Characterizing dynamics of built-up height in China from 2005 to 2020 based on GEDI, Landsat, and PALSAR data

Abstract

The unprecedented urbanization in China has driven rapid urban and rural development in recent decades. While existing studies have extensively focused on horizontal urban expansion, research on vertical urban expansion patterns remains limited. To address this gap, this study proposed a Multi-Temporal Built-up Height estimation Network (MTBH-Net) to estimate 30-m China Multi-Temporal Built-up Height (CMTBH-30) by integrating Global Ecosystem Dynamics Investigation (GEDI), Landsat, and PALSAR data. Specifically, we introduced sample migration to generate reference built-up height data and applied the Continuous Change Detection and Classification (CCDC) disturbance feature to reduce inconsistency in unchanged built-up areas. Validation using the GEDI test set demonstrated that CMTBH-30 achieved RMSEs of 5.10 m, 5.53 m, 6.16 m, and 6.21 m for 2005, 2010, 2015, and 2020. Further validation with field-collected data yielded an RMSE of 4.54 m. Additionally, CMTBH-30 is consistent with the 3D-GIoBFP dataset, achieving RMSEs ranging from 5.34 m to 8.95 m across ten cities. Our findings reveal an increase in average built-up heights in China from 10.28 m in 2005 to 10.92 m in 2020, reflecting an upward trend in urban development. Additionally, the standard deviation of built-up heights rises from 5.16 m in 2005 to 7.71 m in 2020, indicating increased height variation nationwide. Regional analysis from 2005 to 2020 highlights notable vertical growth in newly expanded built-up areas in Macau (+14.4 m), Hong Kong (+12.3 m), and Guangdong (+12.3 m), while Qinghai (+3.8 m) and Chongqing (+3.0 m) also experienced significant growth in stable built-up areas. Heilongjiang, Jilin, Hebei, and Taiwan exhibited minimal growth. The CMTBH-30 dataset effectively captures fine-grained built-up heights, addressing the gap in multi-temporal built-up height estimation. This study provides a new dimension for urban research and is valuable for a multitude of applications such as urban planning, disaster management, and sustainable development. The CMTBH-30 dataset is available at https://data-starcloud.pcl.ac.cn/iearthdata/.