Environmental Monitoring and Assessment

, Volume 140, Issue 1–3, pp 131–145 | Cite as

Remote sensing of aquatic vegetation: theory and applications

  • Thiago S. F. SilvaEmail author
  • Maycira P. F. Costa
  • John M. Melack
  • Evlyn M. L. M. Novo


Aquatic vegetation is an important component of wetland and coastal ecosystems, playing a key role in the ecological functions of these environments. Surveys of macrophyte communities are commonly hindered by logistic problems, and remote sensing represents a powerful alternative, allowing comprehensive assessment and monitoring. Also, many vegetation characteristics can be estimated from reflectance measurements, such as species composition, vegetation structure, biomass, and plant physiological parameters. However, proper use of these methods requires an understanding of the physical processes behind the interaction between electromagnetic radiation and vegetation, and remote sensing of aquatic plants have some particular difficulties that have to be properly addressed in order to obtain successful results. The present paper reviews the theoretical background and possible applications of remote sensing techniques to the study of aquatic vegetation.


Remote sensing Macrophytes Aquatic vegetation 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Ackleson, S. G., & Klemas, V. (1987). Remote sensing of submerged aquatic vegetation in lower Chesapeake bay: A comparison of Landsat MSS to TM imagery. Remote Sensing of Environment, 22, 235–248.CrossRefGoogle Scholar
  2. Alberotanza, L., Brando, V. E., Ravagnan, G., & Zandonella, A. (1999). Hyperspectral aerial images. A valuable tool for submerged vegetation recognition in the Ortobello lagoons, Italy. International Journal of Remote Sensing, 20(3), 235–248.CrossRefGoogle Scholar
  3. Anstee, J., Dekker, A., Brando, N., Pinnel, N., Byrne, G., Danieal, P., et al. (2001). Hyperspectral imaging for benthic species recognition in shallow coastal waters. In Proceedings of the International Geoscience and Remote Sensing Symposium ’01 (Vol. 6. pp. 2513–1515).Google Scholar
  4. Armstrong, R. A. (1993). Remote sensing of submerged vegetation canopies for biomass estimation. International Journal of Remote Sensing, 14(3), 621–627.CrossRefGoogle Scholar
  5. Austin, A., & Adams, R. (1978). Aerial color and color infrared survey of marine plant resources. Photogrammetric Engineering and Remote Sensing, 44(4), 469–480.Google Scholar
  6. Bajjouk, T., Guillaumont, B., & Populus, J. (1996). Application of airborne imaging spectrometry system data to intertidal seaweed classification and mapping. Hydrobiologia, 326/327, 463–471.CrossRefGoogle Scholar
  7. Baker, C., Lawrence, R., Montagne, C., & Patten, D. (2006). Mapping wetlands and riparian areas using Landsat ETM+ imagery and decision-tree-based models. Wetlands, 26(2), 465–474.CrossRefGoogle Scholar
  8. Benton, A. R., & Newman, R. M. (1976). Color aerial photography for aquatic plant monitoring. Journal of Aquatic Plant Management, 14, 14–16.Google Scholar
  9. Berk, A., Anderson, G., Bernstein, L., Acharya, P., Dothe, H., Matthew, M., et al. (1999). MODTRAN4 radiative transfer modeling for atmospheric correction. In Proceedings of SPIE – The International Society for Optical Engineering (Vol. 3756, pp. 348–353).Google Scholar
  10. Best, R. G., Wehde, M. E., & Linder, R. L. (1981). Spectral reflectance of hydrophytes. Remote Sensing of Environment, 11, 27–35.CrossRefGoogle Scholar
  11. Brennan, R., & Webster, T. L. (2006). Object-oriented land cover classification of lidar-derived surfaces. Canadian Journal of Remote Sensing, 32(2), 162–172.Google Scholar
  12. Chavez Jr., P. S. (1988). An improved dark-object subtraction technique for atmospheric scattering correction of multi-spectral data. Remote Sensing of Environment, 24, 459–479.CrossRefGoogle Scholar
  13. Chavez Jr., P. S. (1996). Image-based atmospheric corrections – revisited and improved. Photogrammetric Engineering and Remote Sensing, 62(9), 1025–1036.Google Scholar
  14. Chopra, R., Verma, V. K., & Sharma, P. K. (2001). Mapping, monitoring and conservation of Haruke wetland ecosystem, Punjab, India, through remote sensing. International Journal of Remote Sensing, 22(1), 89–98.CrossRefGoogle Scholar
  15. Costa, M. (2005). Estimate of net primary productivity of aquatic vegetation of the Amazon floodplain using Radarsat and JERS-1. International Journal of Remote Sensing, 26(20), 4527–4536.CrossRefGoogle Scholar
  16. Costa, M. P. F. (2004). Use of SAR satellites for mapping zonation of vegetation communities in the Amazon floodplain. International Journal of Remote Sensing, 25(10), 1817–1835.CrossRefGoogle Scholar
  17. Costa, M. P. F., Niemann, O., Novo, E., & Ahern, F. (2002). Biophysical properties and mapping of aquatic vegetation during the hydrological cycle of the Amazon floodplain using JERS-1 and Radarsat. International Journal of Remote Sensing, 23(7), 1401–1426.CrossRefGoogle Scholar
  18. Dierssen, H. M., & Zimmerman, R. (2003). Ocean color remote sensing of seagrass and bathymetry in the Bahamas Banks by high-resolution airborne imagery. Limnology and Oceanography, 48(1), 444–455.CrossRefGoogle Scholar
  19. Dutra, L. V., Treuhaft, R., Mura, J. C., Santos, J. R. D., & Freitas, C. D. C. (2007). Estimating 3-dimensional structure of tropical forests from radar multi-baseline interferometry: The Tapajós FLONA case. In Anais do XIII Simpósio Brasileiro De Sensoriamento Remoto. Florianópolis, Brasil (pp. 1657–1662).Google Scholar
  20. Edwards, R. W., & Brown, M. W. (1960). An aerial photographic method for studying the distribution of aquatic macrophytes in shallow waters. Journal of Ecology, 48, 161–163.CrossRefGoogle Scholar
  21. Everitt, J. H., Yang, C., Escobar, D. E., Webster, C. F., Lonard, R. I., & Davis, M. R. (1999). Using remote sensing and spatial information technologies to detect and map two aquatic macrophytes. Journal of Aquatic Plant Management, 37, 71–80.Google Scholar
  22. Filippi, A. M., & Jensen, J. R. (2006). Fuzzy learning vector quantization for hyperspectral coastal vegetation classification. Remote Sensing of Environment, 100(4), 512–530.CrossRefGoogle Scholar
  23. Ford, J., & Casey, D. (1988). Shuttle radar mapping with diverse incidence angles in the rainforest of Borneo. International Journal of Remote Sensing, 9(5), 927–943.CrossRefGoogle Scholar
  24. Fyfe, S. K. (2003). Spatial and temporal variation in spectral reflectance: Are seagrasses spectrally distinct?. Limnology and Oceanography, 48(1), 464–479.CrossRefGoogle Scholar
  25. Graciani, S. D., & Novo, E. M. L. M. (2003). Determinação da cobertura de macrófitas aquáticas em reservatórios tropicais. In Anais do XI Simpósio Brasileiro de Sensoriamento Remoto. (pp. 2509–2516).Google Scholar
  26. Haack, B., & Bechdo, M. (2000). Integrating multisensor data and RADAR texture measures for land cover mapping. Computers & Geosciences, 26, 411–421.CrossRefGoogle Scholar
  27. Han, L., & Rundquist, D. (2003). The spectral responses of Ceratophyllum demersum at varying depths in an experimental tank. International Journal of Remote Sensing, 24(4), 859–864.CrossRefGoogle Scholar
  28. Heege, T., Bogner, A., & Pinnel, N. (2003). Mapping of submerged aquatic vegetation with a physically based process chain. In SPIE Proceedings on Remote Sensing (Vol. 5233). CD-ROM.Google Scholar
  29. Hess, L., Melack, J., Filoso, S., & Wang, Y. (1995). Delineation of inundated area and vegetation along the Amazon floodplain with the SIR-C synthetic aperture radar. IEEE Transactions on Geoscience and Remote Sensing, 33(4), 896–904.CrossRefGoogle Scholar
  30. Hess, L., Melack, J., Novo, E. M. L. M., Barbosa, C. C. F., & Gastil, M. (2003). Dual-season mapping of wetland inundation and vegetation for the Central Amazon Basin. Remote Sensing of Environment, 87, 404–428.CrossRefGoogle Scholar
  31. Hess, L., Melack, J., & Simonett, D. S. (1990). Radar detection of flooding beneath the forest canopy: A review. International Journal of Remote Sensing, 11(7), 1313–1325.CrossRefGoogle Scholar
  32. Hess, L. L., Novo, E. M. L. M., Slaymaker, D. M., Holt, J., Steffen, C., Valeriano, D. M., et al. (2002). Geocoded digital videography for validation of land cover mapping in the Amazon basin. International Journal of Remote Sensing, 23(7), 1527–1555.CrossRefGoogle Scholar
  33. Hopkinson, C., Chasmer, L., Lim, K., Treitz, P., & Creed, I. (2006). Towards a universal lidar canopy height indicator. Canadian Journal of Remote Sensing, 32(2), 139–152.Google Scholar
  34. Hopkinson, C., Chasmer, L. E., Sass, G., Creed, I., Sitar, M., Kalbfleisch, W., & Treitz, P. (2005). Vegetation class dependent errors in lidar ground elevation and canopy height estimates in a boreal wetland environment. Canadian Journal of Remote Sensing, 31(2), 191–206.Google Scholar
  35. Jakubauskas, M., Kindscher, K., Fraser, A., Debinski, D., & Price, K. P. (2000). Close-range remote sensing of aquatic macrophyte vegetation cover. International Journal of Remote Sensing, 21(8), 3533–3538.CrossRefGoogle Scholar
  36. Jensen, J. R., H. M. E., & Christensen, E. (1986). Remote sensing inland wetlands: A multispectral approach. Photogrammetric Engineering and Remote Sensing, 52(1), 87–100.Google Scholar
  37. Jensen, J. R., Narumalani, S., Weatherbee, O., & Mackey, J. H. E. (1993). Measurement of seasonal and yearly cattail and waterlily changes using multidate SPOT panchromatic data. Photogrammetric Engineering and Remote Sensing, 59(4), 519–525.Google Scholar
  38. Jensen, J. R., Rutchey, K., Koch, M., & Narumalani, S. (1995). Inland wetland change detection in the Everglades water conservation area 2A using a time series of normalized remotely sensed data. Photogrammetric Engineering and Remote Sensing, 61(2), 199–209.Google Scholar
  39. Junk, W. (Ed.) (1997). The Central Amazon Floodplain: Ecology of a Pulsing System, Vol. 126 of Ecological Studies. Springer.Google Scholar
  40. Kasischke, E. S., & Borgeau-Chavez, L. L. (1997). Monitoring south Florida wetlands using ERS-1 SAR imagery. Photogrammetric Engineering and Remote Sensing, 63(3), 281–291.Google Scholar
  41. Kasischke, E. S., Smith, K. B., Borgeau-Chavez, L. L., Romanowicz, E. A., Brunzell, S., & Richardson, C. J. (2003). Effects of seasonal hydrologic patterns in south Florida wetlands on radar backscatter measured from ERS-2 SAR imagery. Remote Sensing of Environment, 88, 423–441.CrossRefGoogle Scholar
  42. Kirk, J. T. O. (1994) Light and Photosynthesis in Aquatic Ecosystems, 2nd edn. Cambridge University Press.Google Scholar
  43. Komatsu, T., Igarashi, C., Tatsukawa, K., Sultana, S., Matsuoka, Y., & Harada, S. (2003). Use of multi-beam sonar to map seagrass beds in Otsuchi Bay on the Sanriku coast of Japan. Aquatic Living Resources, 16, 23–230.CrossRefGoogle Scholar
  44. Kotchenova, S. Y., Song, X., Shabanov, N. V., Potter, C. S., Knyazikhin, Y., & Myeni, R. B. (2004). Lidar remote sensing for modeling gross primary production of deciduous forests. Remote Sensing of Environment, 92, 158–172.CrossRefGoogle Scholar
  45. LaCapra, V. C., Melack, J. M., Gastil, M., & Valeriano, D. (1996). Remote sensing of foliar chemistry of inundated rice with imaging spectrometry. Remote Sensing of Environment, 55(1), 50–58.CrossRefGoogle Scholar
  46. Le Toan, T., Ribbes, F., Wang, L., Floury, N., Ding, K., King, J. A., et al. (1997). Rice crop mapping and monitoring using ERS-1 data based on experiment and modeling results. IEEE Transactions on Geoscience and Remote Sensing, 35(1), 41–56.CrossRefGoogle Scholar
  47. Lewis, A., & Henderson, F. M. (1998). Manual of Remote Sensing, Vol. 2, Chapt. Radar fundamentals: The geoscience perspective (3rd edn., pp. 131–187). New York: Wiley.Google Scholar
  48. Lu, Z., Kwoun, O., & Rykhus, R. (2007). Interferometric synthetic aperture radar (InSAR): Its past, present and future. Photogrammetric Engineering and Remote Sensing, 73(3), 217–221.Google Scholar
  49. Lyzenga, D. R. (1978). Passive remote sensing techniques for mapping water depth and bottom features. Applied Optics, 17, 379–383.CrossRefGoogle Scholar
  50. Maheu-Giroux, M., & de Blois, S. (2005). Mapping the invasive species Phragmites australis in linear wetland corridors. Aquatic Botany, 83, 310–320.CrossRefGoogle Scholar
  51. Maltamo, M., Eerikainen, K., Pitkainen, J., Hyppa, J., & Vemas, M. (2004). Estimation of timber volume and stem density based on scanner laser altimetry and expected size distribution functions. Remote Sensing of Environment, 90, 319–330.CrossRefGoogle Scholar
  52. Malthus, T. J., & George, D. G. (1997). Airborne remote sensing of macrophytes in Cefni reservoir, Anglesley, UK. Aquatic Botany, 58, 317–332.CrossRefGoogle Scholar
  53. Marion, L., & Paillison, J. M. (2003). A mass balance assessment of the contribution of floating-leaved macrophytes in nutrient stocks in an eutrophic macrophyte-dominated lake. Aquatic Botany, 75, 249–260.CrossRefGoogle Scholar
  54. Marshall, T. R., & Lee, P. F. (1994). Mapping aquatic macrophytes through digital image analysis of aerial photographs: An assessment. Journal of Aquatic Plant Management, 32, 61–66.Google Scholar
  55. Moore, K., Wilcox, D., Anderson, B., & Orth, R. (2003). Analysis of historical distribution of SAV in the Easter Shore coastal basins and Mid-Bay island complexes as evidence of historical water quality conditions and a restored bay ecosystem. Special Report in Applied Marine Science and Ocean Engineering 383, Virginia Institute of Marine Science, Annapolis, Maryland.Google Scholar
  56. Moreau, S., & Le Toan, T. (2003). Biomass quantification of Andean wetland forages using ERS satellite SAR data for optmizing livestock management. Remote Sensing of Environment, 84, 477–492.CrossRefGoogle Scholar
  57. Noernberg, M. A., Novo, E., & Krug, T. (1999). The use of biophysical indices and coefficient of variation derived from airborne synthetic aperture radar for monitoring the spread of aquatic vegetation in tropical reservoirs. International Journal of Remote Sensing, 20, 67–82.CrossRefGoogle Scholar
  58. Novo, E. M. L. M., Costa, M. P. F., Mantovani, J. E., & Lima, I. B. T. (2002). Relationship between macrophyte stand variables and radar backscatter at L and C band, Tucurui reservoir, Brazil. International Journal of Remote Sensing, 23, 1241–1260.CrossRefGoogle Scholar
  59. Onaindia, M., Bikuña, B. G., & Benito, I. (1996). Aquatic plants in relation to environmental factors in Northern Spain. Journal of Environmental Management, 47, 123–137.CrossRefGoogle Scholar
  60. Ozesmi, S. L., & Bauer, M. E. (2002). Satellite remote sensing of wetlands. Wetlands Ecology and Management, 10, 381–402.CrossRefGoogle Scholar
  61. Pal, S. R., & Mohanty, P. K. (2002). Use of IRS-1b data for change detection in water quality and vegetation of Chilka lagoon, east coast of India. International Journal of Remote Sensing, 23, 1027–1042.CrossRefGoogle Scholar
  62. Paringit, E. C., Nadaoka, K., Fortes, M. D., Harii, S., Tamura, H., Mistui, J., et al. (2003). Multiangular and hyperspectral reflectance modeling of seagrass beds for remote sensing studies. In Proceedings of the International Geoscience and Remote Sensing Symposium ’03 (Vol. 3. pp. 21–25).Google Scholar
  63. Pasqualini, V., Pergent-Martini, C., Pergent, G., Agreil, M., Skoufas, G., Sourbes, L., et al. (2005). Use of SPOT 5 for mapping seagrasses: An application to Posidonia oceanica. Remote Sensing of Environment, 94, 39–45.CrossRefGoogle Scholar
  64. Patenaude, G., Hill, R. A., Milne, R., Gaveau, D. L. A., Briggs, B. B. J., & Dawson, T. (2004). Quantifying forest above ground content using LiDAR remote sensing. Remote Sensing of Environment, 93, 368–380.CrossRefGoogle Scholar
  65. Peñuelas, J., Filella, I., Gamon, J. A., & Field, C. (1997). Assessing photosynthetic radiation-use efficiency of emergent aquatic vegetation from spectral reflectance. Aquatic Botany, 58, 307–315.CrossRefGoogle Scholar
  66. Peñuelas, J., Gamon, J. A., Griffin, K. L., & Field, C. B. (1993). Assessing community type, plant biomass, pigment composition and photosynthetic efficiency of aquatic vegetation from spectral reflectance. Remote Sensing of Environment, 46, 110–118.CrossRefGoogle Scholar
  67. Pinnel, N., Heege, T., & Zimmermman, S. (2004). Spectral discrimination of submerged macrophytes in lakes using hyperspectral remote sensing data. In SPIE Proceedings on Ocean Optics XVII (Vol. 1. pp. 1–16).Google Scholar
  68. Pope, K. O., Rejmankova, E., Paris, J. F., & Woodruff, R. (1997). Detecting seasonal flooding cycles in marshes of the Yucatán peninsula with SIR-C polarimetric radar imagery. Remote Sensing of Environment, 59, 157–166.CrossRefGoogle Scholar
  69. Popescu, S. C., Wynne, R. H., & Nelson, R. F. (2002). Estimating plot-level tree heights with LiDAR: Local filtering with a canopy-height based variable window size. Computers and Electronics in Agriculture, 37, 71–95.CrossRefGoogle Scholar
  70. Proisy, C., Mougin, E., Fromard, F., & Karam, M. A. (2000). Interpretation of polarimetric radar signatures of mangrove forests. Remote Sensing of Environment, 71, 56–66.CrossRefGoogle Scholar
  71. Rosenthal, W., Blanchard, B., & Blanchard, A. J. (1985). Visible/infrared/microwave agriculture classification, biomass and plant height algorithm. IEEE Transactions on Geoscience and Remote Sensing, 23, 84–89.CrossRefGoogle Scholar
  72. Rosso, P. H., Ustin, S. L., & Hastings, A. (2006). Use of lidar to study changes associated with Spartina invasion in San Francisco Bay marshes. Remote Sensing of Environment, 100, 295–306.CrossRefGoogle Scholar
  73. Santos, J. R. D., Neeff, T., Dutra, L. V., Araujo, L. S., Gama, F. F., & Elmiro, M. A. T. (2004). Tropical forest biomass mapping from dual frequency SAR interferometry (X And P-bands). In ISPRS – International Society For Photogrammetry And Remote Sensing – Technical Commission VII (Vol. 35. pp. 1682–1777).Google Scholar
  74. Sawaya, K., Olmanson, L. G., Heinert, N. J., Brezonik, P. L., & Bauer, M. (2003). Extending satellite remote sensing to local scales: Land and water resource monitoring using high-resolution imagery. Remote Sensing of Environment, 88, 144–156.CrossRefGoogle Scholar
  75. Schulz, M., Rinke, K., & Köller, J. (2003). A combined approach of photogrammetrical methods and field studies to determine nutrient retention by submersed macrophytes in running waters. Aquatic Botany, 76, 17–29.CrossRefGoogle Scholar
  76. Silva, T. S. F. (2004). Imagens EOS-MODIS e Landsat 5 TM no estudo da dinâmica das comunidades de macrófitas na várzea amazônica. Master’s thesis, Instituto Nacional de Pesquisas Espaciais, São José dos Campos, São Paulo, Brazil.Google Scholar
  77. Simard, M., Grandi, G. D., Saatchi, S., & Mayaux, P. (2002). Mapping tropical coastal vegetation using JERS-1 and ERS-1 radar data with a decision tree classifier. International Journal of Remote Sensing, 23(7), 1461–1474.CrossRefGoogle Scholar
  78. Simard, M., Saatchi, S. S., & De Grandi, G. (2000). The use of decision tree and multiscale texture for classification of JERS-1 SAR data over tropical forest. IEEE Transactions on Geoscience and Remote Sensing, 38(5), 2310–2321.CrossRefGoogle Scholar
  79. Simard, M., Zhang, K., Rivera-Monroy, V. H., Ross, M. S., Ruiz, P. L., Castaneda-Moya, E., et al. (2006). Mapping height and biomass of mangrove forests in Everglades National Park with SRTM elevation data. Photogrammetric Engineering and Remote Sensing, 72(3), 299–311.Google Scholar
  80. Song, C., Woodcock, C. E., Seto, K. C., Lenney, M. P., & Macomber, S. A. (2001). Classification and change detection using Landsat TM data: When and how to correct atmospheric effects?. Remote Sensing of Environment, 75, 230–244.CrossRefGoogle Scholar
  81. Sprenkle, E. S., Smock, L. A., & Anderson, J. E. (2004). Distribution and growth of submerged aquatic vegetation in the piedmont section of the James river, Virginia. Southeastern Naturalist, 3(3), 517–530.CrossRefGoogle Scholar
  82. Thomson, A., Fuller, R., Sparks, T., Yates, M., & Eastwood, J. (1998). Ground and airborne radiometry over intertidal surfaces: Waveband selection for cover classification. International Journal of Remote Sensing, 19(6), 1189–1205.CrossRefGoogle Scholar
  83. Thomson, A., Fuller, R., Yates, M., Brown, S., Cox, R., & Wadsworth, R. (2003). The use of airborne remote sensing for extensive mapping of intertidal sediments and saltmarshes in eastern England. International Journal of Remote Sensing, 24(13), 2717–2737.CrossRefGoogle Scholar
  84. Tilley, D. R., Ahmed, M., Son, J. H., & Badrinayanan, H. (2003). Hyperspectral reflectance of emergent macrophytes as an indicator of water column ammonia in an oligohaline, subtropical marsh. Ecological Engineering, 21, 153–163.CrossRefGoogle Scholar
  85. Ulaby, F., Moore, R. K., & Fung, A. K. (1982). Microwave Remote Sensing: Radar remote sensing and surface scattering and emission theory (Vol. II). Norwood, MA: Artech House.Google Scholar
  86. Ulaby, F., Moore, R. K., & Fung, A. K. (1986). Microwave Remote Sensing: From theory to applications. Artech House.Google Scholar
  87. Valta-Hullkonen, K., Pellika, P., Tanskanen, H., Ustinov, A., & Sandman, O. (2003). Digital false colour aerial photographs for discrimination of aquatic macrophyte species. Aquatic Botany, 75, 71–88.CrossRefGoogle Scholar
  88. Vermote, E. F., Tanre, D., Deuze, J. L., Herman, M., & Morcrette, J.-J. (1997). Second simulation of the satellite signal in the solar spectrum, 6S - An overview. IEEE Transactions on Geoscience and Remote Sensing, 35(3), 675–686.CrossRefGoogle Scholar
  89. Vis, C., Hudon, C., & Carignan, R. (2003). An evaluation of approaches used to determine the distribution and biomass of emergent and submerged aquatic macrophytes over large spatial scales. Aquatic Botany, 77, 187–201.CrossRefGoogle Scholar
  90. Wang, C.-K., & Philpot, W. D. (2007). Using airborne bathymetric lidar to detect bottom type variation in shallow waters. Remote Sensing of Environment, 106, 123–135.CrossRefGoogle Scholar
  91. Williams, D. J., Rybicki, N. B., Lombana, A. V., O’Brien, T. M., & Gomez, R. B. (2003). Preliminary investigation of submerged aquatic vegetation mapping using hyperspectral remote sensing. Environmental Monitoring and Assessment, 81, 383–392.CrossRefGoogle Scholar
  92. Zacharias, M., Niemann, O., & Borstad, G. (1992). An assessment and classification of a multispectral bandset for the remote sensing of intertidal seaweeds. Canadian Journal of Remote Sensing, 18(4), 263– 274.Google Scholar
  93. Zhang, X. (1998). On the estimation of biomass of submerged vegetation using Landsat thematic mapper (TM) imagery: A case study of the Honghu Lake, PR China. International Journal of Remote Sensing, 19(1), 11–20.CrossRefGoogle Scholar
  94. Zilioli, E., & Brivio, P. A. (1997). The satellite derived optical information for the comparative assessment of lacustrine water quality. The Science of Total Environment, 196, 229–245.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2007

Authors and Affiliations

  • Thiago S. F. Silva
    • 1
    Email author
  • Maycira P. F. Costa
    • 1
  • John M. Melack
    • 2
  • Evlyn M. L. M. Novo
    • 3
  1. 1.Department of GeographyUniversity of VictoriaVictoriaCanada
  2. 2.Bren School of Environmental Science and ManagementUniversity of CaliforniaSanta BarbaraUSA
  3. 3.Instituto Nacional de Pesquisas EspaciaisSão José dos CamposBrazil

Personalised recommendations