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Land Use/Cover Change and the Main Driving Forces of Shanghai from 1987 to 2007

Author: GuoJiaXiu
Tutor: HuXiaoMeng
School: Shanghai Normal University
Course: Physical Geography
Keywords: Land use cover change Interpretation of remote sensing Support vector machine classification Maximum likelihood method Principal Component Analysis Correlation Analysis
CLC: F301
Type: Master's thesis
Year: 2010
Downloads: 259
Quote: 2
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Abstract


Land use / cover change (LUCC) is hot and one of the cutting-edge issues of global change research, occupies an important position in the study of global environmental change and sustainable development. Remote sensing technology for its macro, fast, dynamic, large-area, etc., has become the primary means of monitoring land use / cover change. In this paper, Shanghai, as the study area, based on two remote sensing images in 1997 and 2002, like natural law and support vector machine classification method to extract the Shanghai Land use cover change. Combined with previous interpreted the 1987 and 2007 land cover information, the use of land use dynamics of indicators and degree of land use analysis of 20 years of land use / cover spatial and temporal changes in Shanghai. Land use / cover change analysis based on the combined 20 years of economic and statistical data analysis of the impact of land use / cover change in population and economic drivers in Shanghai. Finally, according to the previous principal component analysis to get the impact of Shanghai composite score for land use cover change in the driving force, combined with the interpretation and projections Shanghai 1987-2007 the proportion of arable land over the years, the establishment of a regional social - economic - ecological composite index and land cover change model. Through this research, get the following main conclusions: (1) natural law and support vector machine classification method extracted in Shanghai in 1997, 2002, two land use / cover information. In this paper, the comparative classification accuracy of the comprehensive comparison of the two classification methods, for the 1997 TM image classification using support vector machine, For 2002 ETM images, using the maximum likelihood classification. Both the overall classification accuracy of 88.6734% and 87.3009%, respectively, to meet the needs of the research. (2) a single land use dynamics, integrated land use dynamics and degree of land-use analysis of the dynamic changes of land use. The study period, the area of ??arable land and unused land continued to decline, urban and rural construction land, woodland, grassland area continued to increase. Which changes the fastest woodland, 20 years, forest area increased from 28,906 hectares in 1987 to 118,443 hectares in 2007, an increase of 89,437 hectares, a growth rate of 310%, the annual growth rate is 15.5%; reduction of arable land maximum total 167,344 hectares; maximum total increase in the area of ??urban and rural construction land, 123,788 ha; although the grassland area growth, but little change; waters area experienced the first increase after reduction changes, but the magnitude of the overall change . (3) The combination of 20 years, Shanghai's social and economic statistics, regression analysis capabilities using Excel and SPSS statistical analysis of the impact of the Shanghai Municipal land use / cover change the dominant driving force. Analysis of demographic factors, economic factors, land use degree of population differentiation model, the model of the total population and arable land; correlation model of economic development and cultivated land. The results show that the economic development and population growth contributed to the Shanghai land use / cover change. Shanghai land use / cover change also is being affected by other factors, such as policy factors and scientific and technological progress and other factors. (4) combined with principal component analysis of the previous research was Shanghai land use / cover change driving force factor score, regression analysis functionality of Excel, the establishment of regional social - economic - ecological complex system development level ( y) and the area of ??cultivated land area ratio (x) model, the relationship of the two is y =-10.948x 5.8238 (R = -0.973 ** P lt; 0.01) shows that the driving force composite score and the proportion of arable land extremely related. The correlation coefficient R2 = 0.9486.

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