Detecting Long-Term Spatiotemporal Dynamics of Urban Green Spaces with Training Sample Migration Method
Urban green spaces (UGSs) are critical for landscape, ecological, and climate studies. However, the generation of long-term annual UGSs maps is often constrained by the lack of sufficient, high-quality training samples for training classifiers. In this study, we introduce an automatic training sampl...
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MDPI AG
2025-04-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/8/1426 |
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| author | Mengyao Wang Pan Li Chunyu Wang Wei Chen Zhongen Niu Na Zeng Xingxing Han Xinchao Sun |
| author_facet | Mengyao Wang Pan Li Chunyu Wang Wei Chen Zhongen Niu Na Zeng Xingxing Han Xinchao Sun |
| author_sort | Mengyao Wang |
| collection | DOAJ |
| description | Urban green spaces (UGSs) are critical for landscape, ecological, and climate studies. However, the generation of long-term annual UGSs maps is often constrained by the lack of sufficient, high-quality training samples for training classifiers. In this study, we introduce an automatic training sample migration method based on visually interpreted reference data and long-term Landsat imagery, implemented on the Google Earth Engine (GEE) platform, to produce annual UGSs maps for Tianjin from 1984 to 2022. Migrating training samples to each year significantly improved classification performance, especially for UGSs and water bodies. UGSs coverage in sample areas increased from 5% to 38%, resulting in more reliable trend detection. Our spatiotemporal analysis revealed that green coverage in the study area reached up to 40%, dominated by tree cover that is significantly underestimated in existing global and regional land cover products. Distinct temporal patterns emerged between the old built-up area (OBUA) and new built-up area (NBUA). Early UGS decline was largely driven by NBUAs, while post-2007 greening involved both OBUAs and NBUAs, as captured by classification maps and vegetation indices. Our study proposes a scalable and practical framework for long-term land cover mapping in rapidly urbanizing regions, with enhanced potential as higher-resolution data becomes increasingly accessible. |
| format | Article |
| id | doaj-art-a1dba5d094534a77a32d3b90784e8740 |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-a1dba5d094534a77a32d3b90784e87402025-08-20T02:28:20ZengMDPI AGRemote Sensing2072-42922025-04-01178142610.3390/rs17081426Detecting Long-Term Spatiotemporal Dynamics of Urban Green Spaces with Training Sample Migration MethodMengyao Wang0Pan Li1Chunyu Wang2Wei Chen3Zhongen Niu4Na Zeng5Xingxing Han6Xinchao Sun7Institute of Surface-Earth System Science, School of Earth System Science, Tianjin University, Tianjin 300072, ChinaInstitute of Surface-Earth System Science, School of Earth System Science, Tianjin University, Tianjin 300072, ChinaInstitute of Surface-Earth System Science, School of Earth System Science, Tianjin University, Tianjin 300072, ChinaInstitute of Surface-Earth System Science, School of Earth System Science, Tianjin University, Tianjin 300072, ChinaCollege of Hydraulic and Civil Engineering, Ludong University, Yantai 264025, ChinaSchool of Environment and Resources, Zhejiang A & F University, Hangzhou 311300, ChinaInstitute of Surface-Earth System Science, School of Earth System Science, Tianjin University, Tianjin 300072, ChinaInstitute of Surface-Earth System Science, School of Earth System Science, Tianjin University, Tianjin 300072, ChinaUrban green spaces (UGSs) are critical for landscape, ecological, and climate studies. However, the generation of long-term annual UGSs maps is often constrained by the lack of sufficient, high-quality training samples for training classifiers. In this study, we introduce an automatic training sample migration method based on visually interpreted reference data and long-term Landsat imagery, implemented on the Google Earth Engine (GEE) platform, to produce annual UGSs maps for Tianjin from 1984 to 2022. Migrating training samples to each year significantly improved classification performance, especially for UGSs and water bodies. UGSs coverage in sample areas increased from 5% to 38%, resulting in more reliable trend detection. Our spatiotemporal analysis revealed that green coverage in the study area reached up to 40%, dominated by tree cover that is significantly underestimated in existing global and regional land cover products. Distinct temporal patterns emerged between the old built-up area (OBUA) and new built-up area (NBUA). Early UGS decline was largely driven by NBUAs, while post-2007 greening involved both OBUAs and NBUAs, as captured by classification maps and vegetation indices. Our study proposes a scalable and practical framework for long-term land cover mapping in rapidly urbanizing regions, with enhanced potential as higher-resolution data becomes increasingly accessible.https://www.mdpi.com/2072-4292/17/8/1426urban green spacesland cover mappingtraining samplespatiotemporal dynamics |
| spellingShingle | Mengyao Wang Pan Li Chunyu Wang Wei Chen Zhongen Niu Na Zeng Xingxing Han Xinchao Sun Detecting Long-Term Spatiotemporal Dynamics of Urban Green Spaces with Training Sample Migration Method Remote Sensing urban green spaces land cover mapping training sample spatiotemporal dynamics |
| title | Detecting Long-Term Spatiotemporal Dynamics of Urban Green Spaces with Training Sample Migration Method |
| title_full | Detecting Long-Term Spatiotemporal Dynamics of Urban Green Spaces with Training Sample Migration Method |
| title_fullStr | Detecting Long-Term Spatiotemporal Dynamics of Urban Green Spaces with Training Sample Migration Method |
| title_full_unstemmed | Detecting Long-Term Spatiotemporal Dynamics of Urban Green Spaces with Training Sample Migration Method |
| title_short | Detecting Long-Term Spatiotemporal Dynamics of Urban Green Spaces with Training Sample Migration Method |
| title_sort | detecting long term spatiotemporal dynamics of urban green spaces with training sample migration method |
| topic | urban green spaces land cover mapping training sample spatiotemporal dynamics |
| url | https://www.mdpi.com/2072-4292/17/8/1426 |
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