Classification of Seagrass Beds by Coupling Airborne LiDAR Bathymetry Data and Digital Aerial Photographs

  • Satoshi IshiguroEmail author
  • Katsumasa Yamada
  • Takehisa Yamakita
  • Hiroya Yamano
  • Hiroyuki Oguma
  • Tsuneo Matsunaga
Part of the Ecological Research Monographs book series (ECOLOGICAL)


Evaluation of the spatial distribution pattern of patchy and fragmental seagrass beds, as hotspots of faunal biodiversity and of high primary productivity, is key to the robust understanding of the ecological state and of the effects of environmental changes on biota in coastal areas. Supervised classification of aerial photographs and satellite imagery is used for assessing the state of shallow-water bottom features (i.e., substrata), such as rock and seagrass patches. For accurate classification, it is important to measure the topography of the seabed extensively and at high resolution, because the color of aerial photographs must be corrected for depth. This is difficult, however, because the shallowness of the water restricts the movements of survey vessels. We generated a digital surface model (DSM) of shallow-water bottom features via airborne LiDAR bathymetry and then used the DSM and digital aerial photographs to classify the bottom features. We conducted simultaneous bathymetry and aerial photography of a bay on the east coast of Tohoku, Japan, using a Fugro LADS Mk 3 system for bathymetry (at 5-m resolution) and a RedLake image sensor for aerial photography (at 0.4-m resolution). After using the topographic data to correct for absorption, we classified the imagery to reveal the distribution of seagrass beds. The estimated distribution corresponded with empirical observations.


Airborne LiDAR Supervised classification Shallow-water bottom features Absorption correction Seagrass 



We gratefully thank Y. Mochizuki, M. Nakaoka, M. Tamaoki, N. Nakajima, N. Takamura, M. Kurosawa, A. Shirai, and the staff at the International Coastal Research Center of the Ocean Research Institute (University of Tokyo) for the help with field data collection. This research was supported in part by the Center Project of the National Institute for Environmental Studies and Center for Environmental Biology and Ecosystem Studies (no. 1112AF001) and conducted by the National Institute of Advanced Industrial Science and Technology (no. 1112ZZ002).


