Wet channel network extraction by integrating LiDAR intensity and elevation data

Water Resources Research

Volumn 51, Issue 12, 2015, Pages 10029-10046

  Hooshyar, Milad ^a   Kim, Seoyoung ^a   Wang, Dingbao ^a   Medeiros, Stephen C ^a  

^a University of Central Florida   (United States)

Author keywords

edge; LiDAR; temporary stream; valley network; wet channel network

Indexed keywords

CATCHMENTS; GAUSSIAN DISTRIBUTION; GROUNDWATER; INFRARED DEVICES; LIGHT; OPTICAL RADAR; SURFACE WATERS; WATER ABSORPTION;

CHANNEL NETWORK; CHANNEL NETWORK EXTRACTION; EDGE; FREQUENCY DISTRIBUTIONS; GAUSSIAN MIXTURE MODEL; SURFACE WATER AND GROUNDWATERS; TEMPORARY STREAM; VALLEY NETWORK;

IMAGE PROCESSING;

CATCHMENT; CHANNEL MORPHOLOGY; DIGITAL IMAGE; ELEVATION; EXTRACTION METHOD; GAUSSIAN METHOD; GROUNDWATER-SURFACE WATER INTERACTION; HEADWATER; HYDROGRAPH; INTEGRATED APPROACH; LIDAR; NEAR INFRARED; PIXEL; POWER LAW; SIGNAL PROCESSING; SPATIAL RESOLUTION; STREAM; TEMPORAL VARIATION; TOPOGRAPHY; VALLEY;

LAKE TAHOE; UNITED STATES;

EID: 84956757408     PISSN: 00431397     EISSN: 19447973     Source Type: Journal    
DOI: 10.1002/2015WR018021     Document Type: Article

References (61)

