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Spectral fingerprinting: sediment source discrimination and contribution modelling of artificial mixtures based on VNIR-SWIR spectral properties

  • Analysis And Modelling Of Sediment Transfer In Mediterranean River Basins
  • Published:
Journal of Soils and Sediments Aims and scope Submit manuscript

Abstract

Purpose

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).

Materials and methods

To further explore the applicability of VNIR-SWIR spectral data for sediment tracing purposes, source samples were collected in the Isábena watershed, a 445 km2 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.

Results and discussion

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.

Conclusions

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.

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References

  • Alatorre LC, Beguería S (2009) Identification of eroded areas using remote sensing in a badlands landscape on marls. Catena 76:182–190

    Article  Google Scholar 

  • Bachmann M (2007) Automatisierte Ableitung von Bodenbedeckungsgraden durch MESMA-Entmischung. Dissertation PhD, University of Würzburg, Germany

  • Bayer A (2013) Methodological developments for mapping soil constituents using imaging spectroscopy. Dissertation PhD, University of Potsdam, Germany

  • Bayer A, Bachmann M, Müller A, Kaufmann H (2012) A comparison of feature-based MLR and PLS regression techniques for the prediction of three soil constituents in a degraded South African ecosystem. Appl Environ Soil Sci, Article ID 971252. doi:10.1155/2012/971252

  • Ben-Dor E, Chabrillat S, Dematte JAM, Taylor GR, Hill J, Whiting ML, Sommer S (2009) Using imaging spectroscopy to study soil properties. Remote Sens Environ 113:S38–S55

    Article  Google Scholar 

  • Beven K, Binley A (1992) The future of distributed models: model calibration and uncertainty prediction. Hydrol Process 6:279–298

    Article  Google Scholar 

  • Brosinsky A, Foerster S, Segl K, López-Tarazón JA, Piqué G, Bronstert A (2014) Spectral fingerprinting: characterising suspended sediment sources by the use of VNIR-SWIR spectral information. J Soils Sediments (this issue)

  • Chabrillat S, Eisele, A, Guillaso S, Rogaß C, Ben-Dor E, Kaufmann H (2011) HYSOMA: an easy-to-use software interface for soil mapping applications of hyperspectral imagery. 7th EARSeL SIG Imaging Spectroscopy workshop, Edinburgh, Scotland

  • Clark RN (1999) Chapter 1: spectroscopy of rocks and minerals and principles of spectroscopy. In: Rencz A (ed) Remote sensing for the earth sciences, vol. 3: manual of remote sensing. John Wiley and Sons, New York, pp 3–58

    Google Scholar 

  • Collins AL, Walling DE (2002) Selecting fingerprint properties for discriminating potential suspended sediment sources in river basins. J Hydrol 261:218–244

    Article  CAS  Google Scholar 

  • Collins AL, Walling DE (2004) Documenting catchment suspended sediment sources: problems, approaches and prospects. Prog Phys Geogr 28:159–196

    Article  Google Scholar 

  • Collins AL, Walling DE, Leeks GJL (1997) Source type ascription for fluvial suspended sediment based on a quantitative composite fingerprinting technique. Catena 29:1–27

    Article  CAS  Google Scholar 

  • Collins AL, Walling DE, Webb L, King P (2010) Apportioning catchment scale sediment sources using a modified composite fingerprinting technique incorporating property weighting and prior information. Geoderma 155:249–261

    Article  Google Scholar 

  • Commission Internationale de l’Eclairage (CIE) (1931) CIE proceedings. Cambridge University Press, Cambridge

    Google Scholar 

  • Davis CM, Fox JF (2009) Sediment fingerprinting: review of the method and future improvements for allocating nonpoint source pollution. J Environ Eng 135:490–504

    Article  CAS  Google Scholar 

  • Evrard O, Poulenard J, Némery J, Ayrault S, Gratiot N, Duvert C, Prat C, Lefèvre I, Bonté P, Esteves M (2013) Tracing sediment sources in a tropical highland catchment of central Mexico by using conventional and alternative fingerprinting methods. Hydrol Process 27:911–922

