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

Authors :
Roy, Hari
Fox, Dennis
Emsellem, Karine
Études des Structures, des Processus d’Adaptation et des Changements de l’Espace (ESPACE )
Université de Montpellier (UM)-Université de Provence - Aix-Marseille 1-Avignon Université (AU)-Centre National de la Recherche Scientifique (CNRS)-Université de Nice Sophia-Antipolis (UNSA)-Université de la Méditerranée - Aix-Marseille 2
Études des Structures, des Processus d’Adaptation et des Changements de l’Espace (ESPACE)
Université Nice Sophia Antipolis (... - 2019) (UNS)
COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Avignon Université (AU)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)
Université Nice Sophia Antipolis (1965 - 2019) (UNS)
Source :
LNCS Transactions on Computational Science, LNCS Transactions on Computational Science, Springer, 2014, 8582, pp.315-330, Transactions on Computational Science, Transactions on Computational Science, 2014, 8582, pp.315-330
Publication Year :
2014
Publisher :
HAL CCSD, 2014.

Abstract

International audience; 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 Km 2) 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 = 0.99 and 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.

Details

Language :
English
ISSN :
18664733 and 18664741
Database :
OpenAIRE
Journal :
LNCS Transactions on Computational Science, LNCS Transactions on Computational Science, Springer, 2014, 8582, pp.315-330, Transactions on Computational Science, Transactions on Computational Science, 2014, 8582, pp.315-330
Accession number :
edsair.dedup.wf.001..924f867b7b31fb9154ecb51704c0a4e1