A review of wetland remote sensing

Sensors (Switzerland)

Volumn 17, Issue 4, 2017, Pages

  Guo, Meng ^a   Li, Jing ^b   Sheng, Chunlei ^b   Xu, Jiawei ^a   Wu, Li ^c  

^a NORTHEAST NORMAL UNIVERSITY   (China)

^b NORTHEAST INSTITUTE OF GEOGRAPHY AND AGRICULTURAL ECOLOGY   (China)

^c HEILONGJIANG ACADEMY OF AGRICULTURAL SCIENCES   (China)

Author keywords

LiDAR; Optical sensor; Radar; Remote sensing; Wetland

Indexed keywords

BIODIVERSITY; CLIMATE CHANGE; DECISION TREES; ECOSYSTEMS; OPTICAL RADAR; OPTICAL SENSORS; RADAR; SEA LEVEL; SPECTROSCOPY; TRACKING RADAR; WATER POLLUTION; WATER QUALITY; WETLANDS;

BIOMASS ESTIMATION; HYPER-SPECTRAL IMAGERIES; LIGHT DETECTION AND RANGING; MANGROVE FOREST; OBJECT-BASED CLASSIFICATIONS; SUPERVISED AND UNSUPERVISED CLASSIFICATION; TEMPORAL INFORMATION; WETLAND CLASSIFICATION;

REMOTE SENSING;

REMOTE SENSING; WETLAND;

REMOTE SENSING TECHNOLOGY; WETLANDS;

EID: 85017267862     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s17040777     Document Type: Review

References (283)

