Study on monitoring taipei land-use change

Yeong Kuan Chen; Chi Chuan Cheng; Su Fen Wang; Rong Peng Wang

Study on monitoring taipei land-use change

Yeong Kuan Chen, Chi Chuan Cheng, Su Fen Wang, Rong Peng Wang

Department of Geography

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

This study applies remote sensing, GIS, landscape indices, and multivariate statistical analysis to monitor spatial and temporal land-use variations of Taipei districts from 1993 to 2007. The process includes the calculation of landscape indices from land-use maps and the performance of multivariate statistical analysis with three cases (i.e., cluster analysis with original landscape indices, principal component analysis, and factor analysis). The result from landscape indices indicates that variations exist among districts. As for multivariate statistical analysis, three cases achieve the same result. The results are concluded as follows: The integration of spatial technologies and landscape indices is a timely and useful approach to monitor Taipei land-use change. In addition, multivariate statistical analysis is a feasible approach for land-use change analysis. Particularly, principal component analysis and factor analysis are good for reducing the number of landscape indices, and cluster analysis is good for analyzing the spatial and temporal variations of Taipei districts. Therefore, the result obtained from this study can be a reference of Taipei urban planning in future.

Original language	English
Title of host publication	31st Asian Conference on Remote Sensing 2010, ACRS 2010
Pages	1479-1485
Number of pages	7
Publication status	Published - 2010 Dec 1
Event	31st Asian Conference on Remote Sensing 2010, ACRS 2010 - Hanoi, Viet Nam Duration: 2010 Nov 1 → 2010 Nov 5

Publication series

Name	31st Asian Conference on Remote Sensing 2010, ACRS 2010
Volume	2

Other

Other	31st Asian Conference on Remote Sensing 2010, ACRS 2010
Country	Viet Nam
City	Hanoi
Period	10-11-01 → 10-11-05

All Science Journal Classification (ASJC) codes

Computer Networks and Communications

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Cite this

Chen, Y. K., Cheng, C. C., Wang, S. F., & Wang, R. P. (2010). Study on monitoring taipei land-use change. In 31st Asian Conference on Remote Sensing 2010, ACRS 2010 (pp. 1479-1485). (31st Asian Conference on Remote Sensing 2010, ACRS 2010; Vol. 2).

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abstract = "This study applies remote sensing, GIS, landscape indices, and multivariate statistical analysis to monitor spatial and temporal land-use variations of Taipei districts from 1993 to 2007. The process includes the calculation of landscape indices from land-use maps and the performance of multivariate statistical analysis with three cases (i.e., cluster analysis with original landscape indices, principal component analysis, and factor analysis). The result from landscape indices indicates that variations exist among districts. As for multivariate statistical analysis, three cases achieve the same result. The results are concluded as follows: The integration of spatial technologies and landscape indices is a timely and useful approach to monitor Taipei land-use change. In addition, multivariate statistical analysis is a feasible approach for land-use change analysis. Particularly, principal component analysis and factor analysis are good for reducing the number of landscape indices, and cluster analysis is good for analyzing the spatial and temporal variations of Taipei districts. Therefore, the result obtained from this study can be a reference of Taipei urban planning in future.",

author = "Chen, {Yeong Kuan} and Cheng, {Chi Chuan} and Wang, {Su Fen} and Wang, {Rong Peng}",

year = "2010",

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N2 - This study applies remote sensing, GIS, landscape indices, and multivariate statistical analysis to monitor spatial and temporal land-use variations of Taipei districts from 1993 to 2007. The process includes the calculation of landscape indices from land-use maps and the performance of multivariate statistical analysis with three cases (i.e., cluster analysis with original landscape indices, principal component analysis, and factor analysis). The result from landscape indices indicates that variations exist among districts. As for multivariate statistical analysis, three cases achieve the same result. The results are concluded as follows: The integration of spatial technologies and landscape indices is a timely and useful approach to monitor Taipei land-use change. In addition, multivariate statistical analysis is a feasible approach for land-use change analysis. Particularly, principal component analysis and factor analysis are good for reducing the number of landscape indices, and cluster analysis is good for analyzing the spatial and temporal variations of Taipei districts. Therefore, the result obtained from this study can be a reference of Taipei urban planning in future.

AB - This study applies remote sensing, GIS, landscape indices, and multivariate statistical analysis to monitor spatial and temporal land-use variations of Taipei districts from 1993 to 2007. The process includes the calculation of landscape indices from land-use maps and the performance of multivariate statistical analysis with three cases (i.e., cluster analysis with original landscape indices, principal component analysis, and factor analysis). The result from landscape indices indicates that variations exist among districts. As for multivariate statistical analysis, three cases achieve the same result. The results are concluded as follows: The integration of spatial technologies and landscape indices is a timely and useful approach to monitor Taipei land-use change. In addition, multivariate statistical analysis is a feasible approach for land-use change analysis. Particularly, principal component analysis and factor analysis are good for reducing the number of landscape indices, and cluster analysis is good for analyzing the spatial and temporal variations of Taipei districts. Therefore, the result obtained from this study can be a reference of Taipei urban planning in future.

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