Abstract
Compared to natural waters, mine waters represent an extreme water type that is frequently heavily polluted. Although they have been traditionally monitored by in situ measurements of point samples taken at regular intervals, the emergence of a new generation of multispectral and hyperspectral (HS) sensors means that image spectroscopy has the potential to become a modern method for monitoring polluted surface waters. This paper describes an approach employing linear Spectral Unmixing (LSU) for analysis of hyperspectral image data to map the relative abundances of mine water components (dissolved Fe—Fediss, dissolved organic carbon—DOC, undissolved particles). The ground truth data (8 monitored ponds) were used to validate the results of spectral mapping. The same approach applied to HS data was tested using the image data resampled to WorldView2 (WV2) spectral resolution. A key aspect of the image data processing was to define the proper pure image end members for the fundamental water types. The highest correlations detected between the studied water parameters and the fractional images using the HyMap and the resampled WV2 data, respectively, were: dissolved Fe (R2 = 0.74 and R2vw2 = 0.6), undissolved particles (R2 = 0.57 and R2vw2 = 0.49) and DOC (R2 = 0.42 and R2vw2 < 0.40). These fractional images were further classified to create semi-quantitative maps. In conclusion, the classification still benefited from the higher spectral resolution of the HyMap data; however the WV2 reflectance data can be suitable for mapping specific inherent optical properties (SIOPs), which significantly differ from one another from an optical point of view (e.g., mineral suspension, dissolved Fe and phytoplankton), but it seems difficult to differentiate among diverse suspension particles, especially when the waters have more complex properties (e.g., mineral particles, DOC together with tripton or other particles, etc.).
Highlights
Compared to natural waters, mine waters represent an extreme water type that is frequently heavily polluted
The absorption and scattering properties of water are described by its inherent optical properties (IOPs), where the absorption coefficient, volume scattering function and beam attenuation coefficient belong among major IOPs
This paper demonstrates that the linear Spectral Unmixing (LSU) method can be utilized to map selected water parameters at a semi-quantitative level using HyMap or World View2 image data even when there is a limited amount of ground truth data
Summary
Mine waters represent an extreme water type that is frequently heavily polluted. A common way to quantify this contribution is based on the use of specific inherent optical properties (SIOPs) These explain how much each substance contributes to the final absorption and scattering [2] and are estimated from derived IOP values and the measured concentrations of the water constituents [3]. We tested an approach using fundamental water end members derived from the HS images to map the relative abundances of the selected surface water parameters and the ground truth data to validate the results of spectral mapping. The WV2 data, which has sufficient spectral and spatial resolution, exhibit an obvious potential for mine water monitoring and we subsequently tested the same approach employed for the HS data using the HyMap image data resampled to WV2 spectral resolution. A test was performed to discover whether the same approach can be employed successfully using the HyMap image data resampled to World View 2 spectra resolution
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