002/hyp.9750.
[47]
Alho, P.; Vaaja, M.; Kukko, A.; Kasvi, E.; Kurkela, M.; Hyypp？, J.; Hyypp？, H.; Kaartinen, H. Mobile laser scanning in fluvial geomorphology: Mapping and change detection of point bars. Z. Geomorphol. Suppl. Issue 2011, 55, 31–50.
[48]
Hohental, J.; Alho, P.; Hyypp？, J.; Hyypp？, H. Laser scanning applications in fluvial studies. Progr. Phys.Geogr 2011, 35, 782–809.
[49]
Jaakkola, A.; Hyypp？, J.; Hyypp？, H.; Kukko, A. Retrieval algorithms for road surface modelling using laser-based mobile mapping. Sensors 2008, 8, 5238–5249.
[50]
Kukko, A. Road Environment Mapper-3D Data Capturing with Mobile MappingLicentiate’s Thesis, Helsinki University of Technology, Espoo, Finland, 17 September 2009.
[51]
Goulette, F.; Nashashibi, F.; Abuhadrous, I.; Ammoun, S.; Laurgeau, C. An Integrated On-board Laser Range Sensing System for On-the-Way City and Road Modelling. Proceedings of the ISPRS Commission I Symposium—From Sensors to Imagery, Paris, France, 3–5 July 2006; pp. 43:1–43:6.
[52]
Jochem, A.; H？fle, B.; Rutzinger, M. Extraction of vertical walls from mobile laser scanning 441 data for solar potentiaL Assessment. Remote Sens 2011, 3, 650–667.
[53]
Wu, B.; Yu, B.; Yue, W.; Shu, S.; Tan, W.; Hu, C.; Huang, Y.; Wu, J.; Liu, H. A voxel-based method for automated identification and morphological parameters estimation of individual street trees from mobile laser scanning data. Remote Sens 2013, 5, 584–611.
[54]
Wang, Y.; Liang, X.; Flener, C.; Kukko, A.; Kaartinen, H.; Kurkela, M.; Vaaja, M.; Hyypp？, H.; Alho, P. 3D modeling of coarse fluvial sediments based on mobile laser scanning data. Remote Sens 2013, 5, 4571–4592.
[55]
Holopainen, M.; Vastaranta, M.; Kankare, V.; Hyypp？, J.; Liang, X.; Litkey, P.; Yu, X.; Kaartinen, H.; Kukko, A.; Kaasalainen, S.; et al. The use of ALS, TLS and VLS Measurements in Mapping and Monitoring Urban Trees. Proceedings of Joint Urban Remote Sensing Event, JURSE, Munich, Germany, 11–13 April 2011.
[56]
Holopainen, M.; Kankare, V.; Vastaranta, M.; Liang, X.; Lin, Y.; Vaaja, M.; Yu, X.; Hyypp？, J.; Hyypp？, H.; Kaartinen, H.; et al. Urban tree mapping with airborne, terrestrial, and mobile laser scanning. Urban For. Urban Green., 2013. in press.
[57]
Jaakkola, A.; Hyypp？, J.; Kukko, A.; Yu, X.; Kaartinen, H.; Lehtom？ki, M.; Lin, Y. A low-cost multi-sensoral mobile mapping system and its feasibility for tree measurements. ISPRS J. Photogramm. Remote Sens 2010, 65, 514–522.
[58]
Zlinszky, A.; Mücke, W.; Lehner, H.; Briese, A.; Pfeifer, N. Categorizing wetland vegetation by airborne laser scanning on Lake Balaton and Kis-Balaton, Hungary. Remote Sens 2012, 4, 1617–1650.
[59]
Farid, A.; Rautenkranz, D.; Goodrich, D.C.; Marsh, S.E.; Sorooshian, S. Riparian vegetation classification from airborne laser scanning data with an emphasis on cottonwood trees. Can. J. Remote Sens 2006, 32, 15–18.
[60]
Brodu, N.; Lague, D. 3D terrestrial lidar data classification of complex natural scenes using a multi-scale dimensionality criterion: Application in geomorphology. ISPRS J. Photogramm. Remote Sens 2012, 68, 121–134.
[61]
Kukko, A.; Andrei, C.-O.; Salminen, V.-M.; Kaartinen, H.; Chen, Y.; R？nnholm, P.; Hyypp？, H.; Hyypp？, J.; Chen, R.; Haggrén, H.; et al. Road Environment Mapping System of the Finnish Geodetic Institute—FGI ROAMER. Proceedings of ISPRS Workshop on Laser Scanning 2007 and (SilviLaser 2007), Espoo, Finland, 12–14 September 2007; 36, pp. 241–247.
[62]
Kaartinen, H.; Hyypp？, J.; Kukko, A.; Jaakkola, A.; Hyypp？, H. Benchmarking the performance of mobile laser scanning systems using a permanent test field. Sensors 2012, 12, 12814–12835.
[63]
Axelsson, P. DEM Generation from Laser Scanner Data Using Adaptive TIN Models. Proceedings of XIX ISPRS Congress, Amsterdam, The Netherlands, 16–22 July 2000; 33, pp. 110–117.
[64]
Lotsari, E.; Vaaja, M.; Flener, C.; Kaartinen, H.; Kukko, A.; Kasvi, E.; Hyypp？, H.; Hyypp？, J.; Alho, P. Annual bank and point bar morphodynamics of a meandering river based on high-accuracy multi-temporal laser scanning and flow data. Water Resour. Res, 2013. under review.
[65]
McGaughey, R.J. FUSION/LDV: Software for LiDAR Data Analysis and Visualization, FUSION Version 3.30; February 2013. Available online: http://forsys.cfr.washington.edu/fusion/FUSION_manual.pdf (accessed on 11 April 2013).
[66]
Breiman, L. Random forests. Mach. Learn 2001, 45, 5–32.
[67]
Jothityangkoon, C.; Sivapalan, M. Towards estimation of extreme floods: Examination of the roles of runoff process changes and floodplain flows. J. Hydrol 2003, 281, 206–229.
[68]
Makaste, B.; Maas, G.J.; van den Brink, C.; Wolfert, H.P. The influence of floodplain vegetation succession on hydraulic roughness: Is ecosystem rehabilitation in dutch embanked floodplains compatible with flood safety standards? Ambio 2011, 40, 370–376.
[69]
Rinaldi, M.; Mengoni, B.; Luppi, L.; Darby, S.E.; Mosselman, E. Numerical simulation of hydrodynamics and bank erosion in a river bend. Water Resour. Res 2008, 44, 1–17.
[70]
Yu, X.; Hyypp？, J.; Vastaranta, M.; Holopainen, M. Predicting individual tree attributes from airborne laser point clouds based on random forest technique. ISPRS J. Photogramm. Remote Sens 2011, 66, 28–37.
[71]
Hudak, A.; Crookston, N.; Evans, J.; Hall, D.; Falkowski, M. Nearest neighbor imputation of species-level, plot-scale forest structure attributes from LiDAR data. Remote Sens. Environ 2008, 112, 2232–2245.
[72]
Latifi, H.; Nothdurft, A.; Koch, B. Non-parametric prediction and mapping of standing timber volume and biomass in a temperate forest: Application of multiple optical/LiDAR—Derived predictors. Forestry 2010, 83, 395–407.
[73]
Falkowski, M.; Hudak, A.; Crookston, N.; Gessler, P.; Smith, A. Landscape-scale parameterization of a tree-level forest growth model: A k-NN imputation approach incorporating LiDAR data. Can. J. Forest Res 2010, 40, 184–199.
[74]
Wasser, L.; Day, R.; Chasmer, L.; Taylor, A. Influence of vegetation structure on lidar-derived canopy height and fractional cover in forested riparian buffers during leaf-off and leaf-on conditions. PLoS One 2013, 8, e54776.
