Land use change prediction of Wuhan City: a Markov-Monte Carlo approach

Huiqiong Xia; Chunyan Zheng; Hai Liu

doi:10.1117/12.2030262

26 October 2013 Land use change prediction of Wuhan City: a Markov-Monte Carlo approach

Huiqiong Xia, Chunyan Zheng, Hai Liu

Proceedings Volume 8921, MIPPR 2013: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications; 89210M (2013) https://doi.org/10.1117/12.2030262
Event: Eighth International Symposium on Multispectral Image Processing and Pattern Recognition, 2013, Wuhan, China

Abstract

Markov model is found to be beneficial in describing and analyzing land cover change process. The probability of transition between each pair of states is recorded as an element of a transition probability matrix, which is the key factor to obtain a higher precision of prediction in Markov model. In this study, a combined use of RS, GIS, Markov stochastic modeling and Monte Carlo simulating techniques are employed in analyzing and prediction land use/cover changes in Wuhan city. The results indicate that the transition probability matrix derived from Monte Carlo experiment is more accurate for land use prediction, and the prediction results of land use change show that there urban growth is has notable, area of forest land continued decreasing, and that the land use/cover change process would be stable in the future. The study demonstrates remote sensing image is an effective data source and statistical information of land use is a valid supplement for land use/land cover research. Integration of these two kinds of data in Markov - Monte Carlo method can adjust the basis of the same observation time when images are not available every year or at a constant time interval in LUCC modeling. Land use/land cover change information from the prediction results will be beneficial in describing, analyzing the change process of land structure in Wuhan city in next 20 years.

Citation Download Citation

Huiqiong Xia, Chunyan Zheng, and Hai Liu "Land use change prediction of Wuhan City: a Markov-Monte Carlo approach", Proc. SPIE 8921, MIPPR 2013: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 89210M (26 October 2013); https://doi.org/10.1117/12.2030262

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KEYWORDS

Monte Carlo methods

Matrices

Agriculture

Earth observing sensors

Data modeling

Satellite imaging

Satellites

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