Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Urban ecosystems mapping from spaceborne high-resolution optical data
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics. (Geoinformatics)
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics. (Geoinformatics)ORCID iD: 0000-0003-4434-7244
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics. (Geoinformatics)
2014 (English)In: Proc. ‘Dragon 3 Mid-Term Results Symposium’, Chengdu, P.R. China 26–29 May 2014 (ESA SP-724, November 2014), 2014Conference paper, Published paper (Refereed)
Abstract [en]

The potential of high-resolution optical satellite images for mapping of ecologically important urban space is investigated in this study. Both a GeoEye-1 and a Landsat 8 scene over central Shanghai were first segmented by two different algorithms and then classified into seven urban classes by SVM. Shadows in the pan-sharpened GeoEye-1 image were masked out and replaced by the corresponding pan-sharpened classified Landsat 8 image. Largest confusions occurred between sealed and permeable but non-vegetated surfaces, and between low-rise residential and high-rise commercial buildings. Based on the classification result, ecosystem service balances, supply and demand was modelled for each particular land cover class. Classification accuracies of 88% and 91% could be reached, indicating the suitability of the underlying data and method for this application domain. The KTH-SEG segmentation algorithm slightly outperformed the one implemented in eCognition. The highest supply of ecosystem services was found in water bodies whereas high-rise built-up areas revealed largest demands.

Place, publisher, year, edition, pages
2014.
Keyword [en]
Urbanization, SVM, Segmentation, Ecosystem Services, High-Resolution
National Category
Remote Sensing
Research subject
Geodesy and Geoinformatics
Identifiers
URN: urn:nbn:se:kth:diva-160130ISBN: 978-92-9221-288-9 (print)OAI: oai:DiVA.org:kth-160130DiVA: diva2:788788
Conference
Dragon 3 Mid-Term Results Symposium, Chengdu, P.R. China 26–29 May 2014
Note

NV 20150410

Available from: 2015-02-16 Created: 2015-02-16 Last updated: 2015-04-10Bibliographically approved

Open Access in DiVA

No full text

Authority records BETA

Jacob, Alexander

Search in DiVA

By author/editor
Haas, JanJacob, AlexanderBan, Yifang
By organisation
Geodesy and Geoinformatics
Remote Sensing

Search outside of DiVA

GoogleGoogle Scholar

isbn
urn-nbn

Altmetric score

isbn
urn-nbn
Total: 126 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf