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Monitoring impervious surface sprawl in Shanghai using tasseled cap transformation of landsat data
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics. (Geoinformatics)
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics. (Geoinformatics)
2010 (English)Conference paper, Published paper (Other academic)
Abstract [en]

Transformation from non-impervious surface to impervious surface changes the landscapes and also changes the ecological and environmental conditions. Detecting impervious surface growth is vital to monitoring urban development and supporting sustainable city planning. The objective of this research is to conduct detection of impervious surface sprawl using tasseled cap transformation within the conceptual framework of Vegetation-Impervious surface-Soil (V-I-S) model. Landsat-3 MSS images on August 4, 1979 and Gap-filled Landsat-7 ETM+ images on May 22, 2009, covering the Greater Shanghai Area, were used in the case study. The results demonstrated that direct change detection using variables derived from tasseled cap transformation was effective for monitoring impervious surface sprawl. The variables derived from tasseled cap transformation have the potential to link to the components of the V-I-S model. The Greater Shanghai Area experienced high-speed impervious surface sprawl over the past 30 years at the average speed of 38.84km2/year.

Place, publisher, year, edition, pages
2010.
Series
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, ISSN 1682-1750
National Category
Remote Sensing
Identifiers
URN: urn:nbn:se:kth:diva-30447ISI: 000339410100053Scopus ID: 2-s2.0-84897494883OAI: oai:DiVA.org:kth-30447DiVA: diva2:400048
Conference
ISPRS Technical Commission VII Symposium on Advancing Remote Sensing Science, Vienna, Austria, 5-7 July 2010
Note

QC 20150603

Available from: 2011-02-24 Created: 2011-02-24 Last updated: 2017-03-24Bibliographically approved
In thesis
1. Spatial-Temporal Patterns of Urban Growth in Shanghai, China: Monitoring, Analysis, and Simulation
Open this publication in new window or tab >>Spatial-Temporal Patterns of Urban Growth in Shanghai, China: Monitoring, Analysis, and Simulation
2009 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Supporting huge population, megacities are definitely the hot spots of production, consumption, and waste generation. Without careful investment and planning, megacities will be overwhelmed by burgeoning negative impacts on the environment, natural resources, and human health, as well as a host of social and economic issues. The unprecedented combination of economic and population growth since the Reform and Open Policy has led China into transition from a largely rural society to a predominantly urban one. Chinese cities, without question, have not escaped the danger of the series of problems during the rapid progress of urbanization. Therefore, monitoring the spatial-temporal patterns of urban sprawl and their impact on the environment is of critical importance for urban planning and sustainable development, especially in developing Chinese cities such as Shanghai.

To date, few studies have focused on the urban trajectories of Shanghai over the past 30 years from a remote sensing perspective. Most of the studies were concentrated on the technical issues of image processing and classification. Moreover, research on spatial metrics has focused on analyzing remote sensing classification results rather than on the use of interpreting, assessing, and verifying urban simulation results. Furthermore, many researches merely focused on baseline projection and very few studies took into consideration urban growth scenarios so far. As yet there have been no reported scenario simulations of future Shanghai growth with several land-use categories within urban areas.

The overall objective of this research is to investigate the integration of remote sensing, spatial metrics, and spatial-temporal models in the monitoring, analysis, and simulation of urban growth in Shanghai, China. The specific objectives are to: 1). monitor urban dynamics over time with multi-sensor remote sensing images; 2). quantify spatial-temporal properties of urban growth and representing the urban morphological structures by means of spatial metrics; and 3). simulate the geographic extent, patterns, and detailed catalogs of urban growth under different scenarios using Markov-Cellular Automata (Markov-CA) model to support decision making for a more sustainable Shanghai.

Through this study, the combined approach using remotely sensed data with change detection techniques, spatial metrics, and a scenarios-based simulation model proved to be effective to understand, represent, and predict the spatial-temporal dynamics of urban growth. In detail, the segmented-based hierarchy classification and visual interpretation were effective methods to extract urban and industrial land with high-resolution remotely sensed images. Direct change detection using variables derived from tasseled cap transformation was efficient for monitoring impervious surface sprawl. Spatial metrics is a quick and executable way to assessing the impact of urban sprawl on landscape dynamic. Markov-CA model is a useful tool to simulate the scenarios of future urban developments and therefore provides the policy options for sustainable urban planning.

The research results of urban trajectories and impervious surface sprawl showed that Shanghai experienced high-speed urban sprawl and the rate of urban expansion, however, was not homogeneous spatially and temporally. The general annual urban expansion speed was 34.8 km2 per year; nevertheless, it reached 80.2 km2 per year recent six years from 2001 to 2007, while it touched the bottom speed around 14.3 km2 per year during 1979-1989. The expanded area in the Puxi region was 5.23 times of its original area while that of Pudong region was 19.94 times of its original area during 1979-2007. The research results of landscape analysis demonstrated that greenbelt becomes fractured while infrastructural and commercial area is more and more aggregated in the central Shanghai area, and satellite images such as SPOT Pan, XS and Landsat TM with 10-30 meter resolution are sufficient for the landscape dynamic research in central Shanghai area. The results of scenarios-based simulation indicated that built-up areas in Shanghai will increase significantly in 2025 and Shanghai will experience less urban sprawl and retain a better environment in 2025 under service-oriented center (SOC) than under baseline (NS) or manufacturing-dominant center (MDC) scenario. If favorable policy for MDC scenario is adopted, however, there will be a lot of manufacturing industries gathering in Shanghai and more agricultural lands will be encroached.

The present research focused on the analysis of physical and morphological aspects of urban growth. Urban land-use dynamics are, however, intrinsically linked with socio-economic, political, or demographic drivers. Trying to fill in the missing link between traditional urban geography and urban remote sensing & urban simulation and to improve understanding of the interactions between human and natural aspects in the urban socio-ecosystem is the major focus in the next phase of the Ph.D. research.

Keywords: Urban growth, Spatial-temporal pattern, Remote sensing, Spatial metrics, Scenarios-based simulation, Shanghai

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2009. x, 38 p.
Series
Trita-SOM , ISSN 1653-6126 ; 2009-10
Keyword
urban growth, spatial-temporal pattern, remote sensing, spatial metrics, scenarios-based simulation, Shanghai
National Category
Geophysical Engineering
Identifiers
urn:nbn:se:kth:diva-11868 (URN)978-91-7415-532-7 (ISBN)
Presentation
2010-01-21, Seminar Room 4055, Drottning Kristinas väg 30, 1tr, KTH, Stockholm, 10:00 (English)
Opponent
Supervisors
Note
QC 20110224Available from: 2010-01-15 Created: 2010-01-14 Last updated: 2011-02-24Bibliographically approved

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