Skip to main content
Log in

Neural network-based light attenuation model for monitoring seagrass population in the Indian river lagoon

  • Published:
Journal of Intelligent Information Systems Aims and scope Submit manuscript

Abstract

Seagrasses have been considered one of the most critical marine habitat types of coastal and estuarine ecosystems such as the Indian River Lagoon. They are an important part of biological productivity, nutrient cycling, habitat stabilization and species diversity and are the primary focus of restoration efforts in the Indian River Lagoon. The areal extent of seagrasses has declined within segments of the lagoon over the years. Light availability to seagrasses is a major criterion limiting their distribution. Decreased water clarity and resulting reduced light penetration have been cited as the major factors responsible for the decline in seagrasses in the lagoon. Hence, light is a critical factor for the survival of seagrass species. Light attenuation coefficient is an important parameter that indicates the light attenuated by the water column and can therefore be used as an indicator of seagrass vigor. A number of region-specific linear light attenuation models have been proposed in the literature. Though, in practice, linear light attenuation models have been commonly used, there is need for a flexible and robust model that incorporates the non-linearities present in coastal and estuarine environments. This paper presents a neural network based model to estimate light attenuation coefficient from water quality parameters and thereby indirectly monitor seagrass population in the Indian River Lagoon. The proposed neural network models were compared with linear regression models, step-wise linear regression models, model trees and support vector machines. The neural network models performed fairly better compared to the other models considered.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Ackelson, 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.

    Article  Google Scholar 

  • APHA. (1985). Standard methods for examination of water and wastewater (16th edn.). Washington D.C.: American Public Health Association.

    Google Scholar 

  • Bakema, A. H. (1988). Empirical light modeling for a number of Dutch lakes. Delft hydraulics report T387. The Netherlands (in Dutch).

  • Blom, G., Van Duin, E. H. S., & Lijklema, L. (1994). Sediment resuspension and light conditions in some shallow Dutch lakes. Water Science and Technology, 30, 243–252.

    Google Scholar 

  • Buiteveld, H. (1995). A model for calculation of diffuse light attenuation (PAR) and Secchi depth. Netherlands Journal of Aquatic Ecology, 29, 55–65.

    Article  Google Scholar 

  • Bulthius, D. (1984). Control of seagrass Heterozostera tasmanica by benthic screens. Journal of Plant Management, 22, 41–43.

    Google Scholar 

  • Cuthbert, I. D., & del Giorgio, P. (1992). Toward a standard method of measuring color in freshwater. Limnology and Oceanography, 37, 1319–1326.

    Article  Google Scholar 

  • DiToro, D. M. (1978). Optics of turbid estuarine waters: Approximations and applications. Water Research, 12, 1059–1068.

    Article  Google Scholar 

  • Dring, M. J. (1982). The biology of marine plants (p. 199). London: Edward Arnold.

    Google Scholar 

  • Durako, M. J., Murphy, M. D., & Haddad, K. D. (1988). Assessment of fisheries habitat: Northeast Florida. Florida Marine Research Publications no. 45, 51.

  • Environmental Protection Agency. (1979). Methods for chemical analysis of water and wastes. EPA-600/4-79-020.

  • Frank, E., Wang, Y., Inglis, S., Holmes, G., & Witten, I. H. (1998). Using model trees for classification. Machine Learning, 32, 63–76.

    Article  MATH  Google Scholar 

  • Gilmore, R. G. (1987). Subtropical–tropical seagrass communities of the southeastern United States: Fishes and fish communities. In M. J. Durako, R. C. Phillips, & R. R. Lewis III (Eds.), Proceedings of the symposium on subtropical–tropical seagrasses of the Southeastern United States. Florida Dept. of Natural Resources, Bureau of Marine Research Publication Number 42.

  • Green, E. P., Mumby, P. J., Edwards, A. J., & Clark, C. D. (1996). A review of remote sensing for the assessment and management of coastal resources. Coastal Management, 24, 1–40.

    Article  Google Scholar 

  • Haddad, K. D. (1985). Habitats of the Indian river lagoon. In D. Barile (Ed.), Proceedings of the Indian river resources symposium (pp. 23–28). Gainsville, Florida: Florida Sea Grant Project, 84-28.

  • Haigang, Z., Ping, S., & Chuqun, C. (2003). Retrieval of oceanic chlorophyll concentration using support vector machines. IEEE Transactions on Geoscience and Remote Sensing, 41(12), 2947–2951.

    Article  Google Scholar 

  • Hanisak, M. D. (Ed.) (2001). Photosynthetically Active radiation, water quality, and submerged aquatic vegetation in the Indian river lagoon. Report to St. Johns River Water Management District, Harbor Branch Oceanographic Institution.

  • Haykin, S. (1999). Neural networks: A comprehensive foundation (2nd edn.). NJ: Prentice-Hall.

    MATH  Google Scholar 

  • Jeffrey, S. W., & Humphrey, G. F. (1975). New spectrophotometric equations for determining chlorophylls a, b, c1, and c2 in higher plants, algae, and natural phytoplankton. Biochemie und Physiologie der Pflanzen, 167, 223–227.

    Google Scholar 

  • Jianchao, Y., & Chen, C. T. (2001). Comparison of Newton–Gauss with Levenberg Marquardt algorithm for space reselection. 22nd Asian Conference on Remote Sensing, Nov 2001.

  • Kenworthy, W. J., & Haunert, D. E. (Eds.) (1991). The light requirements of seagrasses: Proceedings of a workshop to examine the capability of water quality criteria, standards, and monitoring programs to protect seagrasses. NOAA Technical Memorandum, NMFS-SEFC-287. pp 187.

