Estimating forest aboveground biomass by low density lidar data in mixed broadleaved forests in the Italian pre-alps

Background: Estimation of forest biomass on the regional and global scale is of great importance. Many studies have demonstrated that lidar is an accurate tool for estimating forest aboveground biomass. However, results vary with forest types, terrain conditions and the quality of the lidar data.Methods: In this study, we investigated the utility of low density lidar data (<2 points.m(-2)) for estimating forest aboveground biomass in the mountainous forests of northern Italy. As a study site we selected a 4 km(2) area in the Valsassina mountains in Lombardy Region. The site is characterized by mixed and broad-leaved forests with variable stand densities and tree species compositions, being representative for the entire Pre-Alps region in terms of type of forest and geomorphology. We measured and determined tree height, DBH and tree species for 27 randomly located circular plots (radius = 10 m) in May 2008. We used allometric equations to calculate total aboveground tree biomass and subsequently plot-level aboveground biomass (m.ha(-1)). Lidar data were collected in June 2004.Results: Our results indicate that low density lidar data can be used to estimate forest aboveground biomass with acceptable accuracies. The best height results show a R-2 = 0.87 from final model and the root mean square error (RMSE) 1.02 m (8.3% of the mean). The best biomass model explained 59% of the variance in the field biomass. Leave-one-out cross validation yielded an RMSE of 30.6 mg.ha(-1) (20.9% of the mean).Conclusions: Low-density lidar data can be used to develop a forest aboveground biomass model from plot-level lidar height measurements with acceptable accuracies. In order to monitoring the National Forest Inventory, and respond to Kyoto protocol requirements, this analysis might be applied to a larger area.