1. Barducci, A.; Guzzi, D.; Marcoionni, P.; Pippi, I. Aerospace wetland monitoring by hyperspectral imaging sensors: A case study in the coastal zone of San Rossore Natural Park. J. Environ. Manag. 2009, 90, 2278-2286.
2. Zhang, L.; Wang, M.-H.; Hu, J.; Ho, Y.-S. A review of published wetland research, 1991-2008: Ecological engineering and ecosystem restoration. Ecol. Eng. 2010, 36, 973-980.
3. Fickas, K.C.; Cohen, W.B.; Yang, Z. Landsat-based monitoring of annual wetland change in the Willamette Valley of Oregon, USA from 1972 to 2012. Wetl. Ecol. Manag. 2016, 24, 73-92.
4. Schmidt, K.; Skidmore, A. Spectral discrimination of vegetation types in a coastal wetland. Remote Sens. Environ. 2003, 85, 92-108.
5. Gorham, E. Northern peatlands: Role in the carbon cycle and probable responses to climatic warming. Ecol. Appl. 1991, 1, 182-195.
6. Green, E.P.; Clark, C.D.; Mumby, P.J.; Edwards, A.J.; Ellis, A.C. Remote sensing techniques for mangrove mapping. Int. J. Remote Sens. 1998, 19, 935-956.
7. Nicholls, R.J. Coastal flooding and wetland loss in the 21st century: Changes under the SRES climate and socio-economic scenarios. Glob. Environ. Chang. 2004, 14, 69-86.
8. Park, N.W.; Chi, K.H.; Kwon, B.D. Geostatistical integration of spectral and spatial information for land-cover mapping using remote sensing data. Geosci. J. 2003, 7, 335-341.
9. Wang, L.; Sousa, W.P.; Gong, P. Integration of object-based and pixel-based classification for mapping mangroves with IKONOS imagery. Int. J. Remote Sens. 2004, 25, 5655-5668.
10. Yuan, F.; Sawaya, K.E.; Loeffelholz, B.C.; Bauer, M.E. Land cover classification and change analysis of the Twin Cities (Minnesota) Metropolitan Area by multitemporal Landsat remote sensing. Remote Sens. Environ. 2005, 98, 317-328.
11. Giri, C.; Ochieng, E.; Tieszen, L.L.; Zhu, Z.; Singh, A.; Loveland, T.; Masek, J.; Duke, N. Status and distribution of mangrove forests of the world using earth observation satellite data. Glob. Ecol. Biogeogr. 2011, 20, 154-159.
12. Holden, J. Peatland hydrology and carbon release: Why small-scale process matters. Philos. Trans. R. Soc. Lond. A Math. Phys. Eng. Sci. 2005, 363, 2891-2913.
13. Van der Werf, G.R.; Randerson, J.T.; Giglio, L.; Collatz, G.J.; Kasibhatla, P.S.; Arellano, A.F., Jr. Interannual variability in global biomass burning emissions from 1997 to 2004. Atmos. Chem. Phys. 2006, 6, 3423-3441.
14. Page, S.E.; Siegert, F.; Rieley, J.O.; Boehm, H.-D.V.; Jaya, A.; Limin, S. The amount of carbon released from peat and forest fires in Indonesia during 1997. Nature 2002, 420, 61-65.
15. Toriyama, J.; Takahashi, T.; Nishimura, S.; Sato, T.; Monda, Y.; Saito, H.; Awaya, Y.; Limin, S.H.; Susanto, A.R.; Darma, F.; et al. Estimation of fuel mass and its loss during a forest fire in peat swamp forests of central Kalimantan, Indonesia. For. Ecol. Manag. 2014, 314, 1-8.
16. Rappold, A.G.; Stone, S.L.; Cascio, W.E.; Neas, L.M.; Kilaru, V.J.; Carraway, M.S.; Szykman, J.J.; Ising, A.; Cleve, W.E.; Meredith, J.T.; et al. Peat bog wildfire smoke exposure in rural north Carolina is associated with cardiopulmonary emergency department visits assessed through syndromic surveillance. Environ. Health Persp. 2011, 119, 1415-1420.
17. Li, S.-N.; Wang, G.-X.; Deng, W.; Hu, Y.-M.; Hu, W.-W. Influence of hydrology process on wetland landscape pattern: A case study in the Yellow River Delta. Ecol. Eng. 2009, 35, 1719-1726.
18. Vörösmarty, C.J.; Moore III, B. Modeling basin-scale hydrology in support of physical climate and global biogeochemical studies: An example using the Zambezi River. Surv. Geophys. 1991, 12, 271-311.
19. Slater, L.D.; Reeve, A. Investigating peatland stratigraphy and hydrogeology using integrated electrical geophysics. Geophysics 2002, 67, 365-378.
20. Kuenzer, C.; Bluemel, A.; Gebhardt, S.; Quoc, T.V.; Dech, S. Remote sensing of mangrove ecosystems: A review. Remote Sens. 2011, 3, 878-928.
21. Proulx-Mcinnis, S.; St-Hilaire, A.; Rousseau, A.N.; Jutras, S. A review of ground-penetrating radar studies related to peatland stratigraphy with a case study on the determination of peat thickness in a northern boreal fen in Quebec, Canada. Prog. Phys. Geogr. 2013, 37, 767-786.
22. Devi, G.K.; Ganasri, B.P.; Dwarakish, G.S. Applications of remote sensing in satellite oceanography: A review. Aquat. Procedia 2015, 4, 579-584.
23. Amler, E.; Schmidt, M.; Menz, G. Definitions and mapping of east African wetlands: A review. Remote Sens. 2015, 7, 5256-5282.
24. Zhao, Z.; Zhang, Y.; Liu, L.; Liu, F.; Zhang, H. Recent changes in wetlands on the Tibetan Plateau: A review. J. Geogr. Sci. 2015, 25, 879-896.
25. Henderson, F.M.; Lewis, A.J. Radar detection of wetland ecosystems: A review. Int. J. Remote Sens. 2008, 29, 5809-5835.
26. Adam, E.; Mutanga, O.; Rugege, D. Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: A review. Wetl. Ecol. Manag. 2010, 18, 281-296.
27. Dronova, I. Object-based image analysis in wetland research: A review. Remote Sens. 2015, 7, 6380-6413.
28. Rundquist, D.C.; Narumalani, S.; Narayanan, R.M. A review of wetlands remote sensing and defining new considerations. Remote Sens. Rev. 2001, 20, 207-226.
29. Gao, W.; Guo, H.C. Nitrogen research at watershed scale: A bibliometric analysis during 1959-2011. Scientometrics 2014, 99, 737-753.
30. Chen, S.P. Dictionary of Remote Sensing; Science Press: Beijing, China, 1990; p. 1060.
31. Wu, X.; Chen, X.; Zhan, F.B.; Hong, S. Global research trends in landslides during 1991-2014: A bibliometric analysis. Landslides 2015, 12, 1215-1226.
32. Johannessen, C.L. Marshes prograding in oregon: Aerial photographs. Science 1964, 146, 1575-1578.
33. Pestrong, R. Multiband photos for a tidal marsh. Photogramm. Eng. 1969, 35, 453-470.
34. Reimold, R.J.; Linthurst, R.A. Use of remote sensing for mapping wetlands. J. Transp. Eng. 1975, 101, 189-198.
35. Scarpace, F.L.; Quirk, B.K.; Kiefer, R.W.; Wynn, S.L. Wetland mapping from digitized aerial photography. Photogramm. Eng. Remote Sens. 1982, 47, 829-838.
36. Cline, M.D.; Feagin, R.A.; Yeager, K.M.; Alstyne, J.M.V. Fault-induced wetland loss at Matagorda, Texas, USA: Land cover changes from 1943 to 2008. Geocarto Int. 2011, 26, 633-645.
37. Dale, P.E.R.; Hulsman, K.; Chandica, A.L. Classification of reflectance on colour infrared aerial photographs and sub-tropical salt-marsh vegetation types. Int. J. Remote Sens. 1986, 7, 1783-1788.
38. Lovvorn, J.R.; Kirkpatrick, C.M. Analysis of freshwater wetland vegetation with large-scale color infrared aerial photography. J. Wildl. Manag. 1982, 46, 61-70.
39. Ibrahim, S.; Hashim, I. Classification of mangrove forest by using 1:40 000-scale aerial photograph. For. Ecol. Manag. 1990, 33, 583-592.
40. Tiner, R.W. Use of high-altitude aerial photography for inventorying forested wetlands in the United States. For. Ecol. Manag. 1990, 33, 593-604.
41. Green, E.P.; Ellis, A.C. The assessment of mangrove areas using high resolution multispectral airborne imagery. J. Coast. Res. 1998, 14, 433-443.
42. Everitt, J.H.; Yang, C.; Davis, M.R.; Everitt, J.H.; Davis, M.R. Mapping wild taro with color-infrared aerial photography and image processing. J. Aquat. Plant. Manag. 2007, 45, 106-110.
43. Everitt, J.H.; Yang, C.; Summy, K.R.; Judd, F.W.; Davis, M.R. Evaluation of color-infrared photography and digital imagery to map black mangrove on the Texas Gulf Coast. J. Coast. Res. 2014, 23, 230-235.
44. Everitt, J.H.; Yang, C.; Judd, F.W.; Summy, K.R. Use of archive aerial photography for monitoring black mangrove populations. J. Coast. Res. 2015, 26, 649-653.
45. Tuxen, K.; Schile, L.; Stralberg, D.; Siegel, S.; Parker, T.; Vasey, M.; Callaway, J.; Kelly, M. Mapping changes in tidal wetland vegetation composition and pattern across a salinity gradient using high spatial resolution imagery. Wetl. Ecol. Manag. 2011, 19, 141-157.
46. Martin, R.; Brabyn, L.; Beard, C. Effects of class granularity and cofactors on the performance of unsupervised classification of wetlands using multi-spectral aerial photography. J. Spat. Sci. 2014, 59, 269-282.
