Advances in remote sensing of hydraulic roughness

International Journal of Remote Sensing

Volumn 33, Issue 2, 2012, Pages 630-654

  Forzieri, Giovanni ^a   Castelli, Fabio ^a   Preti, Federico ^a  

^a UNIVERSITY OF FLORENCE   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

BANKS (BODIES OF WATER); ECOSYSTEMS; FLOODS; HYDRAULIC FLUIDS; HYDRAULIC TOOLS; OPTICAL RADAR; VEGETATION;

DATA ASSIMILATION METHODS; ENVIRONMENTAL MONITORING; ESTIMATION TECHNIQUES; FLOODPLAIN ROUGHNESS; HYDRODYNAMIC PROPERTIES; LIGHT DETECTION AND RANGING; MAPPING CAPABILITIES; REMOTE SENSING TECHNIQUES;

REMOTE SENSING;

DATA ASSIMILATION; DETECTION METHOD; ENVIRONMENTAL MODELING; ESTIMATION METHOD; FLOOD WAVE; FLOODPLAIN; FLOW FIELD; HYDRAULIC CONDUCTIVITY; HYDRODYNAMICS; LIDAR; MAPPING; PARAMETERIZATION; REMOTE SENSING; RIPARIAN VEGETATION; RIPARIAN ZONE; SURFACE ROUGHNESS;

EID: 82155170549     PISSN: 01431161     EISSN: 13665901     Source Type: Journal    
DOI: 10.1080/01431161.2010.531788     Document Type: Article

References (122)

1. Akasheh, O.Z., Neale, C.M.U. and Jayanthi, H., 2008, Detailed mapping of riparian vegetation in the middle Rio Grande River using high resolution multi-spectral airborne remote sensing. Journal of Arid Environments, 72, pp. 1734-1744.
2. Andersen, H.E., McGaughey, R.J. and Reutebuch, S.E., 2005, Estimating forest canopy fuel parameters using LiDAR data. Remote Sensing of Environment, 94, pp. 441-449.
3. Anderson, B.G., Rutherfurd, I.D. and Western, A.W., 2006, An analysis of the influence of riparian vegetation on the propagation of flood waves. Environmental Modelling and Software, 21, pp. 1290-1296.
4. Antonarakis, A.S., Richards, K.S. and Brasington, J., 2008a, Object-based land cover classification using airborne LiDAR. Remote Sensing of Environment, 112, pp. 2988-2998.
5. Antonarakis, A.S., Richards, K.S., Brasington, J., Bithell, M. and Muller, E., 2008b, Retrieval of vegetative fluid resistance terms for rigid stems using airborne lidar. Journal of Geophysical Research, 113, G02S07, doi:10.1029/2007JG000543.
6. Arcement G.J. and Schneider, V.R., 1989, Guide for Selecting Manning's Roughness Coefficients for Natural Channels and Flood Plains. Water-Supply Paper 2339 (Washington, DC: US Geological Survey).
7. Ballester-Berman, J.D. and Lopez-Sanchez, J.M., 2005, Retrieval of biophysical parameters of agricultural crops using polarimetric SAR interferometry. IEEE Transactions on Geoscience and Remote Sensing, 43, pp. 683-694.
8. Balzter, H., Baker, J.R., Hallikainen, M. and Tomppo, E., 2002, Retrieval of timber volume and snow water equivalent over a Finnish boreal forest from airborne polarimetric synthetic aperture radar. International Journal of Remote Sensing, 23, pp. 3185-3208.
9. Balzter, H., Rowland, C.S. and Saich, P., 2007, Forest canopy height and carbon estimation at Monks Wood National Nature Reserve, UK, using dual-wavelength SAR interferometry. Remote Sensing of Environment, 108, pp. 224-239.
10. Baptist, M.J., Penning, W.E., Duel, H., Smits, A.J.M., Geerling, G.W., Van der Lee, G.E.M. and Van Alphen, J.S.L., 2004, Assessment of the effects of cyclic floodplain rejuvenation on flood levels and biodiversity along the Rhine River. River Research and Applications, 20, pp. 285-297.
11. Baptist, M.J., Van den Bosch, L.V., Dijkstra, J.T. and Kapinga, S., 2005, Modelling the effects of vegetation on flow and morphology in rivers. Large Rivers, 15, pp. 339-357.
