Quantifying individual tree growth and tree competition using bi-temporal airborne laser scanning data: a case study in the Sierra Nevada Mountains, California

Improved monitoring and understanding of tree growth and its responses to controlling factors are important for tree growth modeling. Airborne Laser Scanning (ALS) can be used to enhance the efficiency and accuracy of large-scale forest surveys in delineating three-dimensional forest structures and under-canopy terrains. This study proposed an ALS-based framework to quantify tree growth and competition. Bi-temporal ALS data were used to quantify tree growth in height (H), crown area (A), crown volume (V), and tree competition for 114,000 individual trees in two conifer-dominant Sierra Nevada forests. We analyzed the correlations between tree growth attributes and controlling factors (i.e. tree sizes, competition, forest structure, and topographic parameters) at multiple levels. At the individual tree level, H had no consistent correlations with controlling factors, A and V were positively related to original tree sizes (R>0.3) and negatively related to competition indices (R<-0.3). At the forest-stand level, H and A were highly correlated to topographic wetness index (|R|>0.7), V was positively related to original tree sizes (|R|>0.8). Multivariate regression models were simulated at individual tree level for H, A, and V with the R-2 ranged from 0.1 to 0.43. The ALS-based tree height estimation and growth analysis results were consistent with field measurements.