access icon free Assessment of Sentinel-2A multispectral image for benthic habitat composition mapping

Sentinel-2A accuracy for benthic habitat composition mapping was tested and compared to ALOS AVNIR-2. Aerial image acquired using custom-made unmanned aerial vehicle was used to train and validate the model. The mapping was conducted regardless of the benthic class and at individual benthic class. Benthic habitat class spatial distribution was obtained using the combination of image segmentation and classification tree analysis. The aerial image was interpreted based on the percentage of the constructed and non-constructed classes. The constructed class includes coral reefs, dead coral, seagrass, and macroalgae, while non-constructed class covers carbonate sand, rock, and rubble. Sentinel-2A produced higher accuracy (92%) than ALOS AVNIR-2 (78%) for benthic habitat spatial distribution mapping. However, in the empirical modelling of benthic habitat composition, ALOS AVNIR-2 (SE 23–24%) produced slightly better accuracy than Sentinel-2A (SE 23–27%). Several factors affected the low accuracy, which include the sub-pixel mixing of benthic habitat and constructed class, the delay between dates of acquisition, and radiometric quality of the images. Since the fundamental relationship between reflectance value and the percentage of the constructed class has been justified and consistent, given more experiments it has the potential to predict benthic habitat composition with higher accuracy in the future.

Inspec keywords: image sensors; image classification; remote sensing; oceanographic techniques; geophysical image processing; geophysical signal processing; bathymetry; image segmentation

Other keywords: custom-made unmanned aerial vehicle; benthic habitat class spatial distribution; classification tree analysis; nonconstructed class covers carbonate sand; benthic habitat spatial distribution mapping; multispectral image; benthic habitat composition mapping; aerial image; ALOS AVNIR-2; image segmentation; individual benthic class; Sentinel-2A

Subjects: Image processing and restoration; Image sensors; Data and information; acquisition, processing, storage and dissemination in geophysics; Oceanographic and hydrological techniques and equipment; Optical, image and video signal processing; Bathymetry and seafloor topography; Instrumentation and techniques for geophysical, hydrospheric and lower atmosphere research; Computer vision and image processing techniques

