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Satellite-based monitoring of tropical seagrass vegetation: current techniques and future developments

  • Soft-Bottom Near-Shore Ecosystems
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Abstract

Decline of seagrasses has been documented in many parts of the world. Reduction in water clarity, through increased turbidity and increased nutrient concentrations, is considered to be the primary cause of seagrass loss. Recent studies have indicated the need for new methods that will enable early detection of decline in seagrass extent and productivity, over large areas. In this review of current literature on coastal remote sensing, we examine the ability of remote sensing to serve as an information provider for seagrass monitoring. Remote sensing offers the potential to map the extent of seagrass cover and monitor changes in these with high accuracy for shallow waters. The accuracy of mapping seagrasses in deeper waters is unclear. Recent advances in sensor technology and radiometric transfer modelling have resulted in the ability to map suspended sediment, sea surface temperature and below-surface irradiance. It is therefore potentially possible to monitor the factors that cause the decline in seagrass status. When the latest products in remote sensing are linked to seagrass production models, it may serve as an early-warning system for seagrass decline and ultimately allow a better management of these susceptible ecosystems.

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References

  • Ananda, J. & G. Herath, 2003. Soil erosion in developing countries: A socio-economic appraisal. Journal of Environmental Management 68: 343–353.

    Article  PubMed  Google Scholar 

  • Baret, F. & T. Fourty, 1997. Radiometric estimates of nitrogen status of leaves and canopies. In Lemaire, G. (ed), Diagnosis of the Nitrogen Status in Crops. Springer Verlag, Berlin.

    Google Scholar 

  • Bazzaz, F. A., 1979. The physiological ecology of plant succession. Annual Review of Ecology and Systematics 10: 351–371.

    Article  Google Scholar 

  • Bell, J. D., D. J. Ferrell, S. E. Mcneill & D. G. Worthington, 1992. Variation in assemblages of fish associated with deep and shallow margins of the seagrass Posidonia australis. Marine Biology (Historical Archive) 114: 667–676.

    Google Scholar 

  • Biber, P. D., M. A. Harwell, J. Cropper & P. Wendell, 2004. Modelling the dynamics of three functional groups of macroalgae in tropical seagrass habitats. Ecological Modelling 175: 25–54.

    Article  Google Scholar 

  • Bilge, F., B. Yazici, T. Dogeroglu & C. Ayday, 2003. Statistical evaluation of remotely sensed data for water quality monitoring. International Journal of Remote Sensing 24: 5317–5326.

    Article  Google Scholar 

  • Boyer, K. E., P. Fong, A. R. Armitage & R. A. Cohen, 2004. Elevated nutrient content of tropical macroalgae increases rates of herbivory in coral, seagrass, and mangrove habitats. Coral Reefs 23: 530–538.

    Google Scholar 

  • Bricaud, A., M. Babin, A. Morel & H. Claustre, 1995. Variability in the chlorophyll specific absorption coefficients of natural phytoplankton: Analysis and parameterization. Journal of Geophysical Research 100: 13321–11332.

    Article  Google Scholar 

  • Brivio, P. A., C. Giardino & E. Zilioli, 2001. Determination of chlorophyll concentration changes in Lake Garda using an image-based radiative transfer code for Landsat TM images. International Jounal of Remote Sensing 22: 487–502.

    Article  Google Scholar 

  • Bukata, R., D. V. Pozdnyakov, J. H. Jerome & F. J. Tanis, 2001. Validation of a radiometric colour model applicable to optically complex water bodies. Remote Sensing of Environment 77: 165–172.

    Article  Google Scholar 

  • Burrage, D. M., M. L. Heron, J. M. Hacker, J. L. Miller, T. C. Stieglitz, C. R. Steinberg & A. Prytz, 2003. Structure and influence of tropical river plumes in the Great Barrier Reef: Application and performance of an airborne sea surface salinity mapping system. Remote Sensing of Environment 85: 204–220.

