The intensification of coastal land-use change in rapidly urbanizing regions demands robust, quantitative approaches to attribute and measure driver impacts on landscape transformation. Advances in scene classification using geographic big data have enabled greater spatial and functional resolution in mapping such changes, yet the relative roles and quantification of anthropogenic versus natural drivers in coastal zones remain poorly resolved. Here, we integrate Landsat and Sentinel-2 remote sensing imagery (1990–2019), OpenStreetMap data, and urban and marine zoning information, employing random forest classification and trajectory analysis, to the coastal zone of the Guangdong–Hong Kong–Macao Greater Bay Area (GBA). Over three decades, approximately 1,730 km² of marine areas – representing 4.8% of the study area – were converted to cropland or urban land. The urban and farmland scenes expanded by 890 km² (2.5%) and 520 km² (1.4%) of the area, respectively. Quantitative attribution showed that human activities accounted for 77.7% of all observed coastal scene changes, with natural factors contributing only 22.3%. These results clarify the scale and dominant drivers of coastal transformation, establishing a quantitative baseline for coastal management. This approach demonstrates how recent advances in scene classification clarify spatially explicit, reproducible insights for sustainable coastal planning and restoration.
Yan et al. (Fri,) studied this question.