Wetland mapping methods and techniques using multisensor, multiresolution remote sensing: Successes and challenges

Remote Sensing of Water Resources, Disasters, and Urban Studies

Volumn 3, Issue , 2015, Pages 191-226

  Mishra, D R ^a   Ghosh, Shuvankar ^a   Hladik, C ^b   O’Connell, Jessica L ^a   Cho, H J ^c  

^a UNIVERSITY OF GEORGIA   (United States)

^b GEORGIA SOUTHERN UNIVERSITY   (Georgia)

^c BETHUNE COOKMAN UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EARTH SCIENCES;

MAPPING METHOD; MULTI SENSOR; MULTI-RESOLUTION REMOTE SENSING;

REMOTE SENSING;

EID: 85041451456     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1201/b19321     Document Type: Chapter

References (196)

1. Abdel-Rahman, E.M., F.B. Ahmed, and R. Ismail. 2013. Random forest regression and spectral band selection for estimating sugarcane leaf nitrogen concentration using EO-1 Hyperion hyperspectral data. Int J Remote Sensing 34:712–728. doi: 10.1080/01431161.2012.713142.
2. Adam, P. 1990. Saltmarsh Ecology. Cambridge, U.K.: Cambridge University Press.
3. Albright, T.P., T.G. Moorhouse, and T.J. McNabb. 2004. The rise and fall of water hyacinth in Lake Victoria and the Kagera River Basin, 1989–2001. J Aquat Plant Manage 42:73–84.
4. Arbiol, R., Y. Zhang, and V. Palà. 2006. Advanced classifcation techniques: A review. In: ISPRS Commission VII Mid-term Symposium Remote Sensing: From Pixels to Processes. Enschede, NL. http://www.icc.cat/index.php/cat/layout/set/print/content/download/3831/12800/fle/advanced_classif-cation_techniques.pdf.
5. Artigas, F.J. and J.S. Yang. 2005. Hyperspectral remote sensing of marsh species and plant vigour gradient in the New Jersey Meadowlands. Int J Remote Sensing 26:5209–5220.
6. ASPRS LiDAR Committee. 2004. ASPRS LiDAR guidelines: Vertical accuracy reporting for LiDAR data. http://www.asprs.org/a/society/committees/LiDAR/Downloads/Vertical_Accuracy_Reporting_for_LiDAR_Data.pdf.
7. Baghdadi, N., M. Bernier, R. Gauthier, and I. Neeson. 2001. Evaluation of C-band SAR data for wetlands mapping. Int J Remote Sensing 22:71–88.
8. Baret, F. and G. Guyot. 1991. Potentials and limits of vegetation indices for LAI and PAR assessment. Remote Sensing Environ 35:161–173.
9. Bedford, B.L., M.R. Walbridge, and A. Aldous. 1999. Patterns in nutrient availability and plant diversity of temperate North American wetlands. Ecology 80:2151–2169. doi: 10.1890/0012-9658(1999)080[2151:PINAAP]2.0.CO;2.
10. Belluco, E., M. Camufo, S. Ferrari, L. Modenese, S. Silvestri, A. Marani, and M. Marani. 2006. Mapping salt-marsh vegetation by multispectral and hyperspectral remote sensing. Remote Sensing Environ. 105:54–67. doi: 10.1016/j. rse.2006.06.006.
11. Benz, U.C., P. Hofmann, G. Willhauck, I. Lingenfelder, and M. Heynen. 2004. Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS J Photogramm Remote Sensing 58:239–258. doi: 10.1016/j.isprsjprs.2003.10.002.
12. Bouma, T.J., M.B. De Vries, E. Low, L. Kusters, P.M.J. Herman, I.C. Tanczos, S. Temmerman, A. Hesselink, P. Meire, and S. van Regenmortel. 2005. Flow hydrodynamics on a mudfat and in salt marsh vegetation: Identifying general relationships for habitat characterisations. Hydrobiologia 540:259–274. doi: 10.1007/s10750-004-7149-0.
13. Boyer, K.E., P. Fong, R.R. Vance, and R.F. Ambrose. 2001. Salicornia virginica in a southern California salt marsh: Seasonal patterns and a nutrient-enrichment experiment. Wetlands 21:315–326. doi: 10.1672/0277-5212(2001)021[0315:SVIASC]2.0.CO;2.
14. Breiman, L., J. Feidman, R. Olshen, and C. Stone. 1984. Classifcation and Regression Trees. Belmont, CA: Wadsworth, Inc.
15. Brennan, R. and T.L. Webster. 2006. Object-oriented land cover classifcation of LiDAR-derived surfaces. Can J Remote Sensing 32:162–172.
16. Brinson, M.M. 1993. A hydrogeomorphic classifcation for wetlands. US Army Corps of Engineers, Washington, DC. http://www.cpe.rutgers.edu/Wetlands/hydrogeomorphic-classifcation-for-wetlands.pdf.
17. Buschmann, C. and E. Nagel. 1993. In vivo spectroscopy and internal optics of leaves as basis for remote sensing of vegetation. Int J Remote Sensing 14: 711–722.
18. Byrd, K.B., J.L. O’Connell, S. Di Tommaso, and M. Kelly. 2014. Evaluation of sensor types and environmental controls on mapping biomass of coastal marsh emergent vegetation. Remote Sensing Environ 149:166–180.
19. Campbell, J.B. 2006. Introduction to Remote Sensing, 4th edn. New York: The Guilford Press.
20. Chen, J., S. Gu, M. Shen, Y. Tang, and B. Matsushita. 2009. Estimating aboveground biomass of grassland having a high canopy cover: An exploratory analysis of in situ hyper-spectral data. Int J Remote Sensing 30:6497–6517.
21. Chen, J.M. and J. Cihlar. 1996. Retrieving leaf area index of boreal conifer forests using Landsat TM images. Remote Sensing Environ 55:153–162.
