• Yingui Cao
  • Chun Yuan
  • Wei Zhou
  • Jing Wang
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 293)


Three Gorges Project is the great project in the world, which accelerates economic development in the reservoir area of Three Gorges Project. In the process of development in the reservoir area of Three Gorges Project, cultivated land has become the important resources, a lot of cultivated land has been occupied and become the constructing land. In the same time, a lot of cultivated land has been flooded because of the rising of the water level. This paper uses the cultivated land areas and social economic indicators of reservoir area of Three Gorges in 1990-2004, takes the statistic analyses and example research in order to analyze the process of cultivated land, get the driving forces of cultivated land change, find the new methods to stimulate and forecast the cultivated land areas in the future, and serve for the cultivated land protection and successive development in reservoir area of Three Gorges. The results indicate as follow, firstly, in the past 15 years, the cultivated land areas has decreased 200142 hm2, the decreasing quantity per year is 13343 hm2. The whole reservoir area is divided into three different areas, they are upper reaches area, belly area and lower reaches area. The trends of cultivated land change in different reservoir areas are similar to the whole reservoir area. Secondly, the curve of cultivated land areas and per capita GDP takes on the reverse U, and the steps between the change rate of cultivated land and the change rate of GDP are different in some years, which indicates that change of cultivated land and change of GDP are decoupling, besides that, change of cultivated land is connection with the development of urbanization and the policy of returning forestry greatly. Lastly, the precision of multi-regression is lower than the BP neural network in the stimulation of cultivated land, then takes use of the BP neural network to forecast the cultivated land areas in 2005, 2010 and 2015, and the forecasting results are reasonable.


Multivariate Regression Linear Reservoir Area Land Change Dynamic Degree Gorge Reservoir Area 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Bai Waiqi, Yan Jianzhong, Zhang Yili. Land Use/Land Cover Change and Driving Forces in the Region of Upper Reaches of the Dadu River. PROGRESS IN GEOGRAPHY. 2004, 23(1): 71–78Google Scholar
  2. Cai Yinyng, Zhang Anlu. Relationships Between Cultivated Land Resource and Economic Development. CHINA POPULATION RESOURCES AND ENVIRONMENT, 2005, 15(5): 52–57Google Scholar
  3. Chen Baiming, Du Hongliang. Decoupling Research of Cultivated Land Occupied and GDP Growth. Resource Science, 2006, 26(4): 42–51Google Scholar
  4. Chen Wei. Study on the Quantitative Change of the Land Demand on the Development of the Economy in Shanghai based on the Model of the BP Nerve Network. CITY MANAGEMENT AND SCIENCE, 2005, 7(2): 80–82Google Scholar
  5. Fan Hong, Zhang Jianping. Study on Land Use/Cover in Arid Valley of Upper Minjiang Watershed. JOURNAL OF DESERT RESEARCH, 2002, 22(3): 273–278Google Scholar
  6. Han Liqun. Theory, Design and Use of Artificial Neural Network. Beijing, 2002Google Scholar
  7. Huang Juan, Diao Chengtai. Analyses of Cultivated Land Change in Recent 10 Years in Chongqing City. China Land Resources Strategy and Regional Adjustment Development. 2006, 407–411Google Scholar
  8. Jin Fengjun, Zhang Xiaoping, Wang Changzheng. Land Use Problems and Intensive Utilization Patterns in the Coastal Regions of China. Resource Science, 2006, 26(5): 53–60Google Scholar
  9. Li Zhaofu, Yang Guishan. Correlation Analysis between Cultivated Land Use Change and Economic Development in Suzhou City over the Past 50 Years. Resource Science, 2005, 27(4): 50–55Google Scholar
  10. Long Hualou, Li Xiubin. Land Use Pattern in Transect of the Yangtse River and Its Influential Factor. ACTA GEOGRAPHICA SINICA, 2001, 56(4): 417–425Google Scholar
  11. OECD. Indicators to measure decoupling of environmental pressure from economic growth. Pairs, 2002Google Scholar
  12. Qu Futian, Wu Limei. Hypothesis and Validationon the Kuznets Curves of Economic Growth and Farmland Conversion. Resource Science, 2004, 26(5): 61–67Google Scholar
  13. Wang Xiulan, Bao Yuhai. Study on the Methods of Land Use Dynamic Change Research. PROGRESS IN GEOGRAPHY, 1999, 18(1): 81–87Google Scholar
  14. Wu Yi, Li Yongle, Hu Qingjun. Mathematics and Statistics. Changsha, 1995Google Scholar
  15. YuJianyng, He Xuhong. Datum statistics analyses and SPSS application. Beijing, 2003Google Scholar
  16. Zhang Zhengdong. Correlation Analysis of Cultivated Land Change with Population Increasing and Economic Growth in Hainan Province in Recent 35 Years. JOURNAL OF DESERT RESEARCH, 2005, 25(5): 273–278Google Scholar
  17. Zhao Jie, Zhao Shidong, Zheng Chunhui. Study on Land Cover/Land Use Change of Naiman Banner since 1980. JOURNAL OF DESERT RESEARCH, 2004, 24(3): 317–322Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.School of Land Science and TechnologyChina University of GeosciencesBeijingP. R.China
  2. 2.Land Use Key laboratoryChina Land Survey and Planning InstituteBeijingP. R.China

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