To understand forest structures, the Geoscience Laser Altimeter System (GLAS) instrument

have been employed to measure and monitor forest canopy with feasibility of acquiring three dimensional

canopy structure information. This study tried to examine the potential of GLAS dataset in measuring forest

canopy structures, particularly maximum canopy height estimation. To estimate maximum canopy height

using feasible GLAS dataset, we simply used difference between signal start and ground peak derived from

Gaussian decomposition method. After estimation procedure, maximum canopy height was derived from

airborne Light Detection and Ranging (LiDAR) data and it was applied to evaluate the accuracy of that of

GLAS estimation. In addition, several influences, such as topographical and biophysical factors, were

analyzed and discussed to explain error sources of direct maximum canopy height estimation using GLAS

data. In the result of estimation using direct method, a root mean square error (RMSE) was estimated at

8.15 m. The estimation tended to be overestimated when comparing to derivations of airborne LiDAR.

According to the result of error occurrences analysis, we need to consider these error sources, particularly

terrain slope within GLAS footprint, and to apply statistical regression approach based on various

parameters from a Gaussian decomposition for accurate and reliable maximum canopy height estimation.

Key Words : Maximum canopy height, Geoscience Laser Altimeter System (GLAS), Airborne Light

Detection and Ranging (LiDAR), Waveform analysis