@article{BrosinskyFoersterSegletal.2014, author = {Brosinsky, Arlena and F{\"o}rster, Saskia and Segl, Karl and Lopez-Tarazon, Jos{\´e} Andr{\´e}s and Pique, Gemma and Bronstert, Axel}, title = {Spectral fingerprinting: characterizing suspended sediment sources by the use of VNIR-SWIR spectral information}, series = {Journal of soils and sediments : protection, risk assessment and remediation}, volume = {14}, journal = {Journal of soils and sediments : protection, risk assessment and remediation}, number = {12}, publisher = {Springer}, address = {Heidelberg}, issn = {1439-0108}, doi = {10.1007/s11368-014-0927-z}, pages = {1965 -- 1981}, year = {2014}, abstract = {Knowledge of sediment sources is a prerequisite for sustainable management practices and may furthermore improve our understanding of water and sediment fluxes. Investigations have shown that a number of characteristic soil properties can be used as "fingerprints" to trace back the sources of river sediments. Spectral properties have recently been successfully used as such characteristics in fingerprinting studies. Despite being less labour-intensive than geochemical analyses, for example, spectroscopy allows measurements of small amounts of sediment material (> 60 mg), thus enabling inexpensive analyses even of intra-event variability. The focus of this study is on the examination of spectral properties of fluvial sediment samples to detect changes in source contributions, both between and within individual flood events. Sediment samples from the following three different origins were collected in the Isabena catchment (445 km(2)) in the central Spanish Pyrenees: (1) soil samples from the main potential source areas, (2) stored fine sediment from the channel bed once each season in 2011 and (3) suspended sediment samples during four flood events in autumn 2011 and spring 2012 at the catchment outlet as well as at several subcatchment outlets. All samples were dried and measured for spectral properties in the laboratory using an ASD spectroradiometer. Colour parameters and physically based features (e.g. organic carbon, iron oxide and clay content) were calculated from the spectra. Principal component analyses (PCA) were applied to all three types of samples to determine natural clustering of samples, and a mixing model was applied to determine source contributions. We found that fine sediment stored in the river bed seems to be mainly influenced by grain size and seasonal variability, while sampling location-and thus the effect of individual tributaries or subcatchments-seem to be of minor importance. Suspended sediment sources were found to vary between, as well as within, flood events; although badlands were always the major source. Forests and grasslands contributed little (< 10 \%), and other sources (not further determinable) contributed up to 40 \%. The analyses further suggested that sediment sources differ among the subcatchments and that subcatchments comprising relatively large proportions of badlands contributed most to the four flood events analyzed. Spectral fingerprints provide a rapid and cost-efficient alternative to conventional fingerprint properties. However, a combination of spectral and conventional fingerprint properties could potentially permit discrimination of a larger number of source types.}, language = {en} } @article{BrosinskyFoersterSegletal.2014, author = {Brosinsky, Arlena and F{\"o}rster, Saskia and Segl, Karl and Kaufmann, Hermann}, title = {Spectral fingerprinting: sediment source discrimination and contribution modelling of artificial mixtures based on VNIR-SWIR spectral properties}, series = {Journal of soils and sediments : protection, risk assessment and remediation}, volume = {14}, journal = {Journal of soils and sediments : protection, risk assessment and remediation}, number = {12}, publisher = {Springer}, address = {Heidelberg}, issn = {1439-0108}, doi = {10.1007/s11368-014-0925-1}, pages = {1949 -- 1964}, year = {2014}, abstract = {Knowledge of the origin of suspended sediment is important for improving our understanding of sediment dynamics and thereupon support of sustainable watershed management. An direct approach to trace the origin of sediments is the fingerprinting technique. It is based on the assumption that potential sediment sources can be discriminated and that the contribution of these sources to the sediment can be determined on the basis of distinctive characteristics (fingerprints). Recent studies indicate that visible-near-infrared (VNIR) and shortwave-infrared (SWIR) reflectance characteristics of soil may be a rapid, inexpensive alternative to traditional fingerprint properties (e.g. geochemistry or mineral magnetism). To further explore the applicability of VNIR-SWIR spectral data for sediment tracing purposes, source samples were collected in the Isabena watershed, a 445 km(2) dryland catchment in the central Spanish Pyrenees. Grab samples of the upper soil layer were collected from the main potential sediment source types along with in situ reflectance spectra. Samples were dried and sieved, and artificial mixtures of known proportions were produced for algorithm validation. Then, spectral readings of potential source and artificial mixture samples were taken in the laboratory. Colour coefficients and physically based parameters were calculated from in situ and laboratory-measured spectra. All parameters passing a number of prerequisite tests were subsequently applied in discriminant function analysis for source discrimination and mixing model analyses for source contribution assessment. The three source types (i.e. badlands, forest/grassland and an aggregation of other sources, including agricultural land, shrubland, unpaved roads and open slopes) could be reliably identified based on spectral parameters. Laboratory-measured spectral fingerprints permitted the quantification of source contribution to artificial mixtures, and introduction of source heterogeneity into the mixing model decreased accuracies for some source types. Aggregation of source types that could not be discriminated did not improve mixing model results. Despite providing similar discrimination accuracies as laboratory source parameters, in situ derived source information was found to be insufficient for contribution modelling. The laboratory mixture experiment provides valuable insights into the capabilities and limitations of spectral fingerprint properties. From this study, we conclude that combinations of spectral properties can be used for mixing model analyses of a restricted number of source groups, whereas more straightforward in situ measured source parameters do not seem suitable. However, modelling results based on laboratory parameters also need to be interpreted with care and should not rely on the estimates of mean values only but should consider uncertainty intervals as well.}, language = {en} }