Coral Reefs

, Volume 23, Issue 1, pp 5–20 | Cite as

Integrating in situ reef-top reflectance spectra with Landsat TM imagery to aid shallow-tropical benthic habitat mapping



This manuscript presents the use of hyperspectral in situ reflectance measurements evaluated above the water surface to train a supervised classification of a simultaneously acquired Landsat 5 TM image. The optical signature of a submerged reef substrate is both attenuated and augmented by the presence of the atmosphere and intervening water column, thereby complicating the link between field and space-borne measurement. The motivation of this manuscript is to quantify the advantage (defined as increase in classification accuracy) conferred by each of five levels of increasingly complex image processing methods to correct for atmospheric and submergence effects. It is found that the maximum overall classification accuracy attained in areas of the image where water depth is unknown was 53%, but with the addition of depth information, accuracy increases to 76%. The results demonstrate the ability of a classifier trained solely by in situ optical measurements to accurately resolve the broad substrate types of a typical Red Sea fringing reef top. Although slightly different accuracy assessment protocols were used, the results also suggest that using in situ spectra for training provided better classification results than using from-image statistics.


In situ spectra Landsat Radiative transfer Classification Red Sea Habitat mapping 


  1. Aas E, Høkedal J (1999) Reflection of spectral sky irradiance on the surface of the sea and related properties. Remote Sens Environ 70:181–190CrossRefGoogle Scholar
  2. Ahmad W, Neil DT (1994) An evaluation of Landsat Thematic Mapper TM digital data for discriminating coral reef zonation: Heron Reef (GBR). Int J Remote Sens 15:2583–2597Google Scholar
  3. Andréfouët S, Kramer P, Torres-Pulliza D, Joyce KE, Hochberg EJ, Garza-Perez R, Mumby PJ, Riegl B, Yamano H, White WH, Zubia M, Brock JC, Phinn SR, Naseer A, Hatcher BG, Muller-Karger FE (2003) Multi-sites evaluation of IKONOS data for classification of tropical coral reef environments. Remote Sens Environ (in press)Google Scholar
  4. Andréfouët S, Muller-Karger FE, Hochberg EJ, Hu C, Carder KL (2001) Change detection in shallow coral reef environments using Landsat 7 ETM+ data. Remote Sens Environ 78:150–162CrossRefGoogle Scholar
  5. Andréfouët S, Roux L, Chancerelle Y, Bonneville A (2000) A fuzzy-possibilistic scheme of study for objects with indeterminate boundaries: Application to French Polynesian reefscapes. IEEE T Geosci Remote 38:257–270CrossRefGoogle Scholar
  6. Armstrong RA (1993) Remote sensing of submerged vegetation canopies for biomass estimation. Int J Remote Sens 14:621–627Google Scholar
  7. Austin RW (1974) The remote sensing of spectral radiance from below the ocean surface. In: Jerlov NG, Nielsen ES (eds) Optical aspects of oceanography. Academic Press London and New York, pp 317–344Google Scholar
  8. Austin RW, Petzold, TJ (1986) Spectral dependence of the diffuse attenuation coefficient of light in ocean waters. Opt Eng 25:471–479Google Scholar
  9. Benny AH, Dawson GJ (1983) Satellite imagery as an aid to bathymetric charting in the Red Sea. Cartogr J 20:5–16Google Scholar
  10. Bierwirth PN, Lee TJ, Burne RV (1993) Shallow sea-floor reflectance and water depth derived by unmixing multispectral imagery. Photogramm Eng Remote Sens 59:331–338Google Scholar
  11. Buddemeier RW, Hopley D (1988) Turn-ons and turn-offs: causes and mechanisms of the initiation and termination of coral reef growth. Proc 6th Int Coral Reef Symp, Townsville, Australia 1:253–261Google Scholar