1. Abrahams, A. D., (1984), Channel networks: A geomorphological perspective, Water Resour. Res., 20 (2), 161-188, doi: 10.1029/WR020i002p00161.
2. Acuña, V., T. Datry, J. Marshall, D. Barceló, C. Dahm, A. Ginebreda, G. McGregor, S. Sabater, K. Tockner, and, M. Palmer, (2014), Why should we care about temporary waterways, Science, 343 (6175), 1080-1081, doi: 10.1126/science.1246666.
3. Antonarakis, A., K. S. Richards, and, J. Brasington, (2008), Object-based land cover classification using airborne LiDAR, Remote Sens. Environ., 112 (6), 2988-2998.
4. Beeson, P., D. Miller, and, C. Duffy, (2011), Identifying ephemeral and perennial stream reaches using apparent thermal inertia of an ungauged basin in Central New Mexico, Geocarto Int., 26 (3), 183-194, doi: 10.1080/10106049.2011.553290.
5. Biswal, B., and, M. Marani, (2010), Geomorphological origin of recession curves, Geophys. Res. Lett., 37, L24403, doi: 10.1029/2010GL045415.
6. Blyth, K., and, J. Rodda, (1973), A stream length study, Water Resour. Res., 9 (5), 1454-1461, doi: 10.1029/WR009i005p01454.
7. Bowen, Z. H., and, R. G. Waltermire, (2002), Evaluation of light detection and ranging (LiDAR) for measuring river corridor topography', J. Am. Water Resour. Assoc., 38 (1), 33-41, doi: 10.1111/j.1752-1688.2002.tb01532.x.
8. Brzank, A., C. Heipke, J. Goepfert, and, U. Soergel, (2008), Aspects of generating precise digital terrain models in the Wadden Sea from LiDAR-water classification and structure line extraction, ISPRS J. Photogramm. Remote Sens., 63 (5), 510-528, doi: 10.1016/j.isprsjprs.2008.02.002.
9. Budyko, M. I., (1971), Climate and Life, Academic Press, N. Y.
10. Camporese, M., C. Paniconi, M. Putti, and, S. Orlandini, (2010), Surface-subsurface flow modeling with path-based runoff routing, boundary condition-based coupling, and assimilation of multisource observation data, Water Resour. Res., 46, W02512, doi: 10.1029/2008WR007536.
11. Canny, J., (1986), A computational approach to edge detection, IEEE Trans. Pattern Anal. Mach. Intel., PAMI-8 (6), 679-698, doi: 10.1109/TPAMI.1986.4767851.
12. Clubb, F. J., S. M. Mudd, D. T. Milodowski, M. D. Hurst, and, L. J. Slater, (2014), Objective extraction of channel heads from high-resolution topographic data, Water Resour. Res., 50, 4283-4304, doi: 10.1002/2013WR015167.
13. Coats, R. N., and, C. R. Goldman, (2001), Patterns of nitrogen transport in streams of the Lake Tahoe Basin, California-Nevada, Water Resour. Res., 37 (2), 405-415, doi: 10.1029/2000WR900219.
14. Day, D., (1978), Drainage density changes during rainfall, Earth Surf. Processes, 3 (3), 319-326, doi: 10.1002/esp.3290030310.
15. Dettinger, M. D., (2013), Projections and downscaling of 21st century temperatures, precipitation, radiative fluxes and winds for the Southwestern US, with focus on Lake Tahoe, Clim. Change, 116 (1), 17-33, doi: 10.1007/s10584-012-0501-x.
16. Do, C. B., and, S. Batzoglou, (2008), What is the expectation maximization algorithm?, Nature Biotechnol., 26 (8), 897-900, doi: 10.1038/nbt1406.
17. Genc, L., S. Smith, and, B. Dewitt, (2005), Using satellite imagery and LiDAR data to corroborate an adjudicated ordinary high water line, Int. J. Remote Sens., 26 (17), 3683-3693, doi: 10.1080/01431160500165922.
18. Godsey, S., and, J. Kirchner, (2014), Dynamic, discontinuous stream networks: Hydrologically driven variations in active drainage density, flowing channels and stream order, Hydrol. Processes, 28 (23), 5791-5803, doi: 10.1002/hyp.10310.
19. Goulsbra, C., M. Evans, and, J. Lindsay, (2014), Temporary streams in a peatland catchment: Pattern, timing, and controls on stream network expansion and contraction, Earth Surf. Processes Landforms, 39 (6), 790-803, doi: 10.1002/esp.3533.
20. Gregory, K., and, D. Walling, (1968), The variation of drainage density within a catchment, Hydrol. Sci. J., 13 (2), 61-68, doi: 10.1080/02626666809493583.
