Accurately estimating building heights is crucial for various applications, including urban planning, climate studies, population estimation, and environmental assessment. However, this remains a challenging task, particularly for large areas. Satellite-based Light Detection and Ranging (LiDAR) has shown promise, but it often faces difficulties in distinguishing building photons from other ground objects. To address this challenge, we propose a novel method that incorporates building footprints, relative positions of building and ground photons, and a self-adaptive buffer for building photon selection. We employ the Ice, Cloud, and Land Elevation Satellite 2 (ICESat-2) photon-counting LiDAR, specifically the ICESat-2/ATL03 data, along with building footprints obtained from the New York City (NYC) Open Data platform. The proposed approach was applied to estimate the heights of 17,399 buildings in NYC, and the results showed strong consistency with the reference building heights. The root mean square error (RMSE) was 8.1 m, and for 71% of the buildings, the mean absolute error (MAE) was less than 3 m. Furthermore, we conducted an extensive evaluation of the proposed approach and thoroughly investigated the influence of terrain, region, building height, building density, and parameter selection. We also verified the effectiveness of our approach in an experimental area in Beijing and compared it with other existing methods. By leveraging ICESat-2 LiDAR data, building footprints, and advanced selection techniques, the proposed approach demonstrates the potential to accurately estimate building heights over broad areas.

Water scarcity is a significant challenge in China, and the South-to-North Water Diversion Project (SNWDP) aims to address the water deficit in the northern region. This study analyses Landsat 5/7/8 remote sensing imagery from 2001 to 2020 on the Google Earth Engine (GEE) cloud platform to assess the impact of the SNWDP on surface water bodies in water-receiving areas. Moreover, by integrating MODIS evapotranspiration data and ERA5 meteorological reanalysis data, this study comprehensively assesses the SNWDP’s contribution to varied surface water body changes. Using an improved multi-temporal water frequency method, this study extracts max, seasonal, and year-long water surface areas. The results reveal that Beijing and Henan provinces have experienced significant increases in water surface areas post-SNWDP, with their max water surface area growing at a rate of 10.42 km²/yr and 33.49 km²/year, respectively. However, water surface areas in several provinces, especially those along the east route, were not observed to increase. The spatially detailed Mann–Kendall test indicates that the expansion of year-long water bodies is mostly concentrated near the central route project, revealing spatial heterogeneity in the water replenishment effect. Cropland and impervious surfaces are the main contributors to transfers in and out of water bodies. Meteorological and water use factors for spatial variations in water changes are also identified. These findings provide insights into the varied hydrological consequences of the SNWDP and contribute to the evaluation and management of similar large-scale water diversion projects around the world.