12. Bates, P., Anderson, M.G., Walling, D.E. and Simm, D., 1992, Modelling floodplain flows using a two-dimensional finite element model. Earth Surface Processes and Landforms, 17, pp. 575-588.
13. Bork, E.E. and Su, J.G., 2007, Integrating LIDAR data and multispectral imagery for enhanced classification of rangeland vegetation: a meta analysis. Remote Sensing of Environment, 111, pp. 11-24.
14. Brandtberg, T., Warner, T., Landenberger, R. and McGraw, J., 2003, Detection and analysis of individual leaf-off tree crowns in small footprint, high sampling density LIDAR data from the eastern deciduous forest in North America. Remote Sensing of Environment, 85, pp. 290-303.
15. Brennan, R. and Webster, T.L., 2006, Object-oriented land cover classification of lidar derived surfaces. Canadian Journal of Remote Sensing, 32, pp. 162-172.
16. Busetto, L., Meroni, M. and Colombo, R., 2008, Combining medium and coarse spatial resolution satellite data to improve the estimation of sub-pixel NDVI time series. Remote Sensing of Environment, 112, pp. 118-131.
17. Campo, L., 2006, Land-atmosphere interactions in a non-hydrostatic atmospheric model with improved satellite and surface assimilation. PhD thesis, University of Florence, Florence, Italy.
18. Campo, L., Caparrini, F. and Castelli, F., 2006, Use of multi-platform, multi-temporal remote-sensing data for calibration of a distributed hydrological model: an application in the Arno basin, Italy. Hydrological Processes, 20, pp. 2693-2712.
19. Castelli, F. and Entekhabi, D., 2002, Models and observations: data assimilation in hydro-logical and geological sciences. Technical report, CNR-MIT cooperation on climate change and hydrological hazards in the Mediterranean Area. Available online at: http://web.mit.edu/darae/WWW/notetot.PDF
20. Chopping, M.J., Rango, A., Havstad, K.M., Schiebe, F.R., Ritchie, J.C., Schmugge, T.J., French, A.N., Su, L., McKee, L. and Dav i s, M.R., 2003, Canopy attributes of desert grassland and transition communities derived from multiangular airborne imagery. Remote Sensing of Environment, 85, pp. 339-354.
21. Chow, V.T. (Ed.), 1959, Open Channel Hydraulics (New York: McGraw-Hill).
22. Cobby, D.M., Mason, D.C. and Davenport, I.J., 2001, Image processing of airborne scanning laser altimetry data for improved river flood modelling. Photogrammetry and Remote Sensing, 56, pp. 121-138.
23. Colombo, R., Bellingeri, D., Fasolini, D. and Marino, C.M., 2003, Retrieval of leaf area index in different vegetation types using high resolution satellite data. Remote Sensing of Environment, 86, pp. 120-131.
24. Cusack, G.A., Hutchinson, M.F. and Kalma, J.D., 1999, Calibrating airborne vegetation data for hydrological applications under dry conditions. International Journal of Remote Sensing, 20, pp. 2221-2233.
25. Darby, S.D., 1999, Effect of riparian vegetation on flow resistance and flood potential. Journal of Hydraulic Engineering, 125, pp. 443-454.
26. Davenport, I.J., Bradbury, R.B., Anderson, G.R.F., Hayman, J.R., Krebs, J.R. and Mason, D.C., 2000, Improving bird population models using airborne remote sensing. International Journal of Remote Sensing, 21, pp. 2705-2717.
27. Davis, P.A., Staid, M.I., Plescia, J.B. and Johnson, J.R., 2002, Evaluation of Airborne Image Data for Mapping Riparian Vegetation within the Grand Canyon, Open-File Report 02-469, 35 p. (Reston, VA: US Geological Survey).
28. Dawson, F.H. and Charlton, F.G., 1988, Bibliography on the Hydraulic Resistance of Vegetated Water Courses (Ambleside: Freshwater Biological Association).
29. Delalieux, S., Somers, B., Hereijgers, B., Verstraeten, W.W., Keulemans, W. and Coppin, P., 2008, A near-infrared narrow-waveband ratio to determine leaf area index in orchards. Remote Sensing of Environment, 112, pp. 3762-3772.
