DOI: https://doi.org/10.14710/geoplanning.4.1.27-40

ACCURACY ASSESSMENTS OF PAN-SHARPENED IMAGE FOR BENTHIC HABITATS MAPPING

*Pramaditya Wicaksono  -  Universitas Gadjah Mada, Indonesia
Faza Adhimah  -  Remote Sensing and Geographic Information System, Vocational School of Universitas Gadjah Mada, Indonesia

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Abstract

Image-sharpening process integrates lower spatial resolution multispectral bands with higher spatial resolution panchromatic band to produce multispectral bands with finer spatial detail called pan-sharpened image. Although the pan-sharpened image can greatly assist the process of information extraction using visual interpretation, the benefit and setback of using pan-sharpened image on the accuracy of digital classification for mapping remain unclear. This research aimed at 1) highlighting the issue of using pan-sharpened image to perform benthic habitats mapping and 2) comparing the accuracy of benthic habitats mapping using original and pan-sharpened bands. In this study, Quickbird image was used and Kemujan Island was selected as the study area. Two levels of hierarchical classification scheme of benthic habitats were constructed based on the composition of in situ benthic habitats. PC Spectral sharpening method was applied on Quickbird image. Image radiometric corrections, PCA transformation, and image classifications were performed on both original and pan-sharpened image. The results showed that the accuracy of benthic habitats classification of pan-sharpened image (maximum overall accuracy 64.28% and 73.30% for per-pixel and OBIA, respectively) was lower than the original image (73.46% and 73.10%, respectively). The main setback of using pan-sharpened image is the inability to correct the sunglint, hence adversely affects the process of water column correction, PCA transformation and image classification. This is mainly because sunglint do not only affect object’s spectral response but also the texture of the object. Nevertheless, the pan-sharpened image can still be used to map benthic habitats using visual interpretation and digital image processing. Pan-sharpened image will deliver better classification accuracy and visual appearance especially when the sunglint is low.

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Keywords: image-sharpening; pan-sharpened; Quickbird; benthic habitats; mapping accuracy

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Section: Original Research
Language : EN
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  1. Benz, U. C., et al. (2004). Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS Journal of Photogrammetry and Remote Sensing, 58(3–4), 239–258. [https://doi.org/10.1016/j.isprsjprs.2003.10.002">CrossRef]

  2. Blaschke, T. (2010). Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 65(1), 2–16. [https://doi.org/10.1016/j.isprsjprs.2009.06.004">CrossRef]  

  3. Blaschke, T., Lang, S., & Hay, G. (2008). Object-Based Image Analysis: Spatial Concepts for Knowledge-Driven Remote Sensing Applications. Springer Berlin Heidelberg. [https://books.google.co.id/books?id=5ktbsV-aSBAC">Google Books]

  4. Congalton, R. G., & Green, K. (2008). Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, Second Edition. CRC Press. [https://books.google.co.id/books?id=T4zj2bnGldEC">Google Books]

  5. Eugenio, F., Marcello, J., & Martin, J. (2015). High-resolution maps of bathymetry and benthic habitats in shallow-water environments using multispectral remote sensing imagery. IEEE Transactions on Geoscience and Remote Sensing, 53(7), 3539–3549. [https://doi.org/10.1109/TGRS.2014.2377300">CrossRef]

  6. Felde, G. W., et al. (2003). Analysis of Hyperion data with the FLAASH atmospheric correction algorithm. In Geoscience and Remote Sensing Symposium, 2003. IGARSS’03. Proceedings. 2003 IEEE International (Vol. 1, pp. 90–92).

  7. Goodman, J. A., Purkis, S. J., & Phinn, S. R. (2013). Coral reef remote sensing: guide for mapping, monitoring and management. Springer. [https://doi.org/10.1007/978-90-481-9292-2">CrossRef]

  8. Green, E. P., et al. (2005). Remote sensing handbook for tropical coastal management. Paris: Unesco.

  9. Hedley, J. D., Harborne, A. R., & Mumby, P. J. (2005). Technical note: Simple and robust removal of sun glint for mapping shallow‐water benthos. International Journal of Remote Sensing, 26(10), 2107–2112. [https://doi.org/10.1080/01431160500034086">CrossRef]

  10. Hedley, J. D., et al. (2012). Environmental and Sensor Limitations in Optical Remote Sensing of Coral Reefs: Implications for Monitoring and Sensor Design. Remote Sensing, 4(12), 271–302. [https://doi.org/10.3390/rs4010271">CrossRef]

  11. Hochberg, E. J., & Atkinson, M. J. (2000). Spectral discrimination of coral reef benthic communities. Coral Reefs, 19(2), 164–171. [https://doi.org/10.1007/s003380000087">CrossRef]

  12. Jupp, D. L. B. (1988). Background and extensions to depth of penetration (DOP) mapping in shallow coastal waters. In Proceedings of the Symposium on Remote Sensing of the Coastal Zone (p. IV--2).

  13. Kay, S., Hedley, J. D., & Lavender, S. (2009). Sun Glint Correction of High and Low Spatial Resolution Images of Aquatic Scenes: a Review of Methods for Visible and Near-Infrared Wavelengths. Remote Sensing, 1(4), 697–730. [https://doi.org/10.3390/rs1040697">CrossRef]

  14. Krause, K. (2003). Radiance conversion of QuickBird data. Technical Note RS TN Radiometric Radiance

  15. Lyzenga, D. R. (1978). Passive remote sensing techniques for mapping water depth and bottom features. Applied Optics, 17(3), 379. [https://doi.org/10.1364/AO.17.000379">CrossRef]

  16. MacLean, M. G., & Congalton, R. G. (2012). Map accuracy assessment issues when using an object-oriented approach. In Proceedings of the American Society for Photogrammetry and Remote Sensing 2012 Annual Conference (pp. 1–5).

  17. Mather, P. M., & Koch, M. (2011). Computer Processing of Remotely-Sensed Images: An Introduction. Wiley. [https://books.google.co.id/books?id=GWhvDMNh1hAC">Google Books]

  18. Nababan, M. G., Munasik, I. Y., & Kartawijaya, R. P. (2010). Status Ekosistem di Taman Nasional Karimunjawa: 2010. Bogor: Wildlife Conservation Society-Indonesia Program.

  19. Phinn, S. R., Roelfsema, C. M., & Mumby, P. J. (2012). Multi-scale, object-based image analysis for mapping geomorphic and ecological zones on coral reefs. International Journal of Remote Sensing, 33(12), 3768–3797. [https://doi.org/10.1080/01431161.2011.633122">CrossRef]

  20. Richards, J. A. (2013). Remote Sensing Digital Image Analysis. Springer Nature. [https://doi.org/10.1007/978-3-642-30062-2">CrossRef]

  21. Roelfsema, C. M., & Phinn, S. R. (2009). A manual for conducting georeferenced photo transects surveys to assess the benthos of coral reef and seagrass habitats version 3.0.

  22. Roelfsema, C., et al. (2013). Mapping coral reefs at reef to reef-system scales, 10s–1000s km 2 , using object-based image analysis. International Journal of Remote Sensing, 34(18), 6367–6388. [https://doi.org/10.1080/01431161.2013.800660">CrossRef]

  23. Welch, R., & Ehlers, M. (1987). Merging multiresolution SPOT HRV and Landsat TM data. Photogrammetric Engineering and Remote Sensing, 53, 301–303.

  24. Wicaksono, P. (2010). Integrated Model of Water Column Correction Technique for Improving Satellite-Based Benthic Habitat Mapping. Yogyakarta: Gadjah Mada University.

  25. Wicaksono, P. (2014). The use of image rotations on multispectral-based benthic habitats mapping. In The 12th Biennial Conference of PORSEC 2014.

  26. Wicaksono, P. (2015). Mapping Seagrass Leaf Area Index, Standing Crop, and Above Ground Carbon Stock Using Compressed Remote Sensing Data. In 1st International Conference of Indonesian Society for Remote Sensing 2015.

  27. Wicaksono, P. (2016). Improving the accuracy of Multispectral-based benthic habitats mapping using image rotations: the application of Principle Component Analysis and Independent Component Analysis. European Journal of Remote Sensing, 433–463. [https://doi.org/10.5721/EuJRS20164924">CrossRef]

  28. Wicaksono, P., Akhyar, P. A., & Hidayat, A. (2015). Pemetaan Habitat Bentik Sebagai Dasar Pengelolaan Wilayah Pesisir dan Pulau-Pulau Kecil (Studi Kasus Pulau Menjangan Besar dan Menjangan Kecil Kepulauan Karimunjawa). In Seminar Nasional Pengelolaan Pesisir & Daerah Aliran Sungai ke-1 Universitas Gadjah Mada, Yogyakarta. Retrieved from https://www.researchgate.net/publication/283443268_Pemetaan_Habitat_Bentik_Sebagai_Dasar_Pengelolaan_Wilayah_Pesisir_dan_Pulau-Pulau_Kecil_Studi_Kasus_Pulau_Menjangan_Besar_dan_Menjangan_Kecil_Kepulauan_Karimunjawa">

  29. Wicaksono, P., & Hafizt, M. (2013). Mapping Seagrass from Space: Addressing the Complexity of Seagrass LAI Mapping. European Journal of Remote Sensing, 18–39. [https://doi.org/10.5721/EuJRS20134602">CrossRef]

  30. Zhang, C., et al. (2013). Object-based benthic habitat mapping in the Florida Keys from hyperspectral imagery. Estuarine, Coastal and Shelf Science, 134, 88–97. [https://doi.org/10.1016/j.ecss.2013.09.018">CrossRef]


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