  1. Boström C, Jackson EL, Simenstad CA (2006) Seagrass landscapes and their effects on associated fauna: a review. Estuar Coast Shelf Sci 68:383–403CrossRefGoogle Scholar
  2. Chavez PS Jr (1988) An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sens Environ 24:459–479CrossRefGoogle Scholar
  3. Duffy EJ (2006) Biodiversity and the functioning of seagrass ecosystems. Mar Ecol Prog Ser 311:233–250CrossRefGoogle Scholar
  4. Green EP, Mumby PJ, Edwards AJ, Clark CD (2000) Remote sensing handbook for tropical coastal management. UNESCO, ParisGoogle Scholar
  5. Heck KL Jr, Valentine JF (2006) Plant–herbivore interactions in seagrass meadows. J Exp Mar Biol Ecol 330:420–436CrossRefGoogle Scholar
  6. Hemminga MA, Duarte CM (2000) Seagrass ecology. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  7. Hovel KA, Fonseca MS (2005) Influence of seagrass landscape structure on the juvenile blue crab habitat-survival function. Mar Ecol Prog Ser 300:170–191CrossRefGoogle Scholar
  8. Kuroishi Y, Ando H, Fukushima Y (2002) A new hybrid geoid model for Japan, GSIGEO2000. J Geodesy 76:428–436CrossRefGoogle Scholar
  9. JMA (Japan Meteorological Agency) (2013) Tidal data web site (in Japanese). Available from URL: Accessed 7 Nov 2013
  10. Larkum AW, Orth RRJ, Duarte CM (eds) (2006) Seagrasses: biology, ecology, and conservation. Springer, DordrechtGoogle Scholar
  11. Sagawa T, Mikami A, Komatsu T, Kosaka N, Kosako A, Miyazaki S, Takahashi M (2008) Mapping seagrass beds using IKONOS satellite image and side scan sonar measurements: a Japanese case study. Int J Remote Sens 28:281–291CrossRefGoogle Scholar
  12. Sakamoto SX, Sasa S, Sawayama S, Tsujimoto R, Terauchi G, Yagi H, Komatsu T (2012) Impact of huge tsunami in March 2011 on seaweed bed distributions in Shizugawa Bay, Sanriku Coast, revealed by remote sensing. Proc SPIE 8525. doi: 10.1117/12.999308 Google Scholar
  13. Sakuno Y, Kunii H (2013) Estimation of growth area of aquatic macrophytes expanding spontaneously in Lake Shinji using ASTER data. Int J Geosci 4:1–5CrossRefGoogle Scholar
  14. Sleeman JC, Kendrick GA, Boggs GS, Hegge BJ (2005) Measuring fragmentation of seagrass landscapes: which indices are most appropriate for detecting change? Mar Freshw Res 56:851–864CrossRefGoogle Scholar
  15. Sugimori Y, Sakamoto W (1990) Ocean environmental optical science. Tokai University Press, Tokyo (in Japanese)Google Scholar
  16. Wabnitz CC, Andréfouët S, Torres-Pulliza D, Müller-Karger FE, Kramer PA (2008) Regional-scale seagrass habitat mapping in the Wider Caribbean region using Landsat sensors: applications to conservation and ecology. Remote Sens Environ 112:3455–3467CrossRefGoogle Scholar
  17. Waycott M, Duarteb CM, Carruthers TJB, Orth RJ, Dennison WC, Olyarnick S, Calladine A, Fourqurean JW, Heck KL Jr, Randall Hughes A, Kendrick GA, Judson Kenworthy W, Short FT, Williams SL (2009) Accelerating loss of seagrasses across the globe threatens coastal ecosystems. Proc Natl Acad Sci U S A 106:12377–12381CrossRefPubMedPubMedCentralGoogle Scholar
  18. Yamada K, Kumagai NH (2012) Importance of seagrass vegetation for habitat partitioning between closely related species, mobile macrofauna Neomysis (Misidacea). Hydorobiologia 680:125–133CrossRefGoogle Scholar
  19. Yamada K, Hori M, Tanaka Y, Hasegawa N, Nakaoka M (2007) Temporal and spatial macrofaunal community changes along a salinity gradient in seagrass meadows of Akkeshi-ko estuary and Akkeshi Bay, northern Japan. Hydrobiologia 592:345–358CrossRefGoogle Scholar
  20. Yamada K, Hori M, Nakaoka M, Hamaguchi M (2011) Temporal and spatial variation of functional-trait composition (functional diversity) of macro-crustacean community in seagrass meadow. Crustaceana Monogr 15:325–339Google Scholar
  21. Yamakita T, Miyashita T (2014) Landscape mosaicness in the ocean: its significance for biodiversity patterns in benthic organisms and fish. In: Nakano S, Yahara T, Nakashizuka T (eds) Integrative observations and assessments (Ecological research monographs/Asia-Pacific biodiversity observation network). Springer Japan, pp 131–148Google Scholar
  22. Yamakita T, Nakaoka M (2009) Scale dependency in seagrass dynamics: how does the neighboring effect vary with grain of observation? Popul Ecol 51:33–40CrossRefGoogle Scholar
  23. Yamakita T, Watanabe K, Nakaoka M (2011) Asynchronous local dynamics contributes to stability of a seagrass bed in Tokyo Bay. Ecography 34:519–528CrossRefGoogle Scholar
  24. Yamakita T, Taki H, Okabe K, Quantitative effects of terrestrial and oceanic factors on the nationwide distribution of seagrass and Sargasso beds at two different river basin scales (under review)Google Scholar

Copyright information

© Springer Science+Business Media Singapore 2016

Authors and Affiliations

  • Satoshi Ishiguro
    • 1
    Email author
  • Katsumasa Yamada
    • 2
  • Takehisa Yamakita
    • 3
  • Hiroya Yamano
    • 1
  • Hiroyuki Oguma
    • 1
  • Tsuneo Matsunaga
    • 1
  1. 1.National Institute for Environmental StudiesTsukubaJapan
  2. 2.Research Center for Fisheries and Environment in the Ariake and Yatsushiro BaySeikai National Fisheries Research Institute, Fisheries Research AgencyNagasakiJapan
  3. 3.Japan Agency for Marine Earth, Science and TechnologyInstitute of BiogeosciencesYokosukaJapan

Personalised recommendations