Optimizing Spatial Resolution of Imagery for Urban Form Detection—The Cases of France and Vietnam
The multitude of satellite data products available offers a large choice for urban studies. Urban space is known for its high heterogeneity in structure, shape and materials. To approach this heterogeneity, finding the optimal spatial resolution (OSR) is needed for urban form detection from remote s...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2011-09-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | http://www.mdpi.com/2072-4292/3/10/2128/ |
| _version_ | 1818262527922929664 |
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| author | Christiane Weber Anne Puissant Thi Dong-Binh Tran Dominique Badariotti |
| author_facet | Christiane Weber Anne Puissant Thi Dong-Binh Tran Dominique Badariotti |
| author_sort | Christiane Weber |
| collection | DOAJ |
| description | The multitude of satellite data products available offers a large choice for urban studies. Urban space is known for its high heterogeneity in structure, shape and materials. To approach this heterogeneity, finding the optimal spatial resolution (OSR) is needed for urban form detection from remote sensing imagery. By applying the local variance method to our datasets (pan-sharpened images), we can identify OSR at two levels of observation: individual urban elements and urban districts in two agglomerations in West Europe (Strasbourg, France) and in Southeast Asia (Da Nang, Vietnam). The OSR corresponds to the minimal variance of largest number of spectral bands. We carry out three categories of interval values of spatial resolutions for identifying OSR: from 0.8 m to 3 m for isolated objects, from 6 m to 8 m for vegetation area and equal or higher than 20 m for urban district. At the urban district level, according to spatial patterns, form, size and material of elements, we propose the range of OSR between 30 m and 40 m for detecting administrative districts, new residential districts and residential discontinuous districts. The detection of industrial districts refers to a coarser OSR from 50 m to 60 m. The residential continuous dense districts effectively need a finer OSR of between 20 m and 30 m for their optimal identification. We also use fractal dimensions to identify the threshold of homogeneity/heterogeneity of urban structure at urban district level. It seems therefore that our approaches are robust and transferable to different urban contexts. |
| first_indexed | 2024-12-12T19:04:33Z |
| format | Article |
| id | doaj.art-018e7c6d43b046f78543bfdcb6492393 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-12T19:04:33Z |
| publishDate | 2011-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-018e7c6d43b046f78543bfdcb64923932022-12-22T00:14:58ZengMDPI AGRemote Sensing2072-42922011-09-013102128214710.3390/rs3102128Optimizing Spatial Resolution of Imagery for Urban Form Detection—The Cases of France and VietnamChristiane WeberAnne PuissantThi Dong-Binh TranDominique BadariottiThe multitude of satellite data products available offers a large choice for urban studies. Urban space is known for its high heterogeneity in structure, shape and materials. To approach this heterogeneity, finding the optimal spatial resolution (OSR) is needed for urban form detection from remote sensing imagery. By applying the local variance method to our datasets (pan-sharpened images), we can identify OSR at two levels of observation: individual urban elements and urban districts in two agglomerations in West Europe (Strasbourg, France) and in Southeast Asia (Da Nang, Vietnam). The OSR corresponds to the minimal variance of largest number of spectral bands. We carry out three categories of interval values of spatial resolutions for identifying OSR: from 0.8 m to 3 m for isolated objects, from 6 m to 8 m for vegetation area and equal or higher than 20 m for urban district. At the urban district level, according to spatial patterns, form, size and material of elements, we propose the range of OSR between 30 m and 40 m for detecting administrative districts, new residential districts and residential discontinuous districts. The detection of industrial districts refers to a coarser OSR from 50 m to 60 m. The residential continuous dense districts effectively need a finer OSR of between 20 m and 30 m for their optimal identification. We also use fractal dimensions to identify the threshold of homogeneity/heterogeneity of urban structure at urban district level. It seems therefore that our approaches are robust and transferable to different urban contexts.http://www.mdpi.com/2072-4292/3/10/2128/high and very high spatial resolution imagesurban environmenturban form detectionoptimal spatial resolutionvariancefractal dimension |
| spellingShingle | Christiane Weber Anne Puissant Thi Dong-Binh Tran Dominique Badariotti Optimizing Spatial Resolution of Imagery for Urban Form Detection—The Cases of France and Vietnam Remote Sensing high and very high spatial resolution images urban environment urban form detection optimal spatial resolution variance fractal dimension |
| title | Optimizing Spatial Resolution of Imagery for Urban Form Detection—The Cases of France and Vietnam |
| title_full | Optimizing Spatial Resolution of Imagery for Urban Form Detection—The Cases of France and Vietnam |
| title_fullStr | Optimizing Spatial Resolution of Imagery for Urban Form Detection—The Cases of France and Vietnam |
| title_full_unstemmed | Optimizing Spatial Resolution of Imagery for Urban Form Detection—The Cases of France and Vietnam |
| title_short | Optimizing Spatial Resolution of Imagery for Urban Form Detection—The Cases of France and Vietnam |
| title_sort | optimizing spatial resolution of imagery for urban form detection the cases of france and vietnam |
| topic | high and very high spatial resolution images urban environment urban form detection optimal spatial resolution variance fractal dimension |
| url | http://www.mdpi.com/2072-4292/3/10/2128/ |
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