In 2012 verloren we Jean Jacques Peters, voormalig ingenieur van het Waterbouwkundig Laboratorium (1964 tot 1979) en internationaal expert in sedimenttransport, rivierhydraulica en -morfologie. Als eerbetoon aan hem hebben we potamology (http://www.potamology.com/) gecreëerd, een virtueel gedenkarchief dat als doel heeft om zijn manier van denken en morfologische aanpak van rivierproblemen in de wereld in stand te houden en te verspreiden.
Het merendeel van z’n werk hebben we toegankelijk gemaakt via onderstaande zoekinterface.
A wavelet-enhanced inversion method for water quality retrieval from high spectral resolution data for complex waters
Ampe, E.M.; Raymaekers, D.; Hestir, E.L.; Jansen, M.; Knaeps, E.; Batelaan, O. (2015). A wavelet-enhanced inversion method for water quality retrieval from high spectral resolution data for complex waters. IEEE Trans. Giosci. Remote Sens. 53(2): 869-882. https://dx.doi.org/10.1109/TGRS.2014.2330251
In: IEEE transactions on geoscience and remote sensing. Institute of Electrical and Electronics Engineers: New York, N.Y.. ISSN 0196-2892; e-ISSN 1558-0644
Optical remote sensing in complex waters is challenging because the optically active constituents may vary independently and have a combined and interacting influence on the remote sensing signal. Additionally, the remote sensing signal is influenced by noise and spectral contamination by confounding factors, resulting in ill-posedness and ill-conditionedness in the inversion of the model. There is a need for inversion methods that are less sensitive to these changing or shifting spectral features. We propose WaveIN, a wavelet-enhanced inversion method, specifically designed for complex waters. It integrates wavelet-transformed high-spectral resolution reflectance spectra in a multiscale analysis tool. Wavelets are less sensitive to a bias in the spectra and can avoid the changing or shifting spectral features by selecting specific wavelet scales. This paper applied WaveIN to simulated reflectance spectra for the Scheldt River. We tested different scenarios, where we added specific noise or confounding factors, specifically uncorrelated noise, contamination due to spectral mixing, a different sun zenith angle, and specific inherent optical property (SIOP) variation. WaveIN improved the constituent estimation in case of the reference scenario, contamination due to spectral mixing, and a different sun zenith angle. WaveIN could reduce, but not overcome, the influence of variation in SIOPs. Furthermore, it is sensitive to wavelet edge effects. In addition, it still requires in situ data for the wavelet scale selection. Future research should therefore improve the wavelet scale selection.
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