22. Cho, H.J., P. Kirui, and H. Natarajan. 2008. Test of multi-spectral vegetation index for foating and canopy-forming submerged vegetation. Int J Environ Res Public Health 5:289–295.
23. Chust, G., I. Galparsoro, A. Borja, J. Franco, and A. Uriarte. 2008. Coastal and estuarine habitat mapping, using LiDAR height and intensity and multi-spectral imagery. Estuar Coast Shelf Sci 78:633–643. doi: 10.1016/j.ecss.2008.02.003.
24. Cohen, R.A. and P. Fong. 2005. Experimental evidence supports the use of δ 15 N content of the opportunistic green mac-roalga Enteromorpha intestinalis (chlorophyta) to determine nitrogen sources to estuaries. J Phycol 41:287–293. doi: 10.1111/j.1529-8817.2005.04022.x.
25. Congalton, R.G. 1991. A review of assessing the accuracy of clas-sifcations of remotely sensed data. Remote Sensing Environ 37:35–46.
26. Costa, M.P.F. and K.H. Telmer. 2007. Mapping and monitoring lakes in the Brazilian Pantanal wetland using synthetic aperture radar imagery. Aquat Conserv 17:277–288.
27. Cowardin, L.M., V. Carter, F.C. Golet, and E.T. LaRoe. 1979. Classifcation of wetlands and deepwater habitats of the United States. FWS/OBS-79/31. U.S. Fish and Wildlife Service, Washington, DC.
28. Cronk, J.K. and M.S. Fennessy. 2001. Wetland Plants. Boca Raton, FL: Lewis Publication/ CRC Press LLC.
29. Curran, P.J. 1989. Remote sensing of foliar chemistry. Remote Sensing Environ 30:271–278. doi: 10.1016/0034-4257(89)90069-2.
30. Dahl, T.E. 2004. Remote sensing as a tool for monitoring wetland habitat change. U.S. Fish and Wildlife Service, Washington, DC, pp. 1–7.
31. Danks, S.M., E.H. Evans, and P.A. Whittaker. 1984. Photosynthetic systems: structure, function and assembly. New York: John Wiley, 162pp.
32. Darby, F.A. and R.E. Turner. 2008a. Below- and aboveground bio-mass of Spartina alternifora: Response to nutrient addition in a Louisiana salt marsh. Estuaries Coasts 31:326–334.
33. Darby, F.A. and R.E. Turner. 2008b. Efects of eutrophication on salt marsh root and rhizome biomass accumulation. Mar Ecol Prog Ser 363:63–70. doi: 10.3354/meps07423.
34. Deegan, L.A., D.S. Johnson, R.S. Warren, B.J. Peterson, J.W. Fleeger, S. Fagherazzi, and W.M. Wollheim. 2012. Coastal eutrophication as a driver of salt marsh loss. Nature 490:388–392. doi: 10.1038/nature11533.
35. Delalieux, S., B. Somers, S. Hereijgers, W. W. Verstraeten, W. Keulemans, and P. Coppin. 2008. A near-infrared narrow-waveband ratio to determine Leaf Area Index in orchards. Remote Sensing Environ 112:3762–3772.
36. Dronova, I., P. Gong, N.E. Clinton, L. Wang, W. Fu, S. Qi and Y. Liu. 2012. Landscape analysis of wetland plant functional types: The efects of image segmentation scale, vegetation classes and classifcation methods. Remote Sensing Environ 127:357–369.
37. du Plessis W.P. 1999. Linear regression relationships between NDVI, vegetation and rainfall in Etosha National Park, Namibia. J Arid Environ 42: 235–260.
38. Dwivedi, R., B. Rao, and S. Bhattacharya. 1999. Mapping wetlands of the Sundarban Delta and its environs using ERS-1 SAR data. Int J Remote Sensing 20:2235–2247.
39. Evans, T.L. and M. Costa. 2013. Landcover classifcation of the Lower Nhecolândia subregion of the Brazilian Pantanal wetlands using ALOS/PALSAR, RADARSAT-2 and ENVISAT/ ASAR imagery. Remote Sensing Environ 128:118–137. doi:10.1016/j.rse.2012.09.022.
40. Fang, J., S. Piao, and Z. Tang. 2001. Interannual variability in net primary production and precipitation. Science 293:1723.
41. Filippi, A.M. and J.R. Jensen. 2006. Fuzzy learning vector quantization for hyperspectral coastal vegetation classifcation. Remote Sensing Environ 100:512–530.
42. Friess, D.A., T. Spencer, G.M. Smith, I. Möller, S.M. Brooks, and A.G. Tomson. 2012. Remote sensing of geomorphologi-cal and ecological change in response to saltmarsh managed realignment, The Wash, UK. Int J Appl Earth Observ 18:57–68.
43. Gao, B.C. and A.F.H. Goetz. 1995. Retrieval of equivalent water thickness and information related to biochemical components of vegetation canopies from AVIRIS data. Remote Sensing Environ 52:155–162.
44. Gao, X., A.R. Huete, W. Ni, and T. Miura. 2000. Optical-biophysical relationships of vegetation spectra without background contamination. Remote Sensing Environ 74:609–620.
45. Geerken, R., B. Zaitchick, and J.P. Evans. 2005. Classifying range-land vegetation type and coverage from NDVI time series using Fourier fltered cycle similarity. Int J Remote Sensing 26:5535–5554.
46. Geerling, G.W., M. Labrador-Garcia, J.G.P.W. Clevers, A.M.J. Ragas, and A.J.M. Smits. 2007. Classifcation of food-plain vegetation by data fusion of spectral (CASI) and LiDAR data. Int J Remote Sensing 28:4263–4284. doi 10.1080/01431160701241720.
47. Geladi, P. and B.R. Kowalski. 1986. Partial least-squares regression: A tutorial. Anal Chim Acta 185:1–17. doi: 10.1016/0003-2670(86)80028-9.
