Urban lakes change extraction using time series GaoFen-1 satellite imagery
Abstract. Urban lakes serve an indispensable role in maintaining the ecological balance of cities, ensuring flood safety, and providing recreational spaces for tourism. With the development of human activities and economic, the extent of urban lakes are inevitably influenced. Currently, the ability to detect detailed temporal changes in urban lake areas using high resolution data still has limitations. This study proposed a novel method by combining time series Gaofen-1 (GF-1) remote sensing data and random forest machine learning algorithm to explore the urban lakes change Zhushan Lake located in Wuhan. The research conducted the extraction of surface water for Zhushan Lake and its surrounding pit-ponds from 2013 to 2020. And then, a quantitative analysis of the characteristics and driving factors of lake changes is conducted. We find that (1) the accuracy of surface water extraction using the random forest classification method consistently exceeded 96%. The Kappa coefficient ranges from a minimum of 0.86 to a maximum of 0.99. (2) A noticeable decline was observed in the water areas of Zhushan Lake and its surrounding pit-ponds, predominantly along the northwestern shoreline and in the eastern pond regions. This decline is primarily attributed to pressures from building construction. The methodology proposed in this study is suitable for the area management of lakes in urban areas.
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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Urban lakes change extraction using time series GaoFen-1 satellite imagery ; volume:XLVIII-1-2024 ; year:2024 ; pages:463-468 ; extent:6
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ; XLVIII-1-2024 (2024), 463-468 (gesamt 6)
- Classification
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Natürliche Ressourcen, Energie und Umwelt
- Creator
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Luo, Gan
Jiang, Wei
Liu, Lingjia
Wen, Qingke
Liu, Shuo
Sun, Weichao
- DOI
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10.5194/isprs-archives-XLVIII-1-2024-463-2024
- URN
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urn:nbn:de:101:1-2405160448294.070171555065
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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14.08.2025, 10:49 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Luo, Gan
- Jiang, Wei
- Liu, Lingjia
- Wen, Qingke
- Liu, Shuo
- Sun, Weichao
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