Use of LiDAR data to simulate forest net primary production

A methodology has been recently presented to predict the net primary production (NPP) of Mediterranean forests by the integration of conventional and remote sensing data. The methodology is based on the use of two models, C-Fix and BIOME-BGC, whose outputs are combined with estimates of stem volume and tree age in order to predict the NPP of the examined ecosystems. The current work investigates the possibility of deriving these two forest attributes from airborne high resolution LiDAR data. The research concerns the San Rossore pine forest, a test site in Central Italy where several environmental studies have been conducted. First, estimates of stand stem volume and tree age are obtained from LiDAR data by the application of a simplified method trained on existing literature and a few ground measurements. The accuracy of these stand attributes is assessed by comparison with independent ground data derived from a recent forest inventory. Next, the stem volume and tree age estimates are used to drive the NPP modeling strategy, whose outputs are evaluated against inventory measurements of current annual increment (CAI). The simplified LiDAR data processing method produces stand stem volume and tree age estimates having moderate accuracy. These estimates are useful to feed the modeling strategy and predict CAI at stand level. The success of this test offers the possibility of integrating ecosystem modeling techniques and LiDAR data for the simulation of net forest carbon fluxes.