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International Conference on Computational Science and Its Applications

ICCSA 2014: Computational Science and Its Applications – ICCSA 2014 pp 372–385Cite as

Modelling Spatially–Distributed Soil Erosion through Remotely–Sensed Data and GIS

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8582))

Abstract

Estimation of soil erosion using common empirical models has long been an active research topic. Nevertheless, application of those models at basin scale is still a challenge due to data availability and quality. In this study, the Revised Universal Soil Loss Equation (RUSLE) and the Unit Stream Power–based Soil Erosion/Deposition (USPED) were applied and compared to determine the spatial distribution of soil erosion of a coastal watershed in Basilicata, southern Italy. A comprehensive approach that integrates ancillary data, digital terrain model, products derived from satellite remote sensing (multi–temporal Landsat imagery) and GIS techniques was adopted to identify major factors influencing soil erosion. Soil loss and soil erosion/deposition maps were produced. The study provided a reliable prediction of soil erosion rates and definition of erosion–prone areas within the watershed.

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Author information

Authors and Affiliations

  1. Department of Sciences, University of Basilicata, Potenza, Italy

    Antonello Aiello

  2. Institute of Intelligent Systems for Automation, National Research Council of Italy, Bari, Italy

    Maria Adamo

  3. School of Engineering, University of Basilicata, Potenza, Italy

    Filomena Canora

Authors
  1. Antonello Aiello
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  2. Maria Adamo
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  3. Filomena Canora
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Editor information

Editors and Affiliations

  1. School of Engineering, University of Basilicata, 85100, Potenza, Italy

    Beniamino Murgante

  2. Department of Computer and Information Sciences, Covenant University, Ota, Nigeria

    Sanjay Misra

  3. Department of Production and Systems, University of Minho, 4710-057, Braga, Portugal

    Ana Maria A. C. Rocha

  4. DICAR, Polytecnico di Bari, 70125, Bari, Italy

    Carmelo Torre

  5. University of Minho, Braga, Portugal

    Jorge Gustavo Rocha  & Maria Irene Falcão  & 

  6. Monash University, 3800, Clayton, VIC, Australia

    David Taniar

  7. Department of Intelligent Informatics, Kyushu Sangyo University, 2-3-1 Matsukadai, 813-8503, Higashi-ku, Fukuoka, Japan

    Bernady O. Apduhan

  8. Department of Mathematics and Computer Science, University of Perugia, Via Vanvitelli, 1, 06123, Perugia, Italy

    Osvaldo Gervasi

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© 2014 Springer International Publishing Switzerland

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Aiello, A., Adamo, M., Canora, F. (2014). Modelling Spatially–Distributed Soil Erosion through Remotely–Sensed Data and GIS. In: Murgante, B., et al. Computational Science and Its Applications – ICCSA 2014. ICCSA 2014. Lecture Notes in Computer Science, vol 8582. Springer, Cham. https://doi.org/10.1007/978-3-319-09147-1_27

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  • DOI: https://doi.org/10.1007/978-3-319-09147-1_27

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-09146-4

  • Online ISBN: 978-3-319-09147-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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