    Article  Google Scholar 

  • Carder, K. L., F. R. Chen, J. P. Cannizzaro, J. W. Campbell & B. G. Mitchell, 2004. Performance of the MODIS semi-analytical ocean color algorithm for chlorophyll-a. Advances in Space Research 33: 1152–1159.

    Article  CAS  Google Scholar 

  • Carruthers, T. J. B. & D. I. Walker, 1999. Sensitivity of transects across a depth gradient for measuring changes in aerial coverage and abundance of Ruppia megacarpa Mason. Aquatic Botany 65: 281–292.

    Article  Google Scholar 

  • Chauvaud, S., C. Bouchon & R. Maniere, 1998. Remote sensing techniques adapted to high resolution mapping of tropical coastal marine ecosystems (coral reefs, seagrass beds and mangrove). International Journal of Remote Sensing 19: 3625–3639.

    Article  Google Scholar 

  • Cocheret De La Moriniare, E., I. Nagelkerken, H. Meij & G. Velde, 2004. What attracts juvenile coral reef fish to mangroves: habitat complexity or shade? Marine Biology 144: 139–145.

    Article  Google Scholar 

  • Curran, P. J., 1989. Remote sensing of foliar chemistry. Remote Sensing of Environment 30: 271–278.

    Article  Google Scholar 

  • Darecki, M., A. Weeks, S. Sagan, P. Kowalczuk & S. Kaczmarek, 2003. Optical characteristics of two contrasting Case 2 waters and their influence on remote sensing algorithms. Continental Shelf Research 23: 237–250.

    Article  Google Scholar 

  • Deegan, L. A., A. Wright, S. G. Ayvazian, J. T. Finn, H. Golden, R. R. Merson & J. Harrison, 2002. Nitrogen loading alters seagrass ecosystem structure and support of higher trophic levels. Aquatic Conservation: Marine and Freshwater Ecosystems 12: 193–212.

    Article  Google Scholar 

  • Dekker, A. G., V. E. Brando & J. M. Anstee, 2005. Retrospective seagrass change detection in a shallow coastal tidal Australian lake. Remote Sensing of Environment 97: 415–433.

    Article  Google Scholar 

  • Dekker, A. G., V. E. Brando, J. M. Anstee, N. Pinnel, T. Kutser, E. J. Hoogenboom, S. Peters, R. Pasterkamp, R. Vos, C. Olbert & T. J. M. Malthus, 2001a. Spectrometry of water. In Van Der Meer, F. D. & S. M. De Jong (eds), Remote sensing and digital image processing. Kluwer Academic, Dordrecht, 403.

    Google Scholar 

  • Dekker, A. G., R. J. Vos & S. W. M. Peters, 2001b. Comparison of remote sensing data, model results and in situ data for total suspended matter (TSM) in the southern Frisian lakes. The Science of the Total Environment 268: 197–214.

    Article  CAS  Google Scholar 

  • Dey, S. & R. P. Singh, 2003. Comparison of chlorophyll distributions in the northeastern Arabian Sea and southern Bay of Bengal using IRS-P4 Ocean color monitor data. Remote Sensing of Environment 85: 424–428.

    Article  Google Scholar 

  • Duarte, C. M., 1991. Seagrass depth limits. Aquatic Botany 40: 363–377.

    Article  Google Scholar 

  • Duarte, C. M., 2002. The future of seagrass meadows. Environmental Conservation 29: 192–206.

    Article  Google Scholar 

  • Duffy, J. E., J. P. Richardson & E. A. Canuel, 2003. Grazer diversity effects on ecosystem functioning in seagrass beds. Ecology Letters 6: 637–645.

    Article  Google Scholar 

  • Esaias, W. E., M. R. Abbott, I. Barton, O. B. Brown, J. W. Campbell, K. L. Carder, D. K. Clark, R. H. Evans, F. E. Hoge, H. R. Gordon, W. M. Balch, R. Letelier & P. J. Minnett, 1998. An overview of MODIS capabilities for ocean science observations. IEEE Transactions on Geoscience and Remote Sensing 36: 1250–1265.