47. Matthews, S.B. An assessment of bison habitat in the Mills/Mink Lakes area, northwest territories, using Landsat thematic mapper data. Arctic 1991, 44, 2893-2893.
48. Long, B.G.; Skewes, T.D. A technique for mapping mangroves with Landsat TM satellite data and Geographic Information System. Estuar. Coast. Shelf Sci. 1996, 43, 373-381.
49. Hess, L.L.; Novo, E.M.L.M.; Slaymaker, D.M.; Holt, J.; Steffen, C.; Valeriano, D.M.; Mertes, L.A.K.; Krug, T.; Melack, J.M.; Gastil, M. Geocoded digital videography for validation of land cover mapping in the Amazon Basin. Int. J. Remote Sens. 2002, 23, 1527-1555.
50. Rogers, D.J.; Hay, S.I.; Packer, M.J.; Wint, G.R.W. Mapping land-cover over large areas using multispectral data derived from the NOAA-AVHRR: A case study of Nigeria. Int. J. Remote Sens. 1997, 18, 3297-3303.
51. White, D. Defining ecosystem processes of the Australian Great Artesian Basin springs from multi-sensor synergies. In Proceedings of the 20th International Congress on Modelling and Simulation, Adelaide, Australia, 1-6 December 2013; pp. 1938-1944.
52. Rapinel, S.; Hubert-Moy, L.; Clément, B. Combined use of LiDAR data and multispectral earth observation imagery for wetland habitat mapping. Int. J. Appl. Earth Obs. Geoinf. 2015, 37, 56-64.
53. Meng, S.; Pang, Y.; Zhang, Z.; Jia, W.; Li, Z. Mapping aboveground biomass using texture indices from aerial photos in a temperate forest of Northeastern China. Remote Sens. 2016, 8. 230.
54. Pflugmacher, D.; Krankina, O.N.; Cohen, W.B. Satellite-based peatland mapping: Potential of the MODIS sensor. Glob. Planet. Chang. 2007, 56, 248-257.
55. Cai, Y.; Sun, G.; Liu, B. Mapping of water body in poyang lake from partial spectral unmixing of modis data. In Proceedings of the 2005 IEEE International Geoscience and Remote Sensing Symposium (IGARSS ’05), Seoul, Korea, 25-29 July 2005; pp. 4539-4540.
56. Mizuochi, H.; Hiyama, T.; Ohta, T.; Nasahara, K. Evaluation of the surface water distribution in North-Central Namibia based on MODIS and AMSR series. Remote Sens. 2014, 6, 7660-7682.
57. O’Grady, D.; Leblanc, M.; Bass, A. The use of Radar satellite data from multiple incidence angles improves surface water mapping. Remote Sens. Environ. 2014, 140, 652-664.
58. Ordoyne, C.; Friedl, M.A. Using MODIS data to characterize seasonal inundation patterns in the Florida Everglades. Remote Sens. Environ. 2008, 112, 4107-4119.
59. Temimi, M.; Leconte, R.; Brissette, F.; Chaouch, N. Flood and soil wetness monitoring over the Mackenzie River Basin using AMSR-E 37 GHz brightness temperature. J. Hydrol. 2007, 333, 317-328.
60. Dutta, D.; Teng, J.; Vaze, J.; Hughes, J.; Lerat, J.; Marvanek, S. Building Flood Inundation Modelling Capability in River System Models for Water Resources Planning and Accounting; International Association of Hydrological Sciences: Wallingford, UK, 2013; pp. 205-212.
61. Chen, Y.; Huang, C.; Ticehurst, C.; Merrin, L.; Thew, P. An evaluation of MODIS daily and 8-day composite products for floodplain and wetland inundation mapping. Wetlands 2013, 33, 823-835.
62. Huang, C.; Chen, Y.; Wu, J. Mapping spatio-temporal flood inundation dynamics at large river basin scale using time-series flow data and MODIS imagery. Int. J. Appl. Earth Obs. Geoinf. 2014, 26, 350-362.
63. Chakraborty, R.; Ferrazzoli, P.; Rahmoune, R. Application of passive microwave and optical signatures to monitor submerging of vegetation due to floods. Int. J. Remote Sens. 2014, 35, 6310-6328.
64. Kuenzer, C.; Klein, I.; Ullmann, T.; Georgiou, E.; Baumhauer, R.; Dech, S. Remote sensing of river delta inundation: Exploiting the potential of coarse spatial resolution, temporally-dense MODIS time series. Remote Sens. 2015, 7, 8516-8542.
65. Ogilvie, A.; Belaud, G.; Delenne, C.; Bailly, J.S.; Bader, J.C.; Oleksiak, A.; Ferry, L.; Martin, D. Decadal monitoring of the Niger Inner Delta flood dynamics using MODIS optical data. J. Hydrol. 2015, 523, 368-383.
66. Takeuchi, W.; Gonzalez, L. Blending MODIS and AMSR-E to predict daily land surface water coverage. In Proceedings of the International Remote Sensing Symposium (ISRS), Busan, Korea, 27-29 October 2009.
67. Lee, H.; Yuan, T.; Jung, H.C.; Beighley, E. Mapping wetland water depths over the central Congo Basin using PALSAR ScanSAR, Envisat altimetry, and MODIS VCF data. Remote Sens. Environ. 2014, 159, 70-79.
68. Zhou, Y.; Xiao, X.; Qin, Y.; Dong, J.; Zhang, G.; Kou, W.; Jin, C.; Wang, J.; Li, X. Mapping paddy rice planting area in rice-wetland coexistent areas through analysis of Landsat 8 OLI and MODIS images. Int. J. Appl. Earth Obs. Geoinf. 2016, 46, 1-12.
69. Boschetti, M.; Nelson, A.; Nutini, F.; Manfron, G.; Busetto, L.; Barbieri, M.; Laborte, A.; Raviz, J.; Holecz, F.; Mabalay, M. Rapid assessment of crop status: An application of MODIS and SAR data to rice areas in Leyte, Philippines affected by typhoon Haiyan. Remote Sens. 2015, 7, 672-673.
70. Dutta, D.; Das, P.K.; Paul, S.; Sharma, J.R.; Dadhwal, V.K. Assessment of ecological disturbance in the mangrove forest of Sundarbans caused by cyclones using MODIS time-series data (2001-2011). Nat. Hazards 2015, 79, 775-790.
71. Petus, C.; Lewis, M.; White, D. Monitoring temporal dynamics of Great Artesian Basin wetland vegetation, Australia, using MODIS NDVI. Ecol. Indic. 2013, 34, 41-52.
72. Chen, L.; Jin, Z.; Michishita, R.; Cai, J.; Yue, T.; Chen, B.; Xu, B. Dynamic monitoring of wetland cover changes using time-series remote sensing imagery. Ecol. Inform. 2014, 24, 17-26.
73. Zhang, S.; Lei, Y. Monitored landscape change of lake Baiyangdian wetland with dynamic reed area based on remote sensing. Proc. SPIE 2009, 7478, 74781Y.
74. Sivanpillai, R.; Latchininsky, A.V. Mapping locust habitats in the Amudarya River Delta, Uzbekistan with multi-temporal MODIS imagery. Environ. Manag. 2007, 39, 876-886.
75. Kaptué, A.T.; Hanan, N.P.; Prihodko, L. Characterization of the spatial and temporal variability of surface water in the Soudan-Sahel region of Africa. J. Geophys. Res. 2013, 118, 1-12.
76. Klein, I.; Dietz, A.; Gessner, U.; Dech, S.; Kuenzer, C. Results of the global waterpack: A novel product to assess inland water body dynamics on a daily basis. Remote Sens. Lett. 2015, 6, 78-87.
77. Prigent, C.; Papa, F.; Aires, F.; Jimenez, C. Changes in land surface water dynamics since the 1990s and relation to population pressure. Geophys. Res. Lett. 2012, 39, 85-93.
78. Carroll, M.L.; Townshend, J.R.; DiMiceli, C.M.; Noojipady, P.; Sohlberg, R.A. A new global raster water mask at 250 m resolution. Int. J. Digit. Earth 2009, 2, 291-308.
79. Zoffoli, M.L.; Kandus, P.; Madanes, N.; Calvo, D.H. Seasonal and interannual analysis of wetlands in South America using NOAA-AVHRR NDVI time series: The case of the Parana Delta region. Landsc. Ecol. 2008, 23, 833-848.
80. Takeuchi, W.; Tamura, M.; Yasuoka, Y. Estimation of methane emission from west Siberian wetland by scaling technique between NOAA AVHRR and SPOT HRV. Remote Sens. Environ. 2003, 85, 21-29.
81. Moreau, S.; Bosseno, R.; Xing, F.G.; Baret, F. Assessing the biomass dynamics of Andean bofedal and totora high-protein wetland grasses from NOAA/AVHRR. Remote Sens. Environ. 2003, 85, 516-529.
82. Ewiii, R.; Chappell, D.K.; Baldwin, D.G. AVHRR imagery used to identify hurricane damage in a forested wetland of Louisiana. Photogramm. Eng. Remote Sens. 1997, 63, 293-297.
83. Ewiii, R.; Chappell, D.K.; Jacobs, D.M.; Sapkota, S.K.; Dan, G.B. Resource management of forested wetlands: Hurricane impact and recovery mapped by combining Landsat TM and NOAA AVHRR data. Photogramm. Eng. Remote Sens. 1998, 6.4, 733-738.
84. Kalliola, R.; Ruokolainen, K. The dynamics, distribution and classification of swamp vegetation in Peruvian Amazonia. Annales. Botanici. Fennici. 1991, 28, 225-239.
85. Son, N.T.; Chen, C.F.; Chang, N.B.; Chen, C.R. Mangrove mapping and change detection in Ca Mau Peninsula, Vietnam, using Landsat data and object-based image analysis. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2015, 8, 503-510.
