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access icon free Dark target effectiveness for dark-object subtraction atmospheric correction method on mangrove above-ground carbon stock mapping

  • Author(s): Pramaditya Wicaksono 1  and  Muhammad Hafizt 2
    • View affiliations
  • Source: Volume 12, Issue 4, April 2018, p. 582 – 587
    DOI:  10.1049/iet-ipr.2017.0295 , Print ISSN 1751-9659, Online ISSN 1751-9667
© The Institution of Engineering and Technology
Received 05/04/2017, Accepted 03/12/2017, Revised 06/11/2017, Published 12/12/2017

One of the most effective atmospheric correction methods is dark-object subtraction (DOS) method, where the atmospheric offset can be generated from the image itself. The success of DOS strongly relies on the availability and quality of the dark target. Based on the response to the downwelling irradiances, the most effective dark target would be optically-deep water, which is not always available. It is important to assess the alternative dark targets in the absence of the ideal dark target. This research aimed at comparing the effectiveness of different dark targets for DOS method during mangrove above-ground carbon stock (AGC) mapping and comparing the accuracy with robust atmospheric correction FLAASH method. ALOS AVNIR-2 image was used as the test image, and mangrove forest of Karimunjawa and Kemujan Island was selected as the study area. The comparison covers the quality of healthy mangrove reflectance and the accuracy of vegetation indices for mangrove AGC mapping. The results of this research showed that non-ideal dark targets such as cloud-shadow pixels and the minimum value of the image can be used in the absence of ideal dark target, and DOS method is more efficient and effective than more robust atmospheric correction method.

Inspec keywords: vegetation mapping; geophysical image processing

Other keywords: DOS method; optically-deep water; mangrove forest; dark-object subtraction atmospheric correction method; mangrove above-ground carbon stock mapping; dark target quality; ALOS AVNIR-2 image; vegetation index accuracy; dark target availability; mangrove AGC mapping; atmospheric offset; dark target effectiveness; Karimunjawa Island; downwelling irradiances; nonideal dark targets; Kemujan Island

Subjects: Geophysical aspects of vegetation; Instrumentation and techniques for geophysical, hydrospheric and lower atmosphere research; Islands; Geography and cartography computing; Data and information; acquisition, processing, storage and dissemination in geophysics; Optical, image and video signal processing; Other topics in Earth sciences; Computer vision and image processing techniques