    Article  Google Scholar 

  • Ferguson, R. L. & K. Korfmacher, 1997. Remote sensing and GIS analysis of seagrass meadows in North Carolina, USA. Aquatic Botany 58: 241–258.

    Article  Google Scholar 

  • Ferwerda, J. G., A. K. Skidmore & O. Mutanga, 2005. Nitrogen detection with hyperspectral normalized ratio indices across multiple plant species. International Journal of Remote Sensing 26:4083–4095.

    Article  Google Scholar 

  • Gacia, E., M. M. Littler & D. S. Littler, 1999. An experimental test of the capacity of food web interactions (fish—epiphytes—seagrasses) to offset the negative consequences of eutrophication on seagrass communities. Estuarine, Coastal and Shelf Science 48: 757–766.

    Article  Google Scholar 

  • Garnier, J., J. Nemery, G. Billen & S. Thery, 2005. Nutrient dynamics and control of eutrophication in the Marne River system: Modelling the role of exchangeable phosphorus. Journal of Hydrology 304: 397–412.

    Article  CAS  Google Scholar 

  • Gordon, H. R., O. B. Brown & M. M. Jacobs, 1975. Computed relationship between the inherent and apparent optical properties of a flat homogeneous ocean. Applied Optics 14: 417–427.

    CAS  PubMed  Google Scholar 

  • Gordon, H. R. & M. Wang, 1994. Retrieval of water-leaving radiance and aerosol optical thickness over the oceans with SeaWiFS: A preliminary algorithm. Applied Optics 33: 443–452.

    Article  CAS  PubMed  Google Scholar 

  • Green, E. P. & F. T. Short, 2003. World Atlas of Seagrasses. University of California Press.

  • Harrington, J., John A., F. R. Schiebe & J. F. Nix, 1992. Remote sensing of Lake Chicot, Arkansas: Monitoring suspended sediments, turbidity, and Secchi depth with Landsat MSS data. Remote Sensing of Environment 39: 15–27.

    Article  Google Scholar 

  • Hasbún, C. R., A. J. Lawrence, J. H. Samour & S.M. Al-Ghais, 2000. Preliminary observations on the biology of green turtles, Chelonia mydas, from the United Arab Emirates. Aquatic Conservation: Marine and Freshwater Ecosystems 10: 311–322.

    Article  Google Scholar 

  • Haynes, D. & K. Michalek-Wagner, 2000. Water quality in the Great Barrier Reef World Heritage Area: Past perspectives, current issues and new research directions. Marine Pollution Bulletin 41: 428–434.

    Article  CAS  Google Scholar 

  • Heck, K. L., J. R. Pennock, J. F. Valentine, L. D. Coen & S. A. Sklenar, 2000. Effects of nutrient enrichment and small predator density on seagrass ecosystems: An experimental assessment. Limnology and Oceanography 45: 1041–1057.

    Article  CAS  Google Scholar 

  • Hemminga, M. & C. M. Duarte, 2000. Seagrass ecology. Cambridge University Press, Cambridge.

    Google Scholar 

  • Hu, C., Z. Chen, T. D. Clayton, P. Swarzenski, J. C. Brock & F. E. Muller-Karger, 2004. Assessment of estuarine water-quality indicators using MODIS medium-resolution bands: Initial results from Tampa Bay, FL. Remote Sensing of Environment 93: 423–441.

    Article  Google Scholar 

  • Hyndes, G. A., A. J. Kendrick, L. D. Macarthur & E. Stewart, 2003. Differences in the species- and size-composition of fish assemblages in three distinct seagrass habitats with differing plant and meadow structure. Marine Biology 142: 1195–1206.

    Google Scholar 

  • Inversa, O., G. P. Kraemerb, M. Pereza & J. Romeroa, 2004. Effects of nitrogen addition on nitrogen metabolism and carbon reserves in the temperate seagrass Posidonia oceanica. Journal of Experimental Marine Biology and Ecology 303: 97–114.

    Article  CAS  Google Scholar 

  • Jackson, J. B. C., M. X. Kirby, W. H. Berger, K. A. Bjorndal, L. W. Botsford, B. J. Bourque, R. H. Bradbury, R. Cooke, J. Erlandson, J. A. Estes, T. P. Hughes, S. Kidwell, C. B. Lange, H. S. Lenihan, J. M. Pandolfi, C. H. Peterson, R. S. Steneck, M. J. Tegner & R. R. Warner, 2001. Historical overfishing and the recent collapse of coastal ecosystems. Science 293: 629–637.

    Article  PubMed  CAS  Google Scholar 

  • Jones, S. D., J. G. Ferwerda & K. Reinke, 2006. Scaling the walls of history: The perils and pitfalls of landcover change mapping. ISPRS technical committee II Symposium, Vienna.

    Google Scholar 

  • Kester, D. R., M. F. Fox & A. Magnuson, 1996. Modeling, measurements, and satellite remote sensing of biologically active constituents in coastal waters. Marine Chemistry 53: 131–145.

    Article  CAS  Google Scholar 

  • Kirkman, H., 1996. Baseline and Monitoring Methods for Seagrass Meadows. Journal of Environmental Management 47: 191–201.

    Article  Google Scholar 

  • Kishino, M., A. Tanaka & J. Ishizaka, 2005. Retrieval of chlorophyll a, suspended solids, and colored dissolved organic matter in Tokyo Bay using ASTER data. Remote Sensing of Environment 99: 66–74.

    Article  Google Scholar 

  • Kutser, T., A. Herlevi, K. Kallio & H. Arst, 2001. A hyperspectral model for interpretation of passive optical remote sensing data from turbid lakes. The Science of the Total Environment 268: 47–58.

    Article  PubMed  CAS  Google Scholar 

  • Lilienthal, H., S. Haneklaus, E. Schnug & E. Haveresch, 2000. Utilistation of hyperspectral data for the evaluation of the spatial variability of the nitrogen status of wheat. Aspects of Applied Biology 60: 189–194.

    Google Scholar 

  • Maritorena, S. & D. A. Siegel, 2005. Consistent merging of satellite ocean color data sets using a bio-optical model. Remote Sensing of Environment 94: 429–440.

    Article  Google Scholar 

  • Marten, G. C., J. S. Shenk & F. E. Barton, 1985. Near infrared reflectance spectroscopy (NIRS): Analysis of forage quality. U.S. Department of Agriculture.

  • Mélin, F. & G. Zibordi, 2005. Aerosol variability in the Po Valley analyzed from automated optical measurements. Geophysical Research Letters 32: L03810.1–L03810.

    Google Scholar 

  • López-Mendilaharsu, M., S. C. Gardner, J. A. Seminoff & R. Riosmena-Rodriguez, 2005. Identifying critical foraging habitats of the green turtle (Chelonia mydas) along the Pacific coast of the Baja California peninsula, Mexico. Aquatic Conservation: Marine and Freshwater Ecosystems 15: 259–269.

    Article  Google Scholar 

  • Mumby, P. J. & A. J. Edwards, 2002. Mapping marine environments with IKONOS imagery: Enhanced spatial resolution can deliver greater thematic accuracy. Remote Sensing of Environment 82: 248–257.

    Article  Google Scholar 

  • Mumby, P. J., E. P. Green, A. J. Edwards & C. D. Clark, 1999. The cost-effectiveness of remote sensing for tropical coastal resources assessment and management. Journal of Environmental Management 55:157–166.

    Article  Google Scholar 

  • Mutanga, O. & A. K. Skidmore, 2004. Integrating imaging spectroscopy and neural networks to map grass quality in the Kruger National Park, South Africa. Remote Sensing of Environment 90:104–115.

    Article  Google Scholar 

  • Mutanga, O., A. K. Skidmore & H. H. T. Prins, 2003. Predicting in situ pasture quality in the Kruger National Park, South Africa, using continuum-removed absorption features. Remote Sensing of Environment 89: 393–408.

