Comparison of the inversion ability in extrapolating forest canopy height by integration of LiDAR data and different optical remote sensing products

Forest canopy height is an important variable to the modeling of energy over regional and global scales. This paper first examined the relationship between field-surveyed canopy height and LiDAR-derived canopy height, regression between them had an RMSE and R² value of 0.94 m and 0.64. To extrapolate the LiDAR height to a continuous area, we compared the ability of four sources of optical remote sensing data (MODIS BRFs, MODIS NBAR, MISR and SPOT data) in predicting the LiDAR measured canopy height. Multivariate linear regression and single variable nonlinear regression models were developed, and the best model accurately predicted the LiDAR height using MODIS BRFs data (RMSE=1.2 m, R²= 0.67). This model was applied to the whole study area and finally the canopy height map of the study area was generated.