Estimating forest growth using canopy metrics derived from airborne laser scanner data

Canopy height distributions were created from small-footprint airborne laser scanner data with a sampling density of 0.9–1.2 m− 2 collected over 133 georeferenced field sample plots and 56 forest stands located in young and mature forest. The plot size was 300–400 m2 and the average stand size was 1.7 ha. Spruce and pine were the dominant tree species. Canopy height distributions were created from both first and last pulse data. The laser data were acquired in 1999 and 2001. Height percentiles, mean and maximum height values, coefficients of variation of the heights, and canopy density at different height intervals above the ground were computed from the laser-derived canopy height distributions. Corresponding metrics derived from the 1999 and 2001 laser datasets were compared. Forty-five of 54 metrics derived from the first pulse data changed their values significantly due to forest growth. The upper height percentiles increased their values more than the field-based height growth estimates. The 50 and 90 height percentiles increased by 0.4–1.3 m whereas the field-estimated mean height increased by 0.2–0.9 m. Metrics derived from the last pulse data were less influenced by growth. ree height (hL), basal area (G), and volume (V) were regressed against the laser-derived variables to predict corresponding values of hL, G, and V based on the 1999 and 2001 laser data, respectively. Forest growth was estimated as the difference between the 2001 and 1999 estimates. Laser data were able to predict a significant growth in all the three biophysical variables over the 2-year period. However, the accuracy of the predictions was poor. In most cases the predictions were biased and the precision was low. Finally, several key issues of particular relevance to laser-based monitoring of forest growth are discussed.