86. Mertes, L.A.K.; Smith, M.O.; Adams, J.B. Estimating suspended sediment concentrations in surface waters of the Amazon River wetlands from Landsat images. Remote Sens. Environ. 1993, 43, 281-301.
87. Johnston, R.M.; Barson, M.M. Remote sensing of Australian wetlands: An evaluation of Landsat TM data for inventory and classification. Mar. Freshw. Res. 1993, 44, 235-252.
88. Jensen, J.R.; Rutchey, K.; Koch, M.S.; Narumalani, S. Inland wetland change detection in the everglades water conservation area 2A using a time series of normalized remotely sensed data. Photogramm. Eng. Remote Sens. 1995, 61, 199-209.
89. Laba, M.; Smith, S.D.; Degloria, S.D. Landsat-based land cover mapping in the lower Yuna River watershed in the Dominican republic. Int. J. Remote Sens. 1997, 18, 3011-3025.
90. Florenzano, T.G. Landsat-TM and SPOT-HRV image data applied to geomorphologic mapping in a section of Taquari River, Brazil. Pesquisa Agropecuaria Brasileira 1998, 33, 1721-1727.
91. Gao, J. A comparative study on spatial and spectral resolutions of satellite data in mapping mangrove forests. Int. J. Remote Sens. 1999, 20, 2823-2833.
92. Kovacs, J.M.; Wang, J.; Blanco-Correa, M. Mapping disturbances in a mangrove forest using multi-date Landsat TM imagery. Environ. Manag. 2001, 27, 763-776.
93. Berlangarobles, C.A.; Ruizluna, A. Land use mapping and change detection in the coastal zone of Northwest Mexico using remote sensing techniques. J. Coast. Res. 2002, 18, 514-522.
94. Ibharim, N.A.; Mustapha, M.A.; Lihan, T.; Mazlan, A.G. Mapping mangrove changes in the Matang Mangrove Forest using multi temporal satellite imageries. Ocean Coast. Manag. 2013, 1571, 64-76.
95. Petropoulos, G.P.; Kalivas, D.P.; Griffiths, H.M.; Dimou, P.P. Remote sensing and GIS analysis for mapping spatio-temporal changes of erosion and deposition of two Mediterranean river deltas: The case of the Axios and Aliakmonas rivers, Greece. Int. J. Appl. Earth Obs. 2015, 35, 217-228.
96. Li, L.; Chen, Y.; Xu, T.; Liu, R.; Shi, K.; Huang, C. Super-resolution mapping of wetland inundation from remote sensing imagery based on integration of back-propagation neural network and genetic algorithm. Remote Sens. Environ. 2015, 164, 142-154.
97. Izquierdo, A.E.; Foguet, J.; Grau, H.R. Mapping and spatial characterization of Argentine High Andean peatbogs. Wetl. Ecol. Manag. 2015, 23, 963-976.
98. Dong, Z.; Wang, Z.; Liu, D.; Song, K.; Li, L.; Jia, M.; Ding, Z. Mapping wetland areas using Landsat-derived NDVI and LSWI: A case study of West Songnen plain, Northeast China. J. Indian Soc. Remote Sens. 2014, 42, 1-8.
99. Borro, M.; Morandeira, N.; Salvia, M.; Minotti, P.; Perna, P.; Kandus, P. Mapping shallow lakes in a large South American floodplain: A frequency approach on multitemporal Landsat TM/ETM data. J. Hydrol. 2005, 75, 5-23.
100. Jia, M.; Wang, Z.; Li, L.; Song, K.; Ren, C.; Liu, B.; Mao, D. Mapping China’s mangroves based on an object-oriented classification of Landsat imagery. Wetlands 2014, 34, 277-283.
101. Long, J.; Napton, D.; Giri, C.; Graesser, J. A mapping and monitoring assessment of the Philippines’ mangrove forests from 1990 to 2010. J. Coast. Res. 2013, 30, 260-271.
102. Wang, Y. An efficient method for mapping flood extent in a coastal floodplain using Landsat TM and DEM data. Int. J. Remote Sens. 2002, 23, 3681-3696.
103. Mueller, N.; Lewis, A.; Roberts, D.; Ring, S.; Melrose, R.; Sixsmith, J.; Lymburner, L.; Mcintyre, A.; Tan, P.; Curnow, S. Water observations from space: Mapping surface water from 25years of Landsat imagery across Australia. Remote Sens. Environ. 2016, 174, 341-352.
104. Thomas, R.F.; Kingsford, R.T.; Lu, Y.; Cox, S.J.; Sims, N.C.; Hunter, S.J. Mapping inundation in the heterogeneous floodplain wetlands of the Macquarie Marshes, using Landsat Thematic Mapper. J. Hydrol. 2015, 524, 194-213.
105. Profeti, G.; Macintosh, H. Flood management through Landsat TM and ERS SAR data: A case study. Hydrol. Processes 1997, 11, 1397-1408.
106. Todhunter, P.E.; Rundquist, B.C. Terminal lake flooding and wetland expansion in Nelson county, North Dakota. Phys. Geogr. 2004, 25, 68-85.
107. Overton, I.C. Modelling floodplain inundation on a regulated river: Integrating GIS, Remote Sensing and hydrological models. River Res. Appl. 2005, 21, 991-1001.
108. Frazier, P.; Page, K. The effect of river regulation on floodplain wetland inundation, Murrumbidgee River, Australia. Mar. Freshw. Res. 2006, 57, 133-141.
109. Murray, O.; Thoms, M.; Rayburg, S.; Rowan, J.S.; Duck, R.W.; Werritty, A. The diversity of inundated areas in semiarid flood plain ecosystems. IAHS-AISH Publ. 2006, 306, 277-286.
110. Awadallah, A.G.; Tabet, D. Estimating flooding extent at high return period for ungauged braided systems using remote sensing: A case study of Cuvelai Basin, Angola. Nat. Hazards 2015, 77, 255-272.
111. Carle, M.V.; Sasser, C.E.; Roberts, H.H. Accretion and vegetation community change in the Wax Lake Delta following the historic 2011 Mississippi River flood. J. Coast. Res. 2015, 313, 569-587.
112. More, G.; Serra, P.; Pons, X. Multitemporal flooding dynamics of rice fields by means of discriminant analysis of radiometrically corrected remote sensing imagery. Int. J. Remote Sens. 2011, 32, 1983-2011.
113. Polo, M.J.; Gonzálezdugo, M.P. Tidal flood monitoring in marsh estuary areas from Landsat TM data. Proc. SPIE Int. Soc. Opt. Eng. 2009, 7472, 170-175.
114. Todhunter, P.E.; Rundquist, B.C. Pervasive wetland flooding in the glacial drift prairie of North Dakota (USA). Nat. Hazards 2008, 46, 73-88.
115. Hugh-Jones, M.; Barre, N.; Nelson, G.; Wehnes, K.; Warner, J.; Garvin, J.; Garris, G. Landsat-TM identification of Amblyomma variegatum (Acari: Ixodidae) Habitats in Guadeloupe. Remote Sens. Environ. 1992, 40, 43-55.
116. Harvey, K.R.; Hill, G.J.E. Mapping the nesting habitats of saltwater crocodiles (Crocodylus porosus) in Melacca Swamp and the Adelaide River wetlands, Northern Territory: An approach using remote sensing and GIS. Wildl. Res. 2003, 30, 365-375.
117. Duffett, M.L.; Harvey, K.R.; Hill, G.J.E. Saltwater crocodile nesting habitat suitability mapping with Landsat TM and GIS, Northern Territory, Australia. In Proceedings of the IEEE 2000 International Geoscience and Remote Sensing Symposium (IGARSS 2000), Piscataway, NJ, USA, 24-28 July 2000; pp. 1918-1920.
118. Stevenson, J.C.; Kearney, M.S.; Koch, E.W. Impacts of sea level rise on tidal wetlands and shallow water habitats: A case study from Chesapeake Bay. Tetrahedron Lett. 2002, 9, 467-470.
119. Slattery, S.M.; Alisauskas, R.T. Distribution and habitat use of Ross’s and lesser snow geese during late brood rearing. J. Wildl. Manag. 2007, 71, 2230-2237.
120. Antwi, E.K.; Krawczynski, R.; Wiegleb, G. Detecting the effect of disturbance on habitat diversity and land cover change in a post-mining area using GIS. Landsc. Urban Plan. 2008, 87, 22-32.
121. He, C.; Ishikawa, T.; Sheng, L.; Irie, M. Study on the hydrological conditions for the conservation of the nesting habitat of the red-crowned Crane in Xianghai wetlands, China. Hydrol. Processes 2008, 23, 612-622.
122. Fleskes, J.P.; Gregory, C.J. Distribution and dynamics of waterbird habitat during spring in Southern Oregon-Northeastern California. West. N. Am. Nat. 2010, 70, 26-38.
123. Toral, G.M.; Aragonés, D.; Bustamante, J.; Figuerola, J. Using Landsat images to map habitat availability for waterbirds in rice fields. Ibis 2011, 153, 684-694.
124. Householder, J.E.; Janovec, J.P.; Tobler, M.W.; Page, S.; Lähteenoja, O. Peatlands of the Madre de Dios River of Peru: Distribution, geomorphology, and habitat diversity. Wetlands 2012, 32, 359-368.
125. Wang, H.; Gao, J.; Ren, L.L.; Kong, Y.; Li, H.; Li, L. Assessment of the Red-crowned Crane habitat in the Yellow River Delta Nature Reserve, East China. Reg. Environ. Chang. 2012, 13, 115-123.
126. Delgado, L.E.; Marín, V.H. Interannual changes in the habitat area of the black-necked swan, Cygnus melancoryphus, in the Carlos Anwandter Sanctuary, Southern Chile: A remote sensing approach. Wetlands 2012, 33, 91-99.
