文献类型：会议论文

英文题名：Validation of the ICEsat vegetation product using crown-area-weighted mean height derived using crown delineation with discrete return lidar data

作者：Pang, Yong[1,2] Lefsky, Michael[1] Andersen, Hans-Erik[3] Miller, Mary Ellen[1] Sherrill, Kirk[1]

第一作者：Pang, Yong;庞勇

通信作者：Pang, Y[1]

机构：[1]Colorado State Univ, Coll Nat Resources, Ctr Ecol Applicat Lidar, Ft Collins, CO 80523 USA;[2]Chinese Acad Forestry, Inst Forest Resource & Informat Technol, Beijing 100091, Peoples R China;[3]US Forest Serv, USDA, PNW Res Stn, Anchorage Forestry Sci Lab, Anchorage, AK 99503 USA

会议论文集：Fall Meeting of the American-Geophysical-Union

会议日期：DEC, 2006

会议地点：San Francisco, CA

语种：英文

外文关键词：Aneroid altimeters - Forestry - Radio altimeters - Vegetation

年份：2008

摘要：The Geoscience Laser Altimeter System (GLAS), a spaceborne light detection and ranging (lidar) sensor, has acquired over 250 million lidar observations over forests globally, an unprecedented dataset of vegetation height information. To be useful, GLAS must be calibrated to measurements of height used in forestry inventory and ecology. Airborne discrete return lidar (DRL) can characterize vegetation and terrain surfaces in detail, but its utility as calibration data for GLAS is limited by the lack of a direct relationship between the canopy height measurements collected by airborne and spaceborne lidar systems and coincident field data. We demonstrate that it is possible to use DRL to directly estimate the crown-area-weighted mean height (Hcw), which is conceptually and quantitatively similar to the Lorey's height, which is calculated from forest inventory data, and can be used to calibrate GLAS without the use of field data. For a dataset from five sites in western North America, the two indices of height (Hcw from DRL and Lorey's from forest inventory) are directly related (r(2) = 0.76; RMSE of 3.8 m; intercept and slope of 0.8 m and 0.98, respectively). We derived a relationship between the DRL-derived Hcw and height information from coincident GLAS waveforms; the resulting equation explained 69% of variance, with an RMSE of 6.2 m.