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Use of Multispectral and Hyperspectral Satellite Imagery for Monitoring Waterbodies and Wetlands

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Southern Iraq's Marshes

Part of the book series: Coastal Research Library ((COASTALRL,volume 36))

Abstract

Timely and accurate change detection (CD) of Earth’s surface features is important for understanding interactions between human and natural phenomena. Remote sensing (RS) as the most important information resource plays a role key in monitoring and assessment of the environment. One of most applications of hyperspectral imagery is CD. The hyperspectral imagery provides more details from CD compared to multispectral images. Wetlands are one of the most influential ecosystems in the natural environment for which it is very difficult to find an alternative. The monitoring wetland and waterbody areas based on RS imagery need special techniques due to some limitations (existence noise and condition atmospheric, need to high training data and threshold selection, and complexity of water body areas). Based on these problems it is necessary to CD methods to minimize problems so, this research proposed a framework for hyperspectral CD methods on wetland and water body areas. The proposed method is based on incorporating chronochrome, Z-score analysis, Otsu algorithm, SImplex via Split Augmented Lagrangian (SISAL), Harsanyi–Farrand–Chang (HFC), Pearson correlation coefficient (PCC), and support vector machine (SVM) to detect changes using hyperspectral imagery. The proposed method is applied in four main steps: (1) produce a training data for tuning SVM and kernel parameters, (2) predicted change areas based on a chronochrome algorithm and binary change map obtained using SVM classifier, (3) the amplitude of changes is created by Z-Score analysis and binary change mask, and (4) the multiple change map is produced based on the estimation of number and extraction of endmembers and similarity measure. To evaluate the performance of the proposed method, multi-temporal hyperspectral Hyperion images for Shadegan Wetland were used. The results show high accuracy and low false alarms rate of proposed method methods with an overall accuracy of more than 96%, kappa coefficient of more than 0.82. Besides, the proposed method can provide ‘multiple changes’ as well as the magnitude of the extracted changes.

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Notes

  1. 1.

    Operational Land Imager.

  2. 2.

    Multispectral Scanner System.

  3. 3.

    Enhanced Thematic Mapper.

  4. 4.

    Thematic Mapper.

  5. 5.

    Thermal Infrared Sensor.

  6. 6.

    Enhanced Thematic Mapper Plus.

  7. 7.

    Advanced Land Observation Satellite.

  8. 8.

    Advanced Visible and Near Infrared Radiometer type 2.

  9. 9.

    Phased Array type L-band Synthetic Aperture Radar.

  10. 10.

    Iterative Self-Organizing Data Analysis Techniques.

  11. 11.

    Environmental Mapping and Analysis Program.

  12. 12.

    PRecursore IperSpettrale della Missione Applicativa.

  13. 13.

    Hyperspectral Infrared Imager.

  14. 14.

    Pearson correlation coefficient.

  15. 15.

    Simplex identification via split augmented Lagrangian.

  16. 16.

    Harsanyi–Farrand–Chang.

  17. 17.

    Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes.

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Authors and Affiliations

  1. School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran

    Mahdi Hasanlou & Seyd Teymoor Seydi

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  1. Mahdi Hasanlou
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Correspondence to Mahdi Hasanlou .

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  1. School of Environmental and Animal Sciences, Unitec Institute of Technology, Mt Albert, Auckland, New Zealand

    Laith A. Jawad

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Hasanlou, M., Seydi, S.T. (2021). Use of Multispectral and Hyperspectral Satellite Imagery for Monitoring Waterbodies and Wetlands. In: Jawad, L.A. (eds) Southern Iraq's Marshes. Coastal Research Library, vol 36. Springer, Cham. https://doi.org/10.1007/978-3-030-66238-7_9

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