Wu Shengbiao, Song Yimeng, An Jiafu, Lin Chen, Chen Bin
Future Urbanity & Sustainable Environment (FUSE) Lab, Division of Landscape Architecture, Department of Architecture, Faculty of Architecture, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China.
School of the Environment, Yale University, New Haven, CT, 06511, USA.
Sci Data. 2024 Aug 22;11(1):909. doi: 10.1038/s41597-024-03746-7.
Greenspace, offering multifaceted ecological and socioeconomic benefits to the nature system and human society, is integral to the 11 Sustainable Development Goal pertaining to cities and communities. Spatially and temporally explicit information on greenspace is a premise to gauge the balance between its supply and demand. However, existing efforts on urban greenspace mapping primarily focus on specific time points or baseline years without well considering seasonal fluctuations, which obscures our knowledge of greenspace's spatiotemporal dynamics in urban settings. Here, we combined spectral unmixing approach, time-series phenology modeling, and Sentinel-2 satellite images with a 10-m resolution and nearly 5-day revisit cycle to generate a four-year (2019-2022) 10-m and 10-day resolution greenspace dynamic data cube over 1028 global major cities (with an urbanized area >100 km). This data cube can effectively capture greenspace seasonal dynamics across greenspace types, cities, and climate zones. It also can reflect the spatiotemporal dynamics of the cooling effect of greenspace with Landsat land surface temperature data. The developed data cube provides informative data support to investigate the spatiotemporal interactions between greenspace and human society.
绿色空间为自然系统和人类社会带来多方面的生态和社会效益，是与城市和社区相关的11个可持续发展目标的组成部分。关于绿色空间的时空明确信息是衡量其供需平衡的前提。然而，现有的城市绿色空间制图工作主要集中在特定时间点或基准年份，没有充分考虑季节波动，这模糊了我们对城市环境中绿色空间时空动态的认识。在此，我们将光谱分解方法、时间序列物候模型与分辨率为10米、重访周期近5天的哨兵-2卫星图像相结合，生成了一个覆盖1028个全球主要城市（城市化面积>100平方公里）的四年（2019-2022年）、分辨率为10米和10天的绿色空间动态数据立方体。该数据立方体能够有效捕捉不同绿色空间类型、城市和气候区的绿色空间季节动态。它还能结合陆地卫星地表温度数据反映绿色空间降温效应的时空动态。所开发的数据立方体为研究绿色空间与人类社会之间的时空相互作用提供了丰富的数据支持。