References

    1. 1)
      • 26. Wicaksono, P., Aryaguna, P.A., Lazuardi, W.: ‘Benthic habitat mapping model and cross validation using machine-learning classification algorithms’, Remote Sens.., 2019, 11, p. 1279.
    2. 2)
      • 19. Zhang, C., Selch, D., Xie, Z., et al: ‘Object-based benthic habitat mapping in the Florida keys from hyperspectral imagery’, Estuar. Coast. Shelf Sci., 2013, 134, pp. 8897.
    3. 3)
      • 5. Fauzan, M.A., Kumara, I.S., Yogyantoro, R., et al: ‘Assessing the capability of sentinel-2 data for seagrass cover mapping in Jerowaru, East Lombok’, Indonesian J. Geogr., 2017, 49, (2), pp. 195203.
    4. 4)
      • 18. Bertels, L., Vanderstraete, T., Coillie, S.V., et al: ‘Mapping of coral reefs using hyperspectral CASI data; a case study: Fordata, Tanimbar, Indonesia’, Int. J. Remote Sens., 2008, 29, pp. 23592391.
    5. 5)
      • 14. Hedley, J.D., Harborne, A.R., Mumby, P.J.: ‘Simple and robust removal of sunglint for mapping shallow-water benthos’, Int. J. Remote Sens., 2005, 26, (10), pp. 21072112.
    6. 6)
      • 2. Topouzelis, K., Spondylidis, S.C., Papakonstantinou, A., et al: ‘The use of sentinel-2 imagery for seagrass mapping: Kalloni gulf (Lesvos island, Greece) case study’. Proc. SPIE 9688, Fourth Int. Conf. on Remote Sensing and Geoinformation of the Environment (RSCy2016), 96881F, 2017, doi:10.1117/12.2242887.
    7. 7)
      • 25. Mumby, P.J., Harborne, A.R.: ‘Development of a systematic classification scheme of marine habitats to facilitate regional management and mapping of Caribbean coral reefs’, Biol. Conserv., 1999, 88, pp. 155163.
    8. 8)
      • 29. Joyce, K.E., Phinn, S.R., Roelfsema, C.M.: ‘Live coral cover index testing and application with hyperspectral airborne image data’, Remote Sens.., 2013, 5, pp. 61166137.
    9. 9)
      • 16. Wicaksono, P.: ‘Improving the accuracy of multispectral-based benthic habitats mapping using image rotations: the application of principle component analysis and independent component analysis’, Eur. J. Remote Sens., 2016, 49, pp. 433463.
    10. 10)
      • 9. da Silva, G.C., de Souza, F.E., Marinho-Soriano, E.: ‘Application of ALOS AVNIR-2 for detection of seaweed and seagrass beds on the northeast of Brazil’, Int. J. Remote Sens., 2017, 38, (3), pp. 662678.
    11. 11)
      • 6. Cuevaz-Jimenez, A., Ardisson, P.L., Condal, A.R.: ‘Mapping of shallow coral reefs by colour aerial photography’, Int. J. Remote Sens., 2002, 23, (18), pp. 36973712.
    12. 12)
      • 31. Hossain, M.S., Bujang, J.S., Zakaria, M.H., et al: ‘The application of remote sensing to seagrass ecosystems: an overview and future research prospects’, Int. J. Remote Sens., 2015, 36, (1), pp. 61113.
    13. 13)
      • 11. Green, E.P., Mumby, P.J., Edwards, A. J., et al: ‘Remote sensing handbook for tropical coastal management’, in Edwards, A.J. (Ed.): ‘Coastal management sourcebooks 3’ (UNESCO, Paris, 2000).
    14. 14)
      • 3. Hedley, J., Roelfsema, C., Koetz, B., et al: ‘Capability of the sentinel 2 mission for tropical coral reef mapping and coral bleaching detection’, Remote Sens. Environ., 2012, 120, pp. 145155.
    15. 15)
      • 12. Wicaksono, P., Hafizt, M.: ‘Dark target effectiveness for dark-object subtraction atmospheric correction method on mangrove above-ground carbon stock mapping’, IET Image Process.., 2018, 12, (4), pp. 582587.
    16. 16)
      • 15. Lyzenga, D.R.: ‘Passive remote-sensing techniques for mapping water depth and bottom features’, Appl. Opt., 1978, 17, pp. 379383.
    17. 17)
      • 1. Hedley, J.D., Roelfsema, C.M., Phinn, S.R., et al: ‘Environmental and sensor limitations in optical remote sensing of coral reefs: implications for monitoring and sensor design’, Remote Sens.., 2012, 4, pp. 271302.
    18. 18)
      • 17. Manuputty, A., Gaol, J.L., Agus, S.B., et al: ‘The utilization of depth invariant Index and principle component analysis for mapping seagrass ecosystem of Kotok Island and Karang Bongkok, Indonesia’. IOP Conf. Ser. Earth Environ. Sci., 2017.
    19. 19)
      • 21. Viña, A., Gitelson, A.A.: ‘Sensitivity to foliar anthocyanin content of vegetation indices’, IEEE Geosci. Remote Sens. Lett., 2011, 8, (3), pp. 464468.
    20. 20)
      • 22. Birth, G.S., McVey, G.: ‘Measuring the color of growing turf with a reflectance spectrophotometer’, Agronomy, 1968, 66, pp. 640643.
    21. 21)
      • 32. Penuelas, J., Gamon, J. A., Griffin, K. L., et al: ‘Assessing community type, plant biomass, pigment composition, and photosynthetic efficiency of aquatic vegetation from spectral reflectance’, Remote Sens. Environ., 1993, 46, (2), pp. 110118.
    22. 22)
      • 24. Huete, A., Didan, K., Miura, T., et al: ‘Overview of the radiometric and biophysical performance of the MODIS vegetation indices’, Remote Sens. Environ., 2002, 83, pp. 195213.
    23. 23)
      • 13. Kay, S., Hedley, J.D., Lavender, S.: ‘Sun glint correction of high and low spatial resolution images of aquatic scenes: a review of methods for visible and near-infrared wavelengths’, Remote Sens.., 2009, 1, pp. 697730.
    24. 24)
      • 28. Roelfsema, C.M., Phinn, S.R.: ‘A manual for conducting georeferenced photo transects surveys to assess the benthos of coral reef and seagrass habitats’ (Centre for Remote Sensing & Spatial Information Science, School of Geography, Planning & Environmental Management University of Queensland, Queensland, 2009).
    25. 25)
      • 30. Phinn, S.R., Roelfsema, C.M., Brando, V., et al: ‘Mapping seagrass species, cover and biomass in shallow waters: an assessment of satellite multi-spectral and airborne hyper-spectral imaging systems in Moreton Bay (Australia)’, Remote Sens. Environ., 2008, 112, pp. 34133425.
    26. 26)
      • 20. Wicaksono, P.: ‘Integrated model of water column correction technique for improving satellite-based benthic habitat mapping’ (Universitas Gadjah Mada, Yogyakarta, 2010).
    27. 27)
      • 4. Traganos, D., Reinartz, P.: ‘Mapping Mediterranean seagrasses with sentinel-2 imagery’, Marine Poll. Bull., 2018, 134, pp. 197209.
    28. 28)
      • 10. Tadono, M., Shimada, M., Murakami, H., et al: ‘Calibration of PRISM and AVNIR-2 onboard ALOS ‘Daichi’‘, IEEE trans. Geosci. Remote Sens., 2009, 47, (12), pp. 40424050.
    29. 29)
      • 27. Congalton, R.G., Green, K.: ‘Assessing the accuracy of remotely sensed data: principles and practices. Mapping science’ (CRC Press, 2008).
    30. 30)
      • 8. Wicaksono, P., Hafizt, M.: ‘Mapping seagrass from space: addressing the complexity of seagrass LAI mapping’, Eur. J. Remote Sens., 2013, 46, pp. 1839.
    31. 31)
      • 23. Richardson, A.J., Everitt, J.H.: ‘Using spectra vegetation indices to estimate rangeland productivity’, Geocarto Int., 1992, 7, (1), pp. 6369.
    32. 32)
      • 7. Tran, V.D., Phinn, S.R., Roelfsema, C.: ‘Coral reef mapping in Vietnam's coastal waters from ALOS AVNIR-2 satellite and field survey data’. Proc. ACRS 2010, Hanoi, Vietnam, 2010.
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