Estimating biomass and height using DSM from satellite data and DEM from high-resolution laser scanning data

In this study, dense hemi-boreal forest biomass and height estimation was investigated based on optical satellite data and a high quality Digital Elevation Model (DEM) from airborne laser scanning. This analysis was carried out on data collected 2008-2010 over the test site Remningstorp in southern Sweden. The optical sensors SPOT-5 HRS and ASTER were tested to process a Digital Surface Model (DSM), i.e. the vegetation height above mean sea level, that is used together with the DEM (derived from laser data) to calculate a Canopy Height Model (CHM) as the difference between the former ones. By modeling biomass and height using regression analysis on spectral data from SPOT-5 HRG and height metrics from the CHM an improved Root Mean Squared Error (RMSE) and adjusted R² is expected, compared to using the single data sources alone. The best results showed a relative RMSE for standwise prediction of mean biomass and height of 30.3% and 23.3%, respectively. Adding CHM data to a spectral based (HRG) prediction model improved the mapping accuracy roughly 3%. In conclusion, the estimation accuracy did not improve significantly by adding height metrics to spectral data.