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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2021-02-01
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| Series: | European Journal of Remote Sensing |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/22797254.2020.1811776 |
| _version_ | 1818848975798665216 |
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| author | Liza Stančič Krištof Oštir Žiga Kokalj |
| author_facet | Liza Stančič Krištof Oštir Žiga Kokalj |
| author_sort | Liza Stančič |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-19T06:25:53Z |
| format | Article |
| id | doaj.art-645f31ab296748918a4c11fccc0dc18b |
| institution | Directory Open Access Journal |
| issn | 2279-7254 |
| language | English |
| last_indexed | 2024-12-19T06:25:53Z |
| publishDate | 2021-02-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | European Journal of Remote Sensing |
| spelling | doaj.art-645f31ab296748918a4c11fccc0dc18b2022-12-21T20:32:34ZengTaylor & Francis GroupEuropean Journal of Remote Sensing2279-72542021-02-0154S1314610.1080/22797254.2020.18117761811776Fluvial gravel bar mapping with spectral signal mixture analysisLiza Stančič0Krištof Oštir1Žiga Kokalj2Research Centre of the Slovenian Academy of Sciences and ArtsUniversity of LjubljanaResearch Centre of the Slovenian Academy of Sciences and ArtsThe 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.http://dx.doi.org/10.1080/22797254.2020.1811776geomorphologyriverssub-pixelalpinemultispectral datamapping |
| spellingShingle | Liza Stančič Krištof Oštir Žiga Kokalj Fluvial gravel bar mapping with spectral signal mixture analysis European Journal of Remote Sensing geomorphology rivers sub-pixel alpine multispectral data mapping |
| title | Fluvial gravel bar mapping with spectral signal mixture analysis |
| title_full | Fluvial gravel bar mapping with spectral signal mixture analysis |
| title_fullStr | Fluvial gravel bar mapping with spectral signal mixture analysis |
| title_full_unstemmed | Fluvial gravel bar mapping with spectral signal mixture analysis |
| title_short | Fluvial gravel bar mapping with spectral signal mixture analysis |
| title_sort | fluvial gravel bar mapping with spectral signal mixture analysis |
| topic | geomorphology rivers sub-pixel alpine multispectral data mapping |
| url | http://dx.doi.org/10.1080/22797254.2020.1811776 |
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