Abstract
AbstractAs a fundamental aspect of the urban form, building height is a key attribute for reflecting human activities and human-environment interactions in the urban context. However, openly accessible building height maps covering the whole China remain sorely limited, particularly for spatially informed data. Here we developed a 1 km × 1 km resolution building height dataset across China in 2017 using Spatially-informed Gaussian process regression (Si-GPR) and open-access Sentinel-1 data. Building height estimation was performed using the spatially-explicit Gaussian process regression (GPR) in 39 major Chinese cities where the spatially explicit and robust cadastral data are available and the spatially-implicit GPR for the remaining 304 cities, respectively. The cross-validation results indicated that the proposed Si-GPR model overall achieved considerable estimation accuracy (R2 = 0.81, RMSE = 4.22 m) across the entire country. Because of the implementation of local modelling, the spatially-explicit GPR outperformed (R2 = 0.89, RMSE = 2.82 m) the spatially-implicit GPR (R2 = 0.72, RMSE = 6.46 m) for all low-rise, mid-rise, and high-rise buildings. This dataset, with extensive-coverage and high-accuracy, can support further studies on the characteristics, causes, and consequences of urbanization.
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Library and Information Sciences,Statistics, Probability and Uncertainty,Computer Science Applications,Education,Information Systems,Statistics and Probability
Reference55 articles.
1. Johnson, C. W. & Peirce, N. R. Century of the city: No time to lose. The Rockefeller Foundation (2008).
2. United Nations, U. World urbanization prospects 2018. United Nations Department for Economic and Social Affiars (2018).
3. Acuto, M., Parnell, S. & Seto, K. C. Building a global urban science. Nature Sustainability 1, 2–4, https://doi.org/10.1038/s41893-017-0013-9 (2018).
4. Li, Y., Schubert, S., Kropp, J. P. & Rybski, D. On the influence of density and morphology on the Urban Heat Island intensity. Nat Commun 11, 2647, https://doi.org/10.1038/s41467-020-16461-9 (2020).
5. Sun, Y., Zhang, X., Ren, G., Zwiers, F. W. & Hu, T. Contribution of urbanization to warming in China. Nature Climate Change 6, 706, https://doi.org/10.1038/nclimate2956https://www.nature.com/articles/nclimate2956#supplementary-information (2016).
Cited by
24 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献