Abstract: [Objective] By integration the methods of waveform signal processing, spatial information analyzing and re-construction, and the comprehensive information extraction, a novel approach for the inversion of forest stand characteristics, based on the metrics extracted from the small-footprint full-waveform LiDAR data, was explored in this research.[Method] The subtropical secondary forests in the hilly area of southern Jiangsu were set as a research objective, and based on the pre-processing and analysis of small-footprint full-waveform LiDAR data, the voxel spatial framework was firstly decomposed and the waveform amplitude information was extracted to form a pseudo-vertical waveform model; then, the spatial location and geometric variables were extracted to calculated the point-cloud and waveform based metrics, following by an analysis of correlations for selecting metrics; finally, the inversion models were built by coupling the field-measured forest stand characteristics and validated for accuracy. [Result] 1) For all the LiDAR metrics, Lorey's tree height has the highest sensitivity, following by the volume and above-ground biomass. The basal area has the lowest sensitivity. Whereas, the total return amplitude and the number of peaks have a higher sensitivity of basal area than other metrics. In the group of point-cloud metrics, mean height, height percentiles and upper canopy return point cloud density have a relatively higher correlation with each forest stand characteristic than other metrics; whereas in the group of waveform metrics, the average of medium energy, the standard deviation of return waveform width and the canopy roughness have a high relationship with the forest stand characteristics. 2) Lorey's tree height has the highest estimation accuracy (RMSE=7.26% of the mean of field-measured value), whereas volume, above-ground biomass and basal area have a relatively similar low accuracy (RMSE=15.91%-19.82% of the mean of field-measured value). The number of independent variables was less than 3, and the selected metrics were height percentiles, canopy return point-cloud density, return waveform width and the canopy roughness. 3) The fitted results of the field-measured forest stand characteristics and the cross-validated estimated values showed that Lorey's tree height fitted best (R²=0.85), followed by above-ground biomass (R²=0.68) and volume (R²=0.59), whereas basal area has the lowest R²=0.45. 4) Lorey's tree height, volume and above-ground biomass have a similar spatial distribution, which may be attributed to their inner correlations. Compared with other 3 metrics, the spatial distribution of basal area appeared discontinuous, which may be resulted from the low prediction accuracy.[Conclusion] The results and accuracies of the fitted combo regression model of each forest stand characteristic were better and higher than the point-cloud metrics based models, demonstrated that waveform metrics has a potential to extract the information of the medium and lower level of forest stands. The point-cloud metrics described the forest canopy and the upper 3-D structure and density information, whereas the waveform metrics acquired the canopy and the complete vertical distribution and energy information of the lower part forest. The combination of them enhances the inversion accuracy of the forest stand characteristics.

Key words: small-footprint full-waveform LiDAR, forest stand characteristics, inversion, point-cloud based metrics, waveform based metrics, voxel