Forest canopy height estimation using LiDAR and optical multi-angler data

Tree height is an important parameter in the field of forest resource management, so it is significant in forest resource monitoring and carbon cycling research to obtain accurate, large-scale and spatially continuous tree height. The paper uses the waveform data from space-borne Lidar ICESat/GLAS, and chooses proper algorithms with topographic correction to extract forest tree height average values from GLAS foot-print data. Then, we apply the random forest machine learning algorithm to process tree height values from GLAS foot-print data and MISR/BRF data from multi-angle optical remote sense, and get the tree height map of Tahe Forest Farm, namely, which makes the scale of extracted tree height values extend from point to plane. Finally, the field measured data was utilized to test the results, which showed that R2, RMSE and precision were 0.72, 1.83 m and 85.22%, respectively. This approach integrates Lidar data with multi-angle imagines of optical remote sense,which makes up of the shortcomings of each. In addition, the results also provide accurate reference guidelines for the prediction of forest biomass and carbon storage.