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Spatial-Temporal Patterns of Urban Growth in Shanghai, China: Monitoring, Analysis, and Simulation
KTH, School of Architecture and the Built Environment (ABE), Transport and Economics, Geodesy. (Geoinformatics)
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 [en]
urban growth, spatial-temporal pattern, remote sensing, spatial metrics, scenarios-based simulation, Shanghai
National Category
Geophysical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-11868ISBN: 978-91-7415-532-7 (print)OAI: oai:DiVA.org:kth-11868DiVA: diva2:286574
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
List of papers
1. The Trajectories of Urban Land and Industrial Land in Shanghai over the Past 30 Years
Open this publication in new window or tab >>The Trajectories of Urban Land and Industrial Land in Shanghai over the Past 30 Years
2009 (English)In: 2009 JOINT URBAN REMOTE SENSING EVENT, VOLS 1-3, New York: IEEE , 2009, 1022-1028 p.Conference paper, Published paper (Refereed)
Abstract [en]

With regional economy development and the changes of urban development strategies, Shanghai experienced a distinct land use change process during the past 30 year. To explore the spatial pattern and temporal dynamics of urban land and industrial land over the past years, two independent datasets from multitemporal satellite images based on interpretation with computer-aids and object-oriented classification methods were employed in this study. Preliminary study indicated that Shanghai's urban land experienced continuous increases over the past 30 years. The total urban area of Shanghai increased from 146.1km(2) to 1121.3km(2) during 1979-2007, with an annual urban expansion area of 34.8km(2) per year. The rate of urban expansion, however, was not homogeneous spatially and temporally. 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. Within the extent of 1979's urban land distribution, the told area of industrial land and warehouses way 14.7 km(2) in 1966 and 16.8 km(2) in 2007, with the ratio of the industrial land to the whole territory increased from 10.1% to 11.5% The sizes of industrial land patches within the boundary decreased with the total industrial land area and the number of industrial patches increased. Moreover, the average size of industrial land in the Pudong region (1.6-2.0ha) was larger than that in the Puxi region (1.1-1.8ha). The trajectories of urban land and industrial land in Shanghai can he well explained by the policies of the national and the local government.

Place, publisher, year, edition, pages
New York: IEEE, 2009
Keyword
urban land, industrial land, development trajectory, multitemporal satellite images, Shanghai
National Category
Remote Sensing
Identifiers
urn:nbn:se:kth:diva-30446 (URN)10.1109/URS.2009.5137640 (DOI)000270972300174 ()2-s2.0-70350173602 (Scopus ID)978-1-4244-3460-2 (ISBN)
Conference
JURSE - JOINT URBAN REMOTE SENSING EVENT 2009
Note

QC 20110224

Available from: 2011-02-24 Created: 2011-02-24 Last updated: 2016-05-09Bibliographically approved
2. Monitoring impervious surface sprawl in Shanghai using tasseled cap transformation of landsat data
Open this publication in new window or tab >>Monitoring impervious surface sprawl in Shanghai using tasseled cap transformation of landsat data
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.

Series
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, ISSN 1682-1750
National Category
Remote Sensing
Identifiers
urn:nbn:se:kth:diva-30447 (URN)000339410100053 ()2-s2.0-84897494883 (Scopus ID)
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
3. Analysis of landscape dynamics in Shanghai using landscape metrics: effects of spatial resolution
Open this publication in new window or tab >>Analysis of landscape dynamics in Shanghai using landscape metrics: effects of spatial resolution
2008 (English)In: XXI Congress of International Society for Photogrammetry and Remote Sensing. july, 2008. Beijing, China, 2008Conference paper, Published paper (Refereed)
Abstract [en]

It is increasingly evident that urban sprawl leads to dramatic changes in landscape patterns and thus changes in ecosystem functioning. Analysis of the landscape patterns and their dynamic under urbanization is of great importance for sustainable development, especially in cities with significant changes like Shanghai. The objective of this research is to illustrate the landscape dynamic under the urbanization process in a selected test area of Shanghai in 1991, 1998 and 2007 using multitemtopal remote sensing and landscape metrics; and to determine the optimal resolution suitable for this case study. Preliminary results show that it is a quick and executable way to assessing the impact of urban sprawl on landscape dynamic using remote sensing data and landscape matrices; and the optimal resolution for the case study is 10-30 meters.