21. Gurnell, A., (1978), The dynamics of a drainage network, Nordic Hydrol., 9 (5), 293-306.
22. Hilldale, R. C., and, D. Raff, (2008), Assessing the ability of airborne LiDAR to map river bathymetry, Earth Surf. Processes Landforms, 33 (5), 773-783, doi: 10.1002/esp.1575.
23. Höfle, B., M. Vetter, N. Pfeifer, G. Mandlburger, and, J. Stötter, (2009), Water surface mapping from airborne laser scanning using signal intensity and elevation data, Earth Surf. Processes Landforms, 34 (12), 1635-1649, doi: 10.1002/esp.1853.
24. Hopkinson, C., N. Crasto, P. Marsh, D. Forbes, and, L. Lesack, (2011), Investigating the spatial distribution of water levels in the Mackenzie Delta using airborne LiDAR, Hydrol. Processes, 25 (19), 2995-3011, doi: 10.1002/hyp.8167.
25. Howard, A. D., (1994), A detachment-limited model of drainage basin evolution, Water Resour. Res., 30 (7), 2261-2285, doi: 10.1029/94WR00757.
26. Kollet, S. J., and, R. M. Maxwell, (2006), Integrated surface-groundwater flow modeling: A free-surface overland flow boundary condition in a parallel groundwater flow model, Adv. Water Resour., 29 (7), 945-958, doi: 10.1016/j.advwatres.2005.08.006.
27. Lang, M. W., and, G. W. McCarty, (2009), LiDAR intensity for improved detection of inundation below the forest canopy, Wetlands, 29 (4), 1166-1178, doi: 10.1672/08-197.1.
28. Lashermes, B., E. Foufoula-Georgiou, and, W. E. Dietrich, (2007), Channel network extraction from high resolution topography using wavelets, Geophys. Res. Lett., 34, L23S04, doi: 10.1029/2007GL031140.
29. Le, P. V., and, P. Kumar, (2014), Power law scaling of topographic depressions and their hydrologic connectivity, Geophys. Res. Lett., 41, 1553-1559, doi: 10.1002/2013GL059114.
30. Magirl, C. S., R. H. Webb, and, P. G. Griffiths, (2005), Changes in the water surface profile of the Colorado River in Grand Canyon, Arizona, between 1923 and 2000, Water Resour. Res., 41, W05021, doi: 10.1029/2003WR002519.
31. Mallet, C., and, F. Bretar, (2009), Full-waveform topographic LiDAR: State-of-the-art, ISPRS J. Photogramm. Remote Sens., 64 (1), 1-16, doi: 10.1016/j.isprsjprs.2008.09.007.
32. Marks, K., and, P. Bates, (2000), Integration of high-resolution topographic data with floodplain flow models, Hydrol. Processes, 14 (11-12), 2109-2122, doi: 10.1002/1099-1085(20000815/30)14:11/12<2109::AID-HYP58>3.0.CO;2-1.
33. McFeeters, S., (1996), The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features, Int. J. Remote Sens., 17 (7), 1425-1432, doi: 10.1080/01431169608948714.
34. McGuire, L. A., J. D. Pelletier, and, J. J. Roering, (2014), Development of topographic asymmetry: Insights from dated cinder cones in the western United States, J. Geophys. Res. Earth Surf., 119, 1725-1750, doi: 10.1002/2014JF003081.
35. Meinzer, O. E., (1923), Outline of Ground-Water Hydrology, With Definitions, U.S. Geol. Sur. Water-Supply Pap. 494.
36. Mitášová, H., and, J. Hofierka, (1993), Interpolation by regularized spline with tension: II. Application to terrain modeling and surface geometry analysis, Math. Geol., 25 (6), 657-669, doi: 10.1007/BF00893172.
37. Moon, T. K., (1996), The expectation-maximization algorithm, IEEE Signal Processing Mag., 13 (6), 47-60, doi: 10.1109/79.543975.
38. Morgan, R., (1972), Observations on factors affecting the behaviour of a first-order stream, Trans. Inst. Br. Geogr., 56, 171-185, doi: 10.2307/621547.
39. Orlandini, S., and, G. Moretti, (2009), Determination of surface flow paths from gridded elevation data, Water Resour. Res., 45, W03417, doi: 10.1029/2008WR007099.
40. Orlandini, S., P. Tarolli, G. Moretti, and, G. Dalla Fontana, (2011), On the prediction of channel heads in a complex alpine terrain using gridded elevation data, Water Resour. Res., 47, W02538, doi: 10.1029/2010WR009648.
41. Passalacqua, P., T. Do Trung, E. Foufoula-Georgiou, G. Sapiro, and, W. E. Dietrich, (2010), A geometric framework for channel network extraction from LiDAR: Nonlinear diffusion and geodesic paths, J. Geophys. Res., 115, F01002, doi: 10.1029/2009JF001254.
