Retrieving building height in urban areas using ICESat-2 photon-counting LiDAR data

Abstract

Building height is a pivotal factor in studying urban form and understanding the impacts of the vertical characteristics of urban areas on the environment. The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) employ photon-counting LiDAR to collect Earth’s surface elevation data globally. However, method at ground and building photon classification and subsequently building height extraction from photon-counting LiDAR data is still missing. This study aimed to develop a methodological framework to retrieve building height from ICESat-2 data under various data acquisition conditions stratified by day/night condition, strong/weak beam. First, the noise removal algorithm adopted by ATL03 product is utilized to filter out noise photons and retain signal photons in the raw dataset. Second, the random sample consensus (RANSAC) algorithm and statistical method are adopted to classify the signal photons into ground and building photons according to their characteristics and spatial neighborhood relationship. The building height is then computed by the elevation of building and ground photons. Finally, estimated heights are evaluated using the reference building height derived from terrestrial laser scanning (TLS) data. The results indicate that the proposed methodological framework can effectively identify building and ground photons under different building types. Strong consistence between estimated and reference building height is obtained by quantifying the estimation precision of building height under different data acquisition conditions, root mean square error (RMSE) ranging between 0.35 m and 0.45 m, indicating that the ATL03 data can be utilized to derive the building height in urban areas for all acquisition times and beam intensity. Further analysis demonstrates that strong/weak beam significantly influences the building height estimation compared to day/night condition. Overall, this study provides a method for estimating building height in urban areas using ICESat-2 data, and the findings approve the strong capability of ICESat-2 data in estimating building height.