Monitoring the built-up environment of Shanghai on the street-block level using SAR and volunteered geographic information
In this geo-statistical analysis of change detection, we illustrate the evolution of the built-up environment in Shanghai at the street-block level. Based on two TerraSAR-X image stacks with 36 and 15 images, covering the city centre of Shanghai for the time period from 2008 to 2015, a set of cohere...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2017-07-01
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| Series: | International Journal of Digital Earth |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/17538947.2016.1216616 |
| _version_ | 1797678630036307968 |
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| author | Michael Jendryke Stephen C. McClure Timo Balz Mingsheng Liao |
| author_facet | Michael Jendryke Stephen C. McClure Timo Balz Mingsheng Liao |
| author_sort | Michael Jendryke |
| collection | DOAJ |
| description | In this geo-statistical analysis of change detection, we illustrate the evolution of the built-up environment in Shanghai at the street-block level. Based on two TerraSAR-X image stacks with 36 and 15 images, covering the city centre of Shanghai for the time period from 2008 to 2015, a set of coherence images was created using a small baseline approach. The road network from Open Street Map, a volunteered geographic information product, serves as the input dataset to create street-blocks. A street-block is surrounded by roads and resembles a ground parcel, a real estate property – a cadastral unit. The coherence information is aggregated to these street-blocks for each observation and the variation is analysed over time. An analysis of spatial autocorrelation reveals clusters of similar behaviours. The result is a detailed map of Shanghai highlighting areas of change. We argue that the aggregation and grouping of synthetic aperture radar coherence image information to real-world entities (street-blocks) is comprehensible and relevant to the urban planning process. Therefore, this research is a contribution to the community of urban planners, designers, and government agencies who want to monitor the development of the urban landscape. |
| first_indexed | 2024-03-11T23:02:43Z |
| format | Article |
| id | doaj.art-86291eeecdb4481f8940d7e5553740bc |
| institution | Directory Open Access Journal |
| issn | 1753-8947 1753-8955 |
| language | English |
| last_indexed | 2024-03-11T23:02:43Z |
| publishDate | 2017-07-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj.art-86291eeecdb4481f8940d7e5553740bc2023-09-21T14:38:04ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552017-07-0110767568610.1080/17538947.2016.12166161216616Monitoring the built-up environment of Shanghai on the street-block level using SAR and volunteered geographic informationMichael Jendryke0Stephen C. McClure1Timo Balz2Mingsheng Liao3Wuhan UniversityWuhan UniversityWuhan UniversityWuhan UniversityIn this geo-statistical analysis of change detection, we illustrate the evolution of the built-up environment in Shanghai at the street-block level. Based on two TerraSAR-X image stacks with 36 and 15 images, covering the city centre of Shanghai for the time period from 2008 to 2015, a set of coherence images was created using a small baseline approach. The road network from Open Street Map, a volunteered geographic information product, serves as the input dataset to create street-blocks. A street-block is surrounded by roads and resembles a ground parcel, a real estate property – a cadastral unit. The coherence information is aggregated to these street-blocks for each observation and the variation is analysed over time. An analysis of spatial autocorrelation reveals clusters of similar behaviours. The result is a detailed map of Shanghai highlighting areas of change. We argue that the aggregation and grouping of synthetic aperture radar coherence image information to real-world entities (street-blocks) is comprehensible and relevant to the urban planning process. Therefore, this research is a contribution to the community of urban planners, designers, and government agencies who want to monitor the development of the urban landscape.http://dx.doi.org/10.1080/17538947.2016.1216616sarchange detectionurban areavolunteered geographic information |
| spellingShingle | Michael Jendryke Stephen C. McClure Timo Balz Mingsheng Liao Monitoring the built-up environment of Shanghai on the street-block level using SAR and volunteered geographic information International Journal of Digital Earth sar change detection urban area volunteered geographic information |
| title | Monitoring the built-up environment of Shanghai on the street-block level using SAR and volunteered geographic information |
| title_full | Monitoring the built-up environment of Shanghai on the street-block level using SAR and volunteered geographic information |
| title_fullStr | Monitoring the built-up environment of Shanghai on the street-block level using SAR and volunteered geographic information |
| title_full_unstemmed | Monitoring the built-up environment of Shanghai on the street-block level using SAR and volunteered geographic information |
| title_short | Monitoring the built-up environment of Shanghai on the street-block level using SAR and volunteered geographic information |
| title_sort | monitoring the built up environment of shanghai on the street block level using sar and volunteered geographic information |
| topic | sar change detection urban area volunteered geographic information |
| url | http://dx.doi.org/10.1080/17538947.2016.1216616 |
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