  • Kirk, J. T. O. (1994). Light and photosynthesis in aquatic ecosystems (2nd ed., p. 509). Cambridge: Cambridge University Press.

    Google Scholar 

  • Kirkman, H. (1996). Baseline and monitoring methods for seagrass meadows. Journal of Environmental Management, 47, 191–201.

    Article  Google Scholar 

  • Kirkman, H., & Kirkman, J. (2000). Long-term seagrass meadow monitoring near Perth, Western Australia. Aquatic Botany, 67, 319–332.

    Article  Google Scholar 

  • Larkum, A. W. D., McComb, A. J., & Shepherd, S. A. (1989). Biology of seagrasses (p. 841). New York: Elsevier.

    Google Scholar 

  • Livingston, R. J. (1987). Historic trends of human impacts on seagrass meadows in Florida. In: Symposium proceedings: Pages 139–151 in Subtropical–tropical seagrasses of the Southestern U.S. Florida Marine Research Publication 42.

  • Livingston, R. J., McGlynn, S. E., & Niu, X. (1998). Factors controlling seagrass growth in the gulf coastal system: Water and sediment quality, and light. Aquatic Botany, 60, 135–159.

    Article  Google Scholar 

  • Long, B., Skewes, T. D., & Pointer, I. R. (1994). An efficient method for estimating seagrass biomass. Aquatic Botany, 47, 277–291.

    Article  Google Scholar 

  • Lubbers, L., Boynton, W. R., & Kemp, W. M. (1990). Variations in structure of estuarine fish communities in relation to abundance of submersed plants. Marine Ecology. Progress Series, 65, 1–14.

    Google Scholar 

  • Morris, L. J., & Tomasko, D. A. (Eds.) (1993). Proceedings and conclusions of workshops on: Submerged aquatic vegetation initiative and photosynthetically active radiation. Special Pub. SJ93-SP13. Palatka, FL. SJRWMD. p 224 plus appendices.

  • Mumby, P. J., Green, E. P., Edwards, A. J., & Clark, C. D. (1997). Measurement of seagrass standing stock using satellite and airborne remote sensing. Marine Ecology. Progress Series, 159, 51–60.

    Google Scholar 

  • Onuf, C. P. (1996). Seagrass responses to long-term light reduction by brown tide in upper Laguna Madre, Texas: Distribution and biomass patterns. Marine Ecology. Progress Series, 138, 219–231.

    Google Scholar 

  • Pohle, D. G., Briceji, V. M., & Garcia-Esquivel, Z. (1991). The eelgrass canopy: An above-bottom refuge from benthic predators for juvenile bay scallops Argopecten irradians. Marine Ecology. Progress Series, 74, 47–59.

    Google Scholar 

  • Powell, G. V. N., & Schaffner, F. C. (1991). Water trapping by seagrasses occupying bank habitats in Florida Bay. Estuarine, Coastal and Shelf Science, 32, 43–60.

    Article  Google Scholar 

  • Quinlan, J. R. (1992). Learning with continuous classes. In A. Adams & L. Sterling (Eds.), Proceedings AI’92 (pp. 343–348). Singapore: World Scientific.

    Google Scholar 

  • Ramus, J. (1985). Light. In M. M. Littler & D. S. Littler (Eds.), Handbook of phycological methods: Ecological field methods, macroalgae (pp. 33–52, p. 617). Cambridge: Cambridge University Press.

  • Solomatine, D. P., & Dulal, K. N. (2003). Model trees as an alternative to neural networks in rainfall-runoff modeling. Hydrological Sciences Journal, 48(3), 399–411.

    Article  Google Scholar 

  • Steward, J. S. (2002). Complementary use of different seagrass targets and analytical approaches in the development of PLRGs for the Indian river lagoon. In H. S. Greening (Ed.), Seagrass management: It’s not just nutrients! 2000 Aug 22–24 (pp. 81–90, p. 246). St. Petersburg, FL: Tampa Bay Estuary Program.

  • Thayer, G. W., Kenworthy, W. J., & Fonseca, M. S. (1984). The ecology of seagrass meadows of the atlantic coast: A community porfile. U.S. Fish Widl. Serv. FWS/OBS-84/02.

  • Vapnik, V. N. (2000). The nature of statistical learning theory (2nd edn.). Berlin Heidelberg New York: Springer.

    MATH  Google Scholar 

  • Virnstein, R. W., & Morris, L. J. (1996). Seagrass preservation and restoration: A diagnostic plan for the Indian river lagoon (p. 18). Technical memorandum no.14. Palatka, FL: St. Johns River Water Management District plus appendices.

  • Virnstein, R. W., & Morris, L. J. (2000). Setting seagrass targets for the Indian river lagoon, Florida. In S. A. Bortone (Ed.), Seagrasses: Monitoring, ecology, physiology, and management (pp. 211–218). Boca Raton, FL: CRC.

    Google Scholar 

  • Witten, I. H., & Frank, E. (2000). Data mining: Practical machine learning tools and techniques with Java implementations. San Francisco, USA: Morgan Kaufmann.

    Google Scholar 

  • Wood, S. (1995). Seagrass tells water quality story, FATHOM magazine, URL: http://www.flseagrant.org/science/library/fathom_magazine/volume-7_issue-1/seagrass_tells.htm.

  • Wood, N., & Lavery, P. (2000). Monitoring seagrass ecosystem health—the role of perception in defining health and indicators. Ecosystem Health, 6, 134–148.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to M. T. Musavi.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Musavi, M.T., Ressom, H., Srirangam, S. et al. Neural network-based light attenuation model for monitoring seagrass population in the Indian river lagoon. J Intell Inf Syst 29, 63–77 (2007). https://doi.org/10.1007/s10844-006-0031-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10844-006-0031-y

Keywords

Navigation