References

    1. 1)
      • 39. Liu, G.-R., Liang, C.-K., Kuo, T.-H., et al: ‘Comparison of the NDVI, ARVI and AFRI vegetation index, along with their relations with the AOD using SPOT 4 vegetation data’, Terr. Atmos. Ocean. Sci., 2004, 15, (1), p. 15.
    2. 2)
      • 7. Wicaksono, P., Danoedoro, P., Hartono, H., et al: ‘Preliminary work of mangrove ecosystem carbon stock mapping in small island using remote sensing: above and below ground carbon stock mapping on medium resolution satellite image’, in Neale, C.M.U., Maltese, A. (Eds.): ‘Proc. SPIE Remote Sensing’ (Prague, Czech Republic, 2011), doi: 10.1117/12.897926.
    3. 3)
      • 24. Poungparn, S., Komiyama, A., Intana, V., et al: ‘A quantitative analysis on the root system of a mangrove, xylocarpus granatum koenig’, Tropics, 2002, 12, (1), pp. 35–42.
    4. 4)
      • 6. Sims, D.A., Gamon, J.A.: ‘Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages’, Remote Sens. Environ., 2002, 81, (2–3), pp. 337–354.
    5. 5)
      • 46. Leprieur, C., Kerr, Y.H., Pichon, J.M.: ‘Critical assessment of vegetation indices from AVHRR in a semi-arid environment’, Int. J. Remote Sens., 1996, 17, (13), pp. 2549–2563.
    6. 6)
      • 15. Wicaksono, P., Hafizt, M.: ‘Mapping seagrass from space: addressing the complexity of seagrass LAI mapping’, Eur. J. Remote Sens., 2013, 46, pp. 18–39.
    7. 7)
      • 33. Rouse, J.W., Haas, R.H., Schell, J.A., et al: ‘Monitoring vegetation systems in the great plains with ERTS’, 1974.
    8. 8)
      • 37. Huete, A.R., Liu, H.Q.: ‘An error and sensitivity analysis of the atmospheric and soil correcting variants of the NDVI for the MODIS-EOS’, IEEE Trans. Geosci. Remote Sens., 1994, 32, (4), pp. 897–905.
    9. 9)
      • 38. Kaufman, Y.J., Tanre, D.: ‘Atmospherically resistant vegetation index (ARVI) for EOS-MODIS’, IEEE Trans. Geosci. Remote Sens., 1992, 30, (2), pp. 261–270.
    10. 10)
      • 17. Griffin, M., Burke, H.: ‘Comparison of hyperspectral data for atmospheric effects’, Lincoln Lab. J., 2003, 14, pp. 29–54.
    11. 11)
      • 19. Kruse, F.: ‘Comparison of ATREM, ACORN, and FLAASH atmospheric corrections using Low-altitude AVIRIS data of boulder, CO’ (2004).
    12. 12)
      • 13. Phinn, S., Roelfsema, C., Dekker, A., 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, (8), pp. 3413–3425.
    13. 13)
      • 14. Teillet, P.M., Fedosejevs, G.: ‘On the dark target approach to atmospheric correction of remotely sensed data’, Can. J. Remote Sens., 1995, 21, (4), pp. 374–387.
    14. 14)
      • 30. Murakami, H.: ‘Brightness mutual proofreading of AVNIR-2 and MODIS that uses directionality function of the atmospheric top reflectivity, ALOS special issue’, J. Japan Soc. Photogramm. Remote., 2007, 27, (4), pp. 354–362.
    15. 15)
      • 45. Martin, S.: ‘An introduction to ocean remote Sensing’ (Cambridge University Press, Cambridge, 2013).
    16. 16)
      • 9. Armstrong, R.A.: ‘Remote sensing of submerged vegetation canopies for biomass estimation’, Int. J. Remote Sens., 1993, 14, (3), pp. 621–627.
    17. 17)
      • 27. Komiyama, A., Poungparn, S., Kato, S.: ‘Common allometric equations for estimating the tree weight of mangroves’, J. Trop. Ecol., 2005, 21, (4), pp. 471–477.
    18. 18)
      • 36. Schlerf, M., Atzberger, C., Hill, J.: ‘Remote sensing of forest biophysical variables using HyMap imaging spectrometer data’, Remote Sens. Environ., 2005, 95, (2), pp. 177–194.
    19. 19)
      • 20. Richter, R., Schläpfer, D.: ‘Geo-atmospheric processing of airborne imaging spectrometry data. Part 2: atmospheric/topographic correction’, Int. J. Remote Sens., 2002, 23, (13), pp. 2631–2649.
    20. 20)
      • 10. Chavez, P.S.: ‘Image-based atmospheric corrections – revisited and improved’, Photogramm. Eng. Remote Sens., 1996, 62, (9), pp. 1025–1036.
    21. 21)
      • 1. Liang, S., Fang, H., Chen, M.: ‘Atmospheric correction of landsat ETM + land surface imagery. I. methods’, IEEE Trans. Geosci. Remote Sens., 2001, 39, (11), pp. 2490–2498.
    22. 22)
      • 5. Gamon, J.A., Surfus, J.S.: ‘Assessing leaf pigment content and activity with a reflectometer’, New Phytol., 1999, 143, (1), pp. 105–117.
    23. 23)
      • 43. Jiang, Z., Huete, A.R., Didan, K., et al: ‘Development of a two-band enhanced vegetation index without a blue band’, Remote Sens. Environ., 2008, 112, (10), pp. 3833–3845.
    24. 24)
      • 25. Clough, B., Scott, K.: ‘Allometric relationships for estimating above-ground biomass in six mangrove species’, For. Ecol. Manage., 1989, 27, (2), pp. 117–127.
