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Article Abstract
As a world-class urban agglomeration, the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) has experienced substantial land-cover restructuring driven by urbanization, necessitating high temporal resolution monitoring to capture dynamic surface processes. However, traditional land-cover products cannot capture intra-annual dynamics effectively due to their limited update cycles. Therefore, this study generates a publicly accessible dynamic land-cover dataset for the GBA spanning 2000-2022, with a 30-meter spatial resolution and a 15-day temporal resolution. This dataset achieves an overall accuracy of 97.46%, an average accuracy of 94.5%, an F1 score of 95.19%, and a kappa coefficient of 96.78%. The validation results indicated that this dataset significantly enhances the detection of short-term land cover transitions and facilitates the characterization of critical environmental patterns. We anticipate it will serve as a baseline for monitoring agricultural growing cycles, tracking vegetation phenological shifts, and identifying disturbances in forests and croplands caused by natural hazards or anthropogenic activities.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12361516 | PMC |
| http://dx.doi.org/10.1038/s41597-025-05772-5 | DOI Listing |
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