Extraction of Building Construction Time Using the LandTrendr Model With Monthly Landsat Time Series Data
Extracting building construction time is crucial for effective land resource management and sustainable urban development, particularly in fast-growing urban areas. However, acquiring building construction time remains challenging due to limited observations with multiple changes. To address this is...
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| Format: | Article |
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IEEE
2024-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10552051/ |
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| author | Tengyun Hu Meng Zhang Xuecao Li Tinghai Wu Qiwei Ma Jianneng Xiao Xieqin Huang Jinchen Guo Yangchun Li Donglie Liu |
| author_facet | Tengyun Hu Meng Zhang Xuecao Li Tinghai Wu Qiwei Ma Jianneng Xiao Xieqin Huang Jinchen Guo Yangchun Li Donglie Liu |
| author_sort | Tengyun Hu |
| collection | DOAJ |
| description | Extracting building construction time is crucial for effective land resource management and sustainable urban development, particularly in fast-growing urban areas. However, acquiring building construction time remains challenging due to limited observations with multiple changes. To address this issue, we applied a monthly time series of remote sensing images and the LandTrendr change detection algorithm to extract building construction times. We identified the sensitive index of short wavelength infra-red (SWIR) from satellite observations for detecting changes in building construction, demolition, and reconstruction. Comparing composite results at different temporal intervals revealed that monthly data is more effective in accurately characterizing building changes compared to daily and yearly intervals. Additionally, our improved algorithm in Google Earth Engine identified the maximum change time as the construction turning point at the pixel level. We then revealed Beijing's construction time from 1990 to 2020 by overlaying building footprint data with extracted year information from Landsat images. Our results achieved an 82.32% agreement on identified construction time of buildings with a two-year tolerance strategy using 560 randomly collected building samples. Our results outperformed traditional methods such as annual time series composition with the same LandTrendr algorithm, historical surveying map, and building change time of social big data monitoring, with derived overall accuracies of 68.75%, 74.64%, and 67.47%, respectively, suggesting the good performance of the adopted approach. This study offered a potential avenue for detailed monitoring of urban building changes at a fine-grained spatial scale, with far-reaching implications for sustainable urban development practices. |
| format | Article |
| id | doaj-art-d3e1e5bfcc0c4f8281bb5a6bfc13af20 |
| institution | OA Journals |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-d3e1e5bfcc0c4f8281bb5a6bfc13af202025-08-20T01:47:49ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352024-01-0117183351835010.1109/JSTARS.2024.340915710552051Extraction of Building Construction Time Using the LandTrendr Model With Monthly Landsat Time Series DataTengyun Hu0Meng Zhang1Xuecao Li2https://orcid.org/0000-0002-6942-0746Tinghai Wu3Qiwei Ma4Jianneng Xiao5Xieqin Huang6Jinchen Guo7Yangchun Li8Donglie Liu9https://orcid.org/0009-0000-2733-5829School of Architecture, Tsinghua University, Beijing, ChinaBeijing City Interface Technology Company Ltd., Beijing, ChinaCollege of Land Science and Technology, China Agricultural University, Beijing, ChinaSchool of Architecture, Tsinghua University, Beijing, ChinaPeking University Planning and Design Institute (Beijing) Company Ltd., Beijing, ChinaSurveying and Mapping, Institute Lands and Resource Department of Guangdong Province, Guangzhou, ChinaDepartment of Computer Science and Technology, Tsinghua University, Beijing, ChinaFirst Surveying and Mapping Institute of Guizhou, Guiyang, ChinaGeological Environment Monitoring Institute of Guizhou, Guiyang, ChinaNatural Resources Satellite Remote Sensing Application Center, Guiyang, ChinaExtracting building construction time is crucial for effective land resource management and sustainable urban development, particularly in fast-growing urban areas. However, acquiring building construction time remains challenging due to limited observations with multiple changes. To address this issue, we applied a monthly time series of remote sensing images and the LandTrendr change detection algorithm to extract building construction times. We identified the sensitive index of short wavelength infra-red (SWIR) from satellite observations for detecting changes in building construction, demolition, and reconstruction. Comparing composite results at different temporal intervals revealed that monthly data is more effective in accurately characterizing building changes compared to daily and yearly intervals. Additionally, our improved algorithm in Google Earth Engine identified the maximum change time as the construction turning point at the pixel level. We then revealed Beijing's construction time from 1990 to 2020 by overlaying building footprint data with extracted year information from Landsat images. Our results achieved an 82.32% agreement on identified construction time of buildings with a two-year tolerance strategy using 560 randomly collected building samples. Our results outperformed traditional methods such as annual time series composition with the same LandTrendr algorithm, historical surveying map, and building change time of social big data monitoring, with derived overall accuracies of 68.75%, 74.64%, and 67.47%, respectively, suggesting the good performance of the adopted approach. This study offered a potential avenue for detailed monitoring of urban building changes at a fine-grained spatial scale, with far-reaching implications for sustainable urban development practices.https://ieeexplore.ieee.org/document/10552051/Building footprintschange detectionconstructionmonthly composition |
| spellingShingle | Tengyun Hu Meng Zhang Xuecao Li Tinghai Wu Qiwei Ma Jianneng Xiao Xieqin Huang Jinchen Guo Yangchun Li Donglie Liu Extraction of Building Construction Time Using the LandTrendr Model With Monthly Landsat Time Series Data IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Building footprints change detection construction monthly composition |
| title | Extraction of Building Construction Time Using the LandTrendr Model With Monthly Landsat Time Series Data |
| title_full | Extraction of Building Construction Time Using the LandTrendr Model With Monthly Landsat Time Series Data |
| title_fullStr | Extraction of Building Construction Time Using the LandTrendr Model With Monthly Landsat Time Series Data |
| title_full_unstemmed | Extraction of Building Construction Time Using the LandTrendr Model With Monthly Landsat Time Series Data |
| title_short | Extraction of Building Construction Time Using the LandTrendr Model With Monthly Landsat Time Series Data |
| title_sort | extraction of building construction time using the landtrendr model with monthly landsat time series data |
| topic | Building footprints change detection construction monthly composition |
| url | https://ieeexplore.ieee.org/document/10552051/ |
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