127. Jiang, F.; Qi, S.; Liao, F.; Ding, M.; Wang, Y. Vulnerability of Siberian crane habitat to water level in Poyang Lake wetland, China. Gisci. Remote Sens. 2014, 51, 662-676.
128. Pearlstine, L.G.; Smith, S.E.; Brandt, L.A.; Allen, C.R.; Kitchens, W.M.; Stenberg, J. Assessing state-wide biodiversity in the Florida Gap analysis project. J. Environ. Manag. 2002, 66, 127-144.
129. Tulbure, M.G.; Kininmonth, S.; Broich, M. Spatiotemporal dynamics of surface water networks across a global biodiversity hotspot—implications for conservation. Environ. Res. Lett. 2014, 9, 114012.
130. Wang, Y.; Moskovits, D.K. Tracking fragmentation of natural communities and changes in land cover: Applications of Landsat data for conservation in an urban landscape (Chicago Wilderness). Conserv. Biol. 2001, 15, 835-843.
131. Gross, M.F.; Hardisky, M.A.; Klemas, V.; Wolf, P.L. Quantification of biomass of the marsh grass Spartina alterniflora Loisel using Landsat Thematic Mapper imagery. Photogramm. Eng. Remote Sens. 1987, 53, 1577-1583.
132. Colpaert, A.; Kumpula, J.; Nieminen, M. Reindeer pasture biomass assessment using satellite remote sensing. Arctic 2003, 56, 147-158.
133. Li, X.; Yeh, A.G.O.; Wang, S.; Liu, K.; Liu, X.; Qian, J.; Chen, X. Regression and analytical models for estimating mangrove wetland biomass in South China using Radarsat images. Int. J. Remote Sens. 2007, 28, 5567-5582.
134. Cartus, O.; Kellndorfer, J.; Walker, W.; Franco, C.; Bishop, J.; Santos, L.; Fuentes, J. A national, detailed map of forest aboveground carbon stocks in Mexico. Remote Sens. 2014, 6, 5559-5588.
135. Draper, F.C.; Roucoux, K.H.; Lawson, I.T.; Mitchard, E.T.A.; Honorio Coronado, E.N.; Lähteenoja, O.; Torres Montenegro, L.; Valderrama Sandoval, E.; Zaráte, R.; Baker, T.R. The distribution and amount of carbon in the largest peatland complex in Amazonia. Environ. Res. Lett. 2014, 9, 1-12.
136. Zhang, X.; Huang, X.; Zhao, X.; Lu, R. Impacts of land use change on the vegetation carbon storage in the region around Taihu Lake, China. In Proceedings of the International Symposium on Digital Earth, Beijing, China, 9-12 September 2009.
137. Hamdan, O.; Khairunnisa, M.R.; Ammar, A.A.; Hasmadi, I.M.; Aziz, H.K. Mangrove carbon stock assessment by optical satellite imagery. J. Trop. For. Sci. 2013, 25, 554-565.
138. Jones, T.G.; Ratsimba, H.R.; Ravaoarinorotsihoarana, L.; Cripps, G.; Bey, A. Ecological variability and carbon stock estimates of mangrove ecosystems in Northwestern Madagascar. Forests 2014, 5, 177-205.
139. Yang, F.; Cao, G.; Hou, P. Dynamic detection on water quality and water areas of Nanishu Lakes with remote sensing. Proc. SPIE 2002, 4897, 227-236.
140. Lagos, N.A.; Paolini, P.; Jaramillo, E.; Lovengreen, C.; Duarte, C.; Contreras, H. Environmental processes, water quality degradation, and decline of waterbird populations in the Rio Cruces wetland, Chile. Wetlands 2008, 28, 938-950.
141. Chebud, Y.; Naja, G.M.; Rivero, R.G.; Melesse, A.M. Water quality monitoring using remote sensing and an artificial neural network. Water Air Soil Pollut. 2012, 223, 4875-4887.
142. Torbick, N.; Hession, S.; Hagen, S.; Wiangwang, N.; Becker, B.; Qi, J. Mapping inland lake water quality across the lower Peninsula of Michigan using Landsat TM imagery. Int. J. Remote Sens. 2013, 34, 7607-7624.
143. Choi, M.; Han, S. Remote sensing imageries for land cover and water quality dynamics on the west coast of Korea. Environ. Monit. Assess. 2013, 185, 9111-9124.
144. Kumar, V.; Sharma, A.; Chawla, A.; Bhardwaj, R.; Thukral, A.K. Water quality assessment of river Beas, India, using multivariate and remote sensing techniques. Environ. Monit. Assess. 2016, 188, 1-10.
145. Mccullough, I.M.; Loftin, C.S.; Sader, S.A. Combining lake and watershed characteristics with Landsat TM data for remote estimation of regional lake clarity. Remote Sens. Environ. 2012, 123, 109-115.
146. Lagomasino, D.; Price, R.M.; Whitman, D.; Campbell, P.K.E.; Melesse, A. Estimating major ion and nutrient concentrations in mangrove estuaries in Everglades National Park using leaf and satellite reflectance. Remote Sens. Environ. 2014, 154, 202-218.
147. Roulet, N.T.; Jano, A.; Kelly, C.A.; Klinger, L.F.; Moore, T.R.; Protz, R.; Ritter, J.A.; Rouse, W.R. Role of the Hudson Bay Lowland as a source of atmospheric methane. J. Geophys. Res. Atmos. 1994, 99, 1439-1454.
148. Schiller, C.L.; Hastie, D.R. Exchange of nitrous oxide within the Hudson Bay Lowland. J. Geophys. Res. Atmos. 1994, 99, 1573-1588.
149. Hines, M.E.; Pelletier, R.E.; Crill, P.M. Emissions of sulfur gases from marine and freshwater wetlands of the Florida Everglades—Rates and extrapolation using remote sensing. J. Geophys. Res. Atmos. 1993, 98, 8991-8999.
150. 2 fluxes from canopy to landscape using flux data, footprint modeling, and remote sensing. Glob. Biogeochem. Cycles 2000, 14, 725-744.
151. Wang, Y.; Zhang, S.; Li, Y. Land-cover change, greenhouse gas emission, and climate change: A case study in the Three-River-Plain region of the Northeast China. Proc. SPIE 2004, 5544, 85-95.
152. Bubier, J.; Moore, T.; Savage, K.; Crill, P. A comparison of methane flux in a boreal landscape between dry and a wet year. Glob. Biogeochem. Cycles 2005, 19, 83-100.
153. Poulter, B.; Christensen, N.L.; Halpin, P.N. Carbon emissions from a temperate peat fire and its relevance to interannual variability of trace atmospheric greenhouse gases. J. Geophys. Res. Atmos. 2006, 111, 907-923.
154. Wang, X.; Mannaerts, C.M.; Yang, S.; Gao, Y.; Zheng, D. Evaluation of soil nitrogen emissions from riparian zones coupling simple process-oriented models with remote sensing data. Sci. Total Environ. 2010, 408, 3310-3318.
155. Zhang, Y.; Wang, Y.Y.; Su, S.L.; Li, C.S. Quantifying methane emissions from rice paddies in Northeast China by integrating remote sensing mapping with a biogeochemical model. Biogeosci. Discuss. 2011, 8, 1225-1235.
156. Jørgensen, C.J.; Johansen, K.M.L.; Westergaard-Nielsen, A.; Bo, E. Net regional methane sink in high arctic soils of Northeast Greenland. Nat. Geosci. 2014, 8, 20-23.
157. Qin, Y.; Xiao, X.; Dong, J.; Zhou, Y.; Zhu, Z.; Zhang, G.; Du, G.; Jin, C.; Kou, W.; Wang, J. Mapping paddy rice planting area in cold temperate climate region through analysis of time series Landsat 8 (OLI), Landsat 7 (ETM+) and MODIS imagery. ISPRS J. Photogramm. Remote Sens. 2015, 105, 220-233.
158. Ausseil, A.G.E.; Dymond, J.R.; Shepherd, J.D. Rapid mapping and prioritisation of wetland sites in the Manawatu-Wanganui region, New Zealand. Environ. Manag. 2007, 39, 316-325.
159. Chiu, W.Y.; Couloigner, I. Modified fuzzy c-means classification technique for mapping vague wetlands using Landsat ETM+ imagery. Hydrol. Processes 2006, 20, 3623-3634.
160. Baker, C.; Lawrence, R.; Montagne, C.; Patten, D. Mapping wetlands and riparian areas using Landsat ETM+ imagery and decision-tree-based models. Wetlands 2006, 26, 465-474.
161. Poulin, M.F.; Careau, D.; Rochefort, L.; Desrochers, A. From satellite imagery to peatland vegetation diversity: How reliable are habitat maps? Ecol. Soc. 2002, 6, 1662-1663.
162. Boyd, D.S.; Sanchez-Hernandez, C.; Foody, G.M. Mapping a specific class for priority habitats monitoring from satellite sensor data. Int. J. Remote Sens. 2006, 27, 2631-2644.
163. Maclean, I.; Hassall, M.; Boar, R.R.; Lake, I.R. Effects of disturbance and habitat loss on papyrus-dwelling passerines. Biol. Conserv. 2006, 131, 349-358.
164. Guerrini, L.; Bord, J.P.; Ducheyne, E.; Bouyer, J. Fragmentation analysis for prediction of suitable habitat for vectors: Example of riverine tsetse flies in Burkina Faso. J. Med. Entomol. 2008, 45, 1180-1186.
165. Long, P.R.; Zefania, S.; Ffrench-Constant, R.H.; Székely, T. Estimating the population size of an endangered shorebird, the Madagascar plover, using a habitat suitability model. Anim. Conserv. 2008, 11, 118-127.
166. Hamilton, S.K.; Kellndorfer, J.; Lehner, B.; Tobler, M. Remote sensing of floodplain geomorphology as a surrogate for biodiversity in a tropical river system (Madre de Dios, Peru). Geomorphology 2007, 89, 23-38.
