DOI: 10.1016/j.jag.2015.11.007
Scopus记录号: 2-s2.0-85016235773
论文题名: Remote sensing of impervious surface growth: A framework for quantifying urban expansion and re-densification mechanisms
作者: Shahtahmassebi A ; R ; , Song J ; , Zheng Q ; , Blackburn G ; A ; , Wang K ; , Huang L ; Y ; , Pan Y ; , Moore N ; , Shahtahmassebi G ; , Sadrabadi Haghighi R ; , Deng J ; S
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2016
卷: 46 起始页码: 94
结束页码: 112
语种: 英语
英文关键词: Expansion
; Getis_Ord
; Impervious surface
; ISF
; MESMA
; Re- densification
; Regression residuals
Scopus关键词: algorithm
; Landsat
; mapping
; numerical model
; regression analysis
; remote sensing
; satellite imagery
; spatial analysis
; urban growth
; urbanization
英文摘要: A substantial body of literature has accumulated on the topic of using remotely sensed data to map impervious surfaces which are widely recognized as an important indicator of urbanization. However, the remote sensing of impervious surface growth has not been successfully addressed. This study proposes a new framework for deriving and summarizing urban expansion and re-densification using time series of impervious surface fractions (ISFs) derived from remotely sensed imagery. This approach integrates multiple endmember spectral mixture analysis (MESMA), analysis of regression residuals, spatial statistics (Getis_Ord) and urban growth theories; hence, the framework is abbreviated as MRGU. The performance of MRGU was compared with commonly used change detection techniques in order to evaluate the effectiveness of the approach. The results suggested that the ISF regression residuals were optimal for detecting impervious surface changes while Getis_Ord was effective for mapping hotspot regions in the regression residuals image. Moreover, the MRGU outputs agreed with the mechanisms proposed in several existing urban growth theories, but importantly the outputs enable the refinement of such models by explicitly accounting for the spatial distribution of both expansion and re-densification mechanisms. Based on Landsat data, the MRGU is somewhat restricted in its ability to measure re-densification in the urban core but this may be improved through the use of higher spatial resolution satellite imagery. The paper ends with an assessment of the present gaps in remote sensing of impervious surface growth and suggests some solutions. The application of impervious surface fractions in urban change detection is a stimulating new research idea which is driving future research with new models and algorithms. © 2015 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/80108
Appears in Collections: 气候变化事实与影响
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作者单位: Institute of Remote Sensing and Information System Application, Zhejiang University, Hangzhou, China; Lancaster Environment Centre, Lancaster University, Lancaster, United Kingdom; Department of Geography, Michigan State University, East Lansing, MI, United States; School of Science & Technology, Nottingham Trent University, United Kingdom; Department of Agronomy and Plant Breeding, Mashhad Branch, Islamic Azad University, Iran
Recommended Citation:
Shahtahmassebi A,R,, Song J,et al. Remote sensing of impervious surface growth: A framework for quantifying urban expansion and re-densification mechanisms[J]. International Journal of Applied Earth Observation and Geoinformation,2016-01-01,46