Use of lidar data to simulate forest net primary production

Abstract

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