    25. 25)
      • 47. Meer, F.D., van der Bakker, W.H., et al: ‘Vegetation indices, above ground biomass estimates and the red edge from MERIS’ (International Society for Photogrammetry and Remote Sensing (ISPRS), Amsterdam, 2000).
    26. 26)
      • 4. Nelleman, C., Corcoran, E., Duarte, C.M., et al: ‘Blue carbon. A rapid response assessment’ (United Nations Environment Programme, GRID-Arendal, 2009).
    27. 27)
      • 11. Chavez, P., Berlin, G.L., Mitchell, W.B.: ‘Computer enhancement techniques of landsat MSS digital images for landuse/landcover assessments’. Remote Sensing of Earth Resources, 1977, p. 259.
    28. 28)
      • 31. Green, E., Mumby, P., Edwards, A., et al: ‘Remote sensing handbook for tropical coastal management’ (The United Nations Educational, Scientific and Cultural Organization, Paris, 2000).
    29. 29)
      • 8. Wicaksono, P., Danoedoro, P., Hartono, , et al: ‘Mangrove biomass carbon stock mapping of the karimunjawa islands using multispectral remote sensing’, Int. J. Remote Sens., 2016, 37, (1), pp. 26–52.
    30. 30)
      • 35. Birth, G.S., McVey, G.R.: ‘Measuring the color of growing turf with a reflectance spectrophotometer1’, Agron. J., 1968, 60, (6), p. 640.
    31. 31)
      • 22. Ong, J., Gong, W., Wong, C.: ‘Allometry and partitioning of the mangrove, rhizophora apiculata’, For. Ecol. Manage., 2004, 188, (1–3), pp. 395–408.
    32. 32)
      • 42. Broge, N.H., Leblanc, E.: ‘Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density’, Remote Sens. Environ., 2001, 76, (2), pp. 156–172.
    33. 33)
      • 21. Nababan, M.G., Munasik. Yulianto, I., et al: ‘Status ekosistem taman nasional karimunjawa: 2010’ (2010).
    34. 34)
      • 2. Mahiny, A.S., Turner, B.J.: ‘A comparison of four common atmospheric correction methods’, Photogramm. Eng. Remote Sens., 2007, 73, (4), pp. 361–368.
    35. 35)
      • 32. 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, (1–2), pp. 195–213.
    36. 36)
      • 26. Komiyama, A., Ong, J.E., Poungparn, S.: ‘Allometry, biomass, and productivity of mangrove forests: a review’, Aquat. Bot., 2008, 89, (2), pp. 128–137.
    37. 37)
      • 16. Wicaksono, P.: ‘Mangrove above-ground carbon stock mapping of multi-resolution passive remote-sensing systems’, Int. J. Remote Sens., 2017, 38, (6), pp. 1551–1578.
    38. 38)
      • 18. Kotchenova, S.Y., Vermote, E.F., Matarrese, R., et al: ‘Validation of a vector version of the 6S radiative transfer code for atmospheric correction of satellite data. Part I: path radiance’, Appl. Opt., 2006, 45, (26), pp. 6762–6774.
    39. 39)
      • 44. Pinty, B., Verstraete, M.M.: ‘GEMI: A Non-linear index to monitor global vegetation from satellites’, Vegetatio, 1992, 101, (1), pp. 15–20.
    40. 40)
      • 23. Comley, B.W.T., McGuinness, K.A.: ‘Above and below ground biomass, and allometry, of four common northern Australian mangroves’, Aust. J. Bot., 2005, 53, (5), p. 431.
    41. 41)
      • 12. Hadjimitsis, D.G., Papadavid, G., Agapiou, A., et al: ‘Atmospheric correction for satellite remotely sensed data intended for agricultural applications: impact on vegetation indices’, Nat. Hazards Earth Syst. Sci., 2010, 10, (1), pp. 89–95.
    42. 42)
      • 48. Wamunyima, S.: ‘Estimating fresh grass biomass at landscape level using hyperspectral remote Sensing’ (ITC, Enschede, 2005).
    43. 43)
      • 34. Richardson, A.J., Everitt, J.H.: ‘Using spectral vegetation indices to estimate rangeland productivity’, Geocarto Int., 1992, 7, (1), pp. 63–69.
    44. 44)
      • 40. Gitelson, A.A., Kaufman, Y.J., Stark, R., et al: ‘Novel algorithms for remote estimation of vegetation fraction’, Remote Sens. Environ., 2002, 80, (1), pp. 76–87.
    45. 45)
      • 28. Journal E, I.A.W.A.: ‘Indonesian wood atlas, volume I. Abdurahamin martawijaya, lding kartasujana, kosasi kadir, and sowanda among prawira, vi + 166 pp., illus., 1986’, IAWA J., 1990, 11, (1), pp. 84–84.
    46. 46)
      • 29. Harja, D., Rahayu, S., Pambudi, S.: ‘ICRAF Database – wood density’, http://db.worldagroforestry.org/wd, accessed March 2013.
    47. 47)
      • 41. Vina, A., Gitelson, A.A.: ‘Sensitivity to foliar anthocyanin content of vegetation indices using green reflectance’, IEEE Geosci. Remote Sens. Lett., 2011, 8, (3), pp. 464–468.
    48. 48)
      • 3. Song, C., Woodcock, C.E., Seto, K.C., et al: ‘Classification and change detection using landsat TM data’, Remote Sens. Environ., 2001, 75, (2), pp. 230–244.
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