Improvement of forest canopy height estimation model by combining Geoscience Laser Altimetry System full waveform and multispectral remote sensing data over sloping terrain

Abstract

Light detection and ranging (LiDAR) technology can provide accurate data for the vertical structure of a forest. Such data are fundamental for the study of aboveground biomass in the global carbon cycle. Most studies to date have used Geoscience Laser Altimetry System (GLAS) waveforms and terrain parameters as raw data in creating forest canopy height estimation models. Whether other parameters can be introduced to improve the accuracy of these models remain to be studied. In this study, we introduce parameters for forest texture to augment the topographic index model. Texture parameters were combined with field observations of canopy height to increase the accuracy of canopy height estimates over sloping terrain in the Lushuihe Forest District, a typical forest in the Changbai Mountains, Jilin Province, China. The study area consists mainly of broadleaf forest, coniferous forest, and mixed forest. Fifty-five sample plots covering different terrain reliefs and forest types were selected. The study areas were divided into high and low relief areas (i.e., hills and plains) using four different terrain relief thresholds (5, 10, 15, and 20 m) to select the best partition value. The results showed that the most accurate estimate of canopy height was given by the improved model using a 20-m threshold. Compared with the baseline topographic index model, R2 for leave-one-out cross-validation between the estimates given by the improved model and observed data increased from 0.777 to 0.952 and RMSE decreased from 2.90 to 1.35 m. The increased accuracy of canopy height estimates is due to the correction of GLAS waveform parameters by introducing texture parameters to incorporate changes in canopy structure because of forest aging and different forest types.