    Article  Google Scholar 

  • Myers, R. A. & B. Worm, 2003. Rapid worldwide depletion of predatory fish communities. Nature 423: 280–283.

    Article  PubMed  CAS  Google Scholar 

  • Nezlin, N. P., P. M. Digiacomo, E. D. Stein & D. Ackerman, 2005. Stormwater runoff plumes observed by SeaWiFS radiometer in the Southern California Bight. Remote Sensing of Environment 98: 494–510.

    Article  Google Scholar 

  • Pasqualini, V., C. Pergent-Martini, G. Pergent, M. Agreil, G. Skoufas, L. Sourbes & A. Tsirika, 2005. Use of SPOT 5 for mapping seagrasses: An application to Posidonia oceanica. Remote Sensing of Environment 94: 39–45.

    Article  Google Scholar 

  • Pierson, D. C. & N. Strombeck, 2001. Estimation of radiance reflectance and the concentrations of optically active substances in Lake Malaren, Sweden, based on direct and inverse solutions of a simple model. The Science of the Total Environment 268: 171–188.

    Article  PubMed  CAS  Google Scholar 

  • Plus, M., A. Chapelle, A. Menesguen, J.-M. Deslous-Paoli & I. Auby, 2003. Modelling seasonal dynamics of biomasses and nitrogen contents in a seagrass meadow (Zostera noltii Hornem.): application to the Thau lagoon (French Mediterranean coast). Ecological Modelling 161:211–236.

    Article  Google Scholar 

  • Pope, M. & E. S. Fry, 1997. Absorption spectrum (380–700 nm) of pure water. II. Integrating cavity measurements. Applied Optics 36: 8710–8723.

    Article  CAS  PubMed  Google Scholar 

  • Pozdnyakov, D., R. Shuchman, A. Korosov & C. Hatt, 2005. Operational algorithm for the retrieval of water quality in the Great Lakes. Remote Sensing of Environment 97: 352–370.

    Article  Google Scholar 

  • Rao, C. R. N., L. L. Stowe & E. P. Mcclain, 1989. Remote sensing of aerosols over the oceans using AVHRR data: Theory, practice, and applications. International Journal of Remote Sensing 10: 743–749.

    Article  Google Scholar 

  • Robinson, J. A., K. P. Lulla, M. Kashiwagi, M. Suzuki, M. D. Nellis, C. E. Bussing, W. J. L. Long & L. J. Mckenzie, 2001. Conservation applications of Astronaut photographs of Earth: Tidal-flat loss (Japan), elephant effects on vegetation (Botswana), and seagrass and mangrove monitoring (Australia). Conservation Biology 15: 876–884.

    Article  Google Scholar 

  • Ruhl, C. A., D. H. Schoellhamer, R. P. Stumpf & C. L. Lindsay, 2001. Combined use of remote sensing and continuous monitoring to analyse the variability of suspended-sediment concentrations in San Francisco Bay, California. Estuarine, Coastal and Shelf Science 53: 801–812.

    Article  Google Scholar 

  • Schmidtlein, S. & J. Sassin, 2004. Mapping of continuous floristic gradients in grasslands using hyperspectral imagery. Remote Sensing of Environment 92: 126–138.

    Article  Google Scholar 

  • Secker, J., K. Staenz, R. P. Gauthier & P. Budkewitsch, 2001. Vicarious calibration of airborne hyperspectral sensors in operational environments. Remote Sensing of Environment 76: 81–92.

    Article  Google Scholar 

  • Short, F. T., 2005. Loss and restoration of seagrass ecosystems. 5th International Conference on Environmental Future, ETH Zurich, Switzerland.

  • Short, F. T. & S. Wyllie-Echeverria, 1996. Natural and human-induced disturbance of seagrasses. Environmental Conservation 23: 17–27.

    Article  Google Scholar 

  • Skidmore, A. K., O. Mutanga, K. Schmidt & J. G. Ferwerda, 2005. Modelling herbivore grazing resources using hyperspectral remote sensing and GIS. AGILE 2005: 8th conference on geographic information science, Lisboa, AGILE, Denver, USA.

