Mapping fine-scale building heights in urban agglomeration with spaceborne lidar

Abstract

The increasing availability of 3-D urban data yields new insights into urban developments and their implications for population density, energy consumption, and the carbon budget. However, existing products of urban building heights at a regional or global scale are mostly subject to coarse grid size or long-time lags. Fine-resolution building height maps remain unavailable for most regions to present recent vertical information. Also, the lack of sampled building heights with spatial and temporal consistency at larger scales makes it difficult to update building height maps. The newly available spaceborne lidar data from the Global Ecosystem Dynamics Investigation (GEDI) are expected to overcome these critical barriers. Here we propose a generalizable approach to mapping large-scale distributions of building heights by fusing GEDI-derived relative height metrics, optical data (i.e., Landsat-8 and Sentinel-2), and radar data (Sentinel-1). We applied our approach to the Yangtze River Delta region (YRD), one of China's largest urban agglomerations. We investigated the availability of building height samples generated from GEDI-based relative height metrics and the optimal grid size of building height mapping into 30-m (about footprint level) and 150-m (regional level) grid sizes. We finally developed a random forest model using active and passive sensors to extrapolate GEDI-derived samples discretely to the continuous building height map at the 150-m grid size in 2019. We revealed the spatial distribution patterns of building heights and the urbanization effect on the city's mean building heights in the YRD region. We found that: (1) GEDI-derived building height samples were highly consistent with the reference building height data at the footprint (i.e., Pearson's r = 0.86, RMSE = 9.25 m, n = 237) and regional (i.e., Pearson's r = 0.76, RMSE = 6.36 m, n = 69) levels. (2) The Pearson's correlation coefficient (r) and root mean square error (RMSE) of the final spatially continuous and up-to-date building height map were 0.85 and 5.03 m, respectively. (3) The resulting map showed a strong positive correlation between the city's mean building height and urbanization level. Our work makes it possible to use remotely sensed data to make global maps of building heights at a 150-m scale.