Estimation of forest above-ground biomass using multi-parameter remote sensing data over a cold and arid area

Remote sensing is a valuable tool for estimating forest biomass in remote areas. This study explores retrieval of forest above-ground biomass (AGB) over a cold and arid region in Northwest China, using two different methods (non-parametric and parametric), field data, and three different remote sensing data: a SPOT-5 HRG image, multi-temporal dual-polarization ALOS PALSAR and airborne LiDAR data. The non-parametric method was applied in 300 different configurations, varying both the mathematical formulation and the data input (SPOT-5 and ALOS PALSAR), and the quality of the performance of each configuration was evaluated by Leave One Out (LOO) cross-validation against ground measurements. For the parametric method (the multivariate linear regression), the same remote sensing data were used, but in one additional configuration the airborne LiDAR data were used for stepwise multiple regression.The result of the best performing non-parametric configuration was satisfactory (R = 0.69 and RMSE = 20.7 tons/ha). The results for the parametric method were notoriously inaccurate, except for the case where airborne LiDAR data were included. The regression method with airborne low density LiDAR point cloud data was the best of all tested methods (R = 0.84 and RMSE = 15.2 tons/ha). A cross comparison of the two best results showed that the non-parametric method performs nearly as well as the parametric method with LiDAR data, except for some areas where forests have a very heterogeneous structure. It is concluded that the non-parametric method with SPOT data is able to map forest AGB operatively over the cold and arid region as an alternative to the more expensive airborne LiDAR data.

► Forest AGB was estimated from SPOT-5 and ALOS PALSAR and airborne LiDAR data.
► Both parametric regression and non-parametric k-NN method were applied.
► The k-NN method outperforms than parametric method for SPOT-5 and ALOS PALSAR data.
► The k-NN method performs nearly as well as the parametric method with LiDAR data.