TY - JOUR
T1 - A global product of 150-m urban building height based on spaceborne lidar
AU - Ma, Xiao
AU - Zheng, Guang
AU - Xu, Chi
AU - Moskal, L. Monika
AU - Gong, Peng
AU - Guo, Qinghua
AU - Huang, Huabing
AU - Li, Xuecao
AU - Liang, Xinlian
AU - Pang, Yong
AU - Wang, Cheng
AU - Xie, Huan
AU - Yu, Bailang
AU - Zhao, Bo
AU - Zhou, Yuyu
N1 - Publisher Copyright:
© The Author(s) 2024.
PY - 2024/12
Y1 - 2024/12
N2 - Urban building height, as a fundamental 3D urban structural feature, has far-reaching applications. However, creating readily available datasets of recent urban building heights with fine spatial resolutions and global coverage remains a challenging task. Here, we provide a 150-m global urban building heights dataset around 2020 by combining the spaceborne lidar (Global Ecosystem Dynamics Investigation, GEDI), multi-sourced data (Landsat-8, Sentinel-2, and Sentinel-1), and topographic data. The validation results revealed that the GEDI-estimated building height samples were effective compared to the reference data (Pearson’s r = 0.81, RMSE = 3.58 m). The mapping product also demonstrated good performance, as indicated by its strong correlation with the reference data (Pearson’s r = 0.71, RMSE = 4.73 m). Compared with the currently existing datasets, it holds the ability to provide a spatial resolution (150 m) with a great level of inherent details about the spatial heterogeneity and flexibility of updating using the GEDI samples as inputs. This product will boost future urban studies across many fields, including environmental, ecological, and social sciences.
AB - Urban building height, as a fundamental 3D urban structural feature, has far-reaching applications. However, creating readily available datasets of recent urban building heights with fine spatial resolutions and global coverage remains a challenging task. Here, we provide a 150-m global urban building heights dataset around 2020 by combining the spaceborne lidar (Global Ecosystem Dynamics Investigation, GEDI), multi-sourced data (Landsat-8, Sentinel-2, and Sentinel-1), and topographic data. The validation results revealed that the GEDI-estimated building height samples were effective compared to the reference data (Pearson’s r = 0.81, RMSE = 3.58 m). The mapping product also demonstrated good performance, as indicated by its strong correlation with the reference data (Pearson’s r = 0.71, RMSE = 4.73 m). Compared with the currently existing datasets, it holds the ability to provide a spatial resolution (150 m) with a great level of inherent details about the spatial heterogeneity and flexibility of updating using the GEDI samples as inputs. This product will boost future urban studies across many fields, including environmental, ecological, and social sciences.
UR - https://www.scopus.com/pages/publications/85212488304
U2 - 10.1038/s41597-024-04237-5
DO - 10.1038/s41597-024-04237-5
M3 - 文章
C2 - 39695260
AN - SCOPUS:85212488304
SN - 2052-4463
VL - 11
JO - Scientific Data
JF - Scientific Data
IS - 1
M1 - 1387
ER -