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Integration of multi-sensor analysis and decision tree for evaluation of dual and quad-Pol SAR in L- and C-bands applied for marsh delineation

Author

Listed:
  • João Paulo Delapasse Simioni

    (Research Center on Remote Sensing and Meteorology)

  • Laurindo Antonio Guasselli

    (Research Center on Remote Sensing and Meteorology)

  • Victor Fernandez Nascimento

    (Earth System Science Center)

  • Luis Fernando Chimelo Ruiz

    (Research Center on Remote Sensing and Meteorology)

  • Tassia Fraga Belloli

    (Research Center on Remote Sensing and Meteorology)

Abstract

Marsh is a wetland type characterized by hydromorphic soils, herbaceous vegetation, aquatic and emergent vegetation; usually, the apparent water surface does not exceed 25% of the area. Multi-polarized active remote sensors with different frequencies have characteristics that make them ideal for mapping and delineating marsh areas since they provide information on canopy roughness, vegetation moisture and amount of biomass. Therefore, the main objective of this study is to develop a method based on multi-frequency radar satellites images to delineate marsh areas using decision tree classification. In order to reach this objective, we sought to answer the following questions: (1) Are L-band SAR images more efficient for marshes delineation than C-band SAR images? (2) Is multi-sensor (L and C-band) integration more accurate for marsh areas delineation than a single sensor? and (3) What are the most efficient channels for marshes delineation? Our findings showed that L-band images present greater proportion correct (PC) for marshes delineation compared to C-band images. However, the greatest PC was found using integration of Alos Palsar 1 and Sentinel 1 satellites images, reaching more than 72% of correctness. Regarding the polarization importance to Alos Palsar 1 image, HVVH presented the highest importance, with 29%, followed by VH and HV polarizations, both with 28%. For Sentinel 1 image, the most important polarization was VH, with 22%, followed by VV + VH that presented 20%. HVVH polarization was the most important in Alos and Sentinel images integration, with 35%, followed by Alos Palsar HV and VH, with 34 and 33%, respectively. Thus, we concluded that the method based on SAR multi-frequency data integration used in this study can be easily applied by other researchers interested in marsh delineation since the radar images used are freely available and can be processed and manipulated in free GIS software.

Suggested Citation

  • João Paulo Delapasse Simioni & Laurindo Antonio Guasselli & Victor Fernandez Nascimento & Luis Fernando Chimelo Ruiz & Tassia Fraga Belloli, 2020. "Integration of multi-sensor analysis and decision tree for evaluation of dual and quad-Pol SAR in L- and C-bands applied for marsh delineation," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(6), pages 5603-5620, August.
    • Handle: RePEc:spr:endesu:v:22:y:2020:i:6:d:10.1007_s10668-019-00442-0
    DOI: 10.1007/s10668-019-00442-0
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    References listed on IDEAS

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    1. Mohammed Dabboor & Brian Brisco, 2019. "Wetland Monitoring and Mapping Using Synthetic Aperture Radar," Chapters, in: Didem Gokce (ed.), Wetlands Management - Assessing Risk and Sustainable Solutions, IntechOpen.
    2. N. B. Eniolorunda & S. A. Mashi & G. N. Nsofor, 2017. "Toward achieving a sustainable management: characterization of land use/land cover in Sokoto Rima floodplain, Nigeria," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 19(5), pages 1855-1878, October.
    3. Laurel Ballanti & Kristin B. Byrd & Isa Woo & Christopher Ellings, 2017. "Remote Sensing for Wetland Mapping and Historical Change Detection at the Nisqually River Delta," Sustainability, MDPI, vol. 9(11), pages 1-32, October.
    4. Waheed, T. & Bonnell, R.B. & Prasher, S.O. & Paulet, E., 2006. "Measuring performance in precision agriculture: CART--A decision tree approach," Agricultural Water Management, Elsevier, vol. 84(1-2), pages 173-185, July.
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