Share:


A Sub-pixel visualization method to display fuzzy Phenomena using RGB color composite (case study: mangroves forest)

    Ara Toomanian   Affiliation

Abstract

Natural phenomena boundaries and complexity of features in an urban area due to the low spatial resolution, lead to more pixels of satellite images included in reflectance of multiple land-cover/object components. The sub-pixel information extracting model outputs are fractional cover maps of interested class (end-member), with membership values between zero and one. These maps represented gradient change in only one fuzzy phenomenon boundaries such as vegetation cover. However, in multiple fuzzy class area or complex fuzzy phenomena such as mangrove forests, in the northwest of the Qeshm Island, Hormozgan, Iran, displaying several fractional covers may cause confusion and misunderstanding for the end-user. In this study, an additive color composite and spectral mixture analysis method is utilized for multiple fractional cover representation.  The proposed method is implemented on images acquired from Operational Land Imager (OLI) sensor in the Landsat 8 satellite to extract three fractional covers (water, vegetation, and soil). An RGB color composite was used for each type and percentage of fractional cover for given pixel to display fractional cover separately. Based on such RGB color composite represented both quantitative and qualitative information, we used the RGB color solid cube as map legend for better understanding and map interpretation. The result of this study showed that suggested sub-pixel visualization method, gives new vision to the end-user understanding of fuzzy phenomena.

Keyword : sub-pixel visualization, fuzzy Phenomena, RGB color composite, mangroves forest

How to Cite
Toomanian, A. (2022). A Sub-pixel visualization method to display fuzzy Phenomena using RGB color composite (case study: mangroves forest). Geodesy and Cartography, 48(4), 193–201. https://doi.org/10.3846/gac.2022.16092
Published in Issue
Nov 28, 2022
Abstract Views
97
PDF Downloads
59
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Adams, J. B., Sabol, D. E., Kapos, V., Almeida Filho, R., Roberts, D. A., Smith, M. O., & Gillespie, A. R. (1995). Classification of multispectral im-ages based on fractions of endmembers: Application to land-cover change in the Brazilian Amazon. Remote sensing of Environment, 52(2), 137–154. https://doi.org/10.1016/0034-4257(94)00098-8

Adams, J. B., Smith, M. O., & Johnson, P. E. (1986). Spectral mixture modeling: A new analysis of rock and soil types at the Viking Lander 1 site. Journal of Geophysical Research: Solid Earth, 91(B8), 8098–8112. https://doi.org/10.1029/JB091iB08p08098

Atkinson, P. M. (1997). Mapping sub-pixel boundaries from remotely sensed images. In Z. Kemp (Ed.), Innovations in GIS 4 (pp. 166–180). Taylor and Francis.

Atkinson, P. M. (2009). Issues of uncertainty in super-resolution mapping and their implications for the design of an inter-comparison study. Interna-tional Journal of Remote Sensing, 30, 5293–5308. https://doi.org/10.1080/01431160903131034

Barré, B. A. (2013). Techniques for the visualization of positional geospatial uncertainty [Master thesis]. University of New Orleans.

Burrough, P. A., & Frank, A. (1996). Geographic objects with indeterminate boundaries (1st ed.). CRC Press.

Foody, G. M. (2002). The role of soft classification techniques in the refinement of estimates of ground control point location. Photogrammetric Engi-neering and Remote Sensing, 68, 897–903.

Franke, J., Roberts, D. A., Halligan, K., & Menz, G. (2009). Hierarchical multiple endmember spectral mixture analysis (MESMA) of hyperspectral imagery for urban environments. Remote Sensing of Environment, 113(8), 1712–1723. https://doi.org/10.1016/j.rse.2009.03.018

Gong, Z., Cui, T., Pu, R., Lin, C., & Chen, Y. (2015). Dynamic simulation of vegetation abundance in a reservoir riparian zone using a sub-pixel Mar-kov model. International Journal of Applied Earth Observation and Geoinformation, 35, 175–186. https://doi.org/10.1016/j.jag.2014.09.004

Hengl, T. (2003, January). Visualisation of uncertainty using the HSI colour model: Computations with colours. In 7th International Conference on GeoComputation. University of Southampton, United Kingdom.