    Article  Google Scholar 

  • Fargas D, Martinez-Casasnovas J, Poch R (1997) Identification of critical sediment source areas at a regional level. Phys Chem Earth 22:355–359

    Article  Google Scholar 

  • Francke T, López-Tarazón JA, Vericat D, Bronstert A, Batalla RJ (2008) Flood-based analysis of high-magnitude sediment transport using a non-parametric method. Earth Surf Process Landf 33:2064–2077

    Article  Google Scholar 

  • Franks SW, Rowan JS (2000) Multi-parameter fingerprinting of sediment sources: uncertainty estimation and tracer selection. In: Bentley LR et al (eds) Computational methods in water resources. Balkema, Rotterdam, pp 1067–1074

    Google Scholar 

  • Grimshaw DL, Lewin J (1980) Source identification for suspended sediments. J Hydrol 47:151–162

    Article  Google Scholar 

  • Koiter AJ, Owens PN, Petticrew EL, Lobb DA (2013) The bahavioural characteristics of sediment properties and their implications for sediment fingerprinting as an approach for identifying sediment sources in river basins. Earth Sci Rev 125:24–42

    Article  CAS  Google Scholar 

  • Kooistra L, Wanders J, Epema GF, Leuven RSEW, Wehrens R, Buydens LMC (2003) The potential of field spectroscopy for the assessment of sediment properties in river floodplains. Anal Chim Acta 484:189–200

    Article  CAS  Google Scholar 

  • Krein A, Petticrew EL, Udelhoven T (2003) The use of fine sediment fractal dimensions and color to determine sediment sources in a small watershed. Catena 53:165–179

    Article  CAS  Google Scholar 

  • Lasanta T, Vicente-Serrano SM (2012) Complex land cover change processes in semiarid Mediterranean regions: an approach using Landsat images in northeast Spain. Remote Sens Environ 124:1–14

    Article  Google Scholar 

  • Lawson CL, Hanson RJ (1974) Solving least squares problems. Englewood Cliffs, Prentice-Hall, 340 pp

    Google Scholar 

  • Lees JA (1997) Mineral magnetic properties of mixtures of environmental and synthetic materials: linear additivity and interaction effects. Geophys J Int 131:225–346

    Article  Google Scholar 

  • Legout C, Poulenard J, Nemery J, Navratil O, Grangeon T, Evrard O, Esteves M (2013) Quantifying suspended sediment sources during runoff events in headwater catchments using spectrocolorimetry. J Soils Sediments 13:1478–1492

    Article  Google Scholar 

  • Martínez-Carreras N, Udelhoven T, Krein A, Gallart F, Iffly J, Ziebel J, Hoffmann L, Pfister L, Walling DE (2010a) The use of sediment colour measured by diffuse reflectance spectrometry to determine sediment sources: application to the Attert river catchment (Luxembourg). J Hydrol 382:49–63

    Article  Google Scholar 

  • Martínez-Carreras N, Krein A, Udelhoven T, Gallart F, Iffly JF, Hoffman L, Pfister L, Walling DE (2010b) A rapid spectral-reflectance based fingerprinting approach for documenting suspended sediment sources during storm runoff events. J Soils Sediments 10:400–413

    Article  Google Scholar 

  • Martínez-Carreras N, Krein A, Gallart F, Iffly JF, Pfister L, Hoffmann L, Owens PN (2010c) Assessment of different colour parameters for discriminating potential suspended sediment sources and provenance: a multi-scale study in Luxembourg. Geomorphology 118:118–129

    Article  Google Scholar 

  • Minella JPG, Walling DE, Merten GH (2008) Combining sediment source tracing techniques with traditional monitoring to assess the impact of improved land management on catchment sediment yields. J Hydrol 348:546–563

