Application of GIS and remote sensing for predicting land-use change in the French jura mountains with the lcm model: The impact of variables on the disturbance model

Van Tuan Nghiem, Rachid Nedjai, Van Anh Le, Laure Charleux

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

This research studies the land-use change for the Ain watershed in the Jura Mountains, in the East of France. Using the satellite images LANDSAT in 1975, 1992, 2000 and 2010, the land-use data for the four corresponding years was generated in the first place, and the diachronic land-use analysis from 1975 to 2010 was next carried out. In order to generate the four land-use maps, the satellite images were classified using the Maximum Likelihood supervised classification. The land-use map in 2010 is validated based on the observations from ground visits in 2011. Other land-use maps are validated by the ground truth data established from the auxiliary information layers taken in the area. Based on the trend in the past evolution of land-use, a prediction of future land-use is generated, using the Land Change Modeler (LCM). Next, the multi-temporal analysis of land-use change is carried out and the variables affecting the LCM are evaluated using Multi- Layer Perceptron and Markov transition probabilities. Two pairs of maps (1975, 1992) and (1992, 2000) are used to generate the predictive maps. Copyright

Original languageEnglish (US)
Title of host publication34th Asian Conference on Remote Sensing 2013, ACRS 2013
PublisherAsian Association on Remote Sensing
Pages2598-2605
Number of pages8
ISBN (Print)9781629939100
StatePublished - Jan 1 2013
Event34th Asian Conference on Remote Sensing 2013, ACRS 2013 - Bali, Indonesia
Duration: Oct 20 2013Oct 24 2013

Publication series

Name34th Asian Conference on Remote Sensing 2013, ACRS 2013
Volume3

Other

Other34th Asian Conference on Remote Sensing 2013, ACRS 2013
CountryIndonesia
CityBali
Period10/20/1310/24/13

Keywords

  • GIS
  • LCM
  • Land-use change
  • Markov chain
  • Remote sensing

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