台北市地景變遷監測之研究

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Title:	台北市地景變遷監測之研究
Authors:	王榕芃
Contributors:	景觀學系
Keywords:	地理資訊系統地景變遷地景指數主成份分析因素分析群落分析
Date:	2010
Issue Date:	2011-10-20 10:12:39 (UTC+8)
Abstract:	【摘要】本研究旨在監測台北市從1993年到2007年之地景變遷情形，並探討土地使用發展之影響程度及提供未來發展之建議。研究方法係藉由遙測與地理資訊系統、地景指數量化、Shannon多樣性t檢定方法，擷取台北市和其行政分區之地景變遷資訊，並分析土地使用經15年的變遷後是否對整個地景造成顯著的影響，同時透過多變量統計之主成分分析、因素分析和群落分析，篩選與地景指數有關之共同因素並探討台北市行政分區彼此間之同質性關係。研究結果發現，台北市的土地使用經過15年後，確實有變遷。就建地而言，其數量由1993年的796減為2007年的350；裸露地由1540增為1845；就Shannon多樣性指數和Shannon均勻度指數，均呈現下降趨勢。而利用Shannon多樣性t檢定法檢定結果亦發現，台北市的土地使用變遷確實對整個地景具有顯著的影響效應。至於應用主成份分析與因素分析篩選出4個主成份軸與4個共同因素後之群落分析結果指出，主成份分析之群落分析在1993年和2007年分別將行政區分為4個和2個群組；因素分析之群落分析結果分別將行政區分為6個和2個群組，分析結果顯示群組內的行政分區，確實有明顯的改變。由上述結果可歸納結論為，整合遙測與地理資訊系統、地景指數量化、及Shannon多樣性t檢定法於台北市地景變遷之監測，確實為一有效、可行的方法，同時透過多變量統計分析之應用，可瞭解台北市行政分區間之同質性關係，並解決地景變遷分析時選用地景指數之問題。本研究結果可提供台北市未來土地使用發展模式之參考。關鍵詞：地理資訊系統、地景變遷、地景指數、主成份分析、因素分析、群落　　　　分析【Abstract】 This study focuses on monitoring landscape change of Taipei from 1993 to 2007. The objective is to investigate land use effect and suggest future development. The research processes include applying the technique of remote sensing and GIS, landscape index, and Shannon diversity t-test to acquire the landscape change information of Taipei and its administrative districts; investigating if Taipei’s landscape is obviously affected by land use change within 15 years; deriving common factors from the selected landscape indices and analyzing the similarity among Taipei’s administrative districts via multivariate statistical analysis such as principal component analysis and factor analysis and cluster analysis. The results indicated that Taipei’s landscape had a obvious change after 15 years. The area of build-up increases from 33.65% of 1993 to 41.06% of 2007 and the bare land decreases from 16.59% to 10.54%. Both Shannon diversity index and Shannon Evenness index are decreasing. The result of Shannon diversity t-test also showed that the land use change in Taipei did have an obvious effect for the entire landscape. The application of principal component analysis and factor analysis selected four principal component and four factor of the cluster analysis results indicate, that principal component analysis of cluster analysis in 1993 and 2007, that the administrative districts was clustered into four and two groups; as for the application of factor analysis and cluster analysis, the result pointed out that the administrative districts was clustered into six and two groups no matter in 1993 or 2007 information, but if we investigate the administrative districts within the same group, clearly there was different between 1993 and 2007 information. The above results can be concluded as follows: the application of integrating the technique of remote sensing and GIS, landscape index, and Shannon diversity t-test on Taipei’s landscape change is an effective and feasible approach. Meanwhile, the landscape similarity among different administrative districts and the selection of suitable landscape indices on landscape analysis can be achieved by the use of multivariate statistical analysis. The result obtained from this study can be extended as a reference of future development model for Taipei’s land use. Keywords：Geographical Information System, Landscape Change, Landscape Index, Principal Component Analysis, Factor Analysis, Cluster Analysis
Appears in Collections:	[Department of Landscape Architecture & Graduate Institute of Landscape Architecture ] thesis

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