48. Gesch, D.B. 2009. Analysis of LiDAR elevation data for improved identifcation and delineation of lands vulnerable to sea-level rise. J Coastal Res 25:49–58. doi: 10.2112/Si53-006.1
49. Gilmore, M.S., E.H. Wilson, N. Barrett, D.L. Civco, S. Prisloe, J.D. Hurd, and C. Chadwick. 2008. Integrating multi-temporal spectral and structural information to map wetland vegetation in a lower Connecticut River tidal marsh. Remote Sensing Environ 112:4048–4060. doi: 10.1016/j.rse.2008.05.020.
50. Gilmore, M.S., D.L. Civco, E.H. Wilson, N. Barrett, S. Prisloe, J.D. Hurd, and C. Chadwick. 2010. Remote sensing and in situ measurements for delineation and assessment of coastal marshes and their constituent species. In Remote Sensing of Coastal Environments, J. Wang (ed.). Boca Raton, FL: Springer.
51. Gitelson, A.A. 2004. Wide Dynamic Range Vegetation Index for remote quantifcation of crop biophysical characteristics. J Plant Physiol 161:165–173.
52. Gitelson, A., M. Merzlyak, and H. Lichtenthaler. 1996a. Detection of red edge position and chlorophyll content by refectance measurements near 700 nm. J Plant Physiol 148:501–508.
53. Gitelson, A.A., Y.J. Kaufman, and M.N. Merzlyak. 1996b. Use of green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing Environ 58:289–298.
54. Gitelson A.A., Y.J. Kaufman, R. Stark, and D. Rundquist. 2002. Novel algorithms for 830 remote estimation of vegetation fraction. Remote Sens Environ 80:76–87.
55. Gitelson, A.A., G.P. Keydan, and M.N. Merzlyak. 2006. Tree-band model for noninvasive estimation of chlorophyll, carotenoids, and anthocyanin contents in higher plant leaves. Geophys Res Lett 33:L11402.
56. Gitelson, A.A. and M.N. Merzlyak. 2004. Non-destructive assessment of chlorophyll, carotenoid and anthocyanin content in higher plant leaves: Principles and algorithms. In Remote Sensing for Agriculture and the Environment, S. Stamatiadis, J.M. Lynch, J.S. Schepers (eds.). Larissa, Greece: Ella, pp. 78–94.
57. Gitelson, A.A., A. Vina, T.J. Arkebauer, D.C. Rundquist, G. Keydan, and B. Leavitt. 2003a. Remote estimation of leaf area index and green leaf biomass in maize canopies. Geophys Res Lett 30:1248. doi:10.1029/2002GL016450.
58. Gitelson, A.A., A. Vina, V. Ciganda, and D.C. Rundquist. 2005. Remote estimation of canopy chlorophyll content in crops. Geophys Res Lett 32:L08403.
59. 2 fux in maize. Geophys Res Lett 30:1486. doi: 10.1029/2002GL016543.
60. Goranson, C.E., C.K. Ho, and S.C. Pennings. 2004. Environmental gradients and herbivore feeding preferences in coastal salt marshes. Oecologia. 140:591–600. doi: 10.1007/s00442-004-1615-2.
61. Goward, S.N. and K.F. Huemmrich. 1992. Vegetation canopy PAR absorptance and the normalized diference vegetation index: An assessment using the SAIL model. Remote Sensing Environ 39:119–140.
62. Graets, D. 1990. Remote sensing of terrestrial ecosystem structure: An ecologist’s pragmatic view. Eco Studies 79:5–30.
63. Hall, D.K. 1996. Remote sensing applications to hydrology: Imaging radar. Hydrol Sci J 41:609–624.
64. Hardisky, M.A., F.C. Daiber, C.T. Roman, and V. Klemas. 1984. Remote sensing of biomass and annual net aerial primary productivity of a salt marsh. Remote Sensing Environ. 16:91–106. doi: 10.1016/0034-4257(84)90055-5.
65. Hardisky, M.A., R. Michael Smart, and V. Klemas. 1983. Growth response and spectral characteristics of a short Spartina alternifora salt marsh irrigated with freshwater and sewage efuent. Remote Sensing Environ 13:57–67. doi: 10.1016/0034-4257(83)90027-5.
66. Harris, J. and S. Digby-Argus. 1986. The detection of wetlands on Radar imagery. Proceedings of the Tenth Canadian Symposium on Remote Sensing, Edmonton, Alberta, Canada.
67. Heim, R.R. 2002. A review of twentieth-century drought indices used in the United States. Bull Am Meteorol Soc 83:1149–1165.
68. Hess, L., J. Melack, S. Filoso, and Y. Wang. 1995. Delineation of inundated area and vegetation along the Amazon food-plain with the SIR-C synthetic aperture radar. IEEE Trans Geosci Remote 33:896–904.
69. Hinkle, R.L. and W.J. Mitsch. 2005. Salt marsh vegetation recovery at salt hay farm wetland restoration sites on Delaware Bay. Ecol Eng 25:240–251.
70. Hirano, A., M. Madden, and R. Welch. 2003. Hyperspectral image data for mapping wetland vegetation. Wetlands 23:436–448.
71. Hladik, C. and M. Alber. 2012. Accuracy assessment and correction of a LiDAR-derived salt marsh digital elevation model. Remote Sensing Environ 121:224–235. 10.1016/j.rse.2012.01.018.
72. Hladik, C., J. Schalles, and M. Alber. 2013. Salt marsh elevation and habitat mapping using hyperspectral and LiDAR data. Remote Sensing Environ 139:318–330. doi: 10.1016/j.rse.2013.08.003.
73. Hofeck, J.P. 1995. Classifcation of High Dimensional Multispectral Data. West Lafayette, IN: Purdue University. http://www.state.nj.us/dep/gis/digidownload/metadata/lulc02/anderson2002.html.
74. Huete, A.R. 1998. A soil–adjusted vegetation index (SAVI). Remote Sens Environ 25:295–309.
75. Huete, A., K. Didan, T. Miura, E.P. Rodriguez, X. Gao, and L.G. Ferreira. 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens Environ 83:195–213.
76. Jackson, T., D. Chen, M. Cosh, F. Li, M. Anderson, C. Walthall, P. Doraiswamy, and E.R. Hunt. 2004. Vegetation water content mapping using Landsat data normalized diference water index (NDWI) for corn and soybean. Remote Sensing Environ 92:475–482.
77. Jenkins, J.P., B.H. Braswell, S.E. Frolking, and J.D. Aber. 2002. Detecting and predicting spatial and interannual patterns of temperate forest springtime phenology in the eastern U.S. Geophys Res Lett 29:2201. doi:10.1029/2001GL014008.
78. Jensen, R.R., P. Mausel, N. Dias, R. Gonser, C. Yang, J. Everitt, and R. Fletcher. 2007. Spectral analysis of coastal vegetation and land cover using AISA+ hyperspectral data. Geocarto Int 22:17–28.
79. Kanemasu, E.T. 1974. Seasonal canopy refectance patterns of wheat, sorghum, and soybean. Remote Sensing Environ 3:43–47.
80. Kao, J.T., J.E. Titus, and W.-X. Zhu. 2003. Diferential nitrogen and phosphorus retention by fve wetland plant species. Wetlands 23:979–987. doi:10.1672/0277-5212(2003)023[0979:DNAPRB]2.0.CO;2.
81. Kasischke, E. and L. Bourgeau-Chavez. 1997. Monitoring South Florida wetlands using ERS-1 SAR imagery. Photogramm Eng Remote Sensing 63:281–291.
82. Kasischke, E., J. Melack, and M. Dobson, 1997. The use of imaging radars for ecological applications—A review. Remote Sensing Environ 59:141–156.
83. Kearney, M.S., D. Stutzer, K. Turpie, and J.C. Stevenson. 2009. The efects of tidal inundation on the refectance characteristics of coastal marsh vegetation. J Coastal Res 256:1177–1186. doi: 10.2112/08-1080.1.
84. Keddy, P.A. 2010. Wetland Ecology: Principles and Conservation. New York: Cambridge University Press.
85. Kiage, L.M. and N.D. Walker. 2009. Using NDVI from MODIS to monitor Duckweed bloom in Lake Maracaibo, Venezuela. Water Res Manage 23:1125–1135. doi:10.1007/s11269-008-9318-9.
86. Klemas, V. 2011. Remote sensing of wetlands: Case studies comparing practical techniques. J Coastal Res 27:418–427. doi:10.2112/Jcoastres-D-10-00174.1
87. Kobe, R.K., M. Iyer, and M.B. Walters. 2010. Optimal partitioning theory revisited: Nonstructural carbohydrates dominate root mass responses to nitrogen. Ecology 91:166–179. doi:10.1890/09-0027.1.
88. Kokaly, R.F., G.P. Asner, S.V. Ollinger, M.E. Martin, and C.A. Wessman. 2009. Characterizing canopy biochemistry from imaging spectroscopy and its application to ecosystem studies. Remote Sensing Environ. 113(Suppl. 1):S78–S91. doi:10.1016/j.rse.2008.10.018.
89. Kokaly, R.F. and R.N. Clark. 1999. Spectroscopic determination of leaf biochemistry using band depth analysis of absorption features and stepwise multiple linear regression. Remote Sensing Environ 67:267–287. doi:10.1016/ S0034-4257(98)00084-4.
90. Kovacs, J.M., J.M.L. King, F.F. de Santiago, and F. Flores-Verdugo. 2009. Evaluating the condition of a mangrove forest of the Mexican Pacifc based on an estimated leaf area index mapping approach. Environ Monit Assess 157:137–149.
91. Lang, M. and G. McCarty. 2008. Wetland mapping: History and trends. In Wetlands: Ecology, Conservation and Management. New York: Nova Publishers, pp. 74–112.
92. Lang, M.W. and E.S. Kasischke. 2008. Using C–band synthetic aperture radar data to monitor forested wetland hydrology in Maryland’s coastal plain, USA. IEEE Trans Geosci Remote Sens 46:535–546.
93. Larkin, D.J., S.C. Lishawa, and N.C. Tuchman. 2012. Appropriation of nitrogen by the invasive cattail Typha × glauca. Aquat Bot 100:62–66. doi:10.1016/j.aquabot.2012.03.001.
94. Lieth, H. and R.H. Whittaker. 1975. Primary Production of the Biosphere. New York: Springer-Verlag, 339pp.
95. Lillesand, T.M., R.W. Kiefer, and J.W. Chipman. 2008. Remote Sensing and Image Interpretation, 6th edn. Hoboken, NJ: Wiley, 768pp.
96. Lu, D. and Q. Weng. 2004. Spectral mixture analysis of the urban landscape in Indianapolis with landsat ETM plus imagery. Photogramm Eng Remote Sensing 70:1053–1062.
97. Lu, D. and Q. Weng. 2007. A survey of image classifca-tion methods and techniques for improving classifca-tion performance. Int J Remote Sensing 28:823–870. doi:10.1080/01431160600746456.
98. Lucas, K.L. and G.A. Carter. 2008. The use of hyperspectral remote sensing to assess vascular plant species richness on Horn Island, Mississippi. Remote Sensing Environ 112:3908–3915. doi:10.1016/j.rse.2008.06.009.
99. Marani, M., E. Belluco, S. Ferrari, S. Silvestri, A. D’Alpaos, S. Lanzoni, A. Feola, and A. Rinaldo. 2006. Analysis, synthesis and modelling of high-resolution observations of salt-marsh eco-geomorphological patterns in the Venice lagoon. Estuar Coast Shelf Sci 69:414–426. doi:10.1016/j.ecss.2006.05.021.
100. Masek, J.G., E.F. Vermote, N. Saleous, R. Wolfe, F.G. Hall, F. Huemmrich, F. Gao, et al. 2012. LEDAPS Landsat calibration, refectance, atmospheric correction preprocessing code. Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, TN. doi:10.3334/ORNLDAAC/1080.
101. Mayer, A.L. and R.D. Lopez. 2011. Use of remote sensing to support forest and wetlands policies in the USA. Remote Sensing 3:1211–1233.
102. Mccarthy, M.C. and B.J. Enquist. 2007. Consistency between an allometric approach and optimal partitioning theory in global patterns of plant biomass allocation. Funct Ecol 21:713–720. doi:10.1111/j.1365-2435.2007.01276.x.
103. McKee, K.L. 2011. Biophysical controls on accretion and elevation change in Caribbean mangrove ecosystems. Estuar Coast Shelf Sci 91:475–483. doi:10.1016/j.ecss.2010.05.001.
104. McKee, K.L., D.R. Cahoon, and I.C. Feller. 2007. Caribbean mangroves adjust to rising sea level through biotic controls on change in soil elevation. Global Ecol Biogeogr 16:545–556. doi:10.1111/j.1466-8238.2007.00317.x.
105. Mevik, B.H. and H.R. Cederkvist. 2004. Mean squared error of prediction (MSEP) estimates for principal component regression (PCR) and partial least squares regression (PLSR). J Chemometrics 18:422–429. doi:10.1002/cem.887.
106. Mevik, B.H. and R. Wehrens. 2007. The pls package: Principal component and partial least-squares regression in R. J Stat Sofw 18:1–24.
107. Miller, R.L., M. Fram, R. Fujii, and G. Wheeler. 2008. Subsidence reversal in a re-established wetland in the Sacramento-San Joaquin Delta, California, USA. San Franc Estuary Watershed Sci 6:1–20. http://escholarship.org/uc/item/5j76502x.
108. Mishra, D.R., H.J. Cho, S. Ghosh, A. Fox, C. Downs, P.B.T. Merani, P. Kirui, N. Jackson, and S. Mishra. 2012. Post-spill state of the marsh: Remote estimation of the ecological impact of the Gulf of Mexico oil spill on Louisiana Salt Marshes. Remote Sensing Environ 118:176–185.
109. Mitsch, W.J. and J.G. Gosselink. 2007. Wetlands. Hoboken, NJ: John Wiley and Sons, Inc.
110. Moeslund, J.E., L. Arge, P.K. Bocher, B. Nygaard, and J.C. Svenning. 2011. Geographically comprehensive assessment of salt-meadow vegetation-elevation relations using LiDAR. Wetlands 31:471–482. doi: 10.1007/s13157-011-0179-2.
111. Mokany, K., R.J. Raison, and A.S. Prokushkin. 2006. Critical analysis of root: Shoot ratios in terrestrial biomes. Global Change Biol 12:84–96. doi:10.1111/j.1365-2486.2005.001043.x.
112. Mokhele, T.A. and F.B. Ahmed. 2010. Estimation of leaf nitrogen and silicon using hyperspectral remote sensing. J Appl Remote Sensing 4:1–18. doi:10.1117/1.3525241.
113. Montane, J.M. and R. Torres. 2006. Accuracy assessment of LiDAR saltmarsh topographic data using RTK GPS. Photogramm Eng Remote Sensing 72:961–967.
114. Moore, P.D. 1987. Ecological and hydrological aspects of peat formation. Geol Soc Spec Publ. 32:7–15. doi:10.1144/GSL.SP.1987.032.01.02.
115. Morris, J.T. and B. Haskin. 1990. A 5-yr record of aerial primary production and stand characteristics of Spartina-Alternifora. Ecology 71:2209–2217.
116. Morris, J.T., D. Porter, M. Neet, P.A. Noble, L. Schmidt, L.A. Lapine, and J.R. Jensen. 2005. Integrating LiDAR elevation data, multi-spectral imagery and neural network modelling for marsh characterization. Int J Remote Sensing 26:5221–5234.
117. Morris, J.T., G.P. Shafer, and J.A. Nyman. 2013. Brinson review: Perspectives on the infuence of nutrients on the sus-tainability of coastal wetlands. Wetlands 33(6):975–988. doi:10.1007/s13157-013-0480-3.
118. Myneni, R.B., R.R. Nemani, and S.W. Running. 1997 Estimation of global leaf area index and absorbed par using radiative transfer models. IEEE Trans Geosci Remote 35:1380–1393.
119. Novo, E.M.L.M., M.P.F. Costa, J.E. Mantovani, and I.B.T. Lima. 2002. Relationship between macrophyte stand variables and radar backscatter at L and C band, Tucurui reservoir, Brazil. Int J Remote Sensing 23:1241–1260.
120. Nyman, J.A., R.D. Delaune, H.H. Roberts, and W.H. Patrick. 1993. Relationship between vegetation and soil formation in a rapidly submerging coastal marsh. Mar Ecol Prog Ser 96:269–279.
121. Nyman, J.A., R.J. Walters, R.D. Delaune, and J.W.H. Patrick. 2006. Marsh vertical accretion via vegetative growth. Estuar Coast Shelf Sci 69:370–380.
122. O’Connell, J.L., K.B. Byrd, and M. Kelly. 2014. Remotely-sensed indicators of N-related biomass allocation in Schoenoplectus acu-tus. PLoS One 9:e90870. doi:10.1371/journal.pone.0090870.
123. O’Connell, J.L., K.B. Byrd, and M. Kelly. In review. A hybrid modeling approach for mapping relative diferences in below-ground biomass using spectral refectance, foliar N and plant biophysical data. Remote Sensing Environ.
124. Optech. 2011. Gemini summary specifcation sheet. http://www.geo-konzept.de/data/downloads/altm_gemini.pdf.
125. Ozesmi, S.L. and M.E. Bauer. 2002. Satellite remote sensing of wetlands. Wet Ecol Manage 10:381–402.
126. Pal, M. and P.M. Mather. 2003. An assessment of the efectiveness of decision tree methods for land cover classifcation. Remote Sensing Environ. 86:554–565. doi:10.1016/S0034-4257(03)00132-9.
127. Pengra, B.W., C.A. Johnston, and T.R. Loveland. 2007. Mapping an invasive plant, Phragmites australis, in coastal wetlands using the EO-1 Hyperion hyperspectral sensor. Remote Sensing Environ 108:74–81.
128. Penuelas, J., J.A. Gamon, K.L. Grifn, and C.B. Field. 1993. Assessing community type, plant biomass, pigment composition, and photosynthetic efciency of aquatic vegetation from spectral refectance. Remote Sensing Environ 46:110–118.
129. Phinn, S.R. 1998. A framework for selecting appropriate remotely sensed data dimensions for environmental monitoring and management. Int J Remote Sensing 19:3457–3463. doi:10.1080/014311698214136.
130. Phinn, S., L. Hess, and C.M. Finalyson. 1999. An assessment of the usefulness of remote sensing for coastal wetland inventory and monitoring in Australia. In Techniques for Enhanced Wetland Inventory and Monitoring, C.M. Finlayson and A.G. Spiers (eds.). Canberra, Australian Capital Territory, Australia: Supervising Scientist Environment.
131. Phinn, S.R., D.A. Stow, and J.B. Zedler. 1996. Monitoring wetland habitat restoration in Southern California using airborne multi spectral video data. Restoration Ecol 4:412–422. doi:10.1111/j.1526-100X.1996.tb00194.x.
132. Pohl, C. and J.L. van Genderen. 1998. Multisensor image fusion in remote sensing: concepts, methods and applications. Int J Remote Sensing 19:823–854.
133. Pontius, R.G. and M. Millones. 2011. Death to Kappa: Birth of quantity disagreement and allocation disagreement for accuracy assessment. Int J Remote Sensing 32:4407–4429. doi:10.1080/01431161.2011.552923.
134. Poulter, B. and P.N. Halpin. 2008. Raster modelling of coastal fooding from sea-level rise. Int J Geogr Inf Sci 22:167–182. doi:10.1080/13658810701371858.
135. Ramsey, E. and A. Rangoonwala. 2006. Canopy refectance related to marsh dieback onset and progression in coastal Louisiana. Photogramm Eng Remote Sensing 72:641–652.
136. Rao, B.R.M., R.S. Dwivedi, S.P.S. Kushwaha, S.N. Bhattacharya, J.B. Anand, and S. Dasgupta. Monitoring the spatial extent of coastal wetlands using ERS-1 SAR data. Int J Remote Sens 20:2509–2517.
137. Rasse, D.P., C. Rumpel, and M.F. Dignac. 2005. Is soil carbon mostly root carbon? Mechanisms for a specifc stabilisation. Plant Soil 269:341–356.
138. Reimold, R.J., J.L. Gallagher, and D.E. Tompson. 1973. Remote sensing of tidal marsh. Photogramm Eng Remote Sensing 39:477–488.
139. Rosenqvist, A.K.E., C.M. Finlayson, J. Lowry, and D. Taylor. 2007. The potential of long‐wavelength satellite‐borne radar to support implementation of the Ramsar Wetlands Convention. Aquat Conserv 17:229–244.
140. Rosso, P.H., S.L. Ustin, and A. Hastings. 2006. Use of LiDAR to study changes associated with Spartina invasion in San Francisco Bay marshes. Remote Sensing Environ. 100:295–306. doi:10.1016/j.rse.2005.10.012.
141. Rouse, J.W., R.H. Haas Jr., J.A. Schell, and D.W. Deering. 1974. Monitoring vegetation systems in the Great Plains with ERTS, NASA SP-351. Tird ERTS-1 Symposium, NASA, Washington, DC, Vol. 1, pp. 309–317.
142. Rundquist, D., R. Perk, B. Leavitt, G. Keydan, and A. Gitelson. 2004. Collecting spectral data over cropland vegetation using machine-positioning versus hand-positioning of the sensor. Comput Electron Agron 43:173–178.
143. Ryu, C., M. Suguri, and M. Umeda. 2009. Model for predicting the nitrogen content of rice at panicle initiation stage using data from airborne hyperspectral remote sensing. Biosyst Eng. 104:465–475. doi:10.1016/j.biosystemseng.2009.09.002.
144. Sadro, S., M. Gastil-Buhl, and J. Melack. 2007. Characterizing patterns of plant distribution in a southern California salt marsh using remotely sensed topographic and hyperspec-tral data and local tidal fuctuations. Remote Sensing Environ 110:226–239. doi:10.1016/j.rse.2007.02.024.
145. Sanders, B.F. 2007. Evaluation of on-line DEMs for food inundation modeling. Adv Water Resour 30:1831–1843. doi:10.1016/j.advwatres.2007.02.005.
146. Sartoris, J.J., J.S. Tullen, L.B. Barber, and D.E. Salas. 1999. Investigation of nitrogen transformations in a southern California constructed wastewater treatment wetland. Ecol Eng 14:49–65. doi:10.1016/S0925-8574(99)00019-1.
147. Schalles, J.F., C.M. Hladik, A.A. Lynes, and S.C. Pennings. 2013. Landscape estimates of habitat types, plant bio-mass, and invertebrate densities in a Georgia Salt Marsh. Oceanography 26:88–97.
148. Schmid, K.A., B.C. Hadley, and N. Wijekoon. 2011. Vertical accuracy and use of topographic LIDAR data in coastal marshes. J Coastal Res 27(6A):116–132.
149. Schmidt, K.S. and A.K. Skidmore. 2003. Spectral discrimination of vegetation types in a coastal wetland. Remote Sensing Environ 85:92–108. doi:10.1016/S0034-4257(02)00196-7.
150. Schmidt, K.S., A.K. Skidmore, E.H. Kloosterman, H. Van Oosten, L. Kumar, and J.A.M. Janssen. 2004. Mapping coastal vegetation using an expert system and hyperspectral imagery. Photogramm Eng Remote Sensing 70:703–716.
151. Sellars, J.D. and C.L. Jolls. 2007. Habitat modeling for Amaranthus pumilus: An application of light detection and ranging (LiDAR) data. J Coastal Res 23:1193–1202. doi:10.2112/04-0334.1.
152. Sellers, P.J. 1985. Canopy refectance, photosynthesis and transpiration. Int J Remote Sensing 6:1335–1372.
153. Shekede, M.D., S. Kusangaya, and K. Schmidt. 2008. Spatio-temporal variations of aquatic weeds abundance and coverage in Lake Chivero, Zimbabwe. Phys Chem Earth 33:714–721.
154. Shipley, B. and D. Meziane. 2002. The balanced-growth hypothesis and the allometry of leaf and root biomass allocation. Funct Ecol 16:326–331. doi:10.2307/826585.
155. Silva, T.S.F., M.P. Costa, and J.M. Melack. 2010. Spatial and temporal variability of macrophyte cover and productivity in the eastern Amazon foodplain: A remote sensing approach. Remote Sensing Environ 114:1998–2010.
156. Silvestri, S., M. Marani, and A. Marani. 2003. Hyperspectral remote sensing of salt marsh vegetation, morphology and soil topography. Phys Chem Earth. 28:15–25. doi:10.1016/S1474-7065(03)00004-4.
157. Simard, M., L.E. Fatoyinbo, and N. Pinto. 2010. Mangrove canopy 3D structure and ecosystem productivity using active remote sensing. In Remote Sensing of Coastal Environments, J. Wang (ed.). Boca Raton, FL: CRC Press.
158. Smith, M.-L., S.V. Ollinger, M.E. Martin, J.D. Aber, R.A. Hallett, and C.L. Goodale. 2002. Direct estimation of aboveg-round forest productivity through hyperspectral remote sensing of canopy nitrogen. Ecol Appl. 12:1286–1302. doi:10.1890/1051-0761(2002)012[1286:DEOAFP]2.0.CO;2.
159. Stroppiana, D., M. Boschetti, P.A. Brivio, and S. Bacchi. 2009. Plant nitrogen concentration in paddy rice from feld canopy hyperspectral radiometry. Field Crops Res 111:119–129.
160. Stroppiana, D., F. Fava, M. Baschetti, and P.A. Brivio. 2012. Estimation of nitrogen content in crops and pastures using hyperspectral vegetation indices. In Hyperspectral Remote Sensing of Vegetation, P.S. Tenkabail, J.G. Lyon, and A. Huete (eds.). Boca Raton, FL: CRC Press, pp. 245–262. http://www.crcnetbase.com/doi/pdf/10.1201/b11222-16.
161. Tenkabail, P.S., J.G. Lyon, and A. Huete. 2012. Advances in hyperspectral remote sensing of vegetation and agricultural croplands. In Hyperspectral Remote Sensing of Vegetation, P.S. Tenkabail, J.G. Lyon, and A. Huete (eds.). Boca Raton, FL: Taylor and Francis Group, pp. 28–29.
162. Tenkabail, P.S., R.B. Smith, and E. De-Pauw. 2002. Evaluation of narrowband and broadband vegetation indices for determining optimal hyperspectral wavebands for agricultural crop characteristics. Photogramm Eng Remote Sensing 68:607–621.
163. Tomson, A.G., R.M. Fuller, M.G. Yates, S.L. Brown, R. Cox, and R.A. Wadsworth. 2003. The use of airborne remote sensing for extensive mapping of intertidal sediments and saltmarshes in eastern England. Int J Remote Sensing 24:2717–2737. doi:10.1080/0143116031000066918.
164. Tomson, A.G., A. Huiskes, R. Cox, R.A. Wadsworth, and L.A. Boorman. 2004. Short-term vegetation succession and erosion identifed by airborne remote sensing of Westerschelde salt marshes, the Netherlands. Int J Remote Sensing. 25:4151–4176. doi:10.1080/01431160310001647688.
165. Tian, Y.C., X. Yao, J. Yang, W.X. Cao, D.B. Hannaway, and Y. Zhu. 2011. Assessing newly developed and published vegetation indices for estimating rice leaf nitrogen concentration with ground- and space-based hyperspectral refectance. Field Crops Res 120:299–310. doi:10.1016/j.fcr.2010.11.002.
166. Townend, I., C. Fletcher, M. Knappen, and K. Rossington. 2011. A review of salt marsh dynamics. Water Environ J 25:477–488. doi:10.1111/j.1747-6593.2010.00243.x.
167. Townsend, P. and S. Walsh. 1998. Modeling foodplain inundation using an integrated GIS with radar and optical remote sensing. Geomorphology 21: 295–312.
168. Townsend, P.A. 2000. A quantitative fuzzy approach to assess mapped vegetation classifcations for ecological applications. Remote Sensing Environ 72:253–267.
169. Townsend, P.A. 2002. Relationships between forest structure and the detection of food inundation in forested wetlands using C-band SAR. Int J Remote Sensing 23:443–460.
170. Townsend, P.A., J.R. Foster, R.A. Chastain, and W.S. Currie. 2003. Application of imaging spectroscopy to mapping canopy nitrogen in the forests of the central Appalachian Mountains using Hyperion and AVIRIS. IEEE Trans Geosci Remote 41:1347–1354. doi:10.1109/tgrs.2003.813205.
171. Toyra, J., A. Pietroniro, C. Hopkinson, and W. Kalbfeisch. 2003. Assessment of airborne scanning laser altimetry (LiDAR) in a deltaic wetland environment. Can J Remote Sensing 29:718–728.
172. Toyra, J., A. Pietroniro, L.W. Martz, and T.D. Prowse, 2002. A multi-sensor approach to wetland food monitoring. Hydrol Process 16:1569–1581.
173. Trimble. 2009. Trimble R6 GPS receiver datasheet. http://trl.trimble.com/docushare/dsweb/Get/Document-333155/022543-259H_TrimbleR6GNSS_DS_0413_LR.pdf.
174. Tucker, C.J. 1977. Asymptotic nature of grass canopy spectral refectance. Appl Opt 16:1151–1156.
175. Turner, R.E. 1979. Geographic variations in salt marsh macro-phyte productions: A review. Contrib Mar Sci 20:47–68.
176. Turner, R.E., B.L. Howes, J.M. Teal, C.S. Milan, E.M. Swenson, and D.D.G. Toner. 2009. Salt marshes and eutrophication: An unsustainable outcome. Limnol Oceanogr 54:1634–1642. doi:10.4319/lo.2009.54.5.1634.
177. Turpie, K.R. 2013. Explaining the spectral red-edge features of inundated marsh vegetation. J Coastal Res 290:1111–1117. doi:10.2112/JCOASTRES-D-12-00209.1.
178. USDA. 2010. The PLANTS Database. Baton Rouge, LA: National Plant Data Center. http://plants.usda.gov.
179. Ustin, S.L., D.A. Roberts, S. Jacquemoud, J. Pinzón, M. Gardner, G.J. Scheer, C.M. Castaneda, and A. Palacios. 1998. Estimating canopy water content of chaparral shrubs using optical methods. Remote Sensing Environ 65:280–291.
180. Wang, C., M. Menenti, M.P. Stoll, E. Belluco, and M. Marani. 2007. Mapping mixed vegetation communities in salt marshes using airborne spectral data. Remote Sensing Environ 107:559–570. doi:10.1016/j.rse.2006.10.007.
181. Wang, C., M. Menenti, M.P. Stoll, A. Feola, E. Belluco, and M. Marani. 2009. Separation of ground and low vegetation signatures in LiDAR measurements of salt-marsh environments. IEEE Trans Geosci Remote 47:2014–2023. doi:10.1109/Tgrs.2008.2010490.
182. Wang, L., I. Dronova, P. Gong, W. Yang, Y. Li, and Q. Liu. 2012. A new time series vegetation-water index of phonological-hydrolog-ical trait across species and functional types for Poyang Lake wetland ecosystem. Remote Sensing Environ 125:49–63.
183. Wang, Y. 2010. Remote sensing of coastal environments: An overview. In Remote Sensing of Coastal Environments, J. Wang (ed.). Boca Raton, FL: CRC Press.
184. Webster, T.L., D.L. Forbes, E. MacKinnon, and D. Roberts. 2006. Flood-risk mapping for storm-surge events and sea-level rise using LiDAR for southeast New Brunswick. Can J Remote Sensing 32:194–211.
185. Wiegert, R.G. and B.J. Freeman. 1990. Tidal marshes of the southeast Atlantic coast: A community profle. U.S. Department of Interior, Fish and Wildlife Service, Biological Report. 85(7.29), Washington, DC.
186. Wilson, B.A. and H. Rashid. 2005. Monitoring the 1997 food in the Red River Valley using hydrologic regimes and RADARSAT imagery. Can Geogr 49:100–109.
187. Wolfe, R., M. Nishihama, A. Fleig, J. Kuyper, D. Roy, J. Storey, and F.S. Patt. 2002. Achieving sub-pixel geolocation accuracy in support of MODIS land science. Remote Sensing Environ 83:31–49.
188. Wright, C. and A. Gallant. 2007. Improved wetland remote sensing in Yellowstone National Park using classifcation trees to combine TM imagery and ancillary environmental data. Remote Sensing Environ. 107:582–605. doi:10.1016/j.rse.2006.10.019.
189. Wulder, M.A., J.G. Masek, W.B. Cohen, T.R. Loveland, and C.E. Woodcock. 2012. Opening the archive: How free data has enabled the science and monitoring promise of Landsat. Remote Sensing Environ 122:2–10. doi:10.1016/j.rse.2012.01.010.
190. Yang, C., J.H. Everitt, R.S. Fletcher, J.R. Jensen, and P. W. Mausel. 2009. Mapping black mangrove along the south Texas gulf coast using AISA+ hyperspectral imagery. Photogramm Eng Remote Sensing 75:425–436.
191. Yang, J. and F.J. Artigas. 2010. Mapping salt marsh vegetation by integrating hyperspectral and LiDAR remote sensing. In Remote Sensing of Coastal Environments, Y. Wang (ed.). Boca Raton, FL: CRC Press.
192. Yoder, B.J. and R.H. Waring. 1994. The normalized diference vegetation index of small Douglas-fr canopies with varying chlorophyll concentrations. Remote Sens Environ 49:81–91.
193. Yu, Q., P. Gong, Y.Q. Tian, R. Pu, and J. Yang. 2008. Factors afecting spatial variation of classifcation uncertainty in an image object-based vegetation mapping. Photogramm Eng Remote Sensing 74:1007–1018. doi:10.14358/PERS.74.8.1007.
194. Zedler, J.B. 2001. Handbook for Restoring Tidal Wetlands. Boca Raton, FL: CRC Press.
195. Zhang, C. and Z. Xie. 2012. Combining object-based texture measures with a neural network for vegetation mapping in the Everglades from hyperspectral imagery. Remote Sensing Environ 124:310–320.
196. Zomer, R.J., A. Trabucco, and S.L. Ustin. 2008. Building spectral libraries for wetlands land cover classification and hyperspectral remote sensing. J Environ Manage 90:2170–2177.