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Satellite Monitoring and Impact Assessment of Urban Growth in Stockholm, Sweden between 1986 and 2006
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics (closed 20110301).
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics (closed 20110301).
2010 (English)In: Imagin[e,g] Europe: Proceedings of the 29th Symposium of the European Association of Remote Sensing Laboratories, Chania, Greece / [ed] Ioannis Manakos, Chariton Kalaitzidis, IOS Press, 2010, 131-142 p.Conference paper (Refereed)
Abstract [en]

The objective of this research is to investigate the extent of land-cover change in and around Stockholm from 1986 to 2006 and the nature of the resulting landscape fragmentation with a particular focus on the possible environmental impact. Four scenes of SPOT imagery over the Stockholm area were acquired for this study: two on 13 June 1986, one on 5 August 2006 and one on 4 June 2008. Various image processing and classification algorithms were tested and compared. The best classification results were obtained using an object-based and rule-based approach with texture measures as well as spectral data as inputs. The image pairs from the two decades were classified into seven land cover categories for Stockholm Municipality, i.e., low-density built-up, high-density built-up, industrial areas, open land, forest, mixed forest and open land, and water. The overall accuracies were 93% (kappa: 0.91) for 1986 and 97% (kappa: 0.96) for 2006. Landscape fragmentation and change was evaluated using spatial metrics. The spatial metric results reveal that urban areas increased at the expense of non-built up areas by around 2% both on the municipal and regional levels. The 2006/2008 classification gives evidence of being a more fragmented landscape than that of 1986. While urban areas have become denser within Stockholm municipality, which is in line with the region's development policy, more natural land cover types have at the same time been eroded; a development not in line with the regional goal of maintaining the area's green spaces. The classification technique used on the municipality will be expanded to the region as a whole, and regional trends and consequent recommendations will be the focus of future research

Place, publisher, year, edition, pages
IOS Press, 2010. 131-142 p.
National Category
Remote Sensing Environmental Management
URN: urn:nbn:se:kth:diva-88916DOI: 10.3233/978-1-60750-494-8-131ISBN: 978-1-60750-493-1OAI: diva2:502554
29th Symposium of the European Association of Remote Sensing Laboratories, 15-18 June 2009 Chania, Greece

QC 20120215

Available from: 2012-02-14 Created: 2012-02-14 Last updated: 2014-12-12Bibliographically approved
In thesis
1. Satellie Monitoring of Urban Growth and Indicator-based Assessment of Environmental Impact
Open this publication in new window or tab >>Satellie Monitoring of Urban Growth and Indicator-based Assessment of Environmental Impact
2014 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

One of the major consequences of urbanization is the transformation of land surfaces from rural/natural environments to built-up land that supports diverse forms of human activity. These transformations impact the local geology, climate, hydrology, flora and fauna and human-life supporting ecosystem services in the region. Mapping and analysis of land use/land cover change in urban regions and tracking their environmental impact is therefore of vital importance for evaluating policy options for future growth and promoting sustainable urban development.

The overall objective of this research is to investigate the extent of urban growth and/or sprawl and its potential environmental impact in the regions surrounding a few selected major cities in North America, Europe and Asia using landscape metrics and other environmental indicators to assess the landscape changes. The urban regions examined are the Greater Toronto Area (GTA) in Canada, Stockholm region and County in Sweden and Shanghai in China. The analyses are based on classificatons of optical satellite imagery (Landsat TM/ETM+ or SPOT 1/5) between 1985 and 2010. Maximum likelihood classification (MLC) under urban/rural masks, objectbased image analysis (OBIA) with rule-based classification and support vector machines (SVM) classification methods were used with grey level cooccurrence matrix (GLCM) texture features as input to help obtain higher accuracies. Based on the classification results, landscape metrics, selected environmental indicators and indices, and ecosystem service valuation were calculated and used to estimate environmental impact of urban growth.

The results show that urban areas in the GTA grew by nearly 40% between 1985 and 2005. Results from the landscape metrics and urban compactness indicators show that low-density built-up areas increased significantly in the GTA between 1985 and 2005, mainly at the expense of agricultural areas. The majority of environmentally significant areas were increasingly surrounded by urban areas between 1985 and 2005, furthering their isolation from other natural areas. Urban areas in the Stockholm region increased by 10% between 1986 and 2006. The landscape metrics indicated that natural areas became more isolated or shrank whereas new small urban patches came into being. The most noticeable changes in terms of environmental impact and urban expansion were in the east and north of the study area. Large forested areas in the northeast dropped the most in terms of environmental impact ranking, while the most improved analysis units were close to the central Stockholm area. The study comparing Shanghai and Stockholm County revealed that urban areas increased ten times as much in Shanghai as they did in Stockholm, at 120% and 12% respectively. The landscape metrics results show that fragmentation in both study regions occurred mainly due to the growth of high density built-up areas in previously more natural environments, while the expansion of low density built-up areas was for the most part in conjunction with pre-existing patches. The growth in urban areas resulted in ecosystem service value losses of approximately 445 million USD in Shanghai, mostly due to the decrease in natural coastal wetlands, while in Stockholm the value of ecosystem services changed very little.

This study demonstrates the utility of urban and environmental indicators derived from remote sensing data via GIS techniques in assessing both the spatio-temporal dynamics of urban growth and its environmental impact in different metropolitan regions. High accuracy classifications of optical medium resolution remote sensing data are achieved thanks in part to the incorporation of texture features for both object- and pixel-based classification methods, and to the use of urban/rural masks with the latter. The landscape metrics calculated based on the classifications are useful in quantifying urban growth trends and potential environmental impact as well as facilitating their comparison. The environmental indicator results highlight the challenges in terms of sustainable urban growth unique to each landscape, both spatially and temporally. The next phase of this PhD research will involve finding valid methods of comparing and contrasting urban growth patterns and estimated environmental impact in different regions of the world and further exploration of how to link urbanizing landscapes to changes in ecosystem services via environmental indicators.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2014. xi, 91 p.
TRITA-SOM, ISSN 1653-6126 ; 2014-15
Urban growth, remote sensing, landcover classification, landscape metrics, environmental indicators, environmental impact, Greater Toronto Area, Stockholm, Shanghai
National Category
Environmental Management
urn:nbn:se:kth:diva-157669 (URN)978-91-7595-353-3 (ISBN)
2014-12-12, Seminarierum 4055, 3tr, Drottning Kristinas Väg 30, Stockholm, 10:00 (English)

QC 20141212

Available from: 2014-12-12 Created: 2014-12-12 Last updated: 2016-02-09Bibliographically approved

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Furberg, DorothyBan, Yifang
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