30. Dowling, R. and Accad, A., 2003, Vegetation classification of the riparian zone along the Brisbane river, Queensland, Australia, using light detection and ranging (lidar) data and forward looking video. Canadian Journal of Remote Sensing, 29, pp. 556-563.
31. Dudley, S.J., Bonham, C.D., Abt, S.R. and Fischenich, J.G., 1998, Comparison of methods for measuring woody riparian vegetation density. Journal of Arid Environments, 38, pp. 77-86.
32. Ehlers, M., Gahler, M. and Janowsky, R., 2003, Automated analysis of ultra high resolution remote sensing data for biotope type mapping: new possibilities and challenges. Journal of Photogrammetry and Remote Sensing, 57, pp. 315-326.
33. Elaksher, A.F., 2008, Fusion of hyperspectral images and LIDAR-based DEMs for coastal mapping. Optics and Lasers in Engineering, 46, pp. 493-498.
34. Fahsi, A., Tsegaye, T., Tadesse, W. and Coleman, T., 2000, Incorporation of digital elevation models with Landsat-TM data to improve land cover classification accuracy. Forest Ecology and Management, 128, pp. 57-64.
35. Fischer-Antze, T., Stoesser, T., Bates, P. and Olsen, N.R.B., 2001, 3D numerical modelling of open-channel flow with submerged vegetation. Journal of Hydraulic Research, 39, pp. 303-310.
36. Forzieri, G., 2008, Modelling and remote sensing of vegetation: from river to basin scale. PhD thesis, University of Florence, Florence, Italy.
37. Forzieri, G., Guarnieri, L., Vivoni, E.R., Castelli, F. and Preti, F., 2009, Multiple attribute decision-making for individual tree detection using high-resolution laser scanning. Forest Ecology and Management, 258, pp. 2501-2510.
38. Forzieri, G., Guarnieri, L., Vivoni, E.R., Castelli, F. and Preti, F., 2010a, Spectral-ALS data fusion for different roughness parameterizations of forested floodplains. River Research and Applications. doi:10.1002/rra.1398.
39. Forzieri, G., Moser, G., Vivoni, E.R. and Castelli, F., 2010b, Riparian vegetation mapping for hydraulic roughness estimation using very high resolution remote sensing and LiDAR data fusion. Journal of Hydraulic Engineering, 11, pp. 855-867.
40. Geerling, G.W., Labrador-Garcia, M., Clevers, J.G.P.W., Ragas, A.M.J. and Smits, A.J.M., 2007, Classification of floodplain vegetation by data fusion of spectral (CASI) and LiDAR data. International Journal of Remote Sensing, 28, pp. 4263-4284.
41. Gitelson, A.A., Kaufman, Y.J. and Merzlyak, M.N., 1996, Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment, 58, pp. 289-298.
42. Goetz, S.J., Wright, R.K., Smith, A.J., Zinecker, E. and Schaub, E., 2003, IKONOS imagery for resource management: tree cover, impervious surfaces, and riparian buffer analyses in the mid-Atlantic region. Remote Sensing of Environment, 88, pp. 195-208.
43. Haralick, R.M. and Fu, K.S., 1983, Pattern recognition and classification. In Manual of Remote Sensing, Vol. 1, 2nd ed., R.N. Colwell, D.S. Simonett and F.T. Ulaby (Eds.), pp. 793-805 (Falls Church, VA: American Society of Photogrammetry).
44. Haralick, R.M., Shanmugam, K. and Dinstein, I., 1973, Texture features for image classification. IEEE Transactions on Systems, Man and Cybernetics, 3, pp. 610-621.
45. He, B., 2007, A simple data assimilation method for improving the MODIS LAI time-series data products based on the object analysis and gradient inverse weighted filter. Chinese Optics Letters, 5, pp. 367-369.
46. Hess, L.L., Melack, J., Filoso, S. and Wang, Y., 1995, Delineation of inundated area and vegetation along the Amazon floodplain with the SIR-C synthetic aperture radar. IEEE Transactions on Geoscience and Remote Sensing, 33, pp. 896-903.
47. Hess, L.L., Melack, J.M. and Simonett, D.S., 1990, Radar detection of flooding beneath the forest canopy: a review. International Journal of Remote Sensing, 11, pp. 1313-1325.
48. Hopkinson, C., Lim, K., Chasmer, L.E., Treitz, P., Creed, I.F. and Gynan, C., 2004, Wetland grass to plantation forest - estimating vegetation height from the standard deviation of lidar frequency distributions. In Laser Scanners for Forest and Landscape Assessment, M. Thies, B. Koch, H. Spiecker and H. Weinacker (Eds.), pp. 288-294 (Freiburg: Institute for Forest Growth).
49. Hudak, A.T., Lefsky, M.A., Cohen, W.B. and Berterretche, M., 2002, Integration of lidar and Landsat ETM+ data for estimating and mapping forest canopy height. Remote Sensing of Environment, 82, pp. 397-416.
50. Huete, A.R., 1988, A soil adjusted vegetation index (SAVI). International Journal of Remote Sensing, 9, pp. 295-309.
51. Hughes, G.F., 1968, On the mean accuracy of statistical pattern recognizers. IEEE Transactions on Information Theory, 14, pp. 55-63.
52. Hyyppä, J., Kelle, O., Lehikoinen, M. and Inkinen, M., 2001, A segmentation based method to retrieve stem volume estimates from 3-D tree height models produced by laser scanners. IEEE Transactions on Geoscience and Remote Sensing, 39, pp. 969-975.
53. Jansen, B.J.M. and Backx, J.J.G.M., 1998, Ecotope Mapping Rhine Branches - East 1997. Report No. 98.054 (Lelystad: RIZA) [in Dutch].
54. Jarlan, L., Mangiarotti, S., Mougin, E., Mazzega, P., Hiernaux, P. and Le Dantec, V., 2008, Assimilation of SPOT/VEGETATION NDVI data into a sahelian vegetation dynamics model. Remote Sensing of Environment, 112, pp. 1381-1394.
55. Jarvela, J., 2004, Determination of flow resistance caused by nonsubmerged woody vegetation. International Journal of River Basin Management, 2, pp. 61-70.
56. Johansen, K., Coops, N.C., Gergel, S.E. and Stange, Y., 2007, Application of high spatial resolution satellite imagery for riparian and forest ecosystem classification. Remote Sensing of Environment, 110, pp. 29-44.
57. Johansen, K. and Phinn, S., 2004, Mapping indicators of riparian vegetation health using IKONOS and Landsat-7 ETM+ image data in Australian tropical savannas. In Geoscience and Remote Sensing Symposium, 2004. IGARSS '04, 20-24 September 2004, Anchorage, AK, pp. 1559-1562.
58. Jongschaap, R.E.E. and Schouten, L.S.M., 2005, Predicting wheat production at regional scale by integration of remote sensing data with a simulation model. Agronomy for Sustainable Development, 25, pp. 481-489.
59. Jordan, C.F., 1969, Derivation of leaf area index from quality of light on the forest floor. Ecology, 50, pp. 663-666.
60. Kailath, T., 1967, The divergence and Bhattacharyya distance measures in signal selection. IEEE Transactions on Information Theory, 15, pp. 52-60.
61. Kasischke, E.S., Melack, J.M and Dobson, M.C., 1997, The use of imaging radars for ecological applications - a review. Remote Sensing of Environment, 59, pp. 141-156.
62. Kaufman, Y.J. and Tanre, D., 1992, Atmospherically resistant vegetation index (ARVI) for EOS-MODIS. IEEE Transactions on Geoscience and Remote Sensing, 30, pp. 261-270.
63. Kouwen, N. and Fathi-Moghadam, M., 2000, Friction factors for coniferous trees along rivers. Journal of Hydraulic Engineering, 126, pp. 732-740.
64. Kouwen, N. and Li, R.M., 1980, Biomechanics of vegetated channel linings. Journal of the Hydraulics Division, 106, pp. 1085-1103.
65. Landgrebe, D.A., 2003, Signal Theory Methods in Multispectral Remote Sensing (New York: Wiley).
66. Laurila, H., Karjalainen, M., Hyyppä, J. and Kleemola, J., 2010, Integrating vegetation indices models and phenological classification with composite SAR and optical data for cereal yield estimation in Finland (Part I). Remote Sensing, 2, pp. 76-114.