167. Tan, Q.; Shao, Y.; Yang, S.; Wei, Q. Wetland vegetation biomass estimation using Landsat-7 ETM+data. In Proceedings of the 2003 IEEE International Geoscience and Remote Sensing Symposium (IGARSS ’03), Toulouse, France, 21-25 July 2003; pp. 2629-2631.
168. Li, R.; Liu, J. Estimating wetland vegetation biomass in the Poyang lake of Central China from Landsat ETM data. In Proceedings of the 2004 IEEE International Geoscience and Remote Sensing Symposium (IGARSS ’04), Anchorage, AK, USA, 20-24 September 2004; pp. 4590-4593.
169. Fatoyinbo, T.E.; Marc, S.; Washington-Allen, R.A.; Shugart, H.H. Landscape-scale extent, height, biomass, and carbon estimation of Mozambique’s mangrove forests with Landsat ETM+ and Shuttle Radar Topography Mission elevation data. J. Geophys. Res. Biogeosci. 2008, 113, 1-13.
170. Peregon, A.; Maksyutov, S.; Kosykh, N.P.; Mironycheva-Tokareva, N.P. Map-based inventory of wetland biomass and Net Primary Production in Western Siberia. . J. Geophys. Res. Biogeosci. 2008, 113, 168-182.
171. Anderson, L.O.; Malhi, Y.; Ladle, R.J.; Aragão, L.E.O.C.; Shimabukuro, Y.; Phillips, O.L.; Baker, T.; Costa, A.C.L.; Espejo, J.S.; Higuchi, N. Influence of landscape heterogeneity on spatial patterns of wood productivity, wood specific density and above ground biomass in Amazonia. Biogeosciences 2009, 6, 1883-1902.
172. Fatoyinbo, T.; Simard, M. Height and biomass of mangroves in Africa from ICEsat/GLAS and ARTM. Int. J. Remote Sens. 2013, 34, 668-681.
173. Byrd, K.B.; O’Connell, J.L.; Tommaso, S.D.; Kelly, M. Evaluation of sensor types and environmental controls on mapping biomass of coastal marsh emergent vegetation. Remote Sens. Environ. 2014, 149, 166-180.
174. O’Connell, J.; Byrd, K.; Kelly, M. A hybrid model for mapping relative differences in belowground biomass and root:Shoot ratios using spectral reflectance, foliar N and plant biophysical data within coastal marsh. Remote Sens. 2015, 7, 16480-16503.
175. Crichton, K.A.; Anderson, K.; Bennie, J.J.; Milton, E.J. Characterizing peatland carbon balance estimates using freely available Landsat ETM+ data. Ecohydrology 2014, 8, 493-503.
176. Costantini, M.L.; Rossi, L.; Scialanca, F.; Nascetti, G.; Rossi, D.; Sabetta, L. Association of riparian features and water chemistry with reed litter breakdown in a volcanic lake (Lake Vico, Italy). Aquat. Sci. 2007, 69, 503-510.
177. Akumu, C.E.; Pathirana, S.; Baban, S.; Bucher, D. Modeling methane emission from wetlands in North-Eastern New South Wales, Australia using Landsat ETM+. Remote Sens. 2010, 2, 855-860.
178. Rapinel, S.; Bouzillé, J.B.; Oszwald, J.; Bonis, A. Use of bi-seasonal Landsat-8 imagery for mapping marshland plant community combinations at the regional scale. Wetlands 2015, 35, 1-12.
179. Giardino, C.; Bresciani, M.; Fava, F.; Matta, E.; Brando, V.; Colombo, R. Mapping submerged habitats and mangroves of Lampi Island Marine National Park (Myanmar) from in situ and satellite observations. Remote Sens. 2015, 8, 2.
180. Robinove, C.J. Interpretation of a Landsat image of an unusual flood phenomenon in Australia. Remote Sens. Environ. 1978, 7, 219-225.
181. Pekel, J.F.; Cottam, A.; Gorelick, N.; Belward, A.S. High-resolution mapping of global surface water and its long-term changes. Nature 2016, 540, 418.
182. Feng, M.; Sexton, J.O.; Channan, S.; Townshend, J.R. A global, high-resolution (30-m) inland water body dataset for 2000: First results of a topographic-spectral classification algorithm. Int. J. Digit. Earth 2015, 9, 1-21.
183. Hubert-Moy, L.; Michel, K.; Corpetti, T.; Clement, B. Object-oriented mapping and analysis of wetlands using spot 5 data. In Proceedings of the IEEE International Conference on Geoscience and Remote Sensing Symposium (IGARSS 2006), 31 July-4 August 2006; pp. 3447-3450.
184. Hassan, N.; Hamid, J.R.A.; Adnan, N.A.; Jaafar, M. Delineation of wetland areas from high resolution WorldView-2 data by object-based method. In Proceedings of the IOP Conference Series Earth and Environmental Science, Kuching Sarawak, Malaysia, 26-29 August 2013.
185. Heumann, B.W. An object-based classification of mangroves using a hybrid decision tree—support vector machine approach. Remote Sens. 2011, 3, 2440-2460.
186. Rokitnicki-Wojcik, D.; Wei, A. Transferability of object-based rule sets for mapping coastal high marsh habitat among different regions in Georgian Bay, Canada. Wetl. Ecol. Manag. 2011, 19, 223-236.
187. Lantz, N.J.; Wang, J. Object-based classification of WorldView-2 imagery for mapping invasive common reed. Can. J. Remote Sens. 2014, 39, 328-340.
188. Stavrakoudisa, D.G.; Theocharisa, J.B.; Zalidisb, C.G. A boosted genetic fuzzy classifier for land cover classification of remote sensing imagery. ISPRS J. Photogramm. Remote Sens. 2011, 66, 529-544.
189. Keramitsoglou, I.; Stratoulias, D.; Fitoka, E.; Kontoes, C.; Sifakis, N. A transferability study of the kernel-based reclassification algorithm for habitat delineation. Int. J. Appl. Earth Obs. Geoinf. 2014, 37, 38-47.
190. Kamal, M.; Phinn, S.; Johansen, K. Assessment of mangrove spatial structure using high-spatial resolution image data. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Melbourne, Australia, 21-26 July 2013; pp. 2609-2612.
191. Kamal, M.; Phinn, S.; Johansen, K. Characterizing the spatial structure of mangrove features for optimizing image-based mangrove mapping. Remote Sens. 2014, 6, 984-1006.
192. Xu, X.; Ji, W. Knowledge-based algorithm for satellite image classification of urban wetlands. In Proceedings of the International Conference of Computational Methods in Sciences and Engineering, Athens, Greece, 4-7 April 2014; pp. 285-288.
193. Skurikhin, A.N.; Wilson, C.J.; Liljedahl, A.; Rowland, J.C. Recursive active contours for hierarchical segmentation of wetlands in high-resolution satellite imagery of arctic landscapes. In Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI), San Diego, CA, USA, 6-8 April 2014; pp. 137-140.
194. Fagherazzi, S.; Bortoluzzi, A.; Dietrich, W.E.; Adami, A.; Lanzoni, S.; Marani, M.; Rinaldo, A. Tidal networks: 1. Automatic network extraction and preliminary scaling features from digital terrain maps. Water Resour. Res. 1999, 35, 3891-3904.
195. Midwood, J.D.; Chow-Fraser, P. Changes in aquatic vegetation and fish communities following 5 years of sustained low water levels in coastal marshes of Eastern Georgian Bay, Lake Huron. Glob. Chang. Biol. 2012, 18, 93-105.
196. Leempoel, K.; Satyaranayana, B.; Bourgeois, C.; Zhang, J.; Chen, M.; Wang, J.; Bogaert, J.; Dahdouhguebas, F. Dynamics in mangroves assessed by high-resolution and multi-temporal satellite data: A case study in Zhanjiang Mangrove National Nature Reserve (ZMNNR), P.R. China. Biogeosciences 2013, 10, 5681-5689.
197. Chen, P.; Liew, S.C.; Lim, R.; Kwoh, L.K. Coastal and marine habitat mapping for the straits of Malacca using SPOT and Landsat data. In Proceedings of the 2013 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2013), Melbourne, Australia, 21-26 July 2013; pp. 2431-2434.
198. Blasco, F.; Bellan, M.F.; Chaudhury, M.U. Estimating the extent of floods in Bangladesh using SPOT data. Remote Sens. Environ. 1992, 39, 167-178.
199. Zhu, M.S.; Sun, T.; Shao, D.D. Impact of land reclamation on the evolution of shoreline change and nearshore vegetation distribution in Yangtze River Estuary. Wetlands 2016, 36, 11-17.
200. Zhu, Z.; Zhang, H.; Liu, H. Land cover classification analysis about water and elevation in East Dongting Wetland. In Proceedings of the International Workshop on Earth Observation and Remote Sensing Applications, Changsha, China, 11-14 June 2014; pp. 475-478.
201. Ardli, E.R.; Wolff, M. Land use and land cover change affecting habitat distribution in the Segara Anakan Lagoon, Java, Indonesia. Reg. Environ. Chang. 2009, 9, 235-243.
202. Akumu, C.E.; Mclaughlin, J.W. Modeling peatland carbon stock in a delineated portion of the Nayshkootayaow River watershed in Far North, Ontario using an integrated GIS and remote sensing approach. Catena 2014, 121, 297-306.
203. Mccarthy, M.J.; Merton, E.J.; Muller-Karger, F.E. Improved coastal wetland mapping using very-high 2-meter spatial resolution imagery. Int. J. Appl. Earth Obs. Geoinf. 2015, 40, 11-18.
204. Satyanarayana, B.; Koedam, N.; Smet, K.D.; Nitto, D.D.; Bauwens, M.; Jayatissa, L.P.; Cannicci, S.; Dahdouh-Guebas, F. Long-term mangrove forest development in Sri Lanka: Early predictions evaluated against outcomes using VHR remote sensing and VHR ground-truth data. Mar. Ecol. Progress 2011, 443, 51-63.
205. Neukermans, G.; Dahdouh-Guebas, F.; Kairo, J.G.; Koedam, N. Mangrove species and stand mapping in Gazi Bay (Kenya) using Quickbird satellite imagery. Spat. Sci. 2008, 53, 75-86.
206. Wang, L.; Silváncárdenas, J.L.; Sousa, W.P. Neural network classification of mangrove species from multi-seasonal IKONOS imagery. Photogramm. Eng. Remote Sens. 2008, 74, 921-927.
207. Duke, N.C.; Meynecke, J.O.; Dittmann, S.; Ellison, A.M.; Anger, K.; Berger, U.; Cannicci, S.; Diele, K.; Ewel, K.C.; Field, C.D. A world without mangroves? Science 2007, 317, 41-42.
208. Gao, J. A hybrid method toward accurate mapping of mangroves in a marginal habitat from SPOT multispectral data. Int. J. Remote Sens. 1998, 19, 1887-1899.
209. Conchedda, G.; Durieux, L.; Mayaux, P. An object-based method for mapping and change analysis in mangrove ecosystems. ISPRS J. Photogramm. Remote Sens. 2008, 63, 578-589.
210. Huang, X.; Zhang, L.; Wang, L. Evaluation of morphological texture features for mangrove forest mapping and species discrimination using multispectral IKONOS imagery. IEEE Geosci. Remote Sens. Lett. 2009, 6, 393-397.
211. Tong, P.H.S.; Auda, Y.; Populus, J.; Aizpuru, M.; Habshi, A.A.; Blasco, F. Assessment from space of mangroves evolution in the Mekong Delta, in relation to extensive shrimp farming. Int. J. Remote Sens. 2004, 25, 4795-4812.
212. Vogt, J.; Kautz, M.; Herazo, M.L.F.; Triet, T.; Walther, D.; Saint-Paul, U.; Diele, K.; Berger, U. Do canopy disturbances drive forest plantations into more natural conditions?—A case study from Can Gio Biosphere Reserve, Viet Nam. Glob. Planet. Chang. 2013, 110, 249-258.
213. Kovacs, J.M.; Flores, F. Estimating leaf area index of a degraded mangrove forest using high spatial resolution satellite data. Aquat. Bot. 2004, 80, 13-22.
214. Wang, L.; Sousa, W.P.; Peng, G.; Biging, G.S. Comparison of IKONOS and Quickbird images for mapping mangrove species on the Caribbean coast of Panama. Remote Sens. Environ. 2004, 91, 432-440.
215. Li, S.S.; Tian, Q.J. Mangrove canopy species discrimination based on spectral features of Geoeye-1 imagery. Spectrosc. Spectr. Anal. 2013, 33, 136-141.
216. Blasco, F.; Aizpuru, M. Mangroves along the coastal stretch of the Bay of Bengal: Present status. Indian J. Mar. 2002, 31, 9-20.
217. Belluco, E.; Camuffo, M.; Ferrari, S.; Modenese, L.; Silvestri, S.; Marani, A.; Marani, M. Mapping salt-marsh vegetation by multispectral and hyperspectral remote sensing. Remote Sens. Environ. 2006, 105, 54-67.
218. Rosso, P.H.; Ustin, S.L.; Hastings, A. Mapping marshland vegetation of San Francisco Bay, California, using hyperspectral data. Int. J. Remote Sens. 2005, 26, 5169-5191.
219. Schmid, T.; Koch, M.; Gumuzzio, J.; Mather, P.M. A spectral library for a semi-arid wetland and its application to studies of wetland degradation using hyperspectral and multispectral data. Int. J. Remote Sens. 2004, 25, 2485-2496.
220. Karaman, M.; Budakoglu, M.; Avci, D.U.; Ozelkan, E.; Bulbul, A.; Civas, M.; Tasdelen, S. Determination of seasonal changes in wetlands using CHRIS/Proba Hyperspectral satellite images: A case study from Acigöl (Denizli), Turkey. J. Environ. Biol. 2015, 36, 32-33.
221. Kumar, L.; Sinha, P. Mapping salt-marsh land-cover vegetation using high-spatial and hyperspectral satellite data to assist wetland inventory. Gisci. Remote Sens. 2014, 51, 1-16.
222. Yang, C.H.; Everitt, J.H.; Fletcher, R.S.; Jensen, R.R.; Mausel, P.W. Evaluating AISA+ hyperspectral imagery for mapping black mangrove along the South Texas Gulf Coast. Photogramm. Eng. Remote Sens. 2009, 75, 425-435.
223. Zhang, C. Combining hyperspectral and lidar data for vegetation mapping in the Florida Everglades. Photogramm. Eng. Remote Sens. 2014, 80, 733-743.
224. Wang, L.; Sousa, W.P. Distinguishing mangrove species with laboratory measurements of hyperspectral leaf reflectance. Int. J. Remote Sens. 2009, 30, 1267-1281.
225. Kamal, M.; Phinn, S. Hyperspectral data for mangrove species mapping: A comparison of pixel-based and object-based approach. Remote Sens. 2011, 3, 2222-2242.
226. Kumar, T.; Panigrahy, S.; Kumar, P.; Parihar, J.S. Classification of floristic composition of mangrove forests using hyperspectral data: Case study of Bhitarkanika National Park, India. J. Coast. Conserv. 2013, 17, 121-132.
227. Prospere, K.; Mclaren, K.; Wilson, B. Plant species discrimination in a tropical wetland using in situ hyperspectral data. Remote Sens. 2014, 6, 8494-8523.
228. Artigas, F.J.; Yang, J.S. Hyperspectral remote sensing of marsh species and plant vigour gradient in the New Jersey Meadowlands. Int. J. Remote Sens. 2005, 26, 5209-5220.
229. Andrew, M.E.; Ustin, S.L. The role of environmental context in mapping invasive plants with hyperspectral image data. Remote Sens. Environ. 2008, 112, 4301-4317.
230. Siciliano, D.; Wasson, K.; Potts, D.C.; Olsen, R.C. Evaluating hyperspectral imaging of wetland vegetation as a tool for detecting estuarine nutrient enrichment. Remote Sens. Environ. 2008, 112, 4020-4033.
231. Zhang, C.; Kovacs, J.M.; Wachowiak, M.P.; Floresverdugo, F. Relationship between hyperspectral measurements and mangrove leaf nitrogen concentrations. Remote Sens. 2013, 5, 891-908.
232. Flores-De-Santiago, F.; Kovacs, J.M.; Flores-Verdugo, F. The influence of seasonality in estimating mangrove leaf chlorophyll—A content from hyperspectral data. Wetl. Ecol. Manag. 2013, 21, 193-207.
233. Kalacska, M.; Lalonde, M.; Moore, T.R. Estimation of foliar chlorophyll and nitrogen content in an ombrotrophic bog from hyperspectral data: Scaling from leaf to image. Remote Sens. Environ. 2015, 169, 270-279.
234. Guo, C.; Guo, X. Estimating leaf chlorophyll and nitrogen content of wetland emergent plants using hyperspectral data in the visible domain. Spectrosc. Lett. 2016, 49, 180-187.
235. Li, D.; Guo, Q.; Rahilly, P.J.A.; Phelps, G.M.; Harmon, T.C. Correlation between soil apparent electroconductivity and plant hyperspectral reflectance in a managed wetland. Int. J. Remote Sens. 2011, 32, 2563-2579.
236. Anne, N.J.P.; Abd-Elrahman, A.H.; Lewis, D.B.; Hewitt, N.A. Modeling soil parameters using hyperspectral image reflectance in subtropical coastal wetlands. Int. J. Appl. Earth Obs. Geoinf. 2014, 33, 47-56.
237. Song, C.; White, B.L.; Heumann, B.W. Hyperspectral remote sensing of salinity stress on red (Rhizophora mangle) and white (Laguncularia racemosa) mangroves on Galapagos Islands. Remote Sens. Lett. 2011, 2, 221-230.
238. Wong, F.K.K.; Fung, T. Combining hyperspectral and Radar imagery for mangrove leaf area index modeling. Photogramm. Eng. Remote Sens. 2013, 79, 479-490.
239. Ong, C.; Cudahy, T.; Caccetta, M.; Hick, P. Quantifying dust loading on mangroves using hyperspectral techniques. In Proceedings of the IEEE 2001 International Geoscience and Remote Sensing Symposium (IGARSS ’01), Sydney, Australia, 9-13 July 2001; pp. 296-298.
240. Cole, B.; Mcmorrow, J.; Evans, M. Empirical modelling of vegetation abundance from airborne hyperspectral data for upland peatland restoration monitoring. Remote Sens. 2014, 6, 716-739.
241. Morandeira, N.S.; Grings, F.; Facchinetti, C.; Kandus, P. Mapping plant functional types in floodplain wetlands: An analysis of C-band polarimetric SAR data from RADARSAT-2. Remote Sens. 2016, 8, 1-17.
242. Kasischke, E.S.; Bourgeau-Chavez, L.L. Monitoring South Florida wetlands using ERS-1 SAR imagery. Photogramm. Eng. Remote Sens. 1997, 63, 281-291.
243. Maillard, P.; Alencarsilva, T.; Clausi, D.A. An evaluation of RADARSAT-1 and ASTER data for mapping Veredas (Palm Swamps). Sensors 2008, 8, 6055-6076.
244. Marechal, C.; Pottier, E.; Hubertmoy, L.; Rapinel, S. One year wetland survey investigations from quad-pol RADARSAT-2 time-series SAR images. Can. J. Remote Sens. 2012, 38, 240-252.
245. Touzi, R.; Deschamps, A.; Rother, G. Wetland characterization using polarimetric RADARSAT-2 capability. Can. J. Remote Sens. 2007, 33, S56-S67.
246. Bartsch, A.; Wagner, W.; Scipal, K.; Pathe, C.; Sabel, D.; Wolski, P. Global monitoring of wetlands--the value of ENVISAT ASAR global mode. J. Environ. Manag. 2009, 90, 2226-2233.
247. Betbeder, J.; Rapinel, S.; Corpetti, T.; Pottier, E.; Corgne, S.; Moy, L.H. Multi-temporal classification of TerraSAR-X data for wetland vegetation mapping. SPIE Remote Sens. 2013, 8887, 88871B.
248. Ferreira-Ferreira, J.; Silva, T.S.F.; Streher, A.S.; Affonso, A.G.; Furtado, L.F.D.A.; Forsberg, B.R.; Valsecchi, J.; Queiroz, H.L. Combining ALOS/PALSAR derived vegetation structure and inundation patterns to characterize major vegetation types in the Mamirauá Sustainable Development Reserve, Central Amazon floodplain, Brazil. Wetl. Ecol. Manag. 2015, 23, 41-59.
249. Romshoo, S.A.; Shimada, M.; Igarashi, T. Peatland ecosystem characterization employing L-band SAR. In Proceedings of the 2002 IEEE International Geoscience and Remote Sensing Symposium (IGARSS ’02), Toronto, ON, Canada, 24-28 June 2002; pp. 1795-1797.
250. Kushwaha, S.P.S.; Dwivedi, R.S.; Rao, B.R.M. Evaluation of various digital image processing techniques for detection of coastal wetlands using ERS-1 SAR data. Int. J. Remote Sens. 2000, 21, 565-579.
251. Schumann, J.P.; Moller, D.K. Microwave remote sensing of flood inundation. Phys. Chem. Earth Parts A/B/C 2015, 83-84, 84-95.
252. Grings, F.M.; Ferrazzoli, P.; Jacobo-Berlles, J.C.; Karszenbaum, H.; Tiffenberg, J.; Pratolongo, P.; Kandus, P. Monitoring flood condition in marshes using EM models and Envisat ASAR observations. IEEE Trans. Geosci. Remote Sens. 2006, 44, 936-942.
253. Kiage, L.M.; Walker, N.D.; Balasubramanian, S.; Babin, A.; Barras, J. Applications of Radarsat-1 synthetic aperture radar imagery to assess hurricane-related flooding of coastal Louisiana. Int. J. Remote Sens. 2005, 26, 5359-5380.
254. Evans, T.L.; Costa, M.; Telmer, K.; Silva, T.S.F. Using ALOS/PALSAR and RADARSAT-2 to map land cover and seasonal inundation in the Brazilian Pantanal. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2010, 3, 560-575.
255. Horritt, M.S.; Mason, D.C.; Cobby, D.M.; Davenport, I.J.; Bates, P.D. Waterline mapping in flooded vegetation from airborne SAR imagery. Remote Sens. Environ. 2003, 85, 271-281.
256. Brisco, B.; Murnaghan, K.; Wdowinski, S.; Hong, S.H. Evaluation of RADARSAT -2 acquisition modes for wetland monitoring applications. Can. J. Remote Sens. 2015, 41, 1-30.
257. Zhao, L.; Yang, J.; Li, P.; Zhang, L. Seasonal inundation monitoring and vegetation pattern mapping of the Erguna Floodplain by means of a RADARSAT -2 fully polarimetric time series. Remote Sens. Environ. 2014, 152, 426-440.
258. Wang, Y. Seasonal change in the extent of inundation on floodplains detected by JERS-1 Synthetic Aperture Radar data. Int. J. Remote Sens. 2004, 25, 2497-2508.
259. Ramsey, E.; Lu, Z.; Suzuoki, Y.; Rangoonwala, A. Monitoring duration and extent of storm-surge and flooding in western coastal Louisiana marshes with Envisat ASAR data. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2011, 4, 387-399.
260. Alsdorf, D.E.; Smith, L.C.; Melack, J.M. Amazon floodplain water level changes measured with interferometric SIR-C Radar. IEEE Trans. Geosci. Remote Sens. 2001, 39, 423-431.
261. Lu, Z.; Kwoun, O.I. Radarsat-1 and ERS Insar analysis over southeastern coastal Louisiana: Implications for mapping water-level changes beneath swamp forests. IEEE Trans. Geosci. Remote Sens. 2008, 46, 2167-2184.
262. Hong, S.H.; Wdowinski, S.; Kim, S.W. Evaluation of TerraSAR-X observations for wetland Insar application. IEEE Trans. Geosci. Remote Sens. 2010, 48, 864-873.
263. O’Grady, D.; Leblanc, M. Radar mapping of broad-scale inundation: Challenges and opportunities in Australia. Stoch. Environ. Res. Risk Assess. 2014, 28, 29-38.
264. Hidayat, H.; Hoekman, D.H.; Vissers, M.A.M.; Hoitink, A.J.F. Flood occurence mapping of the middle Mahakam lowland area using satelite radar. Hydrol. Earth Syst. Sci. 2012, 16, 1805-1816.
265. Moreau, S.; Toan, T.L. Biomass quantification of Andean wetland forages using ERS satellite SAR data for optimizing livestock management. Remote Sens. Environ. 2003, 84, 477-492.
266. Hamdan, O.; Aziz, H.K.; Hasmadi, I.M. L-band ALOS PALSAR for biomass estimation of Matang mangroves, Malaysia. Remote Sens. Environ. 2014, 155, 69-78.
267. Darmawan, S.; Takeuchi, W.; Vetrita, Y.; Wikantika, K.; Sari, D.K. Impact of topography and tidal height on ALOS PALSAR polarimetric measurements to estimate aboveground biomass of mangrove forest in Indonesia. J. Sens. 2015, 2015, 641798.
268. Shen, G.; Liao, J.; Guo, H.; Liu, J. Poyang Lake wetland vegetation biomass inversion using polarimetric RADARSAT-2 synthetic aperture radar data. J. Appl. Remote Sens. 2015, 9, 451-455.
269. Millin-Chalabi, G.; Mcmorrow, J.; Agnew, C. Detecting a moorland wildfire scar in the Peak District, UK, using synthetic aperture radar from ERS-2 and Envisat ASAR. Int. J. Remote Sens. 2014, 35, 54-69.
270. Taft, O.W.; Haig, S.M.; Kiilsgaard, C. Use of radar remote sensing (RADARSAT) to map winter wetland habitat for shorebirds in an agricultural landscape. Environ. Manag. 2003, 33, 750-763.
271. Forsberg, B.R.; Hashimoto, Y.; Rosenqvist, Å.; Miranda, F.P.D. Tectonic fault control of wetland distributions in the Central Amazon revealed by JERS-1 radar imagery. Quat. Int. 2000, 72, 61-66.
272. Hladik, C.; Alber, M. Accuracy assessment and correction of a LIDAR-derived salt marsh digital elevation model. Remote Sens. Environ. 2012, 121, 224-235.
273. Albarnaz, J.D.; Toso, J.; Corrêa, A.A.; Simes, C.M.O.; Barardi, C.R.M. Assessment of airborne scanning laser altimetry (LIDAR) in a deltaic wetland environment. Can. J. Remote Sens. 2003, 29, 718-728.
274. Richardson, M.C.; Mitchell, C.P.J.; Branfireun, B.A.; Kolka, R.K. Analysis of airborne LiDAR surveys to quantify the characteristic morphologies of northern forested wetlands. J. Geophys. Res. Biogeosci. 2010, 115, 227-235.
275. Zhang, K. Identification of gaps in mangrove forests with airborne LiDAR. Remote Sens. Environ. 2008, 112, 2309-2325.
276. Boehm, H.D.V.; Liesenberg, V.; Limin, S.H. Multi-temporal airborne LiDAR-survey and field measurements of tropical peat swamp forest to monitor changes. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2013, 6, 1524-1530.
277. Zhang, K. Analysis of non-linear inundation from sea-level rise using LiDAR data: A case study for South Florida. Clim. Chang. 2011, 106, 537-565.
278. Moeslund, J.E.; Arge, L.; Bøcher, P.K.; Nygaard, B.; Svenning, J.C. Geographically comprehensive assessment of salt-meadow vegetation-elevation relations using LiDAR. Wetlands 2011, 31, 471-482.
279. Pavri, F.; Dailey, A.; Valentine, V. Integrating multispectral ASTER and LiDAR data to characterize coastal wetland landscapes in the Northeastern United States. Geocarto Int. 2012, 26, 647-661.
280. Huang, C.; Peng, Y.; Lang, M.; Yeo, I.Y.; Mccarty, G. Wetland inundation mapping and change monitoring using Landsat and airborne LiDAR data. Remote Sens. Environ. 2014, 141, 231-242.
281. Chadwick, J. Integrated LiDAR and IKONOS multispectral imagery for mapping mangrove distribution and physical properties. Int. J. Remote Sens. 2011, 32, 6765-6781.
282. Chust, G.; Galparsoro, I.; Borja, Á.; Franco, J.; Uriarte, A. Coastal and estuarine habitat mapping, using LiDAR height and intensity and multi-spectral imagery. Estuar. Coast. Shelf Sci. 2008, 78, 633-643.
283. Allen, T.R.; Wang, Y.; Gore, B. Coastal wetland mapping combining multi-date SAR and LiDAR. Geocarto Int. 2013, 28, 616-631.