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Land Use/Cover Change And The Main Driving Forces Of Shanghai From 1987 To 2007

Posted on:2011-08-17Degree:MasterType:Thesis
Country:ChinaCandidate:J X GuoFull Text:PDF
GTID:2189360302992082Subject:Physical geography
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The research on Land use/cover change has become the hot spot of the geography research and frontier issues. It has played an important role in global environmental change and sustainable development research. Remote sensing technology because of its macro, fast, dynamic, large-scale advantages, has become the primary mean of monitoring of land use / cover change.In this article, Shanghai was selected as the researched area, based on the images of 1997 and 2002, maximum likelihood classification and support vector machines were used to extract the main information of land use cover change in Shanghai. Combined with previous information of interpretation of 1987 and 2007, using the land use dynamics and land use degree index, analyzed the change of the temporal and spatial in Shanghai for 20 years. Based on the results of land use / cover change, combined the statistical datum in Shanghai from 1987-2007, the demographic and economic driving force were analyzed. Finally, according to previous principal component analysis, the composite score of driving force of land use cover changes was obtained. With the method of interpretation and calculation, the proportion of cultivated land from 1987 to 2007 in Shanghai was obtained. A model between regional social - economic - Composite index of ecological and land use/ cover change was established.Through this study, the main conclusions were as follows:(1)With the maximum likelihood classification (MLC)and support vector machine (SVM)classification method, the information of land use/ cover change of 1997and 2002 was obtained. Compared the accuracy of MLC and SVM, for the 1997 TM image, using maximum likelihood classification. For the 2002 ETM image, using support vector machine classification. The overall classification accuracy was 88.6734% and 87.3009%, has met the research needs.(2) According to single land use dynamics, integrated land use dynamics and land-use degree, the dynamic changes of land use was analyzed. The results are: the areas of the cultivated land and unused land continued to decline, the areas of urban and rural construction land, woodland, grassland continued to grow. Among them, the change of the forest is the fastest. 20 years, the area of forest increased from 28,906 hectares in 1987 to 118,443 hectares in 2007, the increment is 89,437 hectares, the growth rate is 310%, the annual growth rate is 15.5%; The reduction amount of cultivated land is the maximum, up to 167344 hectares; The increment of urban construction land is the maximum, up to 123788 hectares; The areas of grassland, although growing, the change is slim; The areas of water has experienced increased first and then decreased, but overall the change was slight.(3) Combined with Shanghai's social and economic statistical data from 1987 to 2007, using statistical regression analysis function of Excel and SPSS, analyzed the dominant driving forces of the land use / cover change. Demographic factors, the model between land use degree and population differentiation,the correlation model between total population and land area were established; Economic factor, the model between the level of economic development and the cultivated area was established. The results showed that: economic development and population growth are the main reasons in Shanghai contributed to land use / cover change. Besides this, the land use / cover change in Shanghai is also being affected by other factors, such as policies and technological progress and other factors.(4) With the previous research "Principal Component Analysis", the overall score of driving forces was obtained. According to the statistical function of Excel, a linear correlation model between regional social-economic-ecological complex system development level (y) and the proportion of cultivated land area (x). The result showed y = 0.1166x-5.53 (R =- 0.973 **, P <0.01), R2 = 0.9384, found that there is a strong correlation between them.
Keywords/Search Tags:LUCC, Interpretation of TM images, Support Vector Machine(SVM), Maximum Likelihood Classification(MLC), Principal component analysis, Correlation analysis
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