42. Pelletier, J. D., (2013), A robust, two-parameter method for the extraction of drainage networks from high-resolution digital elevation models (DEMs): Evaluation using synthetic and real-world DEMs, Water Resour. Res., 49, 75-89, doi: 10.1029/2012WR012452.
43. Perona, P., and, J. Malik, (1990), Scale-space and edge detection using anisotropic diffusion, IEEE Trans. Pattern Anal. Mach. Intell., 12 (7), 629-639, doi: 10.1109/34.56205.
44. Perona, P., T. Shiota, and, J. Malik, (1994), Anisotropic diffusion, in Geometry-Driven Diffusion in Computer Vision, edited, pp. 73-92, Springer Netherlands, doi: 10.1007/978-94-017-1699-4.
45. Rasmussen, C. E., (1999), The infinite Gaussian mixture model, paper presented at Neural Information Processing Systems (NIPS), Denver, Colo.
46. Roberts, M. C., and, O. Archibold, (1978), Variation of drainage density in a small british columbia watershed, J. Am. Water Resour. Assoc., 14, 470-476, doi: 10.1111/j.1752-1688.1978.tb02183.x.
47. Smeeckaert, J., C. Mallet, N. David, N. Chehata, and, A. Ferraz, (2013), Large-scale classification of water areas using airborne topographic LiDAR data, Remote Sens. Environ., 138, 134-148, doi: 10.1016/j.rse.2013.07.004.
48. Smyth, P., (2000), Model selection for probabilistic clustering using cross-validated likelihood, Stat. Comput., 10 (1), 63-72, doi: 10.1049/iet-spr.2009.0235.
49. Sofia, G., P. Tarolli, F. Cazorzi, and, G. Dalla Fontana, (2011), An objective approach for feature extraction: Distribution analysis and statistical descriptors for scale choice and channel network identification, Hydrol. Earth Syst. Sci., 15 (5), 1387-1402, doi: 10.5194/hess-15-1387-2011.
50. Stanley, E. H., S. G. Fisher, and, N. B. Grimm, (1997), Ecosystem expansion and contraction in streams, BioScience, 47, 427-435, doi: 10.2307/1313058.
51. Stoker, J. M., S. K. Greenlee, D. B. Gesch, and, J. C. Menig, (2006), CLICK: The new USGS center for LiDAR information coordination and knowledge, Photogramm. Eng. Remote Sens., 72 (6), 613-616.
52. Tarboton, D. G., (1997), A new method for the determination of flow directions and upslope areas in grid digital elevation models, Water Resour. Res., 33 (2), 309-319, doi: 10.1029/96WR03137.
53. Taylor, A. H., and, R. M. Beaty, (2005), Climatic influences on fire regimes in the northern Sierra Nevada mountains, Lake Tahoe Basin, Nevada, USA, J. Biogeogr., 32 (3), 425-438, doi: 10.1111/j.1365-2699.2004.01208.x.
54. Wang, D., and, L. Wu, (2013), Similarity of climate control on base flow and perennial stream density in the Budyko framework, Hydrol. Earth Syst. Sci., 17 (1), 315-324, doi: 10.5194/hess-17-315-2013.
55. Wei, G. W., (1999), Generalized Perona-Malik equation for image restoration, IEEE Signal Processing Lett., 6 (7), 165-167, doi: 10.1109/97.769359.
56. Wigington, P., T. Moser, and, D. Lindeman, (2005), Stream network expansion: A riparian water quality factor, Hydrol. Processes, 19 (8), 1715-1721, doi: 10.1002/hyp.5866.
57. Wolfe, W., and, G. Zissis, (1985), The Infrared Handbook, Environ. Res. Inst. of Michigan, Ann Arbor.
58. Work, E. A., Jr., and, D. S. Gilmer, (1976), Utilization of satellite data for inventorying prairie ponds and lakes, Photogramm. Eng. Remote Sens., 42 (5), 685-694.
59. Wu, H., C. Liu, Y. Zhang, W. Sun, and, W. Li, (2013), Building a water feature extraction model by integrating aerial image and LiDAR point clouds, Int. J. Remote Sens., 34 (21), 7691-7705, doi: 10.1080/01431161.2013.823674.
60. Xu, H., (2006), Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery, Int. J. Remote Sens., 27 (14), 3025-3033, doi: 10.1080/01431160600589179.
61. Zimmer, M. A., S. W. Bailey, K. J. McGuire, and, T. D. Bullen, (2013), Fine scale variations of surface water chemistry in an ephemeral to perennial drainage network, Hydrol. Processes, 27 (24), 3438-3451, doi: 10.1002/hyp.9449.