Volume II-4/W2
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-4/W2, 163-168, 2015
https://doi.org/10.5194/isprsannals-II-4-W2-163-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-4/W2, 163-168, 2015
https://doi.org/10.5194/isprsannals-II-4-W2-163-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

  10 Jul 2015

10 Jul 2015

USING CA-MARKOV MODEL TO MODEL THE SPATIOTEMPORAL CHANGE OF LAND USE/COVER IN FUXIAN LAKE FOR DECISION SUPPORT

S. H. Li1,2, B. X. Jin2, X. Y. Wei2,3, Y. Y. Jiang4, and J. L. Wang1 S. H. Li et al.
  • 1College of Tourism & Geographic Sciences, Yunnan Normal University,768 Juxian Street in Chengong District, Kunming, Yunnan, China
  • 2Yunnan Provincial Geomatics Centre, 404 West Ring Road, Kunming, Yunnan, China
  • 3College of Geographic Sciences, Nanjing Normal University, No.1,Wenyuan Road,Xianlin University District,Nanjing, China
  • 4Center for Intelligent Spatial Computing, George Mason University, 4400 University Dr., Fairfax, VA, USA

Keywords: LUCC, CA-Markov Model, Dynamic Modelling, Optimized Modelling Scale Combination, Fuxian Lake Watershed

Abstract. Spatiotemporal modelling of land use/cover change (LUCC) has become increasingly important in recent years, especially for environmental change and regional planning. There have been many approaches and software packages for modelling LUCC, but developing a model for a specific region is still a difficult task, because it requires large volume of data input and elaborate model adjustment. Fuxian Lake watershed is one of the most important ecological protection area in China and located in southeast of Kunming city, Yunnan province. In this paper, the CA-Markov model is used to analyse the spatiotemporal LUCC and project its course into the future. Specifically, the model uses high resolution remote sensing images of 2006 and 2009 as input data, and then makes prediction for 2014. A quantitative comparison with remote sensing images of 2014 suggests an overall accuracy of 88%. This spatiotemporal modelling method is expected to facilitate the research of many land cover and use applications modelling.