A Forest height estimation algorithm based on the random forests leaf mean encoding

The forest height estimation systems with GeoScience Laser Altimeter System (GLAS) data have been demonstrated to yield an accurate estimation result in the topographically flat areas with simple forest conditions. However, because of the complex forest structures, the GLAS waveform data is difficult to consistently characterize the forest feature of different forest land cover classes. To obtain an effective and reliable estimation model without additional calibration steps, in this paper, we introduce a forest height estimation algorithm based on random forests leaf mean (FLM) encoding, which combines the decision tree model with linear regression, and obtain a better estimation performance than the single regression model. The experiment results show that the FLM encoding method proposed in this paper can implicitly characterize the complex forest information, and have a positive impact on the overall estimation system performance. The complex forest features can be extracted from GLAS waveform by our method and be used as a supplemental feature to improve the estimation of forest height.