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International Conference on Computational Science and Its Applications

ICCSA 2014: Computational Science and Its Applications – ICCSA 2014 pp 315–330Cite as

Predicting Land Cover Change in a Mediterranean Catchment at Different Time Scales

  • Conference paper

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

Abstract

Land cover has been changing rapidly throughout the world, and this issue is important to researchers, urban planners, and ecologists for sustainable land cover planning for the future. Many modeling tools have been developed to explore and evaluate possible land cover scenarios in future and time scales vary greatly from one study to another. The main objective of this study is to test land cover change prediction at different time scales in a Mediterranean catchment in SE France. Land cover maps were created from aerial photographs (1950, 1982, 2003, 2008, and 2011) of the Giscle catchment (235 Km2) and surfaces were classified into four land cover categories: forest, vineyard, grassland, and built area. Explanatory variables were selected through Cramer’s coefficient. Different time scales were tested in the study: short (2003-2008), intermediate (1982-2003), and long (1950-1982). To test the model’s accuracy, Land Change Modeler (LCM) of IDRISI was used to predict land cover in 2011 and predicted images were compared to a real 2011 map. Kappa index and confusion matrix were used to evaluate the model’s accuracy. Altitude, slope, and distance from roads had the greatest impact on land cover changes among all variables tested. Good to perfect level of spatial and perfect level of quantitative agreement were observed in long to short time scale simulations. Kappa indices (Kquantity = 0.99and Klocation = 0.90) and confusion matrices were good for intermediate and best for short time scale. The results indicate that shorter time scales produce better predictions. Time scale effects have strong interactions with specific land cover dynamics, in which stable land covers are easier to predict than cases of rapid change and quantity is easier to predict than location for longer time periods.

Keywords

  • Time scale
  • Land cover change modeling
  • Mediterranean Europe
  • Land change Modeler (LCM)

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Author information

Authors and Affiliations

  1. UMR 7300 CNRS ESPACE, Université de Nice Sophia Antipolis, BP 3209, 06204, Nice cedex 3, France

    Hari Gobinda Roy, Dennis M. Fox & Karine Emsellem

Authors
  1. Hari Gobinda Roy
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  2. Dennis M. Fox
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  3. Karine Emsellem
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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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Roy, H.G., Fox, D.M., Emsellem, K. (2014). Predicting Land Cover Change in a Mediterranean Catchment at Different Time Scales. In: , 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_23

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  • DOI: https://doi.org/10.1007/978-3-319-09147-1_23

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-09146-4

  • Online ISBN: 978-3-319-09147-1

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