Forest canopy height estimation using ICESat/GLAS data and error factor analysis in Hokkaido, Japan

Spaceborne light detection and ranging (LiDAR) enables us to obtain information about vertical forest structure directly, and it has often been used to measure forest canopy height or above-ground biomass. However, little attention has been given to comparisons of the accuracy of the different estimation methods of canopy height or to the evaluation of the error factors in canopy height estimation. In this study, we tested three methods of estimating canopy height using the Geoscience Laser Altimeter System (GLAS) onboard NASA's Ice, Cloud, and land Elevation Satellite (ICESat), and evaluated several factors that affected accuracy. Our study areas were Tomakomai and Kushiro, two forested areas on Hokkaido in Japan. The accuracy of the canopy height estimates was verified by ground-based measurements. We also conducted a multivariate analysis using quantification theory type I (multiple-regression analysis of qualitative data) and identified the observation conditions that had a large influence on estimation accuracy. The method using the digital elevation model was the most accurate, with a root-mean-square error (RMSE) of 3.2. m. However, GLAS data with a low signal-to-noise ratio (≤10.0) and that taken from September to October 2009 had to be excluded from the analysis because the estimation accuracy of canopy height was remarkably low. After these data were excluded, the multivariate analysis showed that surface slope had the greatest effect on estimation accuracy, and the accuracy dropped the most in steeply sloped areas. We developed a second model with two equations to estimate canopy height depending on the surface slope, which improved estimation accuracy (RMSE = 2.8. m). These results should prove useful and provide practical suggestions for estimating forest canopy height using spaceborne LiDAR.