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Remote sensing is a valuable tool for estimating forest biomass in remote areas. This chapter explores retrieval of forest above-ground biomass (AGB) in an airborne campaign area located at the Qilian Mountains (QMs), using two different methods (non-parametric and parametric), field data, and three different remote sensing data: a Satellite Probatoire d’Observation de la Terre-5 (SPOT-5) high resolution geometrical (HRG) image, multi-temporal dual-polarization Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) and airborne light detection and ranging (LiDAR) data. The nonparametric method was applied in 300 different configurations, varying both the mathematical formulations and the data inputs (SPOT-5 and ALOS PALSAR), and the quality of the performance of each configuration was evaluated by leave-oneout (LOO) cross-validation against ground measurements. For the parametric method (the multiple linear regression), the same remote sensing data were used, but in one additional configuration the airborne LiDAR data were used for stepwise multiple linear regression (SMLR). The result of the best performing nonparametric configuration was satisfactory (R = 0.69 and root mean square error (RMSE) = 20.70 tonnes ha). The results for the parametric methods 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 tonnes 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 nonparametric 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.

Modeling of forest above-ground biomass and evapotransportation dynamics