Estimating the stem carbon production of a coniferous forest using an ecosystem simulation model driven by the remotely sensed red edge

A general forest ecosystem model (FOREST-BGC) driven by remotely sensed and meteorological data was used to estimate stem carbon production (SCP) for a forest in mid-Wales. Key inputs to the model were spatial estimates of leaf area index (LAI) and leaf nitrogen concentration (LNC). The red edge position (REP) was determined for data acquired by the Compact Airborne Spectrographic Imager (CASI) sensor. There was a strong linear correlation (r = 0.94) between LAI and the REP and the relationship was used to obtain spatial estimates of LAI. There was no relationship between LNC and the REP and so spatial estimates of LNC were derived indirectly from LAI. Estimates of SCP generated from FOREST-BGC compared favourably with estimates derived from tree cores (RMSE= +/- 0.34 Mg C ha^-1 yr^-1).