Abstract
In response to the need for a better understanding of biosphere-atmosphere interactions as well as carbon cycles, there is a high demand for monitoring key forest parameters such as biomass and carbon stock. These monitoring tasks provide insight into relevant biogeochemical processes as well as anthropogenic impacts on the environment. Recent advances in remote sensing techniques such as Light Detection and Ranging (LiDAR) enable scientists to nondestructively identify structural and biophysical characteristics of forests. This study quantified forest biomass and carbon stock at the plot level from small-footprint full-waveform LiDAR data collected over a montane mixed forest in September 2011, using seven modeling methods: ordinary least squares, generalized additive model, Cubist, bagging, random forest, boosted regression trees, and support vector regression (SVR). Results showed that higher percentiles of canopy height and intensity made significant contributions to the predictions, while other explanatory variables related to canopy geometric volume, structure, and canopy coverage were generally not as important. Boosted regression trees provided the highest accuracy for model calibration, whereas SVR and ordinary least squares performed slightly better than the other models in model validation. In this study, the simple ordinary least squares approach performed just as well as any advanced machine learning method.