Applying object-based approach to monitor urban expansion in Ha Long city, Vietnam, during 2000-2023 from multi-date Landsat satellite imagery
Rapid urban expansion in Vietnam presents significant management and environmental challenges. This study quantifies urban growth in the Ha Long City area using a time series of six Landsat images acquired from 2000 to 2023. To address the heterogeneity of the urban class, an object-based (OB) image...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | European Journal of Remote Sensing |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/22797254.2024.2398108 |
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| author | Anh Tuan Vu Duc Anh Ngo Thi Phuong Hao Nguyen Cong Giang Nguyen |
| author_facet | Anh Tuan Vu Duc Anh Ngo Thi Phuong Hao Nguyen Cong Giang Nguyen |
| author_sort | Anh Tuan Vu |
| collection | DOAJ |
| description | Rapid urban expansion in Vietnam presents significant management and environmental challenges. This study quantifies urban growth in the Ha Long City area using a time series of six Landsat images acquired from 2000 to 2023. To address the heterogeneity of the urban class, an object-based (OB) image analysis approach was employed. Instead of relying on spectral channels, Principal component analysis (PCA) was utilized during segmentation, followed by classification using the Random Forest (RF) algorithm. For post-classification processing, a logical filter was applied to confirm the classification results at a detailed level, using additional information from the general classification results. Unconfirmed objects were subsequently verified through visual interpretation. The accuracy of the post-classified process results of this study ranges from 83.44% to 96.64% (producer accuracy) and from 83.48% to 91.27% (user accuracy). The results show that the urban area expanded more than four times, with the most significant growth occurring between 2000 and 2005, and between 2015 and 2020. Notably, land reclamation contributed significantly to urban growth. Understanding these trends is crucial for informed urban planning and environmental management in the region. |
| format | Article |
| id | doaj-art-3920001b846e41e78739d8bb3f4db95b |
| institution | OA Journals |
| issn | 2279-7254 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | European Journal of Remote Sensing |
| spelling | doaj-art-3920001b846e41e78739d8bb3f4db95b2025-08-20T01:58:55ZengTaylor & Francis GroupEuropean Journal of Remote Sensing2279-72542024-12-0157110.1080/22797254.2024.2398108Applying object-based approach to monitor urban expansion in Ha Long city, Vietnam, during 2000-2023 from multi-date Landsat satellite imageryAnh Tuan Vu0Duc Anh Ngo1Thi Phuong Hao Nguyen2Cong Giang Nguyen3Earth Observation System Department, Vietnam National Space Center (VNSC), Vietnam Academy of Science and Technology (VAST), Hanoi, VietnamEarth Observation System Department, Vietnam National Space Center (VNSC), Vietnam Academy of Science and Technology (VAST), Hanoi, VietnamSpatial Informative System and Modelling Department, Vietnam National Space Center (VNSC), Vietnam Academy of Science and Technology (VAST), Hanoi, VietnamFaculty of Civil Engineering, Hanoi Architectural University, Hanoi, VietnamRapid urban expansion in Vietnam presents significant management and environmental challenges. This study quantifies urban growth in the Ha Long City area using a time series of six Landsat images acquired from 2000 to 2023. To address the heterogeneity of the urban class, an object-based (OB) image analysis approach was employed. Instead of relying on spectral channels, Principal component analysis (PCA) was utilized during segmentation, followed by classification using the Random Forest (RF) algorithm. For post-classification processing, a logical filter was applied to confirm the classification results at a detailed level, using additional information from the general classification results. Unconfirmed objects were subsequently verified through visual interpretation. The accuracy of the post-classified process results of this study ranges from 83.44% to 96.64% (producer accuracy) and from 83.48% to 91.27% (user accuracy). The results show that the urban area expanded more than four times, with the most significant growth occurring between 2000 and 2005, and between 2015 and 2020. Notably, land reclamation contributed significantly to urban growth. Understanding these trends is crucial for informed urban planning and environmental management in the region.https://www.tandfonline.com/doi/10.1080/22797254.2024.2398108Urban expansionHa Long CityLandsat imageobject-based classificationRFPCA |
| spellingShingle | Anh Tuan Vu Duc Anh Ngo Thi Phuong Hao Nguyen Cong Giang Nguyen Applying object-based approach to monitor urban expansion in Ha Long city, Vietnam, during 2000-2023 from multi-date Landsat satellite imagery European Journal of Remote Sensing Urban expansion Ha Long City Landsat image object-based classification RF PCA |
| title | Applying object-based approach to monitor urban expansion in Ha Long city, Vietnam, during 2000-2023 from multi-date Landsat satellite imagery |
| title_full | Applying object-based approach to monitor urban expansion in Ha Long city, Vietnam, during 2000-2023 from multi-date Landsat satellite imagery |
| title_fullStr | Applying object-based approach to monitor urban expansion in Ha Long city, Vietnam, during 2000-2023 from multi-date Landsat satellite imagery |
| title_full_unstemmed | Applying object-based approach to monitor urban expansion in Ha Long city, Vietnam, during 2000-2023 from multi-date Landsat satellite imagery |
| title_short | Applying object-based approach to monitor urban expansion in Ha Long city, Vietnam, during 2000-2023 from multi-date Landsat satellite imagery |
| title_sort | applying object based approach to monitor urban expansion in ha long city vietnam during 2000 2023 from multi date landsat satellite imagery |
| topic | Urban expansion Ha Long City Landsat image object-based classification RF PCA |
| url | https://www.tandfonline.com/doi/10.1080/22797254.2024.2398108 |
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