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REMOTE SENSING FOR LAND & RESOURCES    2014, Vol. 26 Issue (2) : 184-189     DOI: 10.6046/gtzyyg.2014.02.29
Technology Application |
Simulation and forecast of land cover pattern in Qinhuai River Basin based on the CA-Markov model
CHEN Ailing1,2, DU Jinkang1,2
1. Department of Geographical Information Science, Nanjing University, Nanjing 210046, China;
2. Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210046, China
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Abstract  Based on the classified result of Landsat ETM+ remote sensing images of 2006 and 2009, the paper simulated and forecasted land cover types of Qinhuai River Basin in the future by using the CA-Markov model. In the model-building process, the transition probability matrix and the transition area matrix were obtained through the Markov model, which determined the conversion rules and iterative times of the CA model. The land cover pattern of the study area in 2012 and 2018 was simulated and forecasted with the CA-Markov model. Then the forecast result was compared with the actual classified data of 2012 to verify the forecast accuracy. The raster number forecast error of each land cover type is not higher than 6.5%, and the spatial location accuracy is 76.5%. The forecast results show that the paddy field decreased to 33.3% and the impervious surface reached 31.1% of Qinhuai River Basin in 2018. Most of the paddy field converted into impervious surface. The impervious surface of urban areas expands obviously in such urban areas as Nanjing, Lukou, Jurong and Lishui. The methods can provide a basis for dynamic monitoring as well as sustainable development of Qinhuai River Basin.
Keywords Three Gorges Reservoir Area      landslide monitoring      GPS survey      height      precision analysis     
:  TP79  
Issue Date: 28 March 2014
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LIN Hao
FAN Jinghui
HONG Youtang
TU Pengfei
GUO Xiaofang
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LIN Hao,FAN Jinghui,HONG Youtang, et al. Simulation and forecast of land cover pattern in Qinhuai River Basin based on the CA-Markov model[J]. REMOTE SENSING FOR LAND & RESOURCES, 2014, 26(2): 184-189.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2014.02.29     OR     https://www.gtzyyg.com/EN/Y2014/V26/I2/184
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