Hengl, T., Walvoort, D. J., & Brown, A. (2002, January). Pixel (PM) and colour mixture (CM): GIS techniques for visualization of fuzziness and un-certainty of natural resource inventories. In 5th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences (Accuracy 2002). Delft University Press.

Hengl, T., Walvoort, D. J., Brown, A., & Rossiter, D. G. (2004). A double continuous approach to visualization and analysis of categorical maps. In-ternational Journal of Geographical Information Science, 18(2), 183–202. https://doi.org/10.1080/13658810310001620924

Jensen, J. R. (2005). Introductory digital image processing (3rd ed.). Prentice Hall.

Myint, S. W., & Okin, G. S. (2009). Modelling land‐cover types using multiple endmember spectral mixture analysis in a desert city. International Journal of Remote Sensing, 30(9), 2237–2257. https://doi.org/10.1080/01431160802549328

Peng, Y., Chen, G., Tian, G., & Yang, X. (2009). Niches of plant populations in mangrove reserve of Qi’ao Island, Pearl River Estuary. Acta Ecologica Sinica, 29(6), 357–361. https://doi.org/10.1016/j.chnaes.2009.09.017

Powell, R. L., Roberts, D. A., Dennison, P. E., & Hess, L. L. (2007). Sub-pixel mapping of urban land cover using multiple endmember spectral mix-ture analysis: Manaus, Brazil. Remote Sensing of Environment, 106(2), 253–267. https://doi.org/10.1016/j.rse.2006.09.005

Reschke, J., & Hüttich, C. (2014). Continuous field mapping of Mediterranean wetlands using sub-pixel spectral signatures and multi-temporal Landsat data. International Journal of Applied Earth Observation and Geoinformation, 28, 220–229. https://doi.org/10.1016/j.jag.2013.12.014

Reynolds, R. (2002). Oceanography. The Gulf ecosystem: Health and sustainability (pp. 55–64). Backhuys Publishers. https://doi.org/10.14321/j.ctt1tm7jkg.11

Roberts, D. A., Gardner, M., Church, R., Ustin, S., Scheer, G., & Green, R. O. (1998). Mapping chaparral in the Santa Monica Mountains using multi-ple endmember spectral mixture models. Remote Sensing of Environment, 65(3), 267–279. https://doi.org/10.1016/S0034-4257(98)00037-6

Roberts, D. A., Adams, J. B., & Smith, M. O. (1990, May 20–24). Transmission and scattering of light by leaves: Effects on spectral mixtures. In Pro-ceedings of IGARSS (pp. 1381–1384). College Park, Maryland, USA. IEEE. https://doi.org/10.1109/IGARSS.1990.688757

Shahraki, M., Saint-Paul, U., Krumme, U., & Fry, B. (2016). Fish use of intertidal mangrove creeks at Qeshm Island, Iran. Marine Ecology Progress Series, 542, 153–166. https://doi.org/10.3354/meps11546

Small, C. (2005). A global analysis of urban reflectance. International Journal of Remote Sensing, 26(4), 661–681. https://doi.org/10.1080/01431160310001654950

Thu, P. M., & Populus, J. (2007). Status and changes of mangrove forest in Mekong Delta: Case study in Tra Vinh, Vietnam. Estuarine, Coastal and Shelf Science, 71(1), 98–109. https://doi.org/10.1016/j.ecss.2006.08.007

Westland, S., & Cheung, V. (2012). RGB systems. In J. Chen, W. Cranton, & M. Fihn (Eds.). Handbook of visual display technology (pp. 147–153). Springer. https://doi.org/10.1007/978-3-540-79567-4_12

Wu, C. (2004). Normalized spectral mixture analysis for monitoring urban composition using ETM+ imagery. Remote Sensing of Environment, 93(4), 480–492. https://doi.org/10.1016/j.rse.2004.08.003

Zhang, Q. (2008). Animated representation of uncertainty and fuzziness in spatial planning maps [MSc Thesis]. ITC, Enschede.

Zlinszky, A., & Kania, A. (2016, June). Will it blend? Visualization and accuracy evaluation of high-resolution fuzzy vegetation maps. In International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXIII ISPRS Congress (Vol. XLI-B2, pp. 335–342). https://doi.org/10.5194/isprsarchives-XLI-B2-335-2016