Monitoring changes in urban green spaces using Sentinel 2 data, a case study from Vietnam
Urban green space (UGS) plays an important role in improving the urban environment, enhancing the quality of life and contributing to sustainable urban development. However, rapid urbanization has led to significant changes in land area, especially the decline of UGS. This study is focused on monito...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Association of Surveyors of Slovenia (Zveza geodetov Slovenije)
2025-06-01
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| Series: | Geodetski Vestnik |
| Subjects: | |
| Online Access: | https://www.geodetski-vestnik.com/en/clanek/10.15292/geodetski-vestnik.2025.02.205-219 |
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| Summary: | Urban green space (UGS) plays an important role in improving the urban environment, enhancing the quality of life and contributing to sustainable urban development. However, rapid urbanization has led to significant changes in land area, especially the decline of UGS. This study is focused on monitoring of UGS in Thanh Hoa city, Vietnam from 2016 to 2024 in a 4-year cycle, using Sentinel-2 images and machine learning algorithms. Sentinel-2 multi-temporal data were used to calculate spectral indices including NDVI, NDWI, NDBI, BUI and SAVI. Next, the study used two machine learning algorithms, Random Forest (RF) and Support Vector Machine (SVM), to classify 3 types of land cover: vegetation, non-vegetation and water bodies. Among these, the most accurate classification result was used to reclassify the area into two groups: UGSs (vegetation) and Others (non-vegetation and water). Segmentation was performed using the Simple Non-Iterative Clustering (SNIC) algorithm to refine the classification boundaries. The analysis revealed significant changes in UGS over time, highlighting the impact of urban expansion on UGS distribution. These findings provide valuable insights into the dynamics of green space development and degradation in Thanh Hoa city and the method proposed in the study can be applied to other urban areas in the world for assessing urban green space changes. |
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| ISSN: | 0351-0271 1581-1328 |