Fluvial gravel bar mapping with spectral signal mixture analysis

The paper presents a method for mapping fluvial gravel bars based on Sentinel-2 and Landsat imagery. The proposed method therefore uses spectral signal mixture analysis (SSMA) because its results allow the development of land cover fraction maps for surface water, gravel, and vegetation. The method...

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Bibliographic Details
Published in:European journal of remote sensing 2021-02, Vol.54 (sup1), p.31-46
Main Authors: Stančič, Liza, Oštir, Krištof, Kokalj, Žiga
Format: Article
Language:English
Subjects:
alpine
Bars (landforms)
Classification
Geomorphology
Gravel
Land cover
Land use
Landsat
Landsat satellites
Mapping
Mountainous areas
multispectral data
Remote sensing
Remote sensing systems
River basins
rivers
Satellite imagery
Spectra
sub-pixel
Surface water
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Summary:The paper presents a method for mapping fluvial gravel bars based on Sentinel-2 and Landsat imagery. The proposed method therefore uses spectral signal mixture analysis (SSMA) because its results allow the development of land cover fraction maps for surface water, gravel, and vegetation. The method is validated on a spatially heterogeneous mountainous area in the upper Soča river basin in north-west Slovenia, Central Europe. Unmixing results in highly accurate fraction maps with MAE of around 0.1. Gravel fractions are mapped the most accurately, indicating that the approach can be used successfully for fluvial gravel bar mapping. Endmember sets selected automatically perform slightly worse (MAE higher by at most 0.05) than sets selected manually based on high resolution reference data. Both Sentinel-2 and Landsat imagery can be used for accurate mapping with differences between the two remote sensing systems within 0.05 MAE. For the study area, the SSMA-based soft classification method is more accurate for land cover mapping than a Spectral Angle Mapping-based hard classification. The method is promising for an effective use in other cases where highly accurate subpixel information is needed, because it is able to detect small-scale changes that could go unnoticed with hard classification mapping.
ISSN:2279-7254
2279-7254
DOI:10.1080/22797254.2020.1811776