    Article  Google Scholar 

  • Ministerio de Medio Ambiente y Medio Rural y Marino (MARM) (2008) Mapa de cultivos y aprovechamientos 1:50,000, Madrid, Spain

  • Motha JA, Wallbrink PJ, Hairsine PB, Grayson RB (2003) Determining the sources of suspended sediment in a forested catchment in southeastern Australia. Water Resour Res 39:1056

    Article  Google Scholar 

  • Mukundan R, Walling DE, Gellis AC, Slattery MC, Radcliffe DE (2012) Sediment source fingerprinting: transforming from a research tool to a management tool. J Am Water Res Assoc 48:1241–1257

    Article  Google Scholar 

  • Müller EN, Güntner A, Francke T, Mamede G (2010) Modeling sediment export, retention and reservoir sedimentation in drylands with the WASA-SED model. Geosci Model Dev 3:275–291

    Article  Google Scholar 

  • Navratil O, Evrard O, Esteves M, Legout C, Ayrault S, Némery J, Mate-Marin A, Ahmadi M, Lefèvre I, Poirel A, Bonté P (2012) Temporal variability of suspended sediment sources in an alpine catchment combining river/rainfall monitoring and sediment fingerprinting. Earth Surf Process Landf 37:828–846

    Article  Google Scholar 

  • Owens PN, Walling DE (2002) The phosphorus content of fluvial sediment in rural and industrialized river basins. Water Res 36:685–701

    Article  CAS  Google Scholar 

  • Owens PN, Batalla RJ, Collins AJ, Gomez B, Hicks DM, Horowitz AJ, Kondolf GM, Marden M, Page MJ, Peacock DH, Petticrew EL, Salomons W, Trustrum NA (2005) Fine-grained sediment in river systems: environmental significance and management issues. River Res Appl 21:693–717

    Article  Google Scholar 

  • Peart MR, Walling DE (1986) Fingerprinting sediment sources: the example of a drainage basin in Devon, UK. In: Hadley RF (ed) Drainage basin sediment delivery. IAHS Publication 159, IAHS Press, Wallingford, pp 41–55

  • Poulenard J, Perrette Y, Fanget B, Quetin P, Trevisan D, Dorioz JM (2009) Infrared spectroscopy tracing sediment sources in a small rural watershed (French Alps). Sci Total Environ 407:2808–2819

    Article  CAS  Google Scholar 

  • Poulenard J, Legout C, Némery J, Bramorski J, Navratil O, Douchin A, Fanget B, Perrette Y, Evrard O, Esteves M (2012) Tracing sediment sources during floods using diffuse reflectance infrared fourier transformed spectrometry (DRIFTS) a case study in a highly erosive mountainous catchment (Southern French Alps). J Hydrol 414–415:452–462

    Article  Google Scholar 

  • Reimann C, Filzmoser P, Garrett R, Dutter R (2008) Statistical data analysis explained. Applied environmental statistics with R. John Wiley & Sons Ltd., Chichester, pp 362

  • Richter N (2010) Pedogenic iron oxide determination of soil surfaces from laboratory spectroscopy and HyMap image data—a case study in Cabo de Gata-Níjar Natural Park, SE Spain. Unpublished Dissertation PhD, Humboldt University Berlin, Germany

  • Rowan JS, Goodwill P, Franks SW (2000) Uncertainty estimation in fingerprinting suspended sediment sources. In: Foster IDL (ed) Tracers in geomorphology. Wiley, Chichester, pp 279–290

    Google Scholar 

  • Savitzky A, Golay MJ (1964) Smoothing and differentiation of data by simplified least square procedures. Anal Chem 36:1627–1639

    Article  CAS  Google Scholar 

  • Small IF, Rowan JS, Franks SW, Wyatt A, Duck RW (2004) Bayesian sediment fingerprinting provides a robust tool for environmental forensic geoscience applications. Geol Soc Lond Spec Publ 232:207–213

