Quantifying highly dynamic urban landscapes: Integrating object-based image analysis with Landsat time series data
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RESEARCH ARTICLE
Quantifying highly dynamic urban landscapes: Integrating object-based image analysis with Landsat time series data Wenjuan Yu . Weiqi Zhou . Chuanbao Jing . Yujia Zhang . Yuguo Qian
Received: 25 February 2020 / Accepted: 29 August 2020 Ó Springer Nature B.V. 2020
Abstract Context Urban landscapes are highly dynamic with changes frequently occurring at short time intervals. Although the Landsat data archive allows the use of high-density time-series data to quantify such dynamics, the approaches that can fully address the spatial and temporal complexity of the urban landscape are still lacking. Objectives A new approach is presented for accurately quantifying urban landscape dynamics. Information regarding when and where a change occurs, what type of change exists, and how often it happens are incorporated. Methods The new approach integrates object-based image analysis and time-series change detection W. Yu W. Zhou (&) C. Jing Y. Qian State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China e-mail: [email protected] W. Zhou C. Jing University of Chinese Academy of Sciences, Beijing 100049, China W. Zhou Beijing Urban Ecosystem Research Station, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
techniques by using all available Landsat images for several decades. This approach was tested on the rapidly urbanizing city of Shenzhen, China from 1986 to 2017. Results Land cover changes in both long- and shorttime intervals can be proficiently detected with an overall accuracy of 90.65% and a user’s accuracy of 92.18% and 82.40% for ‘‘No change’’ and ‘‘Change’’, respectively. The frequency and time of change can be explicitly displayed while incorporating the advantages of object-based image analysis and time-series change detection. The efficiency of the change analysis can be greatly increased because the object-based analysis greatly reduces the number of analyzed units. Conclusion The new approach can accurately and efficiently detect the land cover change for quantifying urban landscape dynamics. Integrating the object and the remotely sensed time-series data has the potential to link the physical and socio-economic properties together for facilitating sustainable landscape planning. Keywords Spatial heterogeneity Land cover change Remote sensing Change detection Frequency Time of change
Y. Zhang School of Geographical Sciences and Urban Planning, Arizona States University, Tempe, AZ 85287, USA
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Landscape Ecol
Introduction Urban landscapes are highly dynamic, involving both outward expansion and internal changes in land cover (Seto et al. 2011; Zhou et al. 2018). Such changes have significant social, environmental, and ecological impacts, and thereby affect urban sustainability (Ewing et al. 2014; Wu 2014; Peng et al. 2016a). A sustainable landscape pattern has been shown to effectiv
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