  12. Capolsini P, Andréfouët S, Rion C, Payri C (2003) A comparison of Landsat ETM+, SPOT HRV, Ikonos, ASTER, and airborne MASTER data for coral reef habitat mapping in South Pacific Islands. Can J Remote Sens 29:1–14Google Scholar
  13. Clark CD, Mumby PJ, Chisholm JRM, Jaubert J, Andréfouët S (2000) Spectral discrimination of coral mortality states following a severe bleaching event. Int J Remote Sens 21:2321–2327CrossRefGoogle Scholar
  14. Congalton RG (1991) A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens Environ 37:35–46CrossRefGoogle Scholar
  15. De Vel OY, Bour W (1990) The structural and thematic mapping of coral reefs using high resolution SPOT data: application to the Tétembia reef (New Caledonia). Geocarto Int 2:27–34Google Scholar
  16. Dustan P, Dobson E, Nelson G (2001) Landsat TM: detection of shifts in community composition of coral reefs. Conserv Biol 15:892–902CrossRefGoogle Scholar
  17. Fargion GS, Mueller JL (2000) Ocean optics protocols for satellite ocean color sensor validation, Revision 2, NASA/TM-2000-209966, Goddard Space Flight Space Center, Greenbelt, MarylandGoogle Scholar
  18. Foody GM (2002) Status of land cover classification accuracy assessment. Remote Sens Environ 80:185–201CrossRefGoogle Scholar
  19. Haan de JF, Kokke JMM (1998) Remote sensing algorithm development toolkit 1. Operationalization of atmospheric correction methods for tidal and inland waters. Netherlands Remote Sensing Board (BCRS) publication. Rikjkswaterstaat Survey DeptGoogle Scholar
  20. Hochberg EJ, Atkinson MJ (2000) Spectral discrimination of coral reef benthic communities. Coral Reefs 19:164–171CrossRefGoogle Scholar
  21. Hochberg EJ, Atkinson MJ (2003) Capabilities of remote sensors to classify coral, algae, and sand as pure and mixed spectra. Remote Sens Environ 85:174–189CrossRefGoogle Scholar
  22. Holden H, LeDrew E (1998) Spectral discrimination of healthy and non-healthy corals based on cluster analysis, principal components analysis, and derivative spectroscopy. Remote Sens Environ 65:217–224CrossRefGoogle Scholar
  23. Holden H, LeDrew E (2001) Effects of the water column on hyperspectral reflectance of submerged coral reef features. Bull Mar Sci 69:685–699Google Scholar
  24. Hughes TP (1994) Catastrophes, phase-shifts and large-scale degradation of a Caribbean coral reef. Science 265:1547–1551Google Scholar
  25. Hughes TP (1999) Off-reef transport of coral fragments at Lizard Island, Australia. Mar Geol 157:1–6CrossRefGoogle Scholar
  26. Irish RR (2000) Landsat 7 science data user’s handbook, NASA Document no 430-15-01-003-0 (
  27. Jerlov NG (1976) Marine optics. Elsevier, AmsterdamGoogle Scholar
  28. Jupp DLB (1988) Background and extensions to depth of penetration (DOP) mapping in shallow coastal waters. Proc Symp on Remote Sensing of the Coastal Zone, Queensland, Australia, 2.2–2.19Google Scholar
  29. Kleypas JA, Buddemeier RW, Gattuso J-P (2001) The future of coral reefs in an age of global change. Int J Earth Sci 90:426–437Google Scholar
  30. Landis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics 33:159–174PubMedGoogle Scholar
  31. Lee Z, Carder KL, Chen RF, Peacock TG (2001) Properties of the water column and bottom derived from AVIRIS data. J Geophys Res 106:11639–11651Google Scholar
  32. Lenny PM, Woodcock CE, Collins JB, Hamdi H (1996) The status of agricultural lands in Egypt: The use of multitemporal NDVI features derived from Landsat. Remote Sens Environ 56:8–20CrossRefGoogle Scholar
  33. Lyzenga D (1978) Passive remote sensing techniques for mapping water depth and bottom features. Appl Optics 17:379–383Google Scholar
  34. Lyzenga DR (1981) Remote sensing of bottom reflectance and water attenuation parameters in shallow water using aircraft and Landsat data. Int J Remote Sens 2:71–82Google Scholar