67. Leckie, D.J., Cloney, E., Jay, C. and Paradine, D., 2005, Automated mapping of stream features with high-resolution multispectral imagery: an example of the capabilities. Photogrammetric Engineering and Remote Sensing, 71, pp. 145-155.
68. Lee, D.S. and Shan, J., 2003, Combining LIDAR elevation data and IKONOS multispectral imagery for coastal classification mapping. Marine Geodesy, 26, pp. 117-127.
69. Lefsky, M.A., Cohen, W.B., Parker, G. and Harding, D., 2002, Lidar remote sensing for ecosystem studies. Bioscience, 52, pp. 19-30.
70. Le Toan, T., Ribbes, F., Wange, L.F., Floury, N., Ding, N. and Kong, K.H., 1997, Rice crop mapping and monitoring using ERS-1 data based on experiment and modeling results. IEEE Transactions on Geoscience and Remote Sensing, 35, pp. 41-56.
71. Liang, S. (Ed.), 2008, Advances in Land Remote Sensing, Modeling, Inversion and Application (New York: Springer-Verlag).
72. Lim, K., Treitz, P., Wulder, M., St-Onge, B. and Flood, M., 2003, Lidar remote sensing of forest structure. Progress in Physical Geography, 27, pp. 88-106.
73. Lucas, R.M., Cronin, N., Lee, A., Moghaddam, M., Witte, C. and Tickle, P., 2006, Empirical relationships between AIRSAR backscatter and LiDAR-derived forest biomass, Queensland, Australia. Remote Sensing of Environment, 100, pp. 407-425.
74. Macklin, M.G. and Lewin, J., 2003, River sediments, great floods and centennial scale Holocene climate change. Journal of Quaternary Science, 18, pp. 101-105.
75. Maltamo, M., Packalén, P., Yu, X., Eerikainen, K., Hyyppa, J. and Pitkanen, J., 2005, Identifying and quantifying structural characteristics of heterogeneous boreal forests using laser scanner data. Forest Ecology and Management, 216, pp. 41-50.
76. Marks, K. and Bates, P., 2000, Integration of high-resolution topographic data with flood-plain flow models. Hydrological Processes, 14, pp. 2109-2122.
77. Martinez, J.M. and Le Toan, T., 2007, Mapping of flood dynamics and spatial distribution of vegetation in the Amazon floodplain using multitemporal SAR data. Remote Sensing of Environment, 108, pp. 209-223.
78. Mertes, L.A.K., Daniel, D.L., Melack, J.M., Nelson, B., Martinelli, L.A. and Forsberg, B.R., 1995, Spatial patterns of hydrology, geomorphology, and vegetation on the floodplain of the Amazon river in Brazil from a remote sensing perspective. Geomorphology, 13, pp. 215-232.
79. Næsset, E. and Økland, T., 2002, Estimating tree height and tree crown properties using airborne scanning laser in a boreal nature reserve. Remote Sensing of Environment, 79, pp. 105-115.
80. Nagler, P.L., Glenn, E.P., Kim, H., Emmerich, W., Scott, R.L., Huxman, T.E. and Huete, A.R., 2007, Relationship between evapotranspiration and precipitation pulses in a semiarid rangeland estimated by moisture flux towers and MODIS vegetation indices. Journal of Arid Environments, 70, pp. 443-462.
81. Nagler, P.L., Glenn, E.P., Thompson, T.L. and Huete, A., 2004, Leaf area index and normalized difference vegetation index as predictors of canopy characteristics and light interception by riparian species on the Lower Colorado River. Agricultural and Forest Meteorology, 125, pp. 1-17.
82. Nicholas, A.P. and McLelland, S.J., 2004, Computational fluid dynamics modelling of three-dimensional processes on natural river floodplains. Journal of Hydraulic Research, 42, pp. 131-143.
83. Pasche, E. and Rouvé, G., 1985, Overbank flow with vegetatively roughened flood plains. Journal of Hydraulic Engineering, 111, pp. 1262-1278.
84. Petryk, S. and Bosmajian, G.B., 1975, Analysis of flow through vegetation. Journal of the Hydraulics Division, 101, pp. 871-884.
