Predicting forest growth based on airborne light detection and ranging data, climate data, and a simplified process-based model.

摘要

Increasing use of airborne light detection and ranging (LiDAR) in forest inventories offers new possibilities to apply process-based forest models (PBM) in practice. We present a new approach, where a simplified PBM is run using inputs derived from the LiDAR data. The PBM was built by combining several existing models together, and it was tested with 52 Scots pine (Pinus sylvestris L.) dominated sample plots in Finland with the LiDAR (PBM_LIDAR) and field (PMB_FIELD) inputs. The results were compared with empirical growth predictions (EM_FIELD) and field reference growth. LiDAR-based stand variables (mean height of tree and crown base and leaf area index) were, on average, well in line with the field measurements. Basal area growth was slightly underestimated with the PBM_LIDAR (bias 4.1%), root mean square prediction error (RMSPE, 26.7%) and overestimated with the PBM_FIELD (bias -10.2%, RMSPE 33.3%), the EM_FIELD being the least biased (bias -1.9%, RMSPE of 24.6%). The bias varied with stand age and fertility. The dependence on field reference growth was highest with EM_FIELD and PBM_LIDAR (R2=0.47 and 0.34, respectively), and lowest with PBM_FIELD (R2=0.18). Despite several development needs, the approach is promising for easy incorporation of canopy and weather data into forest growth predictions.