  35. Ma Z, Redmond RL (1995) Tau coefficients for accuracy assessment of classification of remote sensing data. Photogramm Eng Remote Sens 61:435–439Google Scholar
  36. Maritorena S (1996) Remote sensing of the water attenuation in coral reefs: a case study in French Polynesia. Int J Remote Sens 17:155–166Google Scholar
  37. McCook LJ (1999) Macroalgae, nutrients and phase shifts on coral reefs: scientific issues and management consequences for the Great Barrier Reef. Coral Reefs 18:357–367CrossRefGoogle Scholar
  38. Miller RJ, Dick KJ, Kalinauskas A (1984) Water depth mapping by passive remote sensing. PRAI Project Final Technical Report, Univ. York. Ontario, CanadaGoogle Scholar
  39. Minghelli-Roman A, Chisholm JRM, Marchioretti M, Ripley H, Jaubert JM (2002) Discrimination of coral reflectance spectra in the Red Sea. Coral Reefs 21:307–314Google Scholar
  40. Mobley C (1994) Light and water: radiative transfer in natural waters. Academic Press, New YorkGoogle Scholar
  41. Morel A, Prieur L (1977) Analysis of variations in ocean color. Limnol Oceanogr 22:709–722Google Scholar
  42. Mumby PJ, Clarke CD, Green EP, Edwards AJ (1998) Benefits of water column correction and contextual editing for mapping coral reefs. Int J Remote Sens 19:203–210CrossRefGoogle Scholar
  43. Mumby PJ, Edwards AJ (2002) Mapping marine environments with IKONOS imagery: enhanced spatial resolution can deliver great thematic accuracy. Remote Sens Environ 82:248–257CrossRefGoogle Scholar
  44. Mumby PJ, Green EP, Edwards AJ, Clark CD (1997) Coral reef habitat mapping: how much detail can remote sensing provide? Mar Biol 130:193–202CrossRefGoogle Scholar
  45. Myers MR, Hardy JT, Mazel CH, Dustan P (1999) Optical spectra and pigmentation of Caribbean reef corals and macroalgae. Coral Reefs 18:179–186Google Scholar
  46. Purkis SJ, Kenter JAM, Oikonomou EK, Robinson IS (2002) High-resolution ground verification, cluster analysis and optical model of reef substrate coverage on Landsat TM imagery (Red Sea, Egypt). Int J Remote Sens 23:1677–1698CrossRefGoogle Scholar
  47. Rasser MW, Riegl B (2002) Holocene coral reef rubble and its binding agents. Coral Reefs 21:57–72Google Scholar
  48. Riegl B, Piller WE (2000) Mapping of benthic habitats in northern Safaga Bay (Red Sea, Egypt): a tool for proactive management. Aquat Conserv 10:127–140CrossRefGoogle Scholar
  49. Roelfsema CM, Phinn SR, Dennison WC (2002) Spatial distribution of benthic microalgae on coral reefs determined by remote sensing. Coral Reefs 21:264–274Google Scholar
  50. Song C, Woodcock CE, Seto KC, Lenny MP, Macomber SA (2001) Classification and change detection using Landsat TM data: When and how to correct atmospheric effects? Remote Sens Environ 75:230–244CrossRefGoogle Scholar
  51. Tassan S (1992) An algorithm for the identification of benthic algae in the Venice lagoon from Thematic Mapper data. Int J Remote Sens 13:2887–2909Google Scholar
  52. Tassan S (1996) Modified Lyzenga’s method for macroalgae detection in water with non-uniform composition. Int J Remote Sens 17:1601–1607Google Scholar
  53. Wilson JD (1992) A comparison of procedures for classifying remotely-sensed data using simulated data sets. Int J Remote Sens 13:365–386Google Scholar
  54. Zainal AJM (1994) New technique for enhancing the detection and classification of shallow marine habitats. Mar Tech J 28:68–74Google Scholar
  55. Zainal AJM, Dalby DH, Robinson IS (1993) Monitoring of marine ecological changes on the east coast of Bahrain with Landsat TM. Photogramm Eng Remote Sens 59:415–421Google Scholar

Copyright information

© Springer-Verlag 2003

Authors and Affiliations

  1. 1.Dept Sedimentology, Faculty of Earth and Life SciencesVrije Universiteit AmsterdamAmsterdamThe Netherlands
  2. 2.Institute for Environmental StudiesVrije Universiteit AmsterdamAmsterdamThe Netherlands

Personalised recommendations