 

Keyword
remote sensing, urban, ecology, analysis, change, SPOT
National Category
Remote Sensing
Identifiers
urn:nbn:se:kth:diva-30448 (URN)
Conference
XXI Congress of International Society for Photogrammetry and Remote Sensing (ISPRA). july, 2008. Beijing, China
Note

QC 20110224

Available from: 2011-02-24 Created: 2011-02-24 Last updated: 2016-05-09Bibliographically approved
4. Simulating urban development scenarios using Markov-Cellular automata: a case study of greater Shanghai area, China
Open this publication in new window or tab >>Simulating urban development scenarios using Markov-Cellular automata: a case study of greater Shanghai area, China
2009 (English)In: Computers, Environment and Urban Systems, ISSN 0198-9715, E-ISSN 1873-7587Article in journal (Refereed) Submitted
Abstract [en]

The objective of this paper is to provide a comparative analysis of urban simulations under different scenarios. The objective of this paper is to provide a comparative analysis of urban simulations under different scenarios. Specifically, this research investigates the Markov-Cellular Automata (Markov-CA) model under different development schemes for simulations of the urban expansion scenarios of Greater Shanghai Area (GSA) in 2015 and 2025 in support of sustainable urban development. In this study, multitemporal land cover dataset, derived from remotely sensed images from 1995, 2000 and 2005 were used for simulation and validation. The scenarios of urban expansion patterns of GSA in 2015 and 2025 under three different schemes, i.e., Non-Scheme (NS), Service Oriented Center scheme (SOC) and Manufacture Dominant Center scheme (MDC), were simulated and comparatively analyzed with the aid of landscape metrics. The results demonstrated that GSA will experience less urban sprawl and retain a better environment in 2025 if the service orientated development policy is given priority. If favorable policy for MDC is adopted, however, there will be a lot of manufacturing industries gathering in GSA and more agricultural lands will be encroached. Through this study, the combined approach using remote sensing data, GIS spatial analysis environment and urban simulation model with landscape metrics and scenario analysis proved to be effective to understand, represent and predict the spatial-temporal dynamics of urban evolution, and Markov-CA model is a useful tool for simulating the scenarios of future urban developments, even though there are some issues that need to be investigated in the future, such as the construction of localized schemes, and the incorporation of political, social, economic and cultural factors. Specifically, this research investigates the Markov-Cellular Automata (Markov-CA) model under different development schemes for simulations of the urban expansion scenarios of Greater Shanghai Area (GSA) in 2015 and 2025 in support of sustainable urban development. In this study, multitemporal land cover dataset, derived from remotely sensed images from 1995, 2000 and 2005 were used for simulation and validation. The scenarios of urban expansion patterns of GSA in 2015 and 2025 under three different schemes, i.e., Non-Scheme (NS), Service Oriented Center scheme (SOC) and Manufacture Dominant Center scheme (MDC), were simulated and comparatively analyzed with the aid of landscape metrics. The results demonstrated that GSA will experience less urban sprawl and retain a better environment in 2025 if the service orientated development policy is given priority. If favorable policy for MDC is adopted, however, there will be a lot of manufacturing industries gathering in GSA and more agricultural lands will be encroached. Through this study, the combined approach using remote sensing data, GIS spatial analysis environment and urban simulation model with landscape metrics and scenario analysis proved to be effective to understand, represent and predict the spatial-temporal dynamics of urban evolution, and Markov-CA model is a useful tool for simulating the scenarios of future urban developments, even though there are some issues that need to be investigated in the future, such as the construction of localized schemes, and the incorporation of political, social, economic and cultural factors.

Keyword
Markov-Cellular automata, urban expansion, simulation, scenarios, greater Shanghai area
National Category
Geophysical Engineering
Identifiers
urn:nbn:se:kth:diva-30451 (URN)
Note
QS 20120315Available from: 2011-02-24 Created: 2011-02-24 Last updated: 2017-12-11Bibliographically approved

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Citation style
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