    Article  Google Scholar 

  • Smith HG, Blake WH (2014) Sediment fingerprinting in agricultural catchments: a critical re-examination of source discrimination and data corrections. Geomorphology 204:177–191

    Article  Google Scholar 

  • Somers B, Cools K, Delalieux S, Stuckens J, Van der Zande D, Verstraeten W, Coppin P (2009) Nonlinear hyperspectral mixture analysis for tree cover estimates in orchards. Remote Sens Environ 113:1183–1193

    Article  Google Scholar 

  • Somers B, Asner GP, Tits L, Coppin P (2011) Endmember variability in spectral mixture analysis: a review. Remote Sens Environ 115:1603–1616

    Article  Google Scholar 

  • Valero-Garcés BL, Navas A, Machin J, Walling D (1999) Sediment sources and siltation in mountain reservoirs: a case study from the Central Spanish Pyrenees. Geomorphology 28:23–41

    Article  Google Scholar 

  • Verdú JM (2003) Análisis y modelización de la respuesta hidrológica y fluvial de una extensa cuenca de montaña mediterránea (río Isábena, Pre-Pirineo). Unpublished Dissertation PhD, University of Lleida, Spain

  • Viscarra Rossel RA, Behrens T (2010) Using data mining to model and interpret soil diffuse reflectance spectra. Geoderma 158:46–54

    Article  Google Scholar 

  • Viscarra Rossel RA, Minasny B, Roudier P, McBratney AB (2006a) Color space models for soil science. Geoderma 133:320–337

    Article  CAS  Google Scholar 

  • Viscarra Rossel RA, Walvoort DJJ, McBratney AB, Janik LJ, Skjemstad JO (2006b) Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma 131:59–75

    Article  CAS  Google Scholar 

  • Walden J, Slattery MC, Burt TP (1997) Use of mineral magnetic measurements to fingerprint suspended sediment sources: approaches and techniques for data analyses. J Hydrol 202:353–372

    Article  Google Scholar 

  • Walling DE (2005) Tracing suspended sediment sources in catchments and river systems. Sci Total Environ 344:159–184

    Article  CAS  Google Scholar 

  • Wyszecki G, Stiles WS (1982) Color science: concepts and methods, quantitative data and formulae, Second Editionth edn. Wiley, New York

    Google Scholar 

  • Yu L, Oldfield F (1989) A multivariate mixing model for identifying sediment source from magnetic measurements. Quat Res 32:168–181

    Article  Google Scholar 

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Acknowledgments

This research was carried out within the project “Generation, transport and retention of water and suspended sediments in large dryland catchments: Monitoring and integrated modelling of fluxes and connectivity phenomena” funded by the Deutsche Forschungsgemeinschaft (DFG). The authors would like to thank Arne Brauer, Benjamin Kayatz, Iris Kleine and Charlotte Wilczok from the University of Potsdam for their support of field work and Stefan Lips and Heide Kraudelt for their support of laboratory measurements.

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Authors and Affiliations

  1. University of Potsdam, Institute of Earth and Environmental Science, Karl-Liebknecht-Str. 24-25, 14476, Potsdam, Germany

    Arlena Brosinsky

  2. GFZ German Research Centre for Geosciences, Section 1.4 Remote Sensing, Telegrafenberg, 14473, Potsdam, Germany

    Arlena Brosinsky, Saskia Foerster, Karl Segl & Hermann Kaufmann

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  1. Arlena Brosinsky
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  2. Saskia Foerster
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Correspondence to Arlena Brosinsky.

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Responsible editor: José Carlos de Araújo

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Brosinsky, A., Foerster, S., Segl, K. et al. Spectral fingerprinting: sediment source discrimination and contribution modelling of artificial mixtures based on VNIR-SWIR spectral properties. J Soils Sediments 14, 1949–1964 (2014). https://doi.org/10.1007/s11368-014-0925-1

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  • DOI: https://doi.org/10.1007/s11368-014-0925-1

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