85. Popescu, S.C. and Wynne, R.H., 2004, Seeing the trees in the forest: using LIDAR and mul-tispectral data fusion with local filtering and variable window size for estimating tree height. Photogrammetric Engineering and Remote Sensing, 70, pp. 589-604.
86. Popescu, S.C., Wynne, R.H. and Nelson, R., 2003, Measuring individual tree crown diameter with lidar and assessing its influence on estimating forest volume and biomass. Canadian Journal of Remote Sensing, 29, pp. 564-577.
87. Popescu, S.C. and Zhao, K., 2008, A voxel-based lidar method for estimating crown base height for deciduous and pine trees. Remote Sensing of Environment, 112, pp. 767-781.
88. Reutebuch, S.E., McGaughey, R.J., Andersen, H.E. and Carson, W.W., 2003, Accuracy of a high-resolution lidar terrain model under a conifer forest canopy. Canadian Journal of Remote Sensing, 29, pp. 527-535.
89. Richards, J.A. and Jia, X. (Eds.), 2006, Remote Sensing Digital Image Analysis (Berlin: Springer).
90. Righetti, M. and Armanini, A., 2002, Flow resistance in open channel flows with sparsely distributed bushes. Journal of Hydrology, 269, pp. 55-64.
91. Rignot, E., Way, J., Williams, C. and Viereck, L., 1994, Radar estimates of aboveground biomass in boreal forests of interior Alaska. IEEE Transactions on Geoscience and Remote Sensing, 32, pp. 1117-1124.
92. Rodriguez-Iturbe, I., 2000, Ecohydrology: a hydrologic perspective of climate-soil-vegetation dynamics. Water Resources Research, 36, pp. 3-9.
93. Rodriguez-Iturbe, I. and Porporato, A., 2007, Ecohydrology of Water-Controlled Ecosystems (Cambridge: Cambridge University Press).
94. Rondeaux, G., Steven, M. and Baret, F.H., 1996, Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment, 55, pp. 95-107.
95. Rottensteiner, F., Trinder, J., Clode, S. and Kubik, K., 2005, Using the Dempster-Shafer method for the fusion of LIDAR data and multi-spectral images for building detection. Information Fusion, 6, pp. 283-300.
96. Rouse, J.W., Hass, R.H., Schell, J.A. and Deering, D.W., 1973, Monitoring vegetation systems in the Great Plains with ERTS. In Proceedings of the Third ERTS Symposium, Vol. I, 10-14 December 1973 (Washington, DC: NASA), pp. 309-317.
97. rd Hydrology and Water Resources Symposium, 21-24 May 1996. (Hobart: Institution of Engineers), pp. 397-403.
98. Saadat, H., Bonnell, R., Sharifi, F., Mehuys, G., Namdar, M. and Ale-Ebrahim, S., 2008, Landform classification from a digital elevation model and satellite imagery. Geomorphology, 100, pp. 453-464.
99. Sellin, R.H.J. and van Beesten, D., 2002, Berm vegetation and its effects on flow resistance in a two-stage river channel: an analysis of field data. In River Flow, 4-6 September 2002, Louvain la Neuve, Belgium, D. Bousmar and Y. Zech (Eds.), (Oxford: Taylor & Francis), pp. 319-327 (Belgium).
100. Serpico, S.B. and Moser, G., 2007, Extraction of spectral channels from hyperspectral images for classification purposes. IEEE Transactions on Geoscience and Remote Sensing, 45, pp. 484-495.
101. Smith, A.M., Major, D.J., Hill, M.J., Willms, W.D., Brisco, B. and Lindwall, C.W., 1994, Airborne synthetic aperture radar analysis of rangeland revegetation of a mixed prairie. Journal of Range Management, 47, pp. 385-391.
102. Smith, M.J., Asal, F.F.F. and Priestnall, G., 2004, The use of photogrammetry and LIDAR for landscape roughness estimation in hydrodynamic studies. International Archives of Photogrammetry Remote Sensing and Spatial Information Sciences, 35, pp. 714-719.
103. Sripada, R.P., Heiniger, R.W., White, J.G. and Weisz, R., 2005, Aerial color infrared photography for determining late-season nitrogen requirements in corn. Agronomy Journal, 97, pp. 1443-1451.
104. Stöckli, R., Rutishauser, T., Dragoni, D., O'Keefe, J., Thornton, P.E., Jolly, M., Lu, L. and Denning, A.S., 2008, Remote sensing data assimilation for a prognostic phenology model. Journal of Geophysical Research, 113, G04021, doi:10.1029/2008JG000781.
105. Stoesser, T., Wilson, C.A.M.E., Bates, P. and Dittrich, A., 2003, Application of a 3D numerical model to a river with vegetated floodplains. Journal of Hydroinformatics, 5, pp. 99-112.
106. Straatsma, M.W. and Baptist, M.J., 2008, Floodplain roughness parameterization using airborne laser scanning and spectral remote sensing. Remote Sensing of Environment, 112, pp. 1062-1080.
107. Straatsma, M.W. and Middelkoop, H., 2007, Extracting structural characteristics of herbaceous floodplain vegetation under leaf-off conditions using airborne laser scanner data. International Journal of Remote Sensing, 28, pp. 2447-2467.
108. Straatsma, M.W., Warmink, J.J. and Middelkoop, H., 2008, Two novel methods for fields measurements of hydrodynamic density of floodplain vegetation using terrestrial laser scanning and digital parallel photography. International Journal of Remote Sensing, 29, pp. 1595-1617.
109. Thompson, G.T. and Roberson, J.A., 1976, A theory of flow resistance for vegetated channels. Transactions of the ASABE, 19, pp. 288-293.
110. Townsend, P.A. and Walsh, J., 2001, Remote sensing of forested wetlands: application of multitemporal and multispectral satellite imagery to determine plant community composition and structure in southeastern USA. Plant Ecology, 157, pp. 129-149.
111. Treuhaft, R.N. and Siqueira, P.R., 2000, The vertical structure of vegetated land surfaces from interferometric and polarimetric data. Radio Science, 35, pp. 141-177.
112. Tso, B. and Mather, P.M. (Eds.), 2001, Classification Methods for Remotely Sensed Data (London: Taylor & Francis).
113. Tuominen, S. and Pekkarinen, A., 2005, Performance of different spectral and textural aerial photograph features in multi-source forest inventory. Remote Sensing of Environment, 94, pp. 256-268.
114. Turner, W., Spector, S., Gardiner, N., Flandeland, M., Sterling, E. and Steininger, M., 2003, Remote sensing for biodiversity science and conservation. Trends in Ecology and Evolution, 18, pp. 306-314.
115. Van der Sande, C.J., de Jong, S.M. and de Roo, A.P.J., 2003, A segmentation and classification approach of Ikonos-2 imagery for land cover mapping to assist flood risk and flood damage assessment. International Journal of Applied Earth Observation and Geoinformation, 4, pp. 217-229.
116. Van Stokkom, H.T.C., Smits, A.J.M. and Leuven, R.S.E.W., 2005, Flood defense in the Netherlands: a new era, a new approach. Water International, 30, pp. 76-87.
117. Van Velzen, E.H., Jesse, P., Cornelissen, P. and Coops, H., 2003, Flow Friction of Vegetation in Floodplains. Part 1. Report No. 2003.028 (Arnhem: RIZA) [in Dutch].
118. Wald, L. (Ed.), 2002, Data Fusion: Definitions and Architectures (Paris: Les Presses).
119. Waring, R.H., Way, J., Hunt E.R., Morrissey, L., Ranson, K.J. and Weishampel, J.F., 1995, Imaging radar for ecosystem studies. Bioscience, 45, pp. 715-723.
120. Wegmüller, U. and Werner, C.L., 1997, Retrieval of vegetation parameters with SAR interferometry. IEEE Transactions on Geoscience and Remote Sensing, 35, pp. 18-24.
121. Wulder, M.A., LeDrew, E.L., Franklin, S.E. and Lavigne, M.B., 1998, Aerial image texture information in the estimation of northern deciduous and mixed wood forest leaf area index (LAI). Remote Sensing of Environment, 64, pp. 64-76.
122. Zhou, Y., McLaughlin, D., Entekhabi, D. and Ng, G.-H.C, 2007, An ensemble multiscale filter for large nonlinear data assimilation problems. Monthly Weather Review, 136, pp. 678-698.