1158 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 49, NO. 3, MARCH 2011 ICESat GLAS Data for Urban Environment Monitoring Peng Gong, Zhan Li, Huabing Huang, Guoqing Sun, Senior Member, IEEE, and Lei Wang Abstract—Although the Geoscience Laser Altimeter System (GLAS) onboard the NASA Ice, Cloud and Land Elevation Satel- lite was not designed for urban applications, its 3-D measurement capability over the globe makes it a nice feature for consideration in monitoring urban heights. However, this has not been previously done. In this paper, we report some preliminary assessment of the GLAS data for building height and density estimation in a suburb of Beijing, China. Building heights can be directly calculated from a GLAS data product (GLA14). Because GLA14 limits height levels to six in each ground footprint, we developed a new method to remove this restriction by processing the raw GLAS data. The maximum heights measured in the field at selected GLAS foot- prints were used to validate the GLAS measurement results. By assuming a constant incident energy and surface reflectance within a GLAS footprint, the building density can be estimated from GLA14 or from our newly processed GLAS data. The building density determined from high-resolution images in Google Earth was used to validate the GLAS estimation results. The results indicate that the newly developed method can produce more accurate building height estimation within each GLAS footprint (R 2 =0.937, rmse =6.4 m, and n = 26) than the GLA14 data product (R 2 =0.808, rmse = 11.5 m, and n = 26). However, satisfactory estimation results on building density cannot be ob- tained from the GLAS data with the methods investigated in this paper. Forest cover could be a challenge to building height and density estimation from the GLAS data. It should be addressed in future research. Index Terms—Building density, building height, global change, laser altimetry, urban growth. I. I NTRODUCTION I T HAS been estimated that, by 2050, 69% of the world population will be urbanized, from 29% in 1950 [1]. Urban areas, although covering barely 3%–4% of the land surface on Manuscript received March 2, 2010; revised July 3, 2010; accepted August 5, 2010. Date of publication October 11, 2010; date of current version February 25, 2011. This work was supported in part by the National Natural Science Foundation of China under Grants 30590370 and 40901235 and in part by the National High Technology Research and Development Program of China under Grant 2008AA121702. P. Gong is with the State Key Laboratory of Remote Sensing Science, Beijing 100101, China, and also with the Center for Environmental Measure- ment, Monitoring and Modeling, Department of Environmental Science, Policy and Management, University of California, Berkeley, CA 94720-3114 USA (e-mail: gong@irsa.ac.cn). Z. Li is with the State Key Laboratory of Remote Sensing Science, Beijing 100101, China, and also with the College of Resources and Environment, Graduate University of the Chinese Academy of Sciences, Beijing 100049, China. H. Huang and L. Wang are with the State Key Laboratory of Remote Sensing Science, Beijing 100101, China. G. Sun is with the Department of Geography, University of Maryland, College Park, MD 20742 USA. Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TGRS.2010.2070514 Earth, have a significant global impact. The ecological footprint of city residents with respect to their tremendous demands on materials and energy goes far beyond the physical space of urbanized areas to include not only most agricultural and forested areas but also all areas where mining has occurred. Although urbanization brings mankind more efficient living conditions, it also causes a number of well-known negative effects including traffic congestion, pollution, and faster spread of disease. Finding out how these negative effects can be reduced or avoided in the future and ultimately building more livable cities have been a top problem facing city governors and planners. In order to solve this problem, a better knowledge on where developments have happened and are happening, how cities are shaping up in the future, and how to model urban growth is badly needed. Remote sensing has been widely used in urban morphological measurement and urban growth monitoring and modeling [2]–[13]. Two recent trends in urban remote sensing are the more accurate mapping of specific urban surface cover types using digital aerial images and high spatial resolution satellite data (e.g., [14] and [15]), and the use of digital photogrammetry, interferometric synthetic aperture radar (InSAR), and airborne lidar to measure urban structures in 3-D [16]–[19]. Compared to the traditional 2-D information extracted from remotely sensed data on the horizontal extent of the land use types, the 3-D information about urban structures is complementary, and when combined with urban land use, it allows a more realistic estimation of the spatial and temporal distributions of population in urban areas. This is important to planners and managers who are responsible for land use, transportation, telecommunications, and health care. A new need for the 3-D information in urban areas comes from the global change community where a concern about global carbon budget requires an accurate estimation of com- modity carbon consumptions. Urban structures consuming most of the world production of steel, cement, and wood materials occupy a big portion in the global carbon accounting. More importantly, information about where those high carbon consuming industrial products contained in urban constructions are distributed may be globally demanding in future carbon trade negotiations. Knowledge about the 3-D structure of ur- banized areas in the world in a relatively consistent time frame will thus be helpful to the estimation of where those high carbon commodities will finally end up with. Such information is currently nonexisting. High-resolution remote sensing, InSAR, and airborne lidar technologies are natural sources of data for 3-D information extraction over urban areas. However, they are limited to rel- atively developed countries, and it is difficult to coordinate data 0196-2892/$26.00 © 2010 IEEE