  • Sogard, S. M., G. V. N. Powell & J. G. Holmquist, 1989. Utilization by fishes of shallow, seagrass-covered banks in Florida Bay: 1. Species composition and spatial heterogeneity. Environmental Biology of Fishes (Historical Archive) 24: 53–65.

    Article  Google Scholar 

  • Soukupova, J., B. N. Rock & J. Albrechtova, 2002. Spectral characteristics of lignin and soluble phenolics in the near infrared —a comparative study. International Journal of Remote Sensing 23: 3039–3055.

    Article  Google Scholar 

  • Tanré, D., Y. J. Kaufman, M. Herman & S. D. Matoo, 1997. Remote sensing of aerosol properties over oceans using the MODIS/EOS spectral radiances. Journal of Geophysical Research 102: 16971–16988.

    Article  Google Scholar 

  • Thiemann, S. & H. Kaufmann, 2002. Lake water quality monitoring using hyperspectral airborne data—a semiempirical multisensor and multitemporal approach for the Mecklenburg Lake District, Germany. Remote Sensing of Environment 81: 228–237.

    Article  Google Scholar 

  • Underwood, E., S. Ustin & D. Dipietro, 2003. Mapping nonnative plants using hyperspectral imagery. Remote Sensing of Environment 86: 150–161.

    Article  Google Scholar 

  • Van Duin, E. H. S., G. Blom, F. J. Los, R. Maffione, R. Zimmerman, C. F. Cerco, M. Dortch & E. P. H. Best, 2001. Modeling underwater light climate in relation to sedimentation, resuspension, water quality and autotrophic growth. Hydrobiologia 444: 25–42.

    Article  Google Scholar 

  • Vermaat, J., N. Agawin, M. Fortes, J. Uri, C. Duarte, N. Marba, S. Enriquez & W. K. Van Vierssen, 1997. The capacity of seagrasses to survive increased turbidity and siltation: The significance of growth form and light use. Ambio 26: 499–504.

    Google Scholar 

  • Williams, A. P. & E. R. Hunt, 2002. Estimation of leafy spurge cover from hyperspectral imagery using mixture tuned matching filtering. Remote Sensing of Environment 82: 446–456.

    Article  Google Scholar 

  • Zhang, Y., J. Pulliainen, S. Koponen & M. Hallikainen, 2002. Application of an empirical neural network to surface water quality estimation in the Gulf of Finland using combined optical data and microwave data. Remote Sensing of Environment 81: 327–336.

    Article  Google Scholar 

  • Zhao, D., K. R. Reddy, V. G. Kakani & V. R. Reddy, 2005. Nitrogen deficiency effects on plant growth, leaf photosynthesis, and hyperspectral reflectance properties of sorghum. European Journal of Agronomy 22: 391–403.

    Article  CAS  Google Scholar 

  • Zimmerman, R. C., 2003. A biooptical model of irradiance distribution and photosynthesis in seagrass canopies. Limnology and Oceanography 48: 568–585.

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Mathematical and Geospatial Sciences, Royal Melbourne Institute of Technology, GPO Box 2476V, Melbourne, 3001, Australia

    Jelle G. Ferwerda

  2. International Institute for Geo-information Science and Earth Observation, P.O. Box 6, Enschede, 7500 AA, The Netherlands

    Jan de Leeuw & Zoltan Vekerdy

  3. GeoSys, SA 20, Impasse René Couzinet, BP 65815, Toulouse Cedex 5, 31505, France

    Clement Atzberger

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  1. Jelle G. Ferwerda
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  2. Jan de Leeuw
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  3. Clement Atzberger
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Correspondence to Jelle G. Ferwerda.

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Ferwerda, J.G., de Leeuw, J., Atzberger, C. et al. Satellite-based monitoring of tropical seagrass vegetation: current techniques and future developments. Hydrobiologia 591, 59–71 (2007). https://